RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
8
This work was supported by project JPNP18016, commissioned by the New Energy and
9
Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
10
and JST ALCA-Next (JPMJAN23D3).
11
12
Data availability
13
The source code and sample data (optode and CT images) are available from the
14
GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
16
References
17
Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
18
loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
19
environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Accurate litchi counting from whole-tree images is essential for yield estimation, orchard management, and plant phenotyping, but remains challenging in real orchards because fruits occur in dense, heavily occluded clusters and vary markedly in scale, illumination, and appearance across ripening stages, particularly when green fruits resemble surrounding foliage. Existing methods have shown promise, but their robustness in complex orchard environments remains limited. To address these challenges, we propose FG-LCNet, a two-stage foreground-guided litchi counting framework. In the first stage, an enhanced fruit-cluster detector improves the localization of small and ambiguous clusters under complex canopy backgrounds. In the second stage, the detected foreground regions are fed into a density-regression network with hybrid attention, while a consistency-based training strategy is introduced to improve robustness to appearance and illumination variations. To support this study, a large-scale litchi counting dataset was established, consisting of 1,126 whole-tree images collected from five orchards and spanning three ripening stages, with approximately 120,000 fruit-level dot annotations and more than 20,000 cluster-level bounding boxes. FG-LCNet achieved the best overall counting performance, with an MAE of 7.44 and an RMSE of 11.01. It showed clear advantages in high-density fruit-cluster scenarios and cross-orchard validation, while maintaining competitive results across orchard-region and maturity-stage subsets. The framework further retained inference efficiency suitable for practical deployment. These results indicate that FG-LCNet provides an effective solution for robust litchi counting and offers potential for other clustered fruit-counting tasks.
Why it matches plant phenotyping methods果実数という植物器官形質を whole-tree 画像から推定する二段階画像解析手法を開発し、データセット構築と交差果樹園検証まで行っており、表現型取得・抽出法が中心である。
abstractwe propose FG-LCNet, a two-stage foreground-guided litchi counting framework.
Reproduction assets foundThe paper's implementation code is explicitly stated as publicly available at the authors' GitHub repository (FG-LCNet). The litchi counting dataset (1,126 whole-tree images with ~120,000 dot annotations and 20,000+ bounding boxes) is not yet fully public: a ~100-image annotated subset is promised upon acceptance, and,Code · publicdustry Technology Research System (CARS-32-21), Hainan Modern Agricul-
655
tural Industry Technology System (HNARS-08-G02).
656
Conflicts of Interest
657
The authors declare that there is no conflict of interest regarding the publication of this article.
658
Data Availability
659
The implementation code is publicly available at https://github.com/johnhamtom/FG-LCNet .
660
Upon acceptance, a representative subset of approximately 100 annotated litchi images will be
661
released to support reproducibility and preliminary benchmarking. The full dataset is being further
662
organized for future release. Before full release, the complete dataset can be obtained from the
663
corresponding authorOpen asset ↗johnhamtom/FG-LCNetpdf-raw-page:28 lines:1-81Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole-genome sequencing and mapping of the candidate mutation. This screen led to the identification of a point mutation in CAP-D2 (capd2-2), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery.
Why it matches plant phenotyping methods花粉生存性を画像から自動推定するセグメンテーション手法とソフトウェアPATの開発が研究の中心であり、植物表現型計測ツールとして明確に該当する。
titlePAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' PAT pollen-phenotyping tool (the software implementing the paper's computational analysis, including the fine-tuned CPSAM segmentation model support) as open source on GitHub. Note: the full repository URL in the text (https://github.com/Riha-429[Code · public17
Data availability
428
Pollen Analysis tool (PAT) is available as open source tool at Github repository (https://github.com/Riha-429
Lab/Pollen-Analysis-Tool).
430
Figure legends
431
Fig. 1. Cellpose performance on Alexander-stained anther cross-sections across varying pollen
432
densities.
433
Representative cross-sections of Alexander-stained anthers showing a range of pollen densities, from
434
low (top rows, light staining) to high (bottom rows, dense reOpen asset ↗Pollen-Analysis-Toolpdf-raw-page:17 lines:1-64Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
ArabidopsisRootMorphology / geometry measurementSegmentationRoot system architecture
Introduction Plant parasitism by sedentary plant-parasitic nematodes is a dynamic and continuously evolving process, accompanied by profound remodelling of host root system architecture across distinct infection stages. However, the physiology and anisotropic growth of Arabidopsis thaliana roots under Heterodera schachtii infection, together with complex lateral root proliferation and increasingly dense, overlapping morphology, pose substantial challenges for accurate image segmentation. Methods Here, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features. These adaptations address the unique morphological complexity of the infected root system. Additionally, we integrated Root-TransUNet into a high-throughput phenotyping pipeline and applied it to an existing dataset of ~120,000 images of 362 A. thaliana MAGIC recombinant inbred lines collected over several months of infection. By extracting root system architecture traits, including root surface area and estimated root volume across infection stages, we enabled stage-specific association analyses between host root growth and nematode performance across these genotypes. Results Root-TransUNet achieved strong segmentation performance, demonstrating improved structural continuity and boundary precision compared with widely used CNN- and Transformer-based baselines, including UNet++. Stage-specific analyses revealed that the relationship between host root traits and nematode performance changed as infection progressed. During establishment, nematode number was largely independent of initial root size and varied strongly among genotypes, whereas during the reproductive phase (10-30 dpi), greater root expansion coincided with reduced estimated nematode volume accumulation. Notably, nematode burden was largely independent of host root size before infection, indicating that root quantity was generally not a limiting factor for infection in this experiment. Discussion These results demonstrate that Root-TransUNet can robustly segment infected root systems across a wide range of nematode infection densities, providing a scalable image-analysis framework for studying plant-parasitic nematode parasitism in combination with host root phenotyping.
Why it matches plant phenotyping methods感染根系の画像セグメンテーション手法を開発し、高スループット表現型解析パイプラインに統合して根系形態形質を抽出しており、表現型取得・抽出法が中心的である。
abstractHere, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features.
Reproduction assets foundThe paper analyzes a public BioImages dataset (S-BIAD2402) of ~400,000 RGB root/nematode infection images and provides authors' analysis code on GitHub; both are paper-specific, public, and actionable.Code · publicng molecular signatures, deepening our understanding of host-parasite resource allocation strategies, and establishing a foundation for the discovery of novel resistance mechanisms.
Code and data availability
Python-based source code for automating root analysis using the datasets above is accessible via our GitHub repository ( https://github.com/JieZhou1025/Root-nematode-interaction ).
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2402 .
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
AuthorOpen asset ↗JieZhou1025/Root-nematode-interactionlines:412-424Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Published4 Sept 2026Springer Science and Business Media LLC
Abstract Plant diseases substantially reduce global crop yields, and cotton production is particularly vulnerable to field-acquired variability in symptom appearance, background clutter, and illumination changes that limit the reliability and scalability of expert visual inspection. This study aimed to develop an accurate, computationally efficient, and explainable framework for real-time cotton leaf disease recognition that is suitable for deployment on resource-constrained edge devices. Using the SAR-CLD-2024 dataset (322 RGB images captured under natural agricultural conditions across seven categories, including healthy and diseased leaves), images were preprocessed via resizing and normalization and augmented online in the training set (random rotations, flips, brightness/contrast adjustments, and random cropping). An EfficientNet-B3 backbone initialized with ImageNet-pretrained weights was fine-tuned using categorical cross-entropy loss and Adam optimization, with early stopping, checkpointing, regularization, and a fixed-seed 70/15/15 train–validation–test partition to enhance reproducibility and reduce leakage. Performance was evaluated on an independent test set using accuracy, precision, recall, F1-score, MCC, balanced accuracy, Cohen’s kappa, confusion matrix, multi-class ROC/AUC, and precision–recall analysis, alongside computational benchmarking (parameters, FLOPs, memory, and inference latency) and comparative experiments against contemporary CNN, lightweight, and transformer-based models. The model showed stable convergence over 30 epochs with a small training–validation gap, predominantly correct predictions with limited confusion among visually similar classes, consistently high precision–recall behavior under moderate class imbalance, and stable performance across repeated runs with low variability and a tight confidence interval. Grad-CAM heatmaps localized necrotic lesions, discoloration, and infected tissues while largely ignoring background, and failure cases were associated with early-stage symptoms, occlusion, shadows, and inter-class similarity. Overall, the framework provides a reproducible, interpretable, and efficient solution for cotton leaf disease classification with practical implications for trustworthy, low-latency, on-device decision support in precision agriculture.
Why it matches plant phenotyping methods綿葉の病徴を画像から分類する深層学習手法の開発が中心で、独立テスト、比較評価、計算性能評価、Grad-CAMによる病徴局在化を実施しているため、植物病害フェノタイピング手法に該当する。
abstractThis study aimed to develop an accurate, computationally efficient, and explainable framework for real-time cotton leaf disease recognition
Reproduction assets foundThe paper's Data Availability statement explicitly names the SAR-CLD-2024 cotton leaf dataset used for all experiments as publicly available on Kaggle with a direct URL. No author analysis code or trained model checkpoint is deposited.Dataset · publicntribute to the development of fully automated, scalable, and real-time smart agriculture
systems.
Declaration
Funding
Datta Meghe Institute of Higher Education and Research Wardha, Maharashtra, India
Data Availability: The SAR-CLD-2024 cotton leaf dataset used in this study is publicly
available through the Kaggle platform at: https://www.kaggle.com/datasets/pantho12/sar-cld-2024-dataset-for-cotton
This dataset includes annotated images of various cotton leaf diseases collected under diverse
environmental conditions. All data utilized in this work are freely accessible, and the data
processing methodology has been described in detail to facilitate reproducibility.
Conflict of interest The aOpen asset ↗Kaggle · SAR-CLD-2024pdf-raw-page:32 lines:1-38Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Maize is the staple crop for millions of people in Sub-Saharan Africa, particularly for Zambia. Unfortunately, maize crops are exposed to several serious threats due to their susceptibility to foliar diseases like Maize Rust, Leaf Blight, Leaf Spot, Maize Streak Virus, and Maize Lethal Necrosis that may lead to great yield losses. Conventional methods of crop disease identification consist of field surveys that are not only subjective but also difficult to conduct for smallholder farmers. This paper presents the design and evaluation of a highly optimized version of the EfficientNet-B0 Convolutional Neural Network for the automatic detection of maize leaf diseases using maize leaf images obtained from real-world scenarios. The proposed model utilized the concept of transfer learning with ImageNet pre-trained weights and was trained on the Mendeley Maize Crop Disease (Leaf) Dataset which consists of 30,120 images in nine maize disease classes. The developed fine-tuned EfficientNet-B0 yielded 97.57% classification accuracy, macro precision of 97.61%, macro recall of 97.64%, and macro F1-score of 97.61%. From these results, it is evident that transfer learning and fine-tuning greatly boost maize disease classification accuracy while ensuring high computational efficiency. This study makes a significant contribution to precision agriculture as it offers an accurate and computationally efficient AI-based maize disease classification model, which could help smallholder farmers in early maize disease classification.
Why it matches plant phenotyping methodsトウモロコシ葉画像から病害状態を推定する深層学習モデルを開発・評価しており、植物病害表現型の取得・分類手法が中心的な研究です。
abstractThis paper presents the design and evaluation of a highly optimized version of the EfficientNet-B0 Convolutional Neural Network for the automatic detection of maize leaf diseases using maize leaf images obtained from real-world scenarios.
Reproduction assets foundThe paper's sole qualifying asset is the public Mendeley Maize Crop Disease (Leaf) Dataset of maize leaf images used for all phenotyping/classification measurements, explicitly declared publicly available with a URL. No author analysis code, trained model checkpoints, or other paper-specific assets are disclosed.Dataset · publiconflicts of interest to publish the paper.
Consent to Publish
All authors have read and approved the final version of the manuscript and agree to its
submission to Discover Networks.
Consent to Participate
Not applicable
Data Availability
The Mendeley Maize Crop Disease (Leaf) Dataset used in this study is publicly available at
https://data.mendeley.com/datasets/6w6gsvghfw
Clinical Trial Number
Not applicable.
Ethics Declaration:
Not applicable.
Competing interests
All authors declare no competing interests.Open asset ↗Mendeley · 6w6gsvghfwpdf-raw-page:55 lines:1-22Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Rice panicle blast detection is an important task in plant disease phenotyping. Field-based detection remains challenging because infected spike regions are often small, sparse, elongated, and affected by overlapping panicles, complex backgrounds, and variable illumination. In this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection. The model uses a Lightweight Ghost Backbone (LGB) to reduce redundant computation. It uses Anisotropic Axial Stripe Attention (A2SA) to represent elongated panicle structures. It also uses Focal Multi-Scale Attention (FMSA) for multi-scale feature refinement and Adaptive Geometric Shape IoU (AGS-IoU) for geometry-aware localization. The model was trained and evaluated on a rice panicle image dataset containing 1,055 training images, 69 validation images, and 169 test images. On the test set, RPB-YOLO11 achieved 76.09% mAP50, 45.44% mAP50-95, 73.98% precision, and 72.75% recall with 6.21 GFLOPs. Compared with the YOLO11n baseline, it improved mAP50, mAP50-95, precision, and recall by 2.73, 2.12, 1.64, and 2.11 percentage points, respectively. An Android-oriented inference application supports local image inference, detection visualization, class counting, and diseased-panicle incidence estimation. These results suggest that RPB-YOLO11 provides a practical approach for image-based rice panicle blast survey.
Why it matches plant phenotyping methodsイネ穂いもちの画像検出モデルを開発・比較検証し、罹病穂率を推定する実用アプリまで構築しており、植物病害状態の画像ベース表現型取得が中心である。
abstractIn this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection.
Reproduction assets foundThe paper links a public Hugging Face dataset used to establish the rice panicle blast detection dataset and a public GitHub release (data availability statement) containing the study's datasets/models.Dataset · publicsites, cultivars, growth stages, imaging conditions, and disease severities are still needed to evaluate generalization more fully.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/XuzheYang2Doc/RPB-YOLO11/releases/tag/rpb-yolo11 .
Author contributions
XY: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. XZ: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. CX: Formal analysisOpen asset ↗XuzheYang2Doc/RPB-YOLO11 · rpb-yolo11lines:639-658Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Automated plant disease classification from leaf images demands models that jointly achieve high accuracy and efficient training convergence. Standard deep learning approaches process images through a single feature pathway, limiting their ability to capture diverse visual manifestations such as color changes, texture patterns, and spatial distributions. This paper introduces DMCNNA-FBVL, a framework integrating two complementary innovations: (1) a Deep Multi-Component Neural Network Architecture (DMCNNA) employing three specialized pathways processing color-space statistics, texture descriptors, and raw image features before fusing them through SoftMax-weighted aggregation; and (2) Feedback-Based Validation Learning (FBVL), a training strategy that periodically blends validation-set gradients into weight updates to accelerate convergence. Experiments on the New Plant Diseases Dataset (87,000 images, 38 classes) show that DMCNNA-FBVL achieves 98.7% accuracy, 98.8% precision, 98.6% recall, and 98.7% F1-score, outperforming ResNet-50 by 3.2 percentage points ( 𝑝 < 0 . 0 0 1 ). The primary reported metrics are computed exclusively on an independent 10% hold-out test set, whereas the separate 10% validation partition is used during training for FBVL gradient blending and does not contribute to final test evaluation. Five-fold cross-validation confirms stability (98.7% ± 0.15%). Ablation experiments confirm additive gains, while FBVL reduces wall-clock training time by 15% through faster convergence.
Why it matches plant phenotyping methods葉画像から植物病害を分類するニューラルネットワークと学習戦略の開発・検証が研究の中心であり、植物の病害状態を直接推定している。
abstractAutomated plant disease classification from leaf images demands models that jointly achieve high accuracy and efficient training convergence.
Reproduction assets foundThe paper's plant disease classification experiments use the New Plant Diseases Dataset, which the authors state is publicly available on Kaggle. The authors' code and trained models are only promised 'upon acceptance' (no public URL), so they do not qualify as public assets.Dataset · publicral
monitoring systems.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Data availability
The New Plant Diseases Dataset used in this study is publicly available on Kaggle (https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset). Code and trained models will be made available upon
acceptance.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit
sectors.
Acknowledgments
[Removed for double-blind review.]
CRediT authorship contribution statement
[Removed fOpen asset ↗Kaggle · new-plant-diseases-datasetpdf-raw-page:19 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Southern corn leaf blight (SCLB) is caused by the fungal pathogen Bipolaris maydis (syn. Cochliobolus heterostrophus Drechsler) and is a common disease of fall crops of sweet corn. Phenotyping for SCLB resistance is performed through visual scoring, which is subjective and may limit genetic gain for this quantitative trait. As an alternative, we integrated computer vision (CV)-based phenotyping, genome-wide association studies (GWASs), and predictive breeding approaches to dissect the genetic basis of SCLB resistance. We utilized a sweet corn diversity panel with 693 genotypes, for which whole-genome resequencing produced a high-density single-nucleotide polymorphism (SNP) dataset. Broad-sense heritability for visual scoring ranged from 0.44 to 0.73, while CV-based phenotyping produced estimates ranging from 0.56 to 0.73 in multi-environment resistance trials conducted across 5 years and three locations. We performed GWAS using 16,755,210 SNPs and identified 41 associated SNPs. Genomic selection (GS) models on visual scoring phenotypes achieved moderate prediction accuracies under cross-validation of untested genotypes across characterized environments (0.22-0.47) and high prediction accuracies when predicting tested genotypes in uncharacterized environments (0.49-0.68). Using CV-based phenotypes for GS, we observed prediction accuracies of 0.45-0.47 under the untested genotypes in the characterized environments cross-validation scheme and 0.59-0.62 under the tested genotypes in the uncharacterized environments scheme. GS demonstrated reliability for ranking the individuals across a gradient of environments. These findings identify candidate loci and predictive breeding strategies to accelerate the development of resistant sweet corn cultivars.
Why it matches plant phenotyping methodsCVベースの病害抵抗性表現型測定を視覚評定と比較し、多環境・多年次試験で妥当性を評価しており、フェノタイピング手法が研究の中心である。
abstractPhenotyping for SCLB resistance is performed through visual scoring, which is subjective and may limit genetic gain for this quantitative trait.
Reproduction assets foundThe authors state that all datasets (phenotype data) and analysis code (CV phenotyping script, customized GAPIT script) are publicly available in their GitHub repository, which is listed in allowed_urls.Code · publiche images taken for each plot were saved in JPG format and analyzed using a CV method. Here, we refer to the CV method as a custom Python script written using the OpenCV library version 4.5.0, a set of tools for CV (Bradski, 2000 ). The Python script used for leaf CV image analysis is available in our public GitHub repository ( https://github.com/Resende‐Lab/SCLB‐Disease ).
FIGURE 1
Leaf imaging set up with QR‐coded plot IDs (bottom right) and color checker for computer vision phenotyping of southern corn leaf blight disease severity in sweet corn.
In the CT19 environment, a black cloth attached to a wooden board was used as the background. A wooden frame was used to clamp the leaves down toOpen asset ↗Resende‐Lab/SCLB‐Diseaselines:199-209Dataset · publicBLUP and BayesB model implemented in BGLR.
ACKNOWLEDGMENTS
This work was supported by the National Institute of Food and Agriculture USDA‐NIFA2018‐51181‐28419, USDA‐NIFA2019–05410, and USDA‐NIFA 2022–51181‐38333.
DATA AVAILABILITY STATEMENT
All the datasets and codes used in this study are available in the following repository: https://github.com/Resende‐Lab/SCLB‐Disease
REFERENCES
Amadeu , R. R.
,
Cellon , C.
,
Olmstead , J. W.
,
Garcia , A. A. F.
,
Resende , M. F. R.
, &
Muñoz , P. R.
( 2016 ).Open asset ↗Resende‐Lab/SCLB‐Diseaselines:566-596Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Grain yield is a central target in wheat breeding, yet accurately predicting it remains challenging because it depends on many genes and responds strongly to environmental variation. Genomic selection (GS) has improved breeding efficiency by enabling genome-based prediction of genetic merit, but predictability (PA) for grain yield is often limited under stress environments. At the same time, advances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance and may complement genomic information. In this study, we evaluated genomic and phenomic models for predicting grain yield in elite bread wheat lines across irrigated, drought, and heat-stress environments. Using a sparse phenotyping framework, we compared parametric and non-parametric models. PA was evaluated within environments and under cross-environment sparse phenotyping scenarios. Genomic models provided a stable baseline and enabled effective information sharing across environments when phenotypic data were incomplete. Phenomics-only models captured environment-specific plant responses but were more sensitive to environmental context. Multiomics models that integrated genomic and phenomic information consistently achieved the highest PA, with the largest gains observed under stress conditions. Overall, our results demonstrate that integrating genomics and UAV-based phenomics within sparse phenotyping designs offers a practical and scalable approach to improve grain yield prediction in wheat.
Why it matches plant phenotyping methodsUAV由来のフェノミクスを用いた疎な表現型取得と予測モデルを中心に、環境横断で評価しており、収量という植物形質の推定手法が主要な貢献である。
abstractadvances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance
Reproduction assets foundThe paper's grain yield BLUEs, spectral wavelength BLUEs, and genotypic data are publicly deposited in the CIMMYT data repository (https://doi.org/10.71682/10549399), directly reproducing this paper's phenotyping measurements. No author analysis code with a public URL is stated; other URLs are generic tools/services.Dataset · publicok.com.
Paolo Vitale, Email: p.vitale@cgiar.org.
DATA AVAILABILITY STATEMENT
The datasets generated and analyzed during this study, including best linear unbiased estimates (BLUEs) for grain yield and spectral wavelengths, as well as the corresponding genotypic information, are publicly available in the CIMMYT data repository ( https://doi.org/10.71682/10549399 ).
REFERENCES
Araus, J. L.
,
Kefauver, S. C.
,
Zaman‐Allah, M.
,
Olsen, M. S.
, &
Cairns, J. E.
(2018). Translating high‐throughput phenotyping into genetic gain. Trends in Plant Science, 23(5), 451–466. 10.1016/j.tplants.2018.02.001
Brault, C.
,
Lazerges, J.
,
Doligez, A.
,
Thomas, M.
,
Ecarnot, M.
,
Roumet, P.
,
Bertrand, Y.Open asset ↗CIMMYT data repository · 10.71682/10549399lines:280-433Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Plant diseases are still posing a challenge to the productivity, quality of crops, and food security, especially in locations where field diagnosis is based on manual visual inspec-tion. This paper assesses deep learning network-based automated classification of plant leaf diseases on public RGB leaf-image datasets, such as the Kaggle New Plant Diseases Dataset (Augmented) and PlantVillage images. They investigated four archi-tectures: EfficientNetV2B0, ResNet152V2, DenseNet201, and one hybrid Vision Trans-former (ViT)-based model. The steps of the experiment involved loading the dataset, exploratory analysis, preprocessing, resizing, normalizing, augmentation, transfer learning, independent model training, and evaluation metrics such as accuracy, preci-sion, recall, F1-score, training curves, testing results, and confusion matrices. The hy-brid ViT-based model was reported to have the best accuracy of 99.5%. On the smaller seven class subset of PlantVillage, EfficientNetV2B0 scored 98.11%. On the 38-class dataset, DenseNet201 improved test accuracy (97.34) and validation classification ac-curacy (around 98). ResNet152V2 scored 97.01 on the 38-class test set. The results demonstrate that CNN and transformer-based models can help to recognize plant diseases accurately whereas hybrid attention-based structures provide a promising path to enhance fine-grained classification. Since the model notebooks had varying class settings and splits, the comparison is seen as a model-structured assessment as opposed to a precisely identical benchmark across all architectures.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類する深層学習手法を複数モデルで比較・評価しており、病害表現型の取得・抽出と技術検証が研究の中心である。
abstractThis paper assesses deep learning network-based automated classification of plant leaf diseases on public RGB leaf-image datasets
Reproduction assets foundThe paper's phenotyping inputs are two public plant leaf-image datasets explicitly named in the Data Availability statement: the Kaggle New Plant Diseases Dataset (Augmented) and the PlantVillage dataset, both with public URLs. No author code, models, or supplementary materials are deposited (supplementary materials: 'Dataset · publicy available. Plant leaf images were
obtained from the Kaggle New Plant Diseases Dataset (Augmented) and PlantVillage
datasets. The datasets contain publicly accessible RGB images of healthy and diseased
plant leaves used for supervised image classification research.
DATASET SOURCES
Kaggle New Plant Diseases Dataset (Augmented):
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
PlantVillage Dataset: https://plantvillage.psu.edu/All processed data, experimental configurations, and model implementation details are
described within the manuscript. Additional materials may be made available from the
corresponding author upon reasonable request.
ACKNOWLEDGMENTS
The author acknowOpen asset ↗Kaggle · new-plant-diseases-datasetpdf-raw-page:24 lines:1-23Dataset · publicgmented) and PlantVillage
datasets. The datasets contain publicly accessible RGB images of healthy and diseased
plant leaves used for supervised image classification research.
DATASET SOURCES
Kaggle New Plant Diseases Dataset (Augmented):
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
PlantVillage Dataset: https://plantvillage.psu.edu/All processed data, experimental configurations, and model implementation details are
described within the manuscript. Additional materials may be made available from the
corresponding author upon reasonable request.
ACKNOWLEDGMENTS
The author acknowledges Istanbul Aydin University for academic support and research
guidance duringOpen asset ↗PlantVillagepdf-raw-page:24 lines:1-23Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.
Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。
abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 14 Sept 2026
Abstract Background and aims Plant volatile organic compounds (VOCs) change dynamically with plant development and in response to environmental conditions. However, their potential as non-invasive indicators of phenological progression remains poorly explored. In this study, we developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology. Using soybean ( Glycine max (L.) Merr.), we investigated whether development-associated temporal variation in VOC emissions could delineate and predict developmental phases. Methods We collected VOCs daily under controlled environmental conditions from 16 to 43 days after sowing, spanning the transition from vegetative to reproductive stages, using an automated sampling system coupled with thermal desorption-gas chromatograph-mass spectrometer (TD- GC-MS). To characterise temporal changes in VOC profiles associated with phenological progression, we analysed the daily VOC data using a multi-step pipeline combining statistical filtering and similarity-based network analysis. We defined VOC-derived developmental phases from similarity patterns in the VOC profiles, then developed and evaluated machine-learning models to predict these phases. Key results Seven VOCs exhibited distinct phase-dependent dynamics, including green leaf volatiles and monoterpenes showing characteristic temporal changes during phenological progression. Network-based clustering of VOC profiles resolved five developmental phases closely aligned with conventional developmental stages. A machine-learning model predicted these phases from the VOC profiles with high predictive accuracy on independent test data, demonstrating that phenological progression could be quantitatively inferred from VOC emission patterns. Conclusions Our findings support VOC profiling as a reliable and non-invasive approach for assessing phenological progression in soybean. By extracting temporally structured VOC signals, this framework captures developmental information that may be difficult to obtain through visual observation alone, particularly after canopy closure. VOC profiling offers a practical tool for monitoring crop developmental dynamics and has broader potential for plant phenotyping and precision crop management.
Why it matches plant phenotyping methods自動VOCサンプリング、時系列VOCプロファイリング、機械学習を統合し、VOCから植物の発育段階を非破壊推定する方法を開発・評価しており、フェノタイピング手法が中心である。
abstractwe developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe peak area matrix obtained from the MS- DIAL analysis (Supplementary Dataset S1) was filtered to remove unreliable features.Open asset ↗lines:66-69Code / dataset availability confirmedEurope PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Leaf shape is a fundamental trait of plant ecological strategies, influencing biotic interactions and ecosystem functioning. However, established quantitative metrics fail to capture subtle variations and irregularities, require user-based reference points or are challenging to compare among taxa with broadly different leaf shapes. In addition, established metrics typically conflate (aggregate) leaf edge complexity and macro-shape complexity, despite their independent functional significance and genetic foundations. Here, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity. Based on three case studies, we show that these metrics outperform aggregate metrics in predicting Quercus robur chemical traits, provide more intuitive interspecific classifications, and strongly align with human perception. In addition, edge and macro-shape complexity show high complementarity, while aggregate metrics are highly redundant and typically strongly related to leaf area. Emerging as the strongest predictor of leaf chemistry and key visual cue for complexity as perceived by humans, the effects of edge complexity highlight the under-appreciated functional significance of leaf margins. Our framework and the proposed entropy-based complexity metrics thus promise to help unlock the potential of growing digital image archives of leaves, including images from herbaria and fossils, and are technically readily applicable to shapes of algae, bacteria, pollen, and beyond. The accompanying package ShapeComplexity enables the broad application of entropy-based metrics, providing a powerful tool to explore how the shape of organisms and biological structures influences ecological strategies, biotic interactions, and ecosystem functioning while tracking spatial and temporal variation.
Why it matches plant phenotyping methods葉の画像からエッジ複雑性とマクロ形状複雑性を定量化する新規指標とソフトウェアを開発しており、植物形質抽出法が研究の中心である。
abstractHere, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity.
Reproduction assets foundThe paper's authors publicly release their ShapeComplexity analysis code (Rust) on GitHub, used to compute the paper's leaf edge- and macro-shape complexity metrics. Supplementary data/analysis code are on Dryad, but that URL is not in the allowed list. RMBG is a generic third-party background-removal model, not a phenCode · publicThe complete, open-source Rust-code (The Rust Team, 2025 ) is publicly available on GitHub ( https://github.com/Thornbach/ShapeComplexity ), ensuring transparency and reproducibilityOpen asset ↗Thornbach/ShapeComplexitylines:86-94Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract Deep convolutional networks now classify leaf images on curated benchmarks such as PlantVillage with accuracies close to the measurement ceiling of those datasets, which has shifted the open questions away from raw accuracy toward two under-reported issues: which components of a composite pipeline actually cause the result, and whether the components that matter on laboratory images are the same ones that matter on field photographs. We address both with a classification framework evaluated under a protocol that fixes every development decision before the independent test data are read. A Mask R-CNN stage localizes the dominant leaf, the accepted box is expanded by a validation-selected 5% margin and resized to a shared 224 by 224 input, and the same localized image is passed to ResNet50, InceptionV3, and MobileNetV2 together with an extractor that produces eighteen colour and shape descriptors. The forty-five class probabilities and eighteen descriptors form a sixty-three-dimensional input to a neural meta-classifier developed by three repetitions of stratified five-fold cross-validation. On a locked 3,101-image PlantVillage test partition of fifteen classes the framework reached 99.77% accuracy and 99.75% macro F1 with seven misclassifications, and a one-component-at-a-time ablation confirmed that every stage contributed. The same design was then trained and evaluated entirely within a separate thirteen-class PlantDoc field-image dataset, where it reached 90.03% accuracy and 89.40% macro F1. This is a within-PlantDoc experiment and not a controlled-to-field transfer test, so the figure measures how the pipeline behaves on field imagery rather than how a PlantVillage-trained model survives a domain shift. The central finding comes from running the identical ablation on both datasets: on clean images the ensemble breadth and descriptors provide the incremental gains, but under field conditions the ordering changes, and leaf localization and learned fusion become the decisive components. Removing localization cost 2.83 accuracy points and replacing the neural fusion with soft voting cost a further 2.08 points, the two largest effects on PlantDoc. The contribution is a controlled and transparent account of where each component of a localization-aware plant disease classifier earns its place, and of how that ordering shifts between laboratory and field acquisition.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する分類パイプラインの開発・アブレーション検証が中心であり、単なる病害実験や routine measurement ではない。
titleA Localization-Aware Heterogeneous CNN Ensemble with Neural Meta- Fusion for Plant Disease Classification, with a Component Analysis on Laboratory and Field Images
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe classification subset used here (20,638 images spanning fifteen pepper-bell, potato, and tomato classes) was obtained from PlantVillage, which is openly accessible at https://www.kaggle.com/datasets/emmarex/plantdisease.Open asset ↗Kaggle · emmarex/plantdiseaselines:314-336Code / dataset availability confirmedbioRxiv · checked 5 Sept 2026
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract. In the face of escalating global food demand and increasing climate variability, precise and granular crop yield monitoring is indispensable for maintaining regional agricultural stability. However, current deep learning approaches for yield estimation are severely constrained by their heavy reliance on massive in situ labeled data, which limits their application in data-scarce regions. Furthermore, these models often overlook the essential temporal evolution logic of yield formation and lack a systematic discussion regarding the contribution patterns of different feature dimensions, resulting in a black-box nature of the underlying model mechanisms. To address these challenges, this study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning. The framework's core strength lies in a physiologically complete simulation database, using the WOFOST model to exhaustively cover 30 years of climate variability and habitat combinations across Northeast China (1.24 × 106 km2). A Gated Recurrent Unit (GRU) network was then introduced for end-to-end modeling, accurately capturing the energy accumulation trajectory from vegetative to reproductive growth. Validation against 458 independent ground points (2022–2024) demonstrated robust generalization with an R2 of 0.69, an RMSE of 1.21 t ha−1, and an RRMSE of 13.73 %, despite using no ground data for training. Our analysis revealed that integrating photosynthetic intensity (LAImean), duration (LAD) and peak features (LAImax) across growth stages is critical for accuracy, while omitting early-stage features significantly impairs the model's ability to capture cumulative growth effects. Furthermore, the model successfully captured the spatiotemporal yield anomalies caused by the 2023 typhoon and flooding events. Ultimately, this study generated a 10 m resolution maize yield dataset (2019–2024) for Northeast China. The dataset exhibits consistent interannual stability, with the RRMSE ranging from 7.98 % to 12.92 % and the R2 remaining above 0.44 at the city level. By deeply coupling mechanistic simulation with data mining, this dataset provides detailed support for optimizing agricultural production and guiding farming practices. The Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).
Why it matches plant phenotyping methodsトウモロコシ収量という植物・作物群落の形質を推定する計算フレームワークを開発し、独立地点で性能検証したうえで再利用可能な10 m解像度データセットを生成しており、単なる農業実験の routine measurement ではない。
abstractthis study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning.
Reproduction assets foundThe paper's core output, the NortheastChinaMaizeYield10m maize yield dataset (2019–2024) with accompanying uncertainty layers, is openly deposited on Zenodo with an explicit availability statement and DOI. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).Open asset ↗Zenodo · 10.5281/zenodo.19547014lines:158-191Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate quantification of plant disease severity is essential for evaluating host-pathogen interactions and assessing the effectiveness of disease management strategies. Traditional visual scoring methods and manual estimation of infected tissue are widely used but are often subjective and prone to observer bias. Digital image analysis offers an objective alternative by enabling automated identification and quantification of symptomatic plant tissues based on color and spatial characteristics. Here, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images. The workflow involves acquisition of standardized leaf images, conversion of RGB images into hue-saturation-value (HSV) color space, segmentation of diseased tissue using defined HSV thresholds, refinement of the segmented mask through morphological operations, and extraction of the whole leaf area. The protocol then calculates the diseased area and total leaf area in pixels and computes the percentage of infected tissue. The method uses MATLAB together with the Image Processing Toolbox and can be implemented using simple scripts. This protocol enables rapid and reproducible quantification of disease severity in plant leaves exhibiting visually distinct symptoms such as necrotic lesions or blight patches. By minimizing observer bias and providing quantitative measurements of infected area, the protocol offers a practical and reproducible approach for plant disease phenotyping and evaluation of disease management strategies across diverse plant-pathogen systems where diseased tissues can be clearly distinguished from healthy tissues under reasonably controlled imaging conditions. Key features • A reproducible MATLAB-based workflow for separating diseased and healthy plant tissue using color-space segmentation. • Applicable to plant diseases where symptomatic tissue contrasts clearly with healthy tissue (necrosis, blight lesions, rot patches). • Requires digital leaf images, MATLAB, and the MATLAB Image Processing Toolbox for image processing and disease quantification. • Enables rapid calculation of diseased leaf area and disease severity using automated pixel-based quantification.
Why it matches plant phenotyping methods植物病斑を画像から分割・定量し、感染面積と病害重症度を算出するMATLAB画像解析プロトコルが研究の中心であり、植物病害表現型の取得・抽出手法に該当する。
abstractHere, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images.
Reproduction assets foundThe protocol explicitly deposits its authors' MATLAB image-processing workflow (HSV segmentation, mask refinement, pixel-based disease quantification) in a public GitHub repository with README instructions and example images.Code · publicGitHub repository containing the MATLAB source code, README file with installation and execution instructions, and representative example image(s): https://github.com/pankajborahmajuli-source/Leaf-Disease-Detection-MATLAB-Code/blob/main/README.mdOpen asset ↗Leaf-Disease-Detection-MATLAB-Codehtml-lines:112-148Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background and aims Phytolith analysis is widely applied in palaeoecological and archaeological research, but its interpretive strength depends on the availability of robust modern reference collections. This study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms, including forbs, shrubs, trees, and C3 grasses. Methods Phytoliths were extracted from available plant parts, including leaves, stems, flowers, seeds, seed pods, cones, and roots, depending on sample availability. Morphotypes were identified following ICPN 2.0, with grass silica short cell phytoliths (GSSCPs) further classified by shape and size to examine subfamily-level patterns. Phytolith morphotype percentage data were analysed using Hellinger transformation, PerMANOVA, PCA, LDA, and hierarchical clustering to assess compositional differences among plant growth forms and grass subfamilies. Key results Phytolith production varied strongly among growth forms and plant parts. Grasses were abundant producers, whereas most forbs, shrubs, and trees were trace producers or non-producers. Leaves were the most consistent source of phytoliths, while seeds and seed pods were predominantly non-producers. Grass silica short-cell phytolith (GSSCP) morphotypes showed clear subfamily-level differentiation. Pooideae produced Rondel morphotypes. Danthonoideae produced Rondel as well as wide Bilobate types. Panicoideae and Oryzoideae exhibited a pronounced Bilobate signature, commonly associated with Polylobate and Cross forms. Non-grass taxa (woody, shrubs, and forbs) were dominated by Spheroids, Tracheary elements, Epidermal, and Polygonal sheets and other non-diagnostic forms. Phytolith assemblages differed significantly among plant families, with Poaceae uniquely producing GSSCPs, while non-grass families showed greater overlap in assemblage composition. Conclusion By expanding taxonomic and anatomical coverage, this study strengthens the capabilities of phytoliths in the reconstruction of grasslands and in general paleo vegetation in Australia, especially where other proxies such as pollen are limited.
Why it matches plant phenotyping methods植物部位の珪酸体を抽出・形態分類し、成長形態やイネ科亜科を識別する現代参照コレクションを構築しており、植物形質の取得・判別手法が研究の中心である。
abstractThis study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms
Reproduction assets foundThe authors explicitly state that the R scripts used for data analysis and figure generation are publicly available on their GitHub repository, which directly reproduces this paper's phytolith statistical analyses (PCA, LDA, PerMANOVA, clustering, plots). Supplementary data files contain the paper's measurements but noCode · publicntification of all plant specimens collected for this
study.
A
14 FUNDING
M
15 Funding for this study was provided by ARC Discovery grant DP210100508 and a Ph.D.
D
16 fellowship (UQGSS) to MH.
TE
17 DATA AVAILABILITY
18 The R scripts used for data analysis and figure generation are publicly available on GitHub
EP
19 repository: https://github.com/Manoshi-sporo/Australian-Phytolith-Reference-Collection.
20 CONFLICTS OF INTEREST
CC
21 The authors declare no competing financial or commercial interests.
A
22 AUTHOR CONTRIBUTIONS
23 MH: writing original draft, conceptualization, software, investigation. AC: Supervision, Writing -
24 Review and editing, FM: Supervision, Writing-Review and editing.Open asset ↗Manoshi-sporo/Australian-Phytolith-Reference-Collectionpdf-layout-page:34 lines:1-87Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
Reliable and objective phenotyping is essential for plant breeding programs to characterize genetic variation and accelerate crop improvement. Conventional disease assessment relies on expert visual scoring, which is labor-intensive, subjective, and prone to inter- and intra-rater variability. Although image-based phenotyping methods have been proposed, many require manual intervention, specialized imaging setups, or single time-point measurements, limiting their ability to capture disease progression over time. Here, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images. The pipeline performs semi-automated leaf and automated pustule segmentation from images acquired in situ , enabling objective disease severity estimation with minimal user intervention and without requiring solid backgrounds or manual leaf manipulation or detachment. By extracting temporal traits, including disease severity trajectories and standardized area under the disease progress curve, the method provides a comprehensive characterization of disease development throughout infection. Association between automated and expert assessments was moderate for stripe rust ( R 2 = 0.58) and strong for leaf rust ( R 2 = 0.85), while expert inter-rater reliability was moderate for both diseases (ICC = 0.675 and 0.800, respectively). The proposed approach establishes a scalable and reproducible framework for longitudinal disease phenotyping in controlled environments, with broad applications in disease resistance screening and crop breeding.
Why it matches plant phenotyping methods画像時系列から植物病害の進展と重症度を抽出する半自動・自動解析パイプラインを開発し、専門家評価との比較で検証しており、表現型取得手法が研究の中心です。
abstractHere, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images.
Reproduction assets foundThe paper's Code and Data Availability section explicitly states that software and datasets (the phenotyping pipeline and imaging datasets) are publicly available at the authors' GitHub repository and project website, both of which are in the allowed URL list.Code · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗USask-BINFO/greenskeye_analysislines:195-225Dataset · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗lines:195-225Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically composites spectral observations over fixed calendar windows, implicitly assuming phenological synchrony across fields. We present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction, for a quality trait whose physiology, senescence-linked nitrogen remobilization, contrasts with the season-integrating behavior of yield. Integrating Sentinel-2 imagery (31 vegetation indices, 10 spectral bands), ERA5-Land reanalysis, gSSURGO soil properties, and USGS 3DEP topography across 228 commercial winter wheat fields in western Kansas (2024–2025), we compared six temporal strategies (peakrelative vs. calendar × monthly, biweekly, growth-stage) using three ensemble tree models under nested cross-validation with Boruta feature selection. A single 30-day post-peak window (peak + [16,45] days) was the top-performing and most consistently selected window, chosen in 4 of 5 outer folds, reproducing prior accuracy under random cross-validation (R2 ≈ 0.28); though its advantage over the best calendar window was not statistically significant (paired bootstrap p = 0.08). Under leave-county spatial cross-validation, however, this skill did not transfer across counties (Sentinel-2–only R2 ≈ 0.01; per-county median R 2 = −0.23), indicating the satellite signal supports within-region interpolation but not spatial extrapolation to unseen counties; ablation shows that neither the spectral nor the static features transfer across counties on their own, and the residual crosscounty skill emerges only from their combination. A near-real-time application at ∼3 weeks before harvest retains most within-region skill at a modest accuracy cost. The results delineate where spectral-peak-relative alignment helps, concentrating a senescence-linked signal within region, and where it does not, providing an honest operational baseline for satellite-based grain-quality monitoring.
Why it matches plant phenotyping methods小麦の穀粒タンパク質濃度という植物形質を対象に、衛星時系列のスペクトルピーク相対アラインメントを開発・比較評価し、交差検証で性能と空間移 transfer 性を検証しているため、方法が中心的である。
abstractWe present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction
Reproduction assets foundThe preprint explicitly releases the authors' analysis code (data-acquisition pipeline, feature engineering, cross-validation/modeling, figure scripts) at a public GitHub repository, and a de-identified field-level GPC dataset released alongside the code repository. Both are paper-specific, public, and actionable. The Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗Ciampitti-Labpdf-page:48 lines:1-55Dataset · publica de-identified version of the dataset is released alongside the code repositoryOpen asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Published18 Aug 2026Journal of King Saud University - Computer and Information SciencesCited by 0 · OpenAlex ↗
Plant phenotyping is essential for modern crop breeding, yet traditional static image analysis fails to capture the nonlinear dynamics of plant growth. Existing time-series forecasting models exhibit notable limitations when processing multimodal data: global pooling operations may compress local 2D spatial topology of plants, and shallow feature concatenation may be insufficient for effective cross-modal semantic alignment. Moreover, current methods typically regress absolute morphological states, which may contribute to temporal lag during nonlinear growth spurts. In this paper, we propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth. The network removes the final global pooling and classification layers to preserve spatial topology and incorporates a scalar-guided cross-modal attention module based on the standard query-key-value formulation. This module utilizes 1D morphological features as queries to dynamically weight local visual regions, promoting multimodal feature alignment. Concurrently, a residual incremental forecasting strategy is introduced to predict short-term growth increments rather than absolute states, aiming to improve tracking sensitivity to sudden growth events. Evaluations on the UNL-CPPD maize dataset and supplementary validation on the FIP1 wheat field dataset show that the proposed model achieves competitive single-step forecasting accuracy and favorable temporal trajectory alignment compared with adapted spatiotemporal attention, graph-based, and physics-informed baselines under the evaluated settings. In particular, the FIP1 results suggest that ST-CrossGro-Former can maintain favorable height trajectory alignment under a field-acquired wheat setting, indicating its potential for helping mitigate temporal misalignment in dynamic growth forecasting.
Why it matches plant phenotyping methods植物の動的形態成長を予測する新規クロスモーダル手法を開発し、トウモロコシ・コムギデータセットで評価しており、表現型の抽出・予測手法が中心である。
abstractwe propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth.
Reproduction assets foundThe paper evaluates its ST-CrossGro-Former model on two public plant phenotyping datasets: the UNL-CPPD maize dataset (explicitly stated as publicly available with a repository URL) and the FIP1 wheat field dataset (public dataset from ETH Zürich, with its GigaScience dataset publication DOI). No author analysis code, Dataset · publicThe UNL-CPPD dataset used in this research was acquired from the UNL Plant Phenotyping Datasets repository, accessible at https://plantvision.unl.edu/datasets.Open asset ↗UNL Plant Phenotyping Datasets · UNL-CPPDlines:266-273Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs. Sixteen soybean genotypes were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using six YOLO (You Only Look Once) object-detection architectures (YOLOv7–YOLOv12) to identify the best-performing model for automated analysis. Among the tested object-detection architectures, YOLOv9 achieved the best overall performance for detecting germinated and non-germinated pollen grains in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P < 0.05), with a significant genotype × growth temperature interaction. Invitro incubation temperatures ranging from 10 °C to 45 °C produced a clear thermal response; however, no significant genotype × incubation temperature interaction was detected within either growth temperature regime. Although photosynthetic and physiological traits were measured exploring their relationship with pollen germination, their transient and complex response limited their reliability for predicting reproductive performance. The automated pipeline substantially reduced the time required to evaluate pollen germination. The pipeline processed nearly 5,000 images in approximately one hour, substantially increasing throughput and reducing reliance on manual counting. The findings demonstrate that pollen germination is a promising proxy trait for screening reproductive heat tolerance in soybean. Combining controlled environment phenotyping with YOLO-based object detection enabled efficient, accurate, and scalable pollen analysis, and represents the central methodological advance of this study. YOLOv9 performed best among the tested architectures, although discrepancies from manual counts in some images indicate that additional validation is needed. The weak associations with vegetative physiological traits further support the value of direct pollen-based phenotyping.
Why it matches plant phenotyping methods深層学習による花粉画像解析を中心に、花粉発芽という生殖形質を高速・自動測定するハイスループット表現型解析フレームワークを開発・比較・検証している。
abstractThis study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs.
Reproduction assets foundThe authors state that all data supporting the study, including annotated pollen germination images, computational and statistical codes, and analysis tools, were deposited in Zenodo with a public DOI. This is a paper-specific, publicly actionable asset. LabelMe and Ultralytics YOLO are generic third-party tools, not作者Dataset · publicCommission.
Data availability
All data supporting the findings of this study, including annotated images, computational and
statistical codes, and analysis tools, have been deposited in the Zenodo data repository. Additional
data will be made available upon reasonable request following acceptance of the manuscript.
Repository: https://doi.org/10.5281/zenodo.21685593
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing Interests
Authors declared no competing interests
References
1. FAOSTAT: Crops and livestock products: soybean production data. https://www.fao.org/faostat/
(2022). Accessed 15 Feb 2026.
2. Patel D, Franklin KA. TemperaturOpen asset ↗Zenodo · 10.5281/zenodo.21685593pdf-raw-page:28 lines:1-34Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Boll-opening concentration is critical for mechanical cotton harvesting, yet it is still assessed mainly by manual records and single time-point indicators that miss temporal dynamics. To bridge the lack of a unified workflow linking vision foundation models, multi-temporal boll-opening monitoring, and harvest decision-making, we developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX. DINO-BollGX couples a DINO v3 backbone, a RetinaNet detection head, and an adaptive refinement-and-suppression module for robust open-boll detection under complex field conditions. Using multi-temporal UAV imagery collected over two years for 383 cultivars, we reconstructed plot-scale time series of open-boll counts, derived dynamic features describing progression and intensity changes, and proposed a Cotton Boll-Opening Temporal Stability Index (CTSI) to quantify boll-opening rhythm and concentration; CTSI was further integrated with a time-based risk function to generate harvest decision curves. Under unified data and training settings, DINO-BollGX achieved precision = 0.91, F1 = 0.88, and AP@0.50 = 0.80, and provided accurate boll-count estimation (R 2 =0.98; MAE=3.10), outperforming YOLOv11, YOLOv12, YOLOv13, and RT-DETR. On an independent cross-year dataset acquired at 5 m altitude, it obtained precision = 0.98 and F1 = 0.87. An internal consistency analysis showed that CTSI had the expected negative association with Window_days (r = −0.83) and positive associations with Max_count (r = 0.78) and the boll-opening efficiency index (r = 0.92), reflecting the co-occurrence of temporal compactness and main-phase opening intensity in the cultivar population. CTSI ranged from −2.72 to 4.69 across cultivars, enabling identification of highly synchronized boll-opening. Harvest decision curves indicated that the relative net income index peaked at day 67 after the first observation and a compact optimal harvest window near the end of monitoring; on a fixed harvest date, Kuche 130292 (CTSI=4.69) produced 486 open bolls versus 182 for Xinluzao 36 (CTSI=0.53) and 90 for Andizhan-60 (CTSI=-2.72). Overall, the framework integrates dynamic boll-opening phenotyping with harvest timing optimization, supporting scalable cultivar screening and mechanization-ready deployment, with potential extension to harvest decision scenarios in other crops.
Why it matches plant phenotyping methodsUAV画像と基盤モデルを用いて綿花の開絮を検出・定量し、時系列表現型指標を構築・検証する方法が研究の中心であるため。
abstractwe developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX.
Reproduction assets foundThe paper publicly releases its cotton boll-opening UAV image dataset (3638 patches, 94,774 YOLO-format bounding-box annotations) on GitHub, directly supporting the paper's phenotyping analysis. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicentary information for evaluating cross-scale detection performance and characterizing macroscopic spatial patterns. The 5 m imagery acquired on 18 Sept 2024 is used exclusively for cross-year generalization assessment. All cropped images and the corresponding YOLO-format annotation files have been publicly released on GitHub ( https://github.com/mianchen0529/cotton-boll-dataset/tree/main ) to facilitate further research on cotton phenotyping, agricultural remote sensing, and intelligent analytics.
2.3.
Model construction
2.3.1.
Overall architecture of the DINO-BollGX network
The proposed DINO-BollGX network consists of four stages: image preprocessing, feature extraction, object prediction,Open asset ↗mianchen0529/cotton-boll-datasetlines:63-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published18 Aug 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 0 · OpenAlex ↗
Key message Our scalable two-step method estimates genotypic performance and genetic parameters from ranking data, producing reliable results comparable to quantitative analyses, enabling the integration of ranking data into breeding pipelines. Plant breeding research has chiefly relied on on-station experiments to evaluate varietal performance. Nevertheless, these trials often fail to represent on-farm growing conditions and farmers' preferences, potentially leading to poorly defined breeding targets. Recent work has demonstrated the potential of using on-farm verification trials combined with ranking data to support farmers in evaluating varieties while providing information that is representative of farmers' needs. Despite this potential, scalable methods for quantifying genetic differences and assessing the strength of the genetic signal in such trials remain limited. Here, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters. The approach follows a common strategy in quantitative genetics, in which parameters are estimated from tables of genotypic means and their variances. In our framework, these estimates are obtained from Thurstonian and/or Plackett-Luce models, which treat rankings as observations of an underlying continuous trait associated with genotypic performance. Using simulated data, we showed that genotypic mean estimates derived from ranking analyses are linearly related to those obtained from quantitative trait analyses and that their variances adequately capture estimation uncertainty. We further demonstrated that incorporating these estimates and their variances into a second-step mixed-effects model yields accurate estimates of variance components. Analyses of groundnut, maize, and sweetpotato datasets confirmed the applicability of the approach and showed that ranking data can provide reliable estimates of genetic parameters. We argue that this framework can be scaled to obtain genotypic performance estimates from multi-trial on-farm data.
Why it matches plant phenotyping methods作物品種の遺伝型性能をランキングデータから推定する統計的方法そのものが研究の中心であり、育種に再利用可能な植物性能の推定手法を開発・検証している。
abstractHere, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters.
Reproduction assets foundThe paper's Data availability statement provides public access to the observed groundnut and sweetpotato ranking/trial datasets (Zenodo 17112492), the authors' R functions and simulation workflow (GitHub hdorado/tricot-ranking-analysis, archived Zenodo 17942919), and supplementary material with methods and figures (ZenDataset · publicThe observed data for groundnut and sweetpotato used in this study are publicly available and can be accessed at: Global multi-crop agricultural trial data supported by citizen science, Zenodo [ https://doi.org/10.5281/zenodo.17112492 ]Open asset ↗Zenodo · 10.5281/zenodo.17112492lines:205-225Code · publicThe R functions and simulation workflow used in this study are publicly available at: - Source code available from: [ https://github.com/hdorado/tricot-ranking-analysis ]Open asset ↗GitHub · hdorado/tricot-ranking-analysislines:205-225Code · public- Archived software available from: [ https://doi.org/10.5281/zenodo.17942919 ] - License: [MIT License]Open asset ↗Zenodo · 10.5281/zenodo.17942919lines:205-225Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published17 Aug 2026International Journal of Drug Delivery TechnologyCited by 0 · OpenAlex ↗
Why it matches plant phenotyping methodsトマト葉の病害を画像と深層学習で検出する手法が題名上の中心であり、植物の病害状態を観察的に推定するフェノタイピング研究に該当する。
titleDetection of Tomato Leaf Disease in Leaves with Deep Learning MobileNetV2 with Gaussian and Gabor Preprocessing
Reproduction assets foundThe paper's phenotyping analysis is based on the publicly available PlantVillage plant leaf disease image dataset hosted on Kaggle (54,303 labeled leaf images across 38 classes), which the authors explicitly state was sourced from a publicly available Kaggle dataset. No author-specific code, models, or derived datasetsDataset · publicThe research incorporated PlantVillage dataset(24)
accessible on Kaggle that contains 54,303 plant leaf
images showing both healthy and diseased
conditions spanning across 38 specific categories.Open asset ↗Kagglepdf-raw-page:2 lines:1-105Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Stomatal traits are key microscopic phenotypes for evaluating plant physiology, stress responses, and crop breeding potential. However, in vivo high-magnification microscopy often suffers from a shallow depth of field, causing noticeable defocus blur across different spatial locations and making it difficult to capture clear and complete stomatal structures in a single image. Multi-focus image fusion offers a practical solution, yet existing methods typically rely on supervised training, paired data, or hand-crafted rules, limiting their use in real agricultural microscopy scenarios. In this study, we propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images. The method integrates two-dimensional feature extraction with three-dimensional cross-focal-plane modeling to capture both spatial details and complementary information across focal planes. A max-response-guided spatial gating module is introduced to enhance focused regions while suppressing defocused responses. Additionally, dual sharpness priors based on perceptual features and wavelet high-frequency information enable pixel-wise pseudo-supervised learning without requiring all-in-focus ground-truth images. The model also predicts a probabilistic focal-plane volume for interpretable all-in-focus reconstruction. Experiments on a maize multi-focus image dataset demonstrate that the proposed method achieves superior or competitive performance across multiple fusion metrics, with entropy (EN), edge information preservation ( Q AB∕F ), Chen-Blum contrast metric ( Q CB ), and visual information fidelity for fusion (VIFF) reaching 7.43, 0.21, 0.41, and 1.01, respectively. Ablation studies confirm the effectiveness of the 3D modeling, spatial gating, and dual-prior sharpness supervision. More importantly, when the fused images serve as input to a YOLO-based stomatal instance segmentation model, the proposed method yields the best segmentation accuracy, with mAP50 and mAP50-95 reaching 0.9937 and 0.9121, respectively. Phenotypic measurements derived from the segmentation masks show high consistency with manual annotations, with the highest coefficient of determination R 2 = 0.97 achieved for stomatal count. These results indicate that the framework can act as an effective front-end module for automated microscopic stomatal phenotyping in agriculture.
Why it matches plant phenotyping methods植物の気孔表現型を対象に、マルチフォーカス画像融合、セグメンテーション、形質測定までを中核的に開発・検証しているため。
abstractwe propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images.
Reproduction assets foundThe authors state their data and code are publicly available on GitHub, covering the multi-focus stomatal microscopy dataset and the UMF-stomata fusion/phenotyping code.Code · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:640-655Dataset · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:683-756Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Cross-sectional analysis is considered the reference method for measuring cotton fiber fineness and maturity; however, its widespread use has been limited by labor-intensive sample preparation and manual image analysis. The objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections. A total of 249 composite light microscopy images of cotton fiber cross-sections were collected and manually annotated to generate training and validation datasets. A YOLO11m instance segmentation model was developed to identify cotton fiber and lumen regions and automatically extract quantitative traits, including fiber area, lumen area, fiber perimeter, and lumen perimeter. The workflow integrates automated image segmentation, post-processing, quantitative trait extraction, and data export to facilitate reproducible cotton fiber phenotyping. Model performance was evaluated using mean Average Precision (mAP), and workflow outputs were validated against Adobe Photoshop using descriptive comparisons of six cross-sectional traits. The model achieved Box mAP50 scores of 0.984 for cotton fiber regions and 0.789 for lumen regions, demonstrating high segmentation accuracy. The automated workflow substantially reduced manual analysis time while producing measurements with central tendencies comparable to those obtained using Adobe Photoshop. To facilitate reproducibility and adoption, the workflow, trained model weights, and supporting documentation are publicly available through GitHub and a Hugging Face web application. The workflow substantially increases analytical throughput while providing a reproducible and publicly accessible method for automated cotton fiber cross-sectional phenotyping, facilitating quantitative analysis for cotton genetics and breeding research.
Why it matches plant phenotyping methods綿繊維横断面の画像分割、形質抽出、検証を目的とした再現可能な深層学習ワークフローの開発であり、植物フェノタイピング手法が研究の中心である。
abstractThe objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections.
Reproduction assets foundThe paper's cotton fiber cross-section phenotyping workflow (YOLO11m segmentation pipeline, trained model weights, example images/outputs) is explicitly stated as publicly available via a GitHub repository and a Hugging Face web application, with URLs matching the allowed list.Code · publicThe complete source code, training scripts, dataset configuration, example input images, example outputs,
and supporting documentation are publicly available through the GitHub repository:
https://github.com/RifeLab/cotton-lumen-microOpen asset ↗RifeLab/cotton-lumen-micropdf-page:11 lines:1-47Code · publicThe cotton fiber image analysis workflow is publicly available through a web-based application hosted on
Hugging Face at: https://huggingface.co/spaces/chaneylc/cotton_fiber_microscopy_measureOpen asset ↗pdf-page:11 lines:1-47Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract Purpose Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024). Methods and Results Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R 2 = 0.81–0.95; RMSE = 0.27–0.77 m 2 /m 2 ), followed by CCC (R 2 = 0.58–0.94; RMSE 2 ), while LCC remained less accurately estimated (R 2 = 0.26–0.78; RMSE 2 ). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits. Conclusions The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data.
Why it matches plant phenotyping methodsUAV・近接分光センサーとPROSAIL反転、ANNを用いてLAIや葉・群落クロロフィルを推定し、精度比較と圃場観測との整合性評価を行うことが研究の中心である。
abstractThis study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' UAV image processing code (irradiance normalization, vignetting, exposure compensation, radiometric calibration) in a public GitHub repository under GPL v3.0; other data are available only upon request.Code · publicData availability Code to perform irradiance normalization, vignetting, exposure compensation, and radio-
metric calibration is available at https://github.com/fieldSITES/scripts/tree/main/UAV under GNU General
Public License v3.0. Other data will be made available upon request.Open asset ↗UAVpdf-page:34 lines:1-40Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.
Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。
abstractThis project develops a workable solution.
Reproduction assets foundThe paper builds its multimodal wheat phenotyping analysis on several explicitly cited public data assets: the Kaggle Wheat Plant Diseases image dataset (used for disease classification, Tables 2 and 9), the Global Wheat Head Detection dataset (used for head detection, Tables 1 and 6), FAOSTAT and India Open GovernmentDataset · publicAvailable online at: https://www.fao.org/faostat/ . FAOSTAT statistical database.Open asset ↗lines:1110-1162Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Sustainable agriculture has substantial share on improvement of food security and optimization of resources utilization particularly for high value crops like rice leaf. Rice varieties should be properly classified in order to benefit the harvest management, reduced loss after harvest and improved agriculture methods. The traditional classification method usually brings the low precision and the traditional classification method is also subjected to human error, which is difficult to bring about reliable output. This study introduces an optimized multi-class rice leaf disease classification system utilizing “Rice Feature Selection” (RiceFS) and ensemble machine learning approaches. RiceFS is realized based on a feature selection mechanism based on Recursive Feature Elimination. Selected classifiers such as KNN, Random Forest, Gradient Boosting, Ensemble Learning and Optimized SVM are analyzed based on the extracted subset of features and the proposed system is used to classify the seven classes of rice leaf disease. The experimental results show that the Optimized SVM has the best classification results among the different classifiers with accuracy of 92.10%, Precision of 92.20%, balanced Recall and F1 Score, which shows that Optimized SVM is very effective in multi-class rice leaf disease classification. The performance can be improved by feature reduction, generalization capability and computational complexity reduction, which are realized with the help of RiceFS. The proposed framework is designed to provide an intelligent decision support system for timely intervention, loss minimization and sustainable agriculture. Results indicate that these algorithms are applicable for rice leaf disease classification since they are accurate, reliable and scalable.
Why it matches plant phenotyping methodsイネ葉の病害状態を観察データから分類する計算手法が研究の中心であり、RiceFSと複数の機械学習器を用いた分類フレームワークを開発・評価しているため。
abstractThis study introduces an optimized multi-class rice leaf disease classification system utilizing “Rice Feature Selection” (RiceFS) and ensemble machine learning approaches.
Reproduction assets foundThe paper's RiceFS phenotyping/classification experiments are built on two public Kaggle rice leaf disease image datasets, explicitly cited with URLs and a data availability statement. No author code or models are deposited.Dataset · publicThe RiceFS framework proposed initially
performs a feature selection, followed by training
several classifiers: K-Nearest Neighbors (KNN),
Random Forest (RF), Gradient Boosting (GB),
Ensemble Learning, and Optimized Support
Vector Machine (Optimized SVM). The data is
published on the Kaggle website. The data is
open-source at:
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases [44].
This data consists of 120 jpgs of disease infected
rice leaves. The photos are divided into 3
categories according to the kind of disease. There
are 40 images in each class.
Classes
• Leaf smut
• Brown spot
• Bacterial leaf blight
The datasets are preprocessed by eliminating
redundant information, normOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:11 lines:1-103Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published11 Aug 2026Engineering, Technology & Applied Science ResearchCited by 0 · OpenAlex ↗
Corn is a staple crop of global significance; however, foliar diseases may lead to 30–60% yield loss if not detected at an early stage. Conventional visual inspection is time-consuming, subjective, and difficult to scale for smallholder farmers worldwide. To overcome these issues, we present Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification. CTL-Net, which combines Inception-ResNet-v2 as the backbone and MobileNetV3 as a feature extractor in parallel convolutional streams, simultaneously learns diverse scales of texture information from low-level textures, mid-level structural patterns, and high-level disease semantics of RGB leaf images. Adaptive feature fusion is formulated through learnable weighting coefficients and bi-directional spatial–channel attention mechanisms, which further enhance feature discriminability and robustness. The proposed approach is tested on an extensive dataset of 12,456 images from 10 corn diseases, including Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Cercospora Leaf Spot, acquired under controlled and real-field conditions. CTL-Net attains the highest classification accuracy of 99.42%, outperforming DenseNet121 (97.92%), EfficientNetB3 (97.35%), and general stacking models (97.89%). Robustness experiments demonstrate the effectiveness of the proposed method against illumination variations, additive noise, and partial occlusions. CTL-Net enables real-time inference with a latency of 42 ms on an NVIDIA RTX 3090 GPU. Gradient-weighted Class Activation Mapping++ (Grad-CAM++)-based interpretability analysis results in a mean Intersection over Union (IoU) of 87.6% with expert-annotated disease regions. Five-fold cross-validation, ablation studies, and statistical significance testing (p
Why it matches plant phenotyping methodsトウモロコシ葉画像から病徴・病害状態を推定する深層学習手法を開発し、複数モデルとの比較、頑健性評価、交差検証、アブレーションを行っており、植物フェノタイピング手法が中心である。
abstractwe present Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification.
Reproduction assets foundThe paper states its final curated corn leaf disease dataset (12,456 images, 10 classes) is publicly available via the authors' GitHub repository vishruthkp/maizedataset (reference [30] and Data Availability statement). The Kaggle PlantVillage and Corn or Maize Leaf Disease datasets are cited source inputs, not paper-Dataset · public"Prediction of Crop Yield using Machine Learning," International Available: https://github.com/vishruthkp/maizedataset.Open asset ↗vishruthkp/maizedatasetpdf-page:7 lines:1-53Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Automated plant disease recognition from field photographs remains challenging: models trained on laboratory datasets collapse on in-the-wild images, and the current state of the art on PlantWild-the largest open in-the-wild benchmark (18,542 images, 89 classes)-relies on text prototypes derived from a language model to reach 76.18% top 1 accuracy. We ask whether text supervision is truly necessary or whether the bottleneck is the diversity of the visual representation. Our central methodological finding is that backbone selection and fusion matter far more than classifier-head engineering : across 118 experiments, a simple fixed-weight linear-prototype combination on top of three complementary frozen backbones yields larger and more reproducible gains than any head-level adaptive routing mechanism we test. Specifically, through a systematic study of six frozen vision backbones (three CLIP, two DINOv3, and one DINOv2), three classifier heads, and four fusion configurations, completed in a single day on one consumer GPU, we establish three findings. (i) A single self-supervised backbone (DINOv2 ViT-L/14) already surpasses text-augmented MVPDR (77.56% vs. 76.18%). (ii) Concatenating three complementary backbones (DINOv2 + DINOv3 + CLIP) reaches 80.23% ± 0.41% (five seeds), exceeding the published MVPDR accuracy by +4.05 points and our own reproduction of MVPDR under an identical evaluation protocol by +7.96 points, without any language supervision. (The difference between the two deltas reflects evaluation-protocol differences-our split, model-selection criterion, and training schedule-rather than any discrepancy in the reported numbers; see Section 4.6.6 for a full reconciliation.) (iii) Every form of learned routing we test-per-class gating, backbone gating, sample-wise gating-is inessential; the gain is entirely attributable to backbone diversity and a simple linear-prototype scoring combination. On the smaller PlantDoc benchmark, the same principle transfers but with substantially higher seed variance: the best configuration reaches 80.09% at a favourable seed but 76.97% ± 1.48% over five seeds-a suggestive rather than robust gain. Beyond the accuracy headline, we provide a pathology-aware per-class analysis showing that DINOv2/v3 dominate on fine-texture lesion classes (rusts, mildews, and leaf spots) whilst CLIP's narrow advantage concentrates on organ/species-level identification (rice leaf and potato late blight). All primary claims are validated over five seeds, and all code, feature caches, and result files are released for full reproducibility.
Why it matches plant phenotyping methods植物病害の画像認識を対象に、複数の視覚基盤モデル融合と分類器を体系比較・検証しており、病害状態の推定手法が研究の中心である。
abstractOur central methodological finding is that backbone selection and fusion matter far more than classifier-head engineering
Reproduction assets foundThe paper states that its code, cached features, and result files will be released, but no authors' public URL or repository is provided in the supplied blocks (future-tense availability language only). The DINOv3 GitHub link and OpenReview link are third-party backbone resources, not paper-specific assets.Code · publicAll primary claims are validated over five seeds, and all code, feature caches, and result files are released for full reproducibility.Open asset ↗lines:332-335Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published11 Aug 2026Engineering, Technology & Applied Science ResearchCited by 0 · OpenAlex ↗
In recent years, areca nut plants have been vulnerable to different diseases that appear as distinct colors on leaves, caused by bacteria or fungi. These symptoms disrupt photosynthesis and reduce yield, affecting productivity and crop health. Therefore, accurate plant disease classification is essential for detecting distinct disease shapes and sizes. Existing Deep Learning (DL) models have several limitations that prevent them from distinguishing between various plant diseases due to similar characteristics. To overcome this limitation, a Dynamic elastic boundary strategy Sailfish Optimization Algorithm and Convolution Neural Network (DSFO-CNN) method is proposed to identify and accurately classify arecanut plant diseases. The Visual Geometry Graph-19 (VGG-19) model extracts features that have significant information about disease in arecanut plants. The proposed arecanut plant disease classification model employed feature selection and drop cyclic learning rate, which adjusts the CNN learning rate to efficiently learn the subtle information about various leaf and nut diseases to enhance classification. The experimental results of the DSFO-CNN demonstrate superior performance compared to existing approaches.
Why it matches plant phenotyping methodsアレカヤシの葉・果実に現れる病徴を画像から分類するCNNベース手法を提案・評価しており、植物病害状態の取得・推定が中心的な方法論的貢献である。
abstractTherefore, accurate plant disease classification is essential for detecting distinct disease shapes and sizes.
Reproduction assets foundThe paper's phenotyping inputs are two public image datasets: the collected Arecanut dataset (Kaggle) and the PlantVillage dataset (Kaggle), both explicitly cited and declared openly available. No author analysis code or trained model is released.Dataset · publicDATA AVAILABILITY
The data used in this study are openly available at [19] and
[20].Open asset ↗pdf-page:7 lines:1-63Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants from different accessions grown under different water and nitrogen conditions. From these time series, leaf-level growth dynamics were reconstructed and used to calculate key developmental traits, including phyllochron and relative expansion rates. These measurements were further integrated into the GreenLab model to infer plant-scale parameters such as radiation use efficiency (RUE) and leaf demand parameters. Statistical analyses revealed significant genotype, treatment, and interaction effects on several traits, with stable genotype rankings but contrasted sensitivities to environmental conditions. While whole-rosette growth captured strong and consistent responses, individual leaf contributions to global growth remained limited, highlighting the integrative nature of rosette-scale dynamics. Model-derived parameters provided additional insight into plant strategies, showing that genotypes differ in both the duration and timing of leaf demand, with generally stable patterns across environments. In parallel, QTL mapping was performed using leaf-level measurements and phyllochron traits. This analysis identified shared and trait-specific genetic loci, including loci associated with leaf emergence dynamics that were not detected using traditional rosette-scale integrative traits. Overall, this study demonstrates that combining deep learning-based image analysis with mechanistic modeling enables large-scale and quantitative characterization of plant development, providing biologically interpretable traits and new insights into their genetic and environmental determinism. It highlights the potential of combining high-throughput image-based phenotyping, deep learning, and mechanistic modeling to generate biologically interpretable traits and to assess genetic and environmental influences on these traits.
Why it matches plant phenotyping methods深層学習による葉の自動セグメンテーション・追跡を開発的に適用し、時系列画像から葉レベルおよび植物体レベルの発達形質を定量化しているため、表現型取得・抽出が研究の中心である。
abstractleaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants
Reproduction assets foundThe paper explicitly states that the authors' leaf segmentation software (AraLeaf_segmentation), the GreenLab Arabidopsis model (AraGreenlab), and the statistical analysis code (AraParameters_statistical_analyses) are open-source and publicly hosted on forge.inrae.fr, and that the data and results used in the paper areCode · publicThe leaf segmentation software (AraLeaf_segmentation) and the GreenLab model of Arabidopsis thaliana (AraGreenlab) are open-source software, and distributed under the GNU GPL v3 licence. The two software packages, and the code to run statistical analyses (AraParameters_statistical_analyses) are available at:https://forge.inrae.fr/phenoscope-public/plant_phenomics_suppmat.Open asset ↗phenoscope-public/plant_phenomics_suppmathtml-lines:419-444Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction This study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations. Conventional morphometric analyses of olive endocarps largely rely on manual measurements, limiting reproducibility and quantitative comparison. Methods Ten archaeological endocarps were selected from previously published archaeological assemblages based on the integrity of their outlines, apex-base morphology and overall preservation quality. Quantitative descriptors describing endocarp size, symmetry, curvature and contour geometry were extracted using the OliveID software and compared with a modern morphometric reference database comprising Greek and international olive cultivars. Results Reliable contour extraction and quantitative descriptor computation were successfully achieved for all archaeological specimens despite carbonization. Preliminary comparison of representative morphometric descriptors showed that the archaeological specimens were positioned within the morphometric variation observed among the modern reference collection. Hierarchical clustering consistently associated the archaeological endocarps with the modern Throumbolia morphotype, while distinguishing them from elongated, globular and mucro-bearing cultivars. Discussion These findings demonstrate the feasibility of applying digital image-based morphometric analysis to sufficiently preserved archaeological carbonized olive endocarps and indicate a similar morphometric affinity between the analyzed archaeological material and the modern Throumbolia cultivar. This proof-of-concept study highlights the potential of digital morphometric approaches for quantitative archaeobotanical investigations of archaeological olive remains, while emphasizing the need for larger archaeological datasets and standardized image acquisition to further validate the observed morphometric similarity.
Why it matches plant phenotyping methodsOliveIDを用いて炭化オリーブ内果皮の輪郭からサイズ、対称性、曲率、形状記述子を抽出し、デジタル画像形態計測の適用可能性と再現性を評価している。植物器官の形質抽出法が研究の中心である。
abstractThis study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe quantitative measurements of the archaeological specimens are presented in Supplementary Table 2 , whereas the corresponding mean values and standard errors for the modern cultivars are provided in Supplementary Table 3 .Open asset ↗lines:311-320Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus .
Why it matches plant phenotyping methodsスパーステスト設計とゲノム予測を用いて、複数環境での植物形質予測と表現型測定コスト削減を評価しており、表現型取得・予測手法が研究の中心である。
abstractSparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations.
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit (DOI 10.6084/m9.figshare.31796794) containing the datasets analyzed in this Miscanthus sparse-testing genomic prediction study, including the phenotypic and genotypic data used for the models.Dataset · publicThe datasets analyzed for this study can be found in the figshare repository at https://doi.org/10.6084/m9.figshare.31796794 .Open asset ↗figshare · 10.6084/m9.figshare.31796794lines:603-621Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reliable traits are needed for identification of tea ( Camellia sinensis ) cultivars, yet the stability of leaf morphology and color across leaf positions remains unclear. This study evaluated inter-cultivar variation and positional stability in leaf morphological, RGB color, and SPAD traits in six predominant cultivars. One-year-old shoots were sampled in a completely randomized design, and five fully expanded leaves below the apical bud were analyzed. SPAD values were measured with a chlorophyll meter, and scanned images were used to extract contour and RGB traits. Data were analyzed using ANOVA, correlation analysis, PCA, and discriminant analysis. Leaf morphology differed among cultivars and leaf positions, with significant cultivar-by-position interactions; however, the width-to-length ratio differed among cultivars but remained stable across positions in these cultivars. SPAD values increased with leaf position and were strongly associated with RGB components, being negatively correlated with R and G and positively correlated with B. Morphological traits explained 52.988% of total variance in PCA and yielded 64.6% overall classification accuracy, with LaoHan showing the highest accuracy (83.3%). Misclassification was concentrated among genetically similar cultivars. These findings suggest that stable leaf shape proportions and SPAD-RGB relationships provide useful descriptors, whereas genetic relatedness limits morphology-based cultivar identification under the present conditions.
Why it matches plant phenotyping methods茶品種識別のため、葉の形態・RGB・SPAD特性の取得と安定性、分類性能を中心に評価しており、画像由来形質抽出を含む実質的な表現型解析である。
abstractscanned images were used to extract contour and RGB traits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology15151283/s1 , Table S1: Original data of leaf morphological traits, RGB values, and SPAD values from six tea cultivars in this study.Open asset ↗lines:368-409Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Idesia polycarpa Maxim. is a premier woody oil species in Guizhou Province, China, whose fruit yield and oil quality largely depend on effective pollination and fertilization. However, limited research on pollen viability and germination has hindered industrial progress. To address this gap, a comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay. Scanning electron microscopy (SEM) revealed that I. polycarpa pollen, while genetically conserved at the genus level-characterized by prolate shapes, tricolporate apertures, and reticulate exine ornamentation-exhibits notable micromorphological variation among genotypes. Of the nine staining protocols tested (2,3,5-triphenyl tetrazolium chloride [TTC], carbol fuchsin, acetocarmine, methylene blue, Alexander, peroxidase, 2,5-diphenylmonotetrazolium bromide [MTT], I2-KI, and red ink), TTC and red ink were the most effective, offering clear chromatic distinction between viable and non-viable pollen. Through orthogonal experimental designs, genotype-specific optimal media for in vitro germination were identified: 0.40 g/L H3BO3, 0.01 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.20 g/L KH2PO4 for STZ-6; and 0.20 g/L H3BO3, 0.02 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.10 g/L KH2PO4 for STZ-9. Regression analysis confirmed a highly significant positive correlation (P < 0.01) between in vitro germination rates and the staining results from both TTC and red ink across various concentrations. Notably, 5% TTC and 30% red ink exhibited the highest coefficients of determination. A hierarchical evaluation strategy is thus proposed: the 5% TTC method is recommended for precise laboratory quantification due to its stability, while the 30% red ink method, due to its ease of use, is suited for rapid field-based screening. This study provides valuable insights into the morphological characteristics of I. polycarpa pollen and establishes a standardized evaluation framework, supporting germplasm innovation and optimizing pollination management.
Why it matches plant phenotyping methods花粉の生存性・発芽という植物の生殖形質を対象に、染色法とin vitro発芽法を最適化・検証し、標準化した評価フレームワークを開発しているため、方法論が中心である。
abstracta comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay.
Reproduction assets foundThe article's Data Availability statement points to a public Biostudies deposit containing the study's data (pollen morphology measurements, staining viability counts, and in vitro germination results). No author analysis code or trained models are mentioned.Dataset · publicData Availability: The data that support the findings of this study are openly available in Biostudies at https://doi.org/10.6019/S-BSST3125 .Open asset ↗Biostudies · S-BSST3125lines:176-186Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, TN, USA. Two hundred transgenic switchgrass plants (Panicum virgatum L. "Performer") were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a switchgrass ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high- and low-volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
Why it matches plant phenotyping methods固定・ドローン搭載サンプラー、蛍光測定、風況モデルを組み合わせて植物由来の花粉放出率を推定し、花粉測定技術を評価することが中心であるため、植物の生殖状態・放出特性に関するフェノタイピング手法として採用。
abstractPollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices
Reproduction assets foundThe paper's Data Availability statement deposits all sampling data, modeling code, and simulation results on the Virginia Tech Data Repository (DOI 10.7294/25733604), which is an allowed URL. This directly covers the paper's pollen concentration measurements and Lagrangian stochastic dispersal modeling. Other URLs (e.gDataset · publicAll sampling data, modeling code, and simulation results underlying this manuscript are made available on the Virginia Tech Data Repository at https://doi.org/10.7294/25733604 .Open asset ↗Virginia Tech Data Repository · 10.7294/25733604lines:201-219Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Sugarcane plant diseases pose a significant threat to agricultural productivity, yet early and accurate identification remains challenging for farmers due to the limitations of manual inspection. This study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny, a modern convolutional architecture with a Global Response Normalization (GRN) mechanism, combined with an ensemble Stratified K-Fold Cross Validation strategy (K=6) to improve generalization on real-world field data. A dataset of 2,948 leaf images spanning five classes (Red Rot, Mosaic, Rust, Yellow Leaf, and Healthy) was used, with field-collected images held out as a fixed test set. The ensemble model achieved a mean validation accuracy of 98.49% ± 0.58% across six folds and a test accuracy of 98.39% on 427 unseen field images, with macro-average precision, recall, and F1-score each reaching 98%. ConvNeXt V2 Tiny substantially outperformed ResNet-50 (87.35%) and EfficientNetV2-S (83.37%) under identical experimental settings, demonstrating superior generalization across the domain gap between curated and field data. The primary contribution of this study is the first application of ConvNeXt V2 Tiny with ensemble K-Fold strategy for sugarcane disease classification, offering high accuracy with moderate computational complexity (28.6M parameters) and practical deployability, as demonstrated through the SugarScan web application.
Why it matches plant phenotyping methodsサトウキビ葉画像から病害状態を推定する画像ベースの表現型解析手法が研究の中心であり、モデル性能の検証・比較も実施しているため含める。
abstractThis study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny
Reproduction assets foundThe paper's phenotyping inputs include a public Kaggle dataset (Sugarcane Leaf Disease Dataset, SLD) of sugarcane leaf disease images used for training/validation, plus field-collected images. Only the Kaggle dataset qualifies as a paper-specific public asset with an authors' URL; no author analysis code, trained modelDataset · publicsecondary data from the Sugarcane Leaf Disease Dataset (SLD)
available publicly on Kaggle (https://www.kaggle.com/datasets/pritpal2873/sug arcane-leaf-disease-dataset)Open asset ↗Kaggle · pritpal2873/sugpdf-page:2 lines:54-60Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Introduction: Habitat restoration is necessary for the conservation and management of plant and animal species, especially in rare ecosystems. Drones may be well-suited to monitor changes in plant and animal communities in response to restoration efforts. The objective of the study was to examine whether drone imagery can detect differences in vegetation across multiple contexts. Materials and methods: Using a commercially available drone, I captured and processed aerial imagery with an open-source photogrammetric processing program. Point cloud data were processed to generate a vegetation density index, which was quantified across four cover types and compared between disturbance histories. In addition, using automated radio tracking, I compared vegetation density between used and available locations for Eastern Whip-poor-wills during the day and at night. Results: In August 2024, a drone flight covering a 3.05 km2 area of pine barrens captured 3372 images. Vegetation density differed by cover type (p = 0.001) and was greater in recently disturbed sites (p = 0.002). Scrub oak and recently burned sites had ~30% and ~12% greater vegetation density than deciduous forests and plots > 2 years post-disturbance, respectively. Vegetation density was lower at Eastern Whip-poor-will used locations than at available locations (151.0 vs. 159.7 points/m2, p < 0.001). Conclusions: Analysis of fine-scale differences in vegetation structure was important in discriminating subtle differences in habitat selection for Eastern Whip-poor-wills. This study demonstrated that drones and relatively simple image processing can be practical tools for restoration when quantifying and monitoring vegetation differences in dynamic ecosystems.
Why it matches plant phenotyping methodsドローン画像と点群処理により植生密度・植生構造を定量化する手法を中心に、異なる植生条件での適用性を評価しているため、植物表現型計測の方法適用研究に該当する。
abstractPoint cloud data were processed to generate a vegetation density index, which was quantified across four cover types and compared between disturbance histories.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's drone-derived vegetation density data and related measurements.Dataset · publicThe data supporting the findings of this publication has been made available within a publicly accessible
repository at https://doi.org/10.5281/zenodo.20398090.Open asset ↗Zenodo · 10.5281/zenodo.20398090pdf-page:11 lines:1-49Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Timely and accurate detection of plant diseases is essential for ensuring global food security and supporting sustainable agriculture. Conventional diagnostic approaches, such as manual inspection and laboratory testing, are often time-consuming, labor-intensive, and impractical for large-scale or remote agricultural environments. Although deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved automated plant disease classification, they often lack interpretability and struggle to generalize under diverse field conditions. Methods This study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance. The SE blocks recalibrate channel-wise feature responses to emphasize disease-relevant information while suppressing background noise. To improve model transparency, Grad-CAM, SHAP, and LIME were incorporated to provide visual and feature-level explanations of the model’s predictions. The framework was trained and evaluated using the Plant Pathology 2020 dataset containing four classes: healthy, rust, scab, and multiple diseases. Results AgriX-SENet achieved a training accuracy of 97.47% and a validation accuracy of 95.07%, outperforming fourteen state-of-the-art deep learning models. The classification report demonstrated high precision and recall across most disease categories, although the scab class exhibited comparatively lower recall, indicating an opportunity for further improvement. The explainability analyses consistently showed that the model focused on pathologically relevant regions of leaf images, validating the reliability of its predictions. Discussion The proposed AgriX-SENet framework effectively combines high classification performance with model interpretability, addressing a key limitation of existing CNN-based plant disease detection systems. Its ability to provide accurate and explainable predictions makes it a promising solution for scalable agricultural diagnostics. Future work will focus on improving classification performance for challenging disease categories and optimizing the framework for deployment on mobile and edge computing devices to enable real-time field applications.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する説明可能な深層学習フレームワークを開発・評価しており、植物表現型取得・判定手法が中心である。
abstractThis study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance.
Reproduction assets foundThe paper trains and evaluates AgriX-SENet on the public Plant Pathology 2020 (FGVC7) Kaggle image dataset, which is the paper-specific plant image input for its disease-classification measurements. No author analysis code, trained model checkpoints, or supplementary code/data deposit is mentioned; the data statement (Dataset · publicPlant Pathology 2020 - Fgvc7 . Available online at: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .Open asset ↗Kaggle · plant-pathology-2020-fgvc7lines:895-974Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (ηp2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer (ηp2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems.
Why it matches plant phenotyping methodsRGB画像から芝草キャノピー色を定量化するΔEgなどの指標を導入・比較し、クロロフィルや品質評価との技術的関連性を検証しており、植物表現型取得法が中心である。
abstractWe introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics.
Reproduction assets foundThe paper's Data Availability Statement deposits the phenotype data and the authors' Python image-processing/metric-computation scripts and R statistical analysis scripts in the USDA National Agricultural Library Ag Data Commons, a public repository. The full RGB imagery archive, however, is only available upon requestCode · public2025;23:673–687. doi: 10.1002/lom3.10705.
Associated Data
Data Availability Statement
Data and Python scripts used for image processing and %G, %Gr, %Y, ΔEg, DGCI, HSVi, BA SD , CIELUV v* metric computation, and R scripts used for statistical analysis are be available in the USDA National Agricultural Library Ag Data Commons ( https://agdatacommons.nal.usda.gov/ ), Data for—Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions, accessed on 27 July 2026. The full RGB imagery archive will be made available upon reasonable request.Open asset ↗USDA National Agricultural Library Ag Data Commonslines:691-695Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Covered smut in barley caused by Ustilago hordei leads to yield reduction and quality loss of stored grains and is especially challenging in organic production. However, screening for resistance remains challenging. The goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes. We considered favorable pathogen growth conditions, a sufficient sample size to detect differences among genotypes through a power analysis, sources of disease escape or avoidance, and the infection effect on agronomic traits. Results In the first experiment, twenty genotypes treated with various inoculum concentrations were screened for disease incidence under a speed breeding system. Generally, low infection levels were found, likely due to disease escape or avoidance. Based on a power analysis, we modified the protocol to include more plants and improved pathogen growth conditions under a normal greenhouse system. With the modified protocol, the incidence of covered smut was significantly different among genotypes. The protocol also reduced the number of plants required to detect at least one infected plant. Artificial inoculation significantly decreased germination rates while head emergence, days to heading, and plant height were affected by disease infection in the most susceptible genotypes. We also found that covered smut incidence varied with tiller emergence order. The genotypes 'DH160779' (RES check), PI 270630', 'CIho15270', and 'MTV-color-158' presented potential resistance to covered smut. Conclusion The protocol has a high power to differentiate moderately resistant barley genotypes and we confirmed that specific agronomic traits were affected by disease incidence in susceptible genotypes.
Why it matches plant phenotyping methodsオオムギ病害の抵抗性スクリーニングプロトコルを評価・改良し、検出力と遺伝子型間の識別性能を検証しているため、植物病害表現型の取得法が研究の中心です。
abstractThe goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes.
Reproduction assets foundThe paper's disease-screening and agronomic-trait measurement data are publicly deposited on Zenodo, as stated in the Availability of data and materials section. No author analysis code or trained models are explicitly deposited.Dataset · publicThe data used and/or analyzed in the current study are available through the Zenodo, which is available at Gopinathan, G. (2025). Optimization of a protocol for covered smut in barley [Dataset]. Zenodo. [ 47 ] (https:/doi.org/ https://doi.org/10.5281/zenodo.17906264 ).Open asset ↗Zenodo · 10.5281/zenodo.17906264lines:190-223Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotLeafClassificationDisease symptoms / severity
Early and reliable diagnosis of hibiscus leaf diseases is critical to protect horticultural yield. Yet, it remains challenging under real-time field conditions where uncontrolled lighting, clutter, and the non-contiguous nature of pathological symptoms blur diagnostic cues. To address these challenges, we introduce CNN-FusionViT-GNN. This explainable hybrid multi-branch framework synergizes the fine-grained texture extraction of a DenseNet201 backbone, the global contextual modeling of a Vision Transformer (ViT), and the relational reasoning of a Graph Neural Network (GNN). The model is trained and validated on 'Hibiscus,' a curated field dataset of 1165 images from Bangladesh, which is strategically augmented to 8000 samples for robust training following a strict train-validation-test split. The proposed framework achieves a state-of-the-art accuracy of 98.33% with a macro F1-score of 0.98. The framework's generalization is confirmed through high performance on external datasets: 98.78% accuracy on the 52-class Plant City dataset and 83.88% on the 10-class Tomato Leaf Disease dataset, while maintaining a rapid inference time of 10-45 ms. Furthermore, a multi-faceted Explainable AI (XAI) audit using LIME, Grad-CAM++, ViT Attention Maps, and Occlusion Sensitivity validates that the model's decisions are driven by biologically meaningful symptom patterns rather than background artifacts. This study establishes a computationally efficient, transparent, and robust pathway for automated disease diagnosis in precision agriculture.
Why it matches plant phenotyping methodsハイビスカス葉の病徴を画像から分類するCNN-ViT-GNN手法を開発し、複数データセットで性能検証しているため、植物病害表現型の取得・推定が中心である。
abstractwe introduce CNN-FusionViT-GNN. This explainable hybrid multi-branch framework synergizes the fine-grained texture extraction of a DenseNet201 backbone, the global contextual modeling of a Vision Transformer (ViT), and the relational reasoning of a Graph Neural Network (GNN).
Reproduction assets foundThe paper's primary Hibiscus leaf disease image dataset is publicly deposited on Mendeley Data, and the external Tomato Leaf Disease dataset used for validation is also publicly available on Mendeley Data. No author analysis code or trained model checkpoints are reported.Dataset · publicThe primary dataset generated and analyzed during the current study,“Hibiscus Leaf Diseases Classification Dataset,”is publicly available in Mendeley Data 7 .Open asset ↗Mendeley Datalines:307-347Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract Blanket-rate agrochemical scheduling — a practice wherein the same quantity of fertilizer or pesticide is spread uniformly across an entire field irrespective of spatial or temporal crop need — persists as the dominant farm management paradigm across rural India and large parts of South Asia. This approach generates cascading inefficiencies: excess nitrogen drains into waterways, off-target pesticide deposits devastate pollinators, input costs erode thin profit margins, and wide-scale greenhouse gas release from soil microbial activity accelerates climate change. The study documented here addresses this challenge through a purpose-built, four-layer intelligent field management platform. The platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack. A fine-tuned YOLOv8-L network performs real-time pest and foliar disease localisation; a ResNet-50 backbone quantifies canopy health across five stress gradients; a two-layer stacked LSTM projects short-horizon yield trajectories; and a Deep Q-Network autonomously plans drone spray routes weighted by field-specific prescription maps. Field validation spanned two consecutive growing seasons (Rabi 2022–23 and Kharif 2023–24) across six georeferenced plots covering 4.8 ha at Baramati, Maharashtra. Outcome metrics recorded during head-to-head comparison with conventional practice included a disease detection score of 95.6% mAP, a 47.3% reduction in total nitrogen applied, a 38.1% decrease in pesticide volume, and a 22.4% uplift in harvested grain weight. Together, these field-verified numbers substantiate the operational readiness of integrated AI precision agriculture for smallholder deployment.
Why it matches plant phenotyping methodsマルチスペクトル画像・深層学習による病害局在化とキャノピー健康状態の定量化を中核機能とする統合プラットフォームであり、植物の病害状態・生育状態を直接推定して現地検証している。
abstractThe platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicData Availability The annotated image dataset (14,300 images, 23 classes), trained YOLOv8-L and ResNet-50 weights, LSTM model files, DQN policy checkpoint, and all analysis scripts are archived at https://github.com/precision-agri-ai (Zenodo DOI: 10.5281/zenodo.XXXXXXX).Open asset ↗precision-agri-ailines:161-182Code / dataset availability confirmedOpenAlex · checked 11 Sept 2026
Unmanned aerial vehicle (UAV) photogrammetry offers a cost-effective approach to tree-level detection, however, Structure-from-Motion (SfM) outputs are sensitive to processing choices and site conditions, which can alter canopy representation and reduce individual-tree detection accuracy. Here, we systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection under controlled acquisition conditions. Objectives were to (i) identify an optimal SfM-derived point-cloud configuration for delineating individual trees, and (ii) implement and test a segmentation workflow (local-maxima treetop detection plus Dalponte2016 in lidR) for detecting and counting trees. We assessed RGB-only SfM for individual-tree detection (ITD) across thirteen 1.21-ha loblolly pine ( Pinus taeda ) plots located in two counties in the state of Alabama in the southeastern United States; eight even-aged plantations and five mixed pine-hardwood stands, while holding image acquisition parameters constant. Using Agisoft Metashape Professional (Agisoft LLC, St. Petersburg, Russia), dense-cloud quality (Lowest, Low, Medium, High, Ultra High) and depth-map filtering (Disabled, Mild, Moderate, Aggressive) were varied in a 5 × 4 full-factorial design; assessment metrics included point-cloud density, canopy-surface completeness, canopy-height-model (CHM) agreement with field heights, and ITD precision/recall/F1. We identified a single high-resolution configuration (Ultra High + Disabled) by screening parameter sets for structural accuracy and suppression of false peaks. Using this configuration, CHMs matched field heights in Washington County, Alabama (R 2 = 0.96; RMSE = 0.44 m; bias = − 0.01 m) and in Cullman County, Alabama (R 2 = 0.44; RMSE = 1.14 m; bias = − 0.09 m); pooled performance was R 2 = 0.98; RMSE = 0.54 m; bias = − 0.01 m. ITD accuracy at the primary 3 m match radius yielded a precision of 0.03; recall = 0.29; F1 = 0.05 in the even-aged plantations (Washington) and a precision of 0.03; recall = 0.12; F1 = 0.05 in mixed pine–hardwood stands (Cullman); pooled F1 = 0.05. The selected parameters and workflow are reproducible and transferable, provide insight into RGB-SfM ITD performance, and indicate when lidar remains preferable for crown delineation.
Why it matches plant phenotyping methodsRGB-SfMによる個体樹の検出・樹高推定と、SfM設定およびセグメンテーションワークフローの系統的評価が研究の中心であり、植物の樹冠構造・樹高という形態形質を抽出する方法を検証している。
abstractwe systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection
Reproduction assets foundThe paper's Code availability statement deposits the authors' SfM/ITD processing scripts publicly on OSF (DOI 10.17605/OSF.IO/UXBCZ). Phenotype/field datasets are only available on request, so they are not public assets.Code · publicThe workflow and processing scripts used in this study are publicly available through the Open Science Framework
(OSF) repository: Singh and Narine, [32]. Code Repository for Optimizing SfM Parameters for RGB-Only Individual-Tree
Detection in Loblolly Pine and Mixed Pine-Hardwood Stands. https://doi.org/10.17605/OSF.IO/UXBCZ.Open asset ↗10.17605/OSF.IO/UXBCZpdf-page:12 lines:1-70Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant diseases significantly affect agricultural productivity and global food security, while accurate disease identification remains challenging because of uncertain and overlapping visual symptoms in leaf images. Existing deep learning approaches often require large annotated datasets and suffer from limited interpretability in practical agricultural environments. This study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset. The objective is to develop an interpretable and reliable classification model capable of handling uncertainty in plant disease patterns through feature-driven fuzzy similarity analysis. The methodology integrates image preprocessing, color and texture feature extraction, variance-based feature weighting, prototype generation using K-means clustering, and fuzzy similarity computation using Mahalanobis distance and Gaussian membership functions. RGB, HSV, and Gray-Level Co-occurrence Matrix (GLCM) features are extracted from standardized leaf images and evaluated within an improved fuzzy soft classification framework. Performance comparison is carried out using machine learning models including Support Vector Machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), and Naive Bayes (NB) implemented in Python using Scikit-learn libraries. Experimental simulation results demonstrate that the proposed framework achieves competitive classification performance while preserving interpretability and robustness under uncertain feature distributions. Performance evaluation is conducted through accuracy analysis, ROC-AUC curves, confusion matrices, ablation studies, and Wilcoxon Signed-Rank statistical testing. The proposed Improved Fuzzy Soft model achieved an accuracy of 88.57% which is less than LDA (94.92%), Random Forest (97.78%) and SVM (97.94%) classifiers. However, in the cross data set validation, the proposed Improved Fuzzy Soft model achieved an accuracy of 67.35% which is greater than LDA (51.02%), Random Forest (51.02%) and SVM (55.10%) classifiers. Statistical validation using the Wilcoxon Signed-Rank Test produced a p-value of [Formula: see text], confirming that the performance difference between the Improved Fuzzy Soft framework and the Random Forest classifier is statistically significant under the current experimental setting.
Why it matches plant phenotyping methodsトマト葉画像から植物病害状態を推定する解釈可能な画像解析・分類フレームワークを開発し、複数モデル、交差データセット検証、アブレーション、統計検定で評価しており、病害表現型の取得・抽出手法が中心である。
abstractThis study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset.
Reproduction assets foundThe paper uses public tomato leaf image datasets (PlantVillage and PlantDoc from Kaggle) as phenotyping inputs and states the authors' Improved Fuzzy Soft Framework implementation is publicly available on Zenodo with source code and reproduction instructions.Dataset · publicThe dataset analyzed during the current study are available in the repository:
https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗kaggle.com/datasets/abdallahalidev/plantvillage-datasetpdf-page:24 lines:1-75Code · publicThe implementation of the proposed Improved Fuzzy Soft Framework is publicly available through the Zenodo repository:
https://doi.org/10.5281/zenodo.20570546
The repository contains the source code, documentation, and instructions required to reproduce the experiments reported in
this study.Open asset ↗zenodo · 10.5281/zenodo.20570546pdf-page:25 lines:1-74Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Biotic stress is a major, yet under-quantified, driver of global soybean yield losses, and field-based phenotyping under pest pressure remains a critical bottleneck for crop improvement. Using multi-temporal data from soybean genotypes grown under insecticide-protected and unprotected conditions in Brazil, we present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure. We introduce a three-dimensional metric that jointly captures productivity, feature-level similarity as a proxy for tolerance, and phenological response through days to maturity. This unified formulation enables field-based quantification of pest resilience and replaces labor-intensive and often unreliable direct pest collection and counting. To operationalize this framework, we integrate vegetation indices and self-supervised visual embeddings into a common representation space linking feature stability, performance response and phenological development. This approach enables robust identification of genotypes that maintain feature integrity, minimize developmental delay and sustain yield under pest pressure, with genotypic differences peaking during the pod-fill (R3–R4) and grain-fill (R5.1–R5.5) stages. Overall, this work establishes a scalable, field-ready paradigm for quantifying crop resilience to biotic stress and provides a practical pathway to accelerate breeding for stable yields under real-world agricultural conditions.
Why it matches plant phenotyping methodsUAVによる大規模な圃場フェノタイピング基盤と、植生指数・視覚埋め込みを統合した新しい耐虫性表現型の定量手法が研究の中心である。
abstractwe present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure
Reproduction assets foundThe paper explicitly states that the analysis code is publicly available in the authors' GitHub repository (jianglong26/soybean-insect-resistance), which directly reproduces this paper's phenotyping pipeline (orthomosaic processing, VI/DINOv3 feature extraction, similarity analysis, genotype ranking). The paper also声明sCode · public540 The code used for analysis is available at https://github.com/jianglong26/Open asset ↗pdf-page:16 lines:1-45Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.
Why it matches plant phenotyping methodsUAV multispectral HTPによる小麦黄さび病重症度の推定と、複数の予測モデルの比較・検証が研究の中心であり、植物病害状態を直接推定する実質的なフェノタイピング手法研究である。
abstractHTP data were processed to extract spectral wavelengths and vegetation indices (VIs)
Reproduction assets foundThe article's Data Availability statement deposits the datasets generated and analyzed in this study (yellow rust phenotyping with UAV spectral data and genomic markers in bread and durum wheat) in the CIMMYT repository under DOI 10.71682/10549375, which is an allowed URL. No author analysis code or trained model is avDataset · publicand scalable strategy for YR assessment in wheat breeding.
Funding
The authors gratefully acknowledge financial support from the Government of Mexico through
the “MasAgro – Cultivos para México” initiative.
Data Availability
The datasets generated and/or analyzed during the current study are available in the CIMMYT
repository: https://doi.org/10.71682/10549375.Acknowledgements
We are deeply grateful to Julio Huerta-Espino for his guidance and support throughout all stages
of this manuscript. We also thank Hedilberto Velásquez Miranda for his valuable assistance with
rust visual score phenotyping, and Neftalí Cruz Pérez for his dedicated support in trial sowing and
field management.
Conflict Open asset ↗10.71682/10549375pdf-raw-page:30 lines:1-37Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Orthoimagery derived from unmanned aerial vehicles (UAVs) has become a valuable data source for crop-growth monitoring. Individual plant-level (IPL) information enables high-throughput analyses by capturing plant-to-plant variability within fields. However, reliable IPL-based analysis requires accurate extraction of plant-specific regions, which remains challenging in soybean cultivation due to weed interference and canopy overlap. This study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery. A segmentation model was developed using combinations of RGB and multispectral orthoimagery, and a furrow line detection algorithm was designed to generate IPL ROIs aligned with crop rows. The ensemble model combining U-Net, DeepLabV3+, and SegFormer achieved the most stable performance (F1-score up to 0.94 and IoU up to 0.89). The furrow-guided ROI generation algorithm also accurately estimated crop counts, showing strong agreement with manual observations (R² = 0.90 and RMSE = 6.35). The generated IPL ROIs enabled accurate quantification of growth-related features, with strong agreement between automatically generated and manually delineated ROIs (R² > 0.90). Overall, the proposed preprocessing framework provides a practical and scalable solution for UAV-based high-throughput phenotyping in soybean and other ridge-based cropping systems.
Why it matches plant phenotyping methodsUAV画像から個体単位の植物領域を抽出し、成長形質を定量化する前処理・セグメンテーション手法が研究の中心であるため。
abstractThis study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery.
Reproduction assets foundThe authors state that the complete implementation of their IPL soybean preprocessing pipeline (semantic segmentation + furrow line detection) is publicly available on Zenodo. The annotated sample dataset, however, is only available upon request from the corresponding author, so it is not a public asset.Code · publicThe complete implementation of
this pipeline is publicly available at https://doi.org/10.5281/zenodo.21095307.Open asset ↗zenodo · 10.5281/zenodo.21095307pdf-page:7 lines:1-62Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Aims Climate change is altering northern peatland plant communities, shifting from Sphagnum mosses to vascular plants. This transition impacts ecological functions like carbon sequestration, making long-term vegetation monitoring at the site scale more critical than ever. However, current monitoring methods tend to focus on specific species or functional groups with limited spatial coverage. This study uses remote sensing to infer the spatial structure and temporal variations of peatland plant communities. Location Temperate peatland in Pyrenees Mountains, France (Bernadouze, Vicdessos). Methods Nine plots were selected across diverse microhabitats and sampled three times over the growing season of 2023 (May, June, and July). Plant species abundances were recorded, and 45 vegetation indices were derived from drone and Sentinel-2 multispectral imagery. Five vegetation indices were selected to fit a joint Species Distribution Model (JSDM) and a Random Forests (RF) model, and map species spatial distribution. Principal Coordinates Analysis (PCoA) identified plant community composition, and spatiotemporal variations were quantified in relation to environmental variables. Results Plant species occurrences could be predicted from multispectral imagery using the JSDM, with drone-based inferences (mean R 2 = 0.36) outperforming Sentinel-2 (mean R 2 = 0.29). Model performance was high for abundant species ( R 2 > 0.5), whereas predictions for rare species were less accurate ( R 2 R 2 > 0.65, P R 2 = 0.40; P R 2 = 0.04; P Conclusion This study demonstrates that drone multispectral imagery can be used to predict peatland vegetation richness and community composition and capture fine-scale heterogeneity in a small and fragmented peatland site, outperforming satellite data in spatial precision. Although our model was less accurate using satellite imagery, the use of Sentinel-2 imagery enabled long-term community tracking. By combining both, our predictive modelling framework provides a promising preliminary tool to monitor climate-induced shifts in species distributions, supporting targeted conservation.
Why it matches plant phenotyping methodsドローンおよび衛星マルチスペクトル画像から植物種の空間分布、植生多様性、群集組成を推定する画像・モデリング手法が研究の中心であり、植物状態の測定に直接結びつく。
abstractThis study uses remote sensing to infer the spatial structure and temporal variations of peatland plant communities.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicCodes to replicate main analyses are available at https://github.com/vjassey/peatland_vegetation_mapping .Open asset ↗vjassey/peatland_vegetation_mappinglines:369-375Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Detecting cotton leaf diseases in open-field environments is challenging due to cluttered backgrounds, scale variation, and irregular lesion morphology. Conventional detectors rely on isotropic receptive fields and coupled box-regression losses, which limit their ability to localize elongated lesions with poorly defined boundaries. We present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages. In the backbone, an Anisotropic Morphological Contrast Aggregation module (AMCA) enhances direction-aware representation and lesion-background contrast via re-parameterizable strip convolutions and high-frequency residual extraction. A Dynamic Semantic Boundary Transfer mechanism (DSBT) then captures boundary priors from shallow layers before they are lost to downsampling and injects them into the neck. A Morphological-Spectral Synergistic Feature Pyramid Network (MFS-FPN) preserves these cues during multi-scale fusion through spatial-domain operations compatible with edge hardware. Finally, an Anisotropic Boundary-Decoupled IoU loss (ABD-IoU) independently penalizes each of the four box boundaries and sustains optimization signals in high-IoU regimes via a logarithmic modulation factor. On the self-constructed Complex Cotton Leaf Disease dataset (CCLD; 6,856 images, 6 classes), the method achieves 78.50% mAP@50 and 65.00% mAP@50:95, improving the YOLOv11n baseline by 4.80% and 2.70% with only 2.73 M parameters at 202 FPS. Cross-domain evaluations on PlantDoc and RWD confirm consistent improvements. The framework runs in real time on NVIDIA Jetson edge platforms with INT8 quantization.
Why it matches plant phenotyping methods綿花葉の病斑・病害状態を画像から検出する手法を開発し、複数データセットとベースラインで性能検証しているため、植物表現型取得が中心である。
abstractWe present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages.
Reproduction assets foundThe authors explicitly state that their source code, trained models, and implementation details are publicly available, and the data availability statement points to the same repository, which hosts the self-constructed CCLD cotton leaf disease dataset (6,856 images, 6 classes) used for the paper's phenotyping/disease-Code · publicFurthermore, to facilitate future research, our source code, trained models, and implementation details have been made publicly available at https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:331-343Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:1278-1317Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
To address this challenge, we propose StyleGAN3-T, the translation-equivariant alias-free variant of StyleGAN3, as a generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition. Accurate detection of plant leaf diseases is essential for sustainable agriculture and early intervention. However, deep learning models often struggle with small, imbalanced datasets that limit generalization and robustness. To address this challenge, we propose StyleGAN3-T, a novel alias-free generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition. The proposed approach ensures translation-equivariant, artifact-free image synthesis and enhanced feature diversity. A balanced dataset of 18,000 images was developed by combining real and StyleGAN3-T-generated samples. In pooled GAN benchmarking, StyleGAN2-ADA achieved the strongest generative-quality metrics, whereas StyleGAN3-T was selected as the preferred augmentation model because its alias-free synthesis and spatial consistency yielded superior downstream classification performance in the proposed pipeline.
Why it matches plant phenotyping methods植物病害画像を合成・認識する画像解析手法が研究の中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。
abstractwe propose StyleGAN3-T, a novel alias-free generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition.
Reproduction assets foundThe paper's grape leaf disease image inputs are two publicly available Kaggle datasets explicitly named in the Data Availability statement. No author code, models, or synthetic dataset deposit is provided; other processed data is request-only.Dataset · publictechnical guidance. Y.L. and A.W. supervised the study, provided critical revisions, and contributed to the interpretation of results. All authors reviewed and approved the final manuscript.
Data availability
The datasets analyzed during the current study are publicly available from Kaggle: Grapevine Disease Dataset (Original) (https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original; accessed 13 March 2026; license: MIT) and Grape Leaf Disease 4 Class (https://www.kaggle.com/datasets/jawadulkarim117/grape-leaf-disease-4-class; accessed 13 March 2026; license: CC0: Public Domain). Additional processed metadata, label-harmonization records, dataset split definitions, and other daOpen asset ↗Kaggle · rm1000/grape-disease-dataset-originallines:549-576Dataset · publicthors reviewed and approved the final manuscript.
Data availability
The datasets analyzed during the current study are publicly available from Kaggle: Grapevine Disease Dataset (Original) (https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original; accessed 13 March 2026; license: MIT) and Grape Leaf Disease 4 Class (https://www.kaggle.com/datasets/jawadulkarim117/grape-leaf-disease-4-class; accessed 13 March 2026; license: CC0: Public Domain). Additional processed metadata, label-harmonization records, dataset split definitions, and other data used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Competing inOpen asset ↗Kaggle · jawadulkarim117/grape-leaf-disease-4-classlines:549-576Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The genetic identity of coffee cultivars is fundamental to the specialty coffee sector, where premium prices are paid under the assumption that the purchased planting material corresponds to the declared variety. However, many producing countries lack the certification infrastructure necessary to guarantee this identity in their informal seed systems, exposing producers to undetected varietal non-conformity. In this study, we examine a case from a specialty coffee ( Coffea arabica L.) farm in southern Ecuador where seeds labeled as Sidra (USD 100/kg) and Gesha (USD 500/kg) were purchased without genetic or phytosanitary certification. Using a combination of SSR-based DNA fingerprinting and quantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics, we documented varietal identity and assessed the discriminant capacity of morphological traits across the four resulting morphotypes. Using eleven microsatellite markers for SSR fingerprinting, we found that two of the four morphotypes did not match their declared commercial identity. One plant sold as Sidra was identified as compatible with Batian, a composite variety of Kenyan origin that is genetically unrelated to Ethiopian landraces. The plants acquired as Gesha corresponded to a pure Ethiopian landrace that is genetically similar to, but not identical to, the Panamanian Geisha reference accession T.02722. Only two morphotypes were confirmed as Sidra. Furthermore, the placement of Sidra within the Core Ethiopia genetic group is consistent with prior population-level analyses and with its likely status as a selected Ethiopian landrace rather than a variety of hybrid origin. Morphological linear discriminant analysis achieved 82.4% overall classification accuracy under leave-one-out cross-validation (LOOCV), with internode length dominating the first discriminant function (LD1 = 66.6%). These results demonstrate that varietal nonconformity in the specialty coffee seed sector can extend to the inadvertent introduction of genetically unrelated material and underscore the urgent need for accessible seed certification.
Why it matches plant phenotyping methodsコーヒー品種識別のための形態形質測定と判別分析が研究の中心であり、形態形質の識別性能をLOOCVで検証しているため、植物フェノタイピング手法の適用・検証に該当する。
abstractquantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics
Reproduction assets foundThe paper's morphological/functional trait dataset (used for the phenotyping and LDA analysis) is explicitly stated to be publicly available on Figshare (10.6084/m9.figshare.32841344). No author analysis code repository is stated; other URLs in the text are generic libraries or cited prior work.Dataset · publicThe morphological and functional trait dataset generated and analyzed in this study is publicly available in the Figshare repository at 10.6084/m9.figshare.32841344 .Open asset ↗Figshare · 10.6084/m9.figshare.32841344lines:526-568Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely identification of crop diseases is imperative in precision agriculture to intervene at the right time and maximise yield sustainability. Despite achieving high accuracy, deep learning models are ineffective for rare plant disease classes, mainly due to severe data imbalance and insufficient training samples. Currently, most generative augmentation methods are designed to enhance either visual realism or data diversity, while ignoring methods that are sensitive to early-stage diseases or that control disease progression. In this paper, we propose RareAgriDetectAI, which comprises RareSimGAN, a generative deep learning framework for synthesising images of rare diseases, and a latent traversal mechanism that collaboratively visualises disease progression with increasing severity. We introduce a pipeline for synthesising realistic crop images for data augmentation. We augment the representation of rare classes using synthetic samples in a ResNet50-based classification pipeline. A strong experimental setup, relying on controlled baselines and synthetic-aided training scenarios, was employed. Evaluation on a real dataset shows significant improvement for the rare class ToLCNDV, with recall increasing from 0.42 in the baseline to 0.81 after synthetic augmentation. In contrast, the performance on other common disease classes remains stable. SSIM, Inception Score, and FID metrics were shown to validate generative quality. At the same time, an ablation study identified a suitable augmentation threshold at which sufficient performance is achieved without excessive synthetic data generation. The results further indicate improvements in feature diversity, which translate into earlier disease recognition (before full disease onset) and improved classification robustness with RareSimGAN. The post-framework combines generative modelling and latent space exploration to deliver a low-cost, scalable, and data-efficient solution for agricultural AI systems. RareAgriDetectAI utility can assist in the proactive monitoring of crop health and simulate rare disease scenarios to drive learning that can aid reliable, interpretable deep learning applications in precision agriculture.
Why it matches plant phenotyping methods希少作物病害の画像合成、病徴進行の可視化、早期病害認識を中心とする画像ベースの植物病害フェノタイピング手法であり、生成品質と分類性能も検証している。
abstractwe propose RareAgriDetectAI, which comprises RareSimGAN, a generative deep learning framework for synthesising images of rare diseases, and a latent traversal mechanism that collaboratively visualises disease progression with increasing severity.
Reproduction assets foundThe paper's data availability statement lists the public plant-disease image datasets used (PlantVillage, AI Challenger mirror, PlantDoc, Tomato Leaf Disease), and the code availability statement provides an authors' GitHub repository containing the RareSimGAN implementation, preprocessing, classifier training, andGradDataset · publicThe datasets analysed during the current study are publicly available from the following sources:
•PlantVillage dataset:
https://www.kaggle.com/datasets/emmarex/plantdiseaseOpen asset ↗pdf-page:36 lines:1-66Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping stomatal traits and their developmental plasticity is time-consuming but holds potential to improve water use efficiency and photosynthesis for designing stress-tolerant crops under climate change. Here, we develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning. We (1) analyze over 25,000 images from 60 wheat cultivars grown in growth chamber, greenhouse, and field conditions; (2) investigate the impact of light, temperature, and reduced water and nitrogen supply on stomatal traits and their developmental plasticity across adaxial and abaxial surfaces; and (3) evaluate genetic diversity and breeding progress of stomatal traits. Stomatal traits were highly broad-sense heritable, were largely plastic in response to environmental conditions, and showed genotype-specific responses. Stomatal traits of third leaves under controlled environments with stable light and temperature conditions reliably captured the genetic variance of flag leaves under field conditions. Our data suggests that the upper leaf surface contributed more to transpiration and cooling through consistently higher stomatal density, area, and maximum conductance, while the lower surface facilitated CO₂ diffusion via systematic proper patterning and spacing. Breeding maintains the genetic diversity of stomatal traits, and our pipeline facilitates breeders to target them to enhance water use efficiency in high-yielding modern cultivars.
Why it matches plant phenotyping methods高スループットで14種類の気孔形質を抽出するパイプラインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning.
Reproduction assets foundThe paper's Data and code availability section states that all data are publicly available in a Zenodo repository and that the stomatal identification and trait quantification code is in the authors' public GitLab repository. Both URLs appear verbatim in the supplied blocks and match allowed_urls. The Zenodo DOI in theCode · publicThe code for all the programs in this paper, including the stomatal identification and trait quantification, can be found in our GitLab repository, https://scm.cms.hu-berlin.de/intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotyping .Open asset ↗intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotypinglines:197-215Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
0. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3’s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R 2 = 0.935, n = 74) under simple-background conditions and (R 2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica . Object counts matched manual counts almost exactly for well-separated insects in an insect box (R 2 = 0.9997, n = 12). By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Why it matches plant phenotyping methods画像から花の面積など植物形態形質を抽出する汎用システムを開発し、手動測定との一致で検証しており、植物フェノタイピング手法が中心である。
abstractWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts.
Reproduction assets foundThe paper's Data and code availability statement provides a public Zenodo deposit containing the validation datasets and code used for the EcoMorph phenotyping measurements (floral area, insect morphometrics, counts), plus a public web deployment of the EcoMorph software itself.Code · publicValidation datasets and code are available here on Zenodo
https://zenodo.org/records/20980236.Open asset ↗Zenodo · 20980236pdf-page:2 lines:1-54Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Sweet potato virus disease (SPVD) is one of the most destructive diseases affecting sweet potato production worldwide, causing severe yield losses and posing a significant threat to food security. Vision-based intelligent diagnosis has emerged as a promising solution for large-scale SPVD monitoring due to its low cost and scalability. However, existing publicly available datasets for SPVD are extremely limited and typically focus on a single task, such as disease classification or lesion segmentation, under constrained imaging conditions. This lack of comprehensive, task-oriented datasets significantly restricts the development, evaluation, and fair comparison of advanced computer vision methods for SPVD analysis. In this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks. Rather than constructing a single homogeneous dataset, SPVD-Field is deliberately organized into two complementary task-oriented sub-datasets: SPVD-DET, designed for disease detection with bounding-box annotations, and SPVD-SEG, designed for fine-grained lesion segmentation with pixel-level masks. The two sub-datasets were independently collected using different acquisition protocols optimized for their respective tasks, while sharing a unified semantic definition of SPVD symptoms, crop growth stages, and field environments. SPVD-Field captures substantial real-world variability in imaging scale, viewpoint, illumination, background complexity, and symptom manifestation, reflecting the inherent challenges of fieldbased disease diagnosis. We provide detailed documentation of data acquisition, annotation strategies, and quality control procedures, along with baseline benchmark results for both detection and segmentation tasks to demonstrate the usability and difficulty of the dataset. By offering a structured dataset suite rather than a single-task collection, SPVD-Field aims to support diverse research directions, including detection, segmentation, multi-task learning, and disease severity analysis, and to facilitate reproducible and comparable research in SPVD-related plant phenotyping.
Why it matches plant phenotyping methodsサツマイモの病徴を対象とする画像データセットで、検出・病斑セグメンテーション、データ取得・アノテーション・品質管理、ベンチマークを中心的に提供しており、植物病害状態の画像フェノタイピング手法・データ基盤に該当する。
abstractIn this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks.
Reproduction assets foundThe paper's core asset is the SPVD-Field dataset (SPVD-DET detection images with bounding-box annotations and SPVD-SEG segmentation images with pixel-level masks), explicitly deposited in a public repository via the data availability statement with a DOI link.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://dx.doi.org/10.21227/hq1q-jp43 .Open asset ↗10.21227/hq1q-jp43lines:664-703Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sweet potato is a nutritionally valuable crop that contributes to food security, owing to its storage roots rich in starch, sugars, and antioxidants, while requiring minimal cultivation inputs. Estimating its yield based on visible above-ground traits remains challenging due to weak and inconsistent correlations between shoot biomass and storage root development. Therefore, direct assessment of underground biomass is essential. In this study, we demonstrate the field application of ground penetrating radar (GPR) for non-destructive detection and yield estimation of sweet potato. GPR is a geophysical technique that typically transmits ultra high frequency radio waves into the soil and records reflections from subsurface objects. Electromagnetic wave simulations within the soil-root system revealed GPR signals that strongly correlate with root length, forming the basis for yield quantification. We developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data. By integrating detection and quantification approaches, a linear regression model predicting sweet potato yield from GPR signals achieved moderate accuracy ( R 2 = 0.567, normalized RMSE 0.190). We established a non-destructive and low-labor approach for monitoring root systems, providing a foundation for rapid, scalable, and field-ready yield estimation in sweet potato and other root and tuber crops.
Why it matches plant phenotyping methodsGPRによる地下貯蔵根の検出・定量化と収量推定を中心に、信号処理および画像処理パイプラインを開発・評価しているため、植物フェノタイピング手法として収載する。
abstractWe developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe datasets analyzed during the current study consist of GPR line scans of the field at ALRC and list of sweet potato storage root weights. These data are available together with the analysis scripts on GitHub under open access. All data and scripts are the property of NARO and are distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). The repository can be accessed at: https://github.com/mtei1/GPRScript.Open asset ↗mtei1/GPRScripthtml-lines:240-264Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurate estimation of evapotranspiration (ET) is critical for irrigation management in water-scarce regions such as the Middle East and North Africa (MENA). This study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer, deployed simultaneously over an irrigated late-season potato field (1.8 ha) in the Beqaa Valley, Lebanon. Satellite NDVI observations indicate that the BLS–EC overlap period (13 October–27 November 2021) sampled the crop from peak canopy (NDVI ≈ 0.85–0.90) through the onset of senescence (NDVI ≈ 0.79). The BLS (Scintec BLS900) operated along a 140 m path. The EC system showed incomplete daytime energy-balance closure, with a regression slope of ≈0.69 and a seasonal Bowen-ratio-preserving correction factor of CF = 1.24 (a ~19% closure deficit) was used. Across the matched period, daily H from the BLS was strongly correlated with EC (r ≈ 0.82) but systematically lower, with a regression slope of ≈0.63 that persisted across timescales; this scale-invariant amplitude compression reflects the path-averaged, similarity-based nature of the scintillometer retrieval rather than the EC closure deficit, which instead governs the mean bias. BLS-derived daily ET showed a systematic positive bias relative to uncorrected EC (mean bias error, MBE = +0.30 mm d−1; +16% cumulative). Applying the Bowen-ratio-preserving correction (CF = 1.24) to EC reduced this to MBE = −0.14 mm d−1 (−6%), and the residual-to-LE correction yielded MBE = −0.15 mm d−1 (−6.4%); the latter comparison is only partly independent, as both methods share the same Rn and G. The Bowen-ratio-preserving method is therefore recommended for this dataset. Overall, the BLS captured the temporal variability of crop water use well, but residual-based ET estimates require careful treatment of the energy-balance-closure gap and are sensitive to the high BLS gap fraction (61.6% of 15 min records over the overlap, exceeding 90% at night). Once EC is closure-corrected to serve as the reference, the BLS offers a cost-effective alternative for field-scale ET monitoring in the MENA region, subject to the conditional agreement documented here.
Why it matches plant phenotyping methodsジャガイモ圃場の作物蒸発散量（ET）という生理・水利用状態を対象に、ECとBLSを比較検証し、補正法や測定誤差も評価している。センサー測定法の技術的妥当性が中心であり、単なる routine measurement ではない。
abstractThis study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer
Reproduction assets foundThe paper's flux/ET datasets are only available on request from the corresponding author, so they do not qualify as public assets. However, the Supplementary Information file (available at the MDPI supplementary URL) explicitly contains experiment sensor documentation and field/canopy images (Figures S1–S4: study site,Supplement · publicmeasurements along the beam. Because these results derive from a single crop, season, and phenological window, their generalization awaits multi-site, multi-season replication spanning the full-canopy cycle—the priority for subsequent campaigns.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s26144398/s1 , Figure S1: Study site and potato canopy—Beqaa Valley, Lebanon; Figure S2: Eddy covariance system—full tower view (peak canopy); Figure S3: EC sensor suite close-up and soil sensor installation; Figure S4: BLS900 scintillometer—transmitter, receiver, and meteorological station.
Author Contributions
Conceptualization, HOpen asset ↗lines:251-268Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
Why it matches plant phenotyping methods植物フェノタイピング用の画像解析ソフトウェア開発と、形態形質測定機能の実証が中心である。
abstractPlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions.
Reproduction assets foundThe paper's data availability statement explicitly says that scripts used for the analyses in this paper are publicly available on GitHub (danforthcenter/plantcv-4-paper), and PlantCV source code is available via the PlantCV homepage. This is a paper-specific, public, actionable analysis-code asset.Code · publicerest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Links to code, tutorials, documentation, and other resources
are available on the PlantCV homepage at https://plantcv.org. PlantCV source code is available on GitHub at https://
github.com/danforthcenter/plantcv. Scripts used for analyses
in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-4-paper.O RC I D
HaleySchuhl https://orcid.org/0000-0002-8825-8297
KeelyE. Brown https://orcid.org/0000-0002-5371-5830
ParagK. Bhatt https://orcid.org/0000-0002-0396-6412
DominikSchneider https://orcid.org/0000-0002-5846-5033
Anna L. Casto https://orcid.org/0000-0002-9597-0514
Lucia Acosta-Gamboa https://orcid.org/0000-0001-77Open asset ↗danforthcenter/plantcv-4-paperpdf-raw-page:15 lines:1-92Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of N=150 observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved R2=0.842 and RMSE=0.0705 on a normalized target scale, corresponding to a 7.05% error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached 91.1% accuracy (Cohen’s Kappa =0.91). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline (ΔR2=+0.052) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて植物の水関連形質・水状態を推定する枠組みを開発・検証しており、表現型取得と予測手法が研究の中心である。
abstractWe developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the complete dataset and source code (raw UAV multispectral imagery, Python scripts, IoT sensor logs, CROPWAT 8.0 files, and XGBoost model code) in a public Mendeley Data repository, which directly reproduces this paper's phenotyping measurements and analysis.Dataset · publicThe complete dataset and source code supporting this study are publicly available at Mendeley Data: https://data.mendeley.com/datasets/9xwdvzf3bf/1 (accessed on 20 May 2026). The repository includes: (1) raw multispectral UAV imagery with calibration panel captures; (2) Python scripts for DN-to-reflectance conversion and spectral index extraction; (3) IoT sensor logs (soil moisture, temperature, relative humidity); (4) CROPWAT 8.0 project files for FAO-56 soil water balance simulation; and (5) XGBoost model source code with hyperparameter optimization routines.Open asset ↗Mendeley Data · 9xwdvzf3bf/1lines:193-228Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Wheat is one of the most widely planted staple crops worldwide and underpins global food security. Fungal diseases severely threaten wheat growth and trigger massive yield losses during cultivation. Traditional manual diagnosis is time-consuming and highly subjective, while existing deep learning models often struggle to achieve high accuracy and robustness in complex field environments. Accurate identification of these fungal diseases is therefore vital to secure grain production. Methods This paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification. First, the original basic blocks of ShuffleNetV2 are substituted with SS-Conv-SSM modules to strengthen the extraction of fine-grained lesion features amid visually analogous fungal disease samples; half convolution is embedded to cut down model computational overhead. Second, a Mixed Local Channel Attention (MLCA) unit is attached to the convolution branch of each SS-Conv-SSM module, which adaptively highlights discriminative disease features and filters irrelevant background noise. Standard training configurations and five-fold cross-validation are adopted for fair model evaluation. Results Comparative experiments reveal that the presented network reaches a classification accuracy of 91.35%, which surpasses the original ShuffleNetV2 baseline by 1.16 percentage points. Controlled ablation tests verify the independent performance gain of each core component: the SS-Conv-SSM module raises overall accuracy by 0.89%, and the MLCA mechanism brings an extra 0.27% accuracy increment. Discussion The proposed ShuffleNetV2_SSM_MLCA architecture strikes a favorable trade-off between model lightweight property and classification performance. It delivers a low-computation, high-precision recognition scheme for wheat fungal diseases and lays a solid technical foundation for real-time disease monitoring in intelligent agricultural scenarios.
Why it matches plant phenotyping methods小麦葉片の病斑特徴を画像から抽出し、植物の真菌病状態を分類する軽量深層学習手法を開発・検証しており、病害表現型の取得・推定が研究の中心である。
abstractThis paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification.
Reproduction assets foundThe paper's plant-image measurements are based entirely on publicly available wheat disease image datasets: a primary Kaggle dataset (Wheat Plant Diseases by Kushagra Agarwal) used for model development, and two additional public datasets (Alibaba Cloud Developer Community and CSDN Modelers) used for generalization andDataset · publicThe dataset is publicly available at https://www.kaggle.com/datasets/kushagra3204/wheat-plant-diseases and was accessed on September 5, 2025.Open asset ↗Kaggle · kushagra3204/wheat-plant-diseaseslines:322-374Dataset · publicThe second dataset was contributed by blogger DL data set and released on December 25, 2025 via the CSDN Modelers platform ( https://modelers.csdn.net/69a67f4c7bbde9200b9c3240.html )Open asset ↗CSDN Modelerslines:644-669Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Traditional manual grading of fresh chili peppers suffers from inconsistent quality control and low efficiency. To meet the demand for accurate fruit shape recognition during the post-harvest stage, this study proposes an intelligent recognition method based on an improved DenseNet-121 network. This approach facilitates the application of machine vision in agricultural sorting equipment. DenseNet-121 serves as the backbone network. The Convolutional Block Attention Module (CBAM) is introduced to enhance feature focus on fruit shapes. A regularization strategy (Dropout = 0.3, weight decay = 1 × 10 -4 ) and a cross-entropy loss function with label smoothing (LS = 0.1) are integrated to optimize decision boundaries. These configurations prevent the model from overfitting to hard training labels and yield a robust classification architecture. Experimental results demonstrate that the proposed model achieves a precision of 90.09%, a recall of 89.60%, an F1-score (the harmonic mean of precision and recall) of 89.53%, and an overall accuracy of 89.74%. The model contains 7.09 M parameters and requires a single-frame inference time of 7.35 ms. Comprehensive evaluations indicate that the proposed model achieves an optimal balance among environmental noise robustness, prediction accuracy, and computational efficiency. Consequently, by maintaining high fine-grained classification accuracy alongside a low memory footprint and rapid inference speed, the model demonstrates strong potential for real-time deployment on resource-constrained edge devices within actual agricultural optical sorting equipment.
Why it matches plant phenotyping methodsチリペッパー果実の形状という植物器官形質を画像から分類する深層学習手法の開発・評価が中心であり、単なる品質測定ではない。
abstractExperimental results demonstrate that the proposed model achieves a precision of 90.09%, a recall of 89.60%, an F1-score (the harmonic mean of precision and recall) of 89.53%, and an overall accuracy of 89.74%.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe original image dataset is provided as Supplementary Materials .Open asset ↗lines:30-40Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Cotton leaf diseases present a major threat to global cotton production, significantly impacting both yield and fiber quality. Traditional diagnostic methods are labor-intensive, time-consuming, and demand highly skilled professionals, making them inefficient for large-scale agricultural applications. Although earlier deep learning -based approaches have shown promising results in identifying cotton leaf diseases such as Bacterial Blight, Fusarium Wilt, and Curl Virus Disease, their performance is often limited by complex preprocessing requirements and insufficient generalization to real-world field conditions. To address these challenges, this study proposes and optimized transfer learning-based model, CLDP-CNN, designed to enhance feature extraction and classification efficiency using pre-trained deep neural networks. This study demonstrates the development of Cotton Leaf Disease Prediction Convolutional Neural Network (CLDP-CNN) automatically, utilizing Transfer Learning (TL) which operates on meticulously prepared datasets. Two distinct datasets were used to train the model: the first consisted of field images from cotton farms, while the second was sourced from Kaggle. The main goal of this research examines how the model performs on real-world field datasets. The CLDP-CNN model has proven highly accurate by attaining 99.78% detection success rates for cotton leaf diseases when processing primary dataset which surpasses its secondary dataset accuracy rate of 99.62%. Both the primary dataset and secondary dataset resulted in high accuracy values for the VGG16 pre-trained model which achieved 99.56% accuracy on the primary dataset and 98.82% on the secondary dataset. A web-based application enhances the capabilities of the CLDP-CNN model by providing real-time updates on the health status of cotton plants. This technology empowers farmers with valuable information, enabling them to take timely protective actions to prevent potential severe yield losses in their cotton crops.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を推定する深層学習モデルを開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractThe main goal of this research examines how the model performs on real-world field datasets.
Reproduction assets foundThe paper's cotton leaf disease image datasets are publicly available: the authors' primary field-collected dataset on the first author's GitHub repository, and the secondary Kaggle dataset used for comparison. No analysis code or trained model checkpoints are explicitly deposited.Dataset · publicbia.
Funding: This work was supported by Princess Nourah bint Abdulrahman
University Researchers Supporting Project number (PNURSP2026R760),
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data and code availability
The data that support the findings of this study are openly available in Github and
Kaggle at. https://github.com/mnaeem303/Cotton-Leaf_Disease-Dataset),
and https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset/data
Author Contributions
All the authors (Muhammad Naeem, Muhammad Ibrahim, Nadeem Sarwar, Oumaima
Saidani, Asma Irshad, Muhammad Shadab Alam Hashmi, Muhammad Tayyab
Qammar) contributed equally to this work in their respective meaningOpen asset ↗https://github.com/mnaeem303/Cotton-Leaf_Disease-Datasetpdf-raw-page:29 lines:1-54Dataset · publicbdulrahman
University Researchers Supporting Project number (PNURSP2026R760),
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data and code availability
The data that support the findings of this study are openly available in Github and
Kaggle at. https://github.com/mnaeem303/Cotton-Leaf_Disease-Dataset),
and https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset/data
Author Contributions
All the authors (Muhammad Naeem, Muhammad Ibrahim, Nadeem Sarwar, Oumaima
Saidani, Asma Irshad, Muhammad Shadab Alam Hashmi, Muhammad Tayyab
Qammar) contributed equally to this work in their respective meaningful ways. All the
authors have read and approved the final manuOpen asset ↗pdf-raw-page:29 lines:1-54Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Brinjal (eggplant) is a critical crop in South Asia, especially in Bangladesh, but its production is drastically affected by numerous diseases that inhibit yield and quality. Manual diagnosis of disease is time-consuming, subjective, and prone to errors, necessitating automated, scalable technology. To address these issues, this paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification using Simple Vision Transformer (ViT) with Patch Dropout and Contrastive Captioner (CoCa) methods. One new dataset of 1,823 field-harvested brinjal images encompassing five disease classes including Phomopsis Blight, Fruit and Shoot Borer, Fruit Cracking, Wet Rot, and Healthy samples were prepared through real-world agricultural data collection from Bangladesh. The approach includes extensive preprocessing, class balancing (under-sampling/oversampling), and resilient augmentation methods. The PD-ViCo model significantly improves classification performance under data imbalance with patch dropout regularization and CoCa-style aggregation, resulting in better generalization and robustness. On a range of imbalanced, under-sampled, and oversampled datasets, PD-ViCo achieved a classification accuracy of 99.12% and F1-score of 97.76%, outperforming both ViT and Swin Transformer across all key evaluation metrics. Explainability was also applied using Grad-CAM and Grad-CAM + + , generating visual explanations of model decisions and maintaining conformity to disease-affected regions in the images. These visualizations ensure the credibility of the model and its usability for real agricultural conditions. This study demonstrates that PD-ViCo is a highly accurate, interpretable, and lightweight model for multi-class brinjal disease diagnosis. Not only does it advance state-of-the-art in agricultural AI, but it also provides a valuable dataset and an understandable decision-making protocol that can be applied directly by farmers, agronomists, and agricultural technologists.
Why it matches plant phenotyping methods植物画像から病害状態を分類するモデル、データセット、説明可能性評価を中心に開発・検証しており、植物フェノタイピング手法として適格。
abstractthis paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification
Reproduction assets foundThe paper's own field-harvested brinjal disease image dataset (1,823 images, five classes) is publicly deposited on Mendeley Data, with an explicit availability statement and URL matching an allowed entry. No code or model checkpoint deposit is stated.Dataset · publicThe data utilized in this study is publicly accessible on Mendeley Data Repository at the following link: [ https://data.mendeley.com/datasets/ngc58fsxgd/1 ].Open asset ↗Mendeley Data · ngc58fsxgd/1lines:226-251Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published1 Jul 2026The Plant journal : for cell and molecular biologyCited by 0 · OpenAlex ↗
Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor the presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified the channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo. Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap.
Why it matches plant phenotyping methods植物組織における蛍光シグナルの分離、画像セグメンテーション、定量化ワークフローを開発・比較・検証しており、植物の細胞・細胞小器官状態を測定する方法が中心である。
abstractTo separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe segmentation and histograms were acquired using MATLAB script ( https://github.com/NIB‐SI/Nuclei‐segmentation ). The parameters used in the script to achieve appropriate segmentation are listed on GitHub, Case 1 ( https://github.com/NIB‐SI/Nuclei‐segmentation ).Open asset ↗NIB‐SI/Nuclei‐segmentationlines:255-341Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice leaf diseases pose a major challenge to crop health and agricultural productivity, particularly when timely and accurate diagnosis is required under natural field conditions. The development of automated disease recognition systems depends heavily on the availability of large, well-annotated image datasets. However, many existing rice leaf disease datasets are limited in terms of environmental variability, disease representation, and real-field imaging conditions. To address this gap, this paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors from the experimental fields of the Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh, between July 2023 and July 2024. The dataset contains 4152 images belonging to five classes: Bacterial Leaf Blight, Bacterial Leaf Streak, Sheath Blight, Leaf Blast, and Healthy Leaf. The images were acquired from two rice varieties, BR11 and BRRI dhan34, under natural field conditions across varying illumination environments in order to reflect practical disease recognition scenarios. All images were manually annotated by trained annotators under expert supervision. The dataset is systematically organized and publicly released to support reproducible research in rice disease classification. In addition to dataset presentation, benchmark experiments using Xception, NASNetMobile, and InceptionV3 are provided to demonstrate its applicability for deep learning-based disease recognition. BanglaRiceLeaf is expected to serve as a useful resource for plant disease analysis, comparative model evaluation, and future research in precision agriculture and agricultural computer vision.
Why it matches plant phenotyping methodsイネ葉の病徴・健全状態を画像で分類する公開ベンチマークデータセットであり、データ収集・注釈・ベンチマーク評価が中心です。
abstractthis paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors
Reproduction assets foundThe paper's core asset is the BanglaRiceLeaf rice leaf disease image dataset (4152 field images, five classes), publicly released on Harvard Dataverse with DOI 10.7910/DVN/XAOBYW. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicData Identification Number: https://doi.org/10.7910/DVN/XAOBYW
Direct URL to Data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XAOBYW
Access Instructions: This dataset is publicly available on the Harvard Dataverse repository and can be accessed for academic, research, and instructional purposes.Open asset ↗Harvard Dataverse · doi:10.7910/DVN/XAOBYWhtml-lines:100-131Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Mangroves play a critical role in coastal ecosystem services, particularly through their capacity to sequester large amounts of atmospheric carbon, contributing to climate change mitigation. Developing accurate mangrove carbon models is therefore essential for monitoring ecosystem condition and carbon stocks at relevant scales. This study aimed to estimate mangrove Above-Ground Carbon (AGC) in Baluran National Park by integrating field measurements and remote sensing data within a Machine Learning (ML) framework. The study utilised an extensive field data collection programme of 60 sampling plots of girth at breast height, canopy cover, tree height, and tree density. Mangrove AGC was estimated using allometric equations. AGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE). The RF model, using field-measured tree height, Ratio Vegetation Index (RVI), and Transformed Soil-Adjusted Vegetation Index (TSAVI), achieved the best performance ( R² training = 0.93, R² testing = 0.84, 5-fold CV RMSE = 12.20 Mg C ha⁻¹). Predicted AGC ranged from 5.39 to 57.18 Mg C ha⁻¹ (mean ± Standard Deviation (SD) = 30.43 ± 16.09 Mg C ha⁻¹) and showed improved accuracy compared to the global mangrove biomass dataset of (Simard et al., 2019). A key contribution of this study is the integration of field-measured tree height within a satellite-based ML framework, which enhances the accuracy and ecological relevance of AGC estimation compared to approaches relying solely on spectral predictors or remotely sensed canopy height products, offering a practical and cost-effective alternative for sites where UAV or LiDAR data are unavailable. This approach provides a practical method for regional mangrove carbon monitoring, national carbon accounting and supports climate change mitigation efforts.
Why it matches plant phenotyping methodsマングローブの樹高・樹冠情報と衛星データを統合し、機械学習で個体・プロットレベルの地上部炭素量という植物状態を推定する手法を開発・交差検証しており、単なる生態系測定ではなく表現型取得手法が中心である。
abstractAGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE).
Reproduction assets foundThe authors state that all analysis code (model development, hyperparameter configuration, diagnostics, accuracy assessment) is publicly available in their GitHub repository Mangroves-AGC-Baluran, which reproduces this paper's mangrove AGC machine-learning analysis.Code · publicThe Python codes were available on
h
t
t
p
s
:
/
/
g
i
t
h
u
b
.
c
o
m
/
s
e
f
t
i
a
w
a
n
-
s
r
/
Mangroves-AGC-Baluran.git.Open asset ↗Mangroves-AGC-Baluran.git · Mangroves-AGC-Baluran.gitpdf-raw-page:17 lines:1-379Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The co-evolutionary arms race between crops and their parasites requires continuous identification of new resistance mechanisms. Broomrape (Orobanche cumana), a root parasitic plant, poses a severe threat to sunflower (Helianthus annuus) production, yet the genetic architecture underlying host resistance remains poorly understood. To address this, we established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population. Combining this phenotypic resource with a dual genome-wide association study (GWAS) strategy based on both single nucleotide polymorphisms (SNPs) and k-mers, we highlight the genetic basis of broomrape resistance at unprecedented resolution. Our analyses revealed quantitative trait loci (QTLs) and identified novel candidate genes, including putative leucine-rich repeat receptor kinases potentially involved in parasite recognition and defense activation. Importantly, the k-mer approach circumvented reference genome bias and uncovered key genomic introgressions from wild Helianthus relatives that contribute substantially to resistance. These findings demonstrate the utility of integrating high-resolution phenotyping with advanced association mapping to dissect complex host-parasite interactions. Moreover, they emphasize the enduring value of wild germplasm as a reservoir of adaptive variation, providing crop breeders with crucial tools to counter the rapid evolutionary dynamics of parasitic plants.
Why it matches plant phenotyping methods根部の寄生程度を定量する高スループット表現型解析プラットフォームの確立が明示され、遺伝解析の基盤として方法が実質的に扱われている。
abstractwe established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the paper-specific raw phenotyping images on Zenodo, the k-mer genotype data on the sunflower genome database, and the authors' analysis code on the Hübner lab GitHub repository, all with public URLs.Dataset · publicAll phenotypes raw images for Gadot and Yavor are available through the Zenodo repository ( https://doi.org/10.5281/zenodo.18961268 ).Open asset ↗Zenodo · 10.5281/zenodo.18961268lines:238-238Code · publicCode is accessible through the Hübner lab github: https://github.com/hubner-lab/Sunflower-Broomrape-paper .Open asset ↗Hübner lab github · hubner-lab/Sunflower-Broomrape-paperlines:238-238Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Spatio-Temporal Fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, covering the entire soybean growing season from late June to early November. Near-daily Planet SuperDove imagery with 3 m resolution was used to temporally enhance UAV images, which were acquired at 0.05 m resolution but only at weekly to monthly intervals. Through the downscaling process, the UAV data were converted into a daily dataset with a target spatial resolution of 0.5 m. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms- Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (Fit-FC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)-within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) trajectories, from which the NDVI-based Vegetation Growth Metrics (VGM)85 and the EVI-based VGMmax were derived. The validation results indicated that ESTARFM achieved the highest NDVI performance among the evaluated algorithms, with a Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697. CACAO post-processing further improved these results, with CA-ESTARFM achieving an RMSE of 0.108 and a UIQI of 0.740, corresponding to a 4.4% reduction in RMSE and a 6.2% improvement in UIQI relative to the baseline ESTARFM. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using NDVI-based VGM85 and EVI-based VGMmax showed that CA-ESTARFM remained consistent with simple linear interpolation of UAV observations while retaining finer spatial structure and reducing localized noise in the derived growth metrics. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methodsUAV・衛星画像の時空間融合とCACAO処理により、NDVI/EVIおよび植生成長指標を抽出するワークフローを開発・比較検証しており、植物状態の取得手法が中心である。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Reproduction assets foundThe paper's STF/CACAO analysis code is openly available on Zenodo. The underlying Planet/UAV imagery data are only available from the corresponding author upon request, so they qualify as request_only.Code · publicThe code supporting this study is openly available at Zenodo
(https://doi.org/10.5281/zenodo.20923823).Open asset ↗Zenodo · 10.5281/zenodo.20923823pdf-page:20 lines:1-70Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published30 Jun 2026International Journal for Research in Applied Science and Engineering TechnologyCited by 0 · OpenAlex ↗
The research trains and evaluates multiple CNN architectures, including Basic CNN, AlexNet, VGG16, and EfficientNet B0, to enhance the accuracy of plant disease identification. Each model was tested using the New Plant Diseases Dataset from Kaggle, which includes various plant species and diseases, in order to assess performance, accuracy, and efficiency. The trained models were subsequently integrated into a Marathi language chatbot to facilitate real-time disease detection and provide agricultural guidance. This study provides valuable insights into the strengths and limitations of different models for precision agriculture, especially in applications that support regional languages to encourage accessible and sustainable farming practices. Additionally, a Marathi language chatbot is incorporated, enabling users to obtain plant disease information instantly through a user-friendly web application
Why it matches plant phenotyping methods植物病害状態を画像から識別するCNN群を訓練・評価し、リアルタイム検出システムへ統合しており、病害フェノタイプの取得・推定手法が中心である。
abstractThe research trains and evaluates multiple CNN architectures, including Basic CNN, AlexNet, VGG16, and EfficientNet B0, to enhance the accuracy of plant disease identification.
Reproduction assets foundThe paper's plant-phenotyping input is the public New Plant Diseases Dataset from Kaggle (healthy/diseased leaf images of tomato, potato, corn) used to train and evaluate the CNN ensemble. No author analysis code, trained model checkpoints, or supplementary data deposit is mentioned with an availability statement orURLDataset · publicInitially, the dataset was collected from the New Plant Diseases Dataset available
on Kaggle, which contains images of healthy and diseased plant leaves from various crops such as tomato, potato, and corn.Open asset ↗Kaggle · New Plant Diseases Datasetpdf-raw-page:3 lines:1-44Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Low temperature stress severely restricts the cultivation and distribution of pear ( Pyrus L.) germplasms, frequently resulting in frost injury and yield reduction. To accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress. In this study, 122 pear germplasms were classified into high (HR), medium (MR), and low (LR) cold-tolerance categories based on their semi-lethal temperature (LT 50 ). Further analysis of pear germplasms with different levels of cold resistance revealed that, with decreasing temperature, HR germplasms exhibited smaller increases in relative electrolyte conductivity (REC) and malondialdehyde (MDA) content and higher accumulation of proline (Pro), soluble proteins (SP), soluble sugars (SS), and peroxidase activity compared with LR germplasms. In addition, the peak values of these indicators generally occurred at lower temperatures in HR germplasms. A correlation analysis and principal component analysis indicated that physiological indices, including REC, bound water/free water ratio, SS, and MDA, as well as branch anatomical traits related to xylem and cortex proportions, were closely associated with variation in LT 50 . An integrated assessment using membership function analysis produced rankings consistent with LT 50 -based clustering, supporting the reliability of the multivariate evaluation framework. Overall, this study establishes an integrated, indicator-based approach for evaluating cold resistance in pear germplasm by integrating physiological, biochemical, and anatomical characteristics. These results provide a theoretical basis and methodological reference for screening cold resistance germplasms.
Why it matches plant phenotyping methods生理・生化学・解剖学的形質を統合し、LT50と多変量評価によってナシ遺伝資源の耐寒性を分類・スクリーニングする評価フレームワークが研究の中心である。
abstractTo accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress.
Reproduction assets foundThe article's Data Availability statement links a public Zenodo deposit containing the paper's raw phenotyping data (LT50, physiological/biochemical and anatomical measurements for pear germplasms). Supplemental files also contain germplasm characteristics and LT50 comparisons, but the Zenodo raw-data deposit is the明确,Dataset · publicThe data is available at Zenodo: liu186253. (2025). liu186253/Data: raw data (Version V11). Zenodo. https://doi.org/10.5281/zenodo.17524773 .Open asset ↗Zenodo · 10.5281/zenodo.17524773lines:636-710Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. As key components of agricultural management, planting and harvesting schedules have strongly influenced crop production by defining the length of the crop growing season and shaping the environmental conditions crops experience. Accurate knowledge of these management data is crucial for enhancing crop yield estimates by capturing the timing of crop development relative to weather and soil conditions, assessing climate adaptation by tracking shifts in farming practices over time, and supporting agricultural carbon accounting. Yet, existing planting and harvesting date datasets are largely based on state-level statistics or rule-based calendars that overlook intra-regional variability and the influence of human decision-making. The absence of long-term, high-resolution planting and harvesting date information hinders our ability to reconstruct historical agricultural practices and assess their agronomic and environmental consequences. In this study, we introduce CropPlantHarvest, the first dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution from 2001 to 2024. Planting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates. Harvesting dates are retrieved using the Normalized Harvest Phenology Index (NHPI), a novel index that integrates Normalized Difference Vegetation Index (NDVI) and near-infrared (NIR) reflectance to detect harvesting events by capturing the distinct spectral transition from senescent crops to exposed crop residues. Validation against USDA crop progress reports and field-level dataset demonstrates high accuracy of CropPlantHarvest, with a mean absolute error of approximately 5 d for both crop species. This large spatial and temporal dataset captures management-driven variability in crop season timing and duration, supporting improved modeling of crop yields, greenhouse gas emissions, and resource use. It could also serve as a benchmark for refining remote-sensing phenology products and evaluating the agro-environmental impacts of evolving crop management decisions. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).
Why it matches plant phenotyping methods衛星観測と作物モデルによる圃場レベルの作付・収穫時期推定手法を開発し、NHPIを提案して独立データで検証した大規模データセット研究であり、植物の生育・収穫状態の取得が中心的です。
abstractPlanting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicOur CropPlantHarvest dataset, which provides planting and harvesting dates for corn and soybean fields at 500 m spatial resolution across the U.S. Midwest from 2001 to 2024, can be accessed via Zenodo: https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).Open asset ↗Zenodo · 10.5281/zenodo.16967482lines:322-333Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Global food security is largely based on the accurate and timely diagnosis of crop diseases, where paddy rice is an extremely essential staple of more than half of the world population. The conventional disease identification techniques tend to be laborious, time consuming and demand a great deal of domain knowledge, which becomes a bottleneck in the efficient management of the farms. Although deep learning [and especially Convolutional Neural Networks (CNNs)] have demonstrated a spectacular performance in automated classification of diseases based on leaf images, they tend to overlook important contextual features that are implicitly processed by agronomic experts. The visual defects of a disease might be unclear and this can greatly differ depending on factors like the genetic variety of the plant and the stage of development. We overcome this shortcoming by proposing a new multi-modal deep learning framework, Multi-Modal Factorized Bilinear Pooling (MFBP) model which is capable of a more holistic and precise paddy health measurement. The proposed method is the only one that combines high-level visual information obtained using leaf images and related tabular information, namely the paddy type and number of days. The MFBP model uses Factorized Bilinear Pooling (FBP) rather than the simple feature concatenation which commonly loses the complex relationship between different data types. This systematic method efficiently encodes all the complex interactions between all components of the visual and tabular features vectors in such a way that helps the model to pick up subtle, context-specific patterns. As an example, it will only be possible to educate the model that a specific visual blemish is predictive of a given disease through a specific species at a specific age. We test our model on the Paddy Doctor: Paddy Disease Classification dataset, which is a detailed public dataset comprising of more than 10,000 labeled images and containing relevant metadata, and thus it forms a perfect testing bed to conduct multi-modal research. Through our detailed experiments, we have shown that the proposed MFBP model is much better than a baseline model based on concatenation fusion, which proves that deep, multiplicative interactions can be best modeled in this task. The findings highlight the massive possibilities of multi-modes AI in the development of more robust, more accurate, and more context-aware diagnostic instruments and precision agriculture to enable more sustainable and productive agricultural activities.
Why it matches plant phenotyping methods葉画像とメタデータを統合してイネの健康状態・病害を推定する新規深層学習手法を提案し、ベースライン比較で検証しているため、植物フェノタイピング手法が中心である。
abstractWe overcome this shortcoming by proposing a new multi-modal deep learning framework, Multi-Modal Factorized Bilinear Pooling (MFBP) model which is capable of a more holistic and precise paddy health measurement.
Reproduction assets foundThe paper's phenotyping inputs are the public Kaggle 'Paddy Doctor: Paddy Disease Classification' dataset (10,407 leaf images with tabular metadata for variety and age), explicitly named in the Data Availability statement with a persistent public URL. No author analysis code, trained models, or checkpoints are reportedDataset · publicThe datasets used and/or analysed during the current study are publicly available in the
“Paddy-doctor: paddy disease classification” repository at the following persistent
URL: https://www.kaggle.com/datasets/vbookshelf/paddy-disease-classification.Open asset ↗Kaggle · vbookshelf/paddy-disease-classificationpdf-page:20 lines:1-74Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Image processing plays a vital role in precision agriculture by enabling automated disease detection and crop health monitoring. This research presents a novel Hybrid Vision Transformer with Dense Attention Capsule Network (HVT-DACapNet) model for accurate cotton plant disease detection. The proposed framework integrates Adaptive Wavelet Transform Filtering (AWTF) for noise removal while preserving disease-related features. A Hybrid Vision Transformer (HVT) is employed to extract both local spatial patterns and global contextual dependencies, and the Dense Attention Capsule Network (DACapNet) captures hierarchical spatial relationships with an attention mechanism that emphasizes infected regions. In addition, a hybrid optimization strategy combining Mayfly and Aquila Optimization (HMAO) is used to fine-tune model hyperparameters for improved convergence. The model is evaluated on a publicly available Kaggle cotton leaf disease dataset containing healthy leaves and multiple disease categories including Target Spot, Powdery Mildew, Bacterial Blight, Army Worm, and Aphids, using a 70:15:15 train-validation-test split under the simulation setup and hyperparameter configuration described in the manuscript. The proposed HVT-DACapNet achieves an F1-score of 99.68%, sensitivity of 99.68%, specificity of 98.89%, and an overall accuracy of 99.79%, outperforming existing models such as ConvLSTM-ZOA, GOA, SFO, Inception-V3, and VGG-16.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を推定する深層学習手法を新規開発し、公開データセットで性能評価しているため、植物フェノタイピング手法が中心である。
abstractThis research presents a novel Hybrid Vision Transformer with Dense Attention Capsule Network (HVT-DACapNet) model for accurate cotton plant disease detection.
Reproduction assets foundThe paper's only qualifying asset is the public Kaggle Cotton Plant Disease Dataset used as the input image dataset for all experiments. The authors' code is explicitly not publicly available (institutional restrictions), with only a supplementary algorithm document and on-request implementation details.Dataset · publicthodological workflow of the proposed model. Additional
implementation details may be made available from the corresponding
author upon reasonable request for academic and non-commercial
research purposes.
Data Availability-The datasets generated and/or analysed during the
current study are available in the Kaggle repository,
https://www.kaggle.com/datasets/dhamur/cotton-plant-disease
Author’s contribution – G.Neelavathi– Research proposal –
construction of the workflow and model – Final Drafting– Survey of
Existing works – Improvisation of the proposed model;
Dr.K.Venkatasalam – Initial Drafting of the paper – Collection of
datasets and choice of their suitability – Formulation of pseudOpen asset ↗Kaggle · dhamur/cotton-plant-diseasepdf-raw-page:39 lines:1-42Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically relies on spectral observations composited over fixed calendar windows, implicitly assuming phenological synchrony across fields. This study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction. We integrated Sentinel-2 multispectral imagery (32 vegetation indices, 10 spectral bands), ERA5-Land meteorological reanalysis, gSSURGO soil properties, and USGS 3DEP topographic data, and systematically compared six temporal strategies, the factorial combination of two normalization approaches (peak-relative vs.\calendar) and three resolutions (monthly, biweekly, growth stages), across 228 commercial winter wheat fields in western Kansas (2024--2025). Three ensemble tree models (Random Forest, XGBoost, LightGBM) were trained under nested cross-validation with Boruta feature selection. Peak-relative monthly normalization achieved the highest accuracy (\((R^2 = 0.304 \pm 0.051)\), RMSE \((= 1.11)\)%), explaining an additional 5.1% of variance compared with the best calendar strategy (\((R^2 = 0.253)\)). A single 30-day post-peak window (M\((+)\)1, \((\sim)\)15--45 days after maximum canopy greenness) carried more predictive information than any broader aggregation. SHAP analysis identified topsoil organic matter, SWIR-based senescence indices (NBR2, MIRBI), and grain-filling temperature as the most influential predictors. Three-class quality classification reached 47--49% accuracy (versus 33.3% by chance), indicating practical utility for early grain segregation. While demonstrated for wheat GPC, the framework is transferable to other crop traits with temporally concentrated satellite signals, particularly those tied to specific developmental stages. The results highlight phenological alignment as a generalizable strategy for trait prediction from Earth observation data.
Why it matches plant phenotyping methods衛星リモートセンシング時系列を用いた小麦粒タンパク質濃度予測のため、フェノロジー整列と複数の時間集約戦略を体系的に比較・検証しており、植物形質推定手法が研究の中心である。
abstractThis study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction.
Reproduction assets foundThe paper's data availability statement releases a de-identified field-level GPC dataset alongside a public authors' code repository (Ciampitti-Lab WheatGPCPipeline) implementing the data-acquisition, feature-engineering, and modeling pipeline. Both are paper-specific, public, and actionable.Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurately monitoring alfalfa nutritional quality is essential for optimal pasture management. Yet, current UAV remote sensing methods rely on single-temporal imagery and empirical indices, limiting their ability to handle multi-stage growth dynamics, canopy spectral saturation, and canopy-to-whole-plant scale differences. Furthermore, small sample sizes often cause purely data-driven models to overfit correlations, yielding biologically unrealistic results. Overcoming these challenges, we designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator. After screening 14,960 spectral operators across original and log-transformed spaces, we applied a dual dimensionality reduction strategy to isolate optimal features. Four-band dual-difference structures proved highly sensitive to fiber components (ADF/NDF, |r| = 0.896), while logarithmic decoupling operators accurately isolated protein and nitrogen signals (CP/N, |r| = 0.868). We then engineered a Physics-Informed Sparse Shallow Network (PI-SSN). By leveraging temporal attention decoupling, it adaptively assigns growth-stage weights to different components and uses carbon-nitrogen metabolic constraints to maintain biological accuracy during multi-task retrieval. Multi-stage temporal data significantly boosted accuracy over single-period spectra. PI-SSN delivered exceptional test set coefficients of determination ( R2 ) of 0.812-0.848 and RPDs >2.0 for N, CP, ADF, and NDF, easily outperforming standard baselines. To bridge the canopy-only observation gap, we introduced a 3D allometric transfer operator that incorporates canopy coverage and plant height. This effectively corrected vertical stem-leaf observation biases, enhancing Relative Feed Value (RFV) predictions. Ultimately, this approach offers a powerful new framework for high-throughput forage phenotyping.
Why it matches plant phenotyping methodsUAVリモートセンシングと物理制約ネットワーク、3Dアロメトリック演算子を統合し、アルファルファの栄養品質を推定する手法を開発・検証しており、植物表現型取得が中心である。
abstractwe designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator.
Reproduction assets foundThe paper's authors publicly release the pre-trained PI-SSN model weights, inference code, and usage instructions on GitHub. The raw spectral and ground-truth quality datasets are not public and are available only on request, so they do not qualify as public assets.Code · publiceptualization, Resources, Supervision, Writing-review & editing. Dongyan Zhang: Conceptualization, Funding acquisition, Project Administration, Supervision, Writing-original draft, Writing-review & editing.
Data and code availability
The pre-trained model weights, inference code, and usage instructions are publicly available at https://github.com/AeroPheno/PI-SSN.git . The raw spectral data and ground-truth quality data used in this study are not publicly available due to ongoing collaborative projects, but are available from the corresponding author on reasonable request.
Funding
This work was supported by the 2023 Hohhot to introduce high-level innovative and entrepreneurial talents (teamOpen asset ↗AeroPheno/PI-SSNlines:243-301Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The robust Dutch rose, also known as the Rosa hybrida is distinguished by its vibrant colors, superior product quality, and extended vase life. These rose varieties, originating from Netherlands, have proven highly successful in Indian agricultural conditions and the international export industry. The dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals. The primary purpose of this dataset is to support machine learning activities in agriculture and specifically for tasks such as automatic petal health evaluation and rose variety categorization. Although the rose flower is scientifically rich and has a wide range of industrial uses, it has not been given much attention in machine learning, especially when compared to other plant-based datasets. This study adds to the accuracy of quality assessment through the use of modern computer vision and machine learning methods, thus helping the agriculture sector, rose-based edible product making, and flavor development industries.
Why it matches plant phenotyping methodsバラ花弁画像データセットの構築と、花弁の健康状態・色分類による植物状態評価が研究の中心であり、画像ベースの表現型計測データセットに該当する。
abstractThe dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals.
Reproduction assets foundThe paper's own rose petal image dataset is publicly deposited on Mendeley Data, and the authors' validation/metadata scripts are publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicThe RoseVisuals dataset is publicly available on Mendeley Data at Direct URL to data: https://data.mendeley.com/datasets/f44jwtbfjg/5. Data Identification Number: 10.17632/f44jwtbfjg.5. Repository Name: RoseVisuals.Open asset ↗Mendeley Data · 10.17632/f44jwtbfjg.5html-lines:246-284Code · publicThe RoseVisuals codebase, comprising all validation scripts, is publicly available on GitHub Repository at https://github.com/Arya-S14/RoseVisuals-Validation-Doc.Open asset ↗GitHubhtml-lines:246-284Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The global food security is severely threatened by various bacterial and fungal diseases that significantly degrade the quality, yield and productivity of wheat crop. This increases the need for an accurate and efficient system to improve wheat yield and mitigate these losses by enabling early intervention. The dataset used in this research comprises of 10,000 images from brown rust, yellow rust, powdery mildew, loose smut diseases and healthy wheat plants. The existing neural networks, ensembling and transformer-based models used for classifying wheat diseases are limited by high computational resource requirements that leads to inefficient feature extraction. These challenges are addressed by proposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer, a lightweight and optimized transformer model that enhances feature extraction while significantly reducing computational overhead. In particular, Swin-SHARP results in 82.5% reduction (48.9M to 8.5M parameters), making it an attractive solution for resource-constrained environments. The extracted features are further optimized by integrating the Swin-SHARP transformer with boosted Model-Agnostic Meta-Learning (MAML) and a weighted ensembling strategy to enhance generalization and classification accuracy. Our proposed model achieves a remarkable 98.1% accuracy, significantly outperforming existing CNN-based solutions, ensemble approaches, transformer, and deep learning models. We also cross-validated our proposed model on an unseen wheat plant diseases dataset, achieving 95.57% accuracy. Our proposed model is also compared against prominent models such as Inception-v3, ResNet-18, and VGG-16, which outperforms them by 1.6%, 1.7%, and 2.6%, respectively. The comparison with existing state-of-the-art models, including Sequential CNN, SGDR-S, Inception-v3, Cereal Conv, Darknet-53 CNN, EfficientNet B3, GLNet, CNN & SVM, Customized CNN, CaiT-YOLOv9 and MSFNet revealed that our method outperforms them by 0.6%, 5.8%, 5.3%, 0.75%, 2.8%, 2.68%, 1.42%, 1.3%, 3.31%, 3.29%, and 2.4% respectively. These results demonstrate the effectiveness and practicality of the Swin-SHARP transformer for wheat disease classification, particularly for real-time agricultural applications on mobile and embedded systems aimed at early disease detection and crop management.
Why it matches plant phenotyping methods小麦の病徴画像から植物の病害状態を推定する深層学習手法を開発し、別データセットで交差検証しており、植物表現型取得・判定が研究の中心である。
abstractproposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the authors' wheat disease dataset and analysis code are publicly available on GitHub (https://github.com/SWIN-SHARP/), which is a paper-specific, actionable asset. The paper also uses third-party public datasets (Zindi ICLR Workshop, Mundi, Watershed/GrabcCode · publicThe dataset and code used in this research have made publicly available on https://github.com/SWIN-SHARP/
SWIN-SHARP for reproducibility purposes.Open asset ↗SWIN-SHARPpdf-page:25 lines:1-104Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Three dimensional (3D) instance segmentation is essential for precision characterization of tree architecture at the branch level, which supports both tree fruit crop breeding and the development of robotic systems for orchard management. Existing methods usually use sparse convolution-based operation, which requires a coordinate quantization preprocess to generate sparse tensors, risking the loss of geometric details for fine-grained downstream phenotyping tasks. To overcome this challenge, we developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees. Our hybrid DSPVFormer architecture maximizes the use of raw point features by dynamically mapping and aggregating the raw point features into the sparse voxel embeddings, capturing strong geometric features that may be discarded during quantization. Evaluations demonstrate that DSPVFormer achieved statistically significant improvements over baseline models on most instance segmentation metrics, which are further translated into more accurate phenotyping evaluation including branch counting and pruning map generation. These advances directly benefit downstream applications in plant phenotyping and robotic pruning for tree crops such as apples. Meanwhile, experimental results on phenotyping tasks suggested that phenotyping-specific evaluation metrics should be prioritized over upstream computer vision performance metrics to realize the full potential of high-throughput phenotyping for real-world applications.
Why it matches plant phenotyping methodsリンゴ樹の3D点群から枝レベル形質を抽出するセグメンテーション手法を開発・評価し、枝数や剪定マップへの応用まで検証しており、植物フェノタイピング手法が中心である。
abstractwe developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees.
Reproduction assets foundThe paper states that its data and code (including the DSPVFormer analysis pipeline built on Plant Segmentation Studio) are publicly available at the authors' PSS GitHub repository. The COS dataset of 98 dormant apple tree point clouds is also referenced as accessible via this repository/statement. Other URLs (spconv,mCode · publicThe data and code are available at the PSS GitHub repository: https://github.com/perrydoremi/PlantSegStudio .Open asset ↗https://github.com/perrydoremi/PlantSegStudiolines:383-408Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management. Methods We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening. Results The weighted optimization increased overall F1-scores by 10.0%-18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6-7. Across the interpretability analyses, the 480-550 nm blue-green region emerged as the main source of information for pre-symptomatic detection, whereas the 750-870 nm near-infrared region contributed more general information on disease presence. Discussion These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.
Why it matches plant phenotyping methods小麦の病害状態をハイパースペクトル画像と深層学習で検出する方法が研究の中心であり、時系列評価・モデル比較・性能改善・解釈性分析を含むため、植物フェノタイピング手法として含める。
abstractWe evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe data can be accessed at: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:787-847Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Agricultural disease monitoring remains a critical challenge in precision farming, particularly when deploying computer vision systems on resource-constrained platforms. This study presents a rigorous comparative evaluation of four deep learning architectures—ResNet50, DenseNet121, a Binarized Neural Network (BNN), and YOLOv8-cls—for multi-class plant disease classification using the PlantVillage dataset (15 classes). Unlike prior benchmarking studies, we incorporate statistical validation through repeated stratified experiments (5 runs) and report mean ± standard deviation for accuracy, precision, recall, and F1-score. Results show that while DenseNet121 achieves high classification accuracy (99.48)% ± 0.12), it exhibits significantly higher inference latency. The BNN achieves minimal latency but suffers substantial performance degradation (88.31% ± 0.45). YOLOv8-cls provides the best trade-off, achieving 99.64% ± 0.09 accuracy with low latency (3.3 ms ± 0.2). Statistical comparison using paired t-tests confirms that YOLOv8 significantly outperforms ResNet50 p
Why it matches plant phenotyping methods植物病害を画像から分類する深層学習モデルを比較・反復実験・統計検証しており、植物の病害状態を推定する画像ベース表現型手法の技術評価が中心です。
abstractThis study presents a rigorous comparative evaluation of four deep learning architectures—ResNet50, DenseNet121, a Binarized Neural Network (BNN), and YOLOv8-cls—for multi-class plant disease classification using the PlantVillage dataset (15 classes).
Reproduction assets foundThe authors' Data Availability statement points to a public Zenodo deposit containing the dataset, source code, and models used for this paper's plant disease classification experiments. The PlantDoc Kaggle dataset and Ultralytics GitHub repositories are cited third-party resources, not paper-specific assets.Dataset · publicThe complete dataset is available at [Dataset, Source Code, and Models for: Deep Learning-based Plant Disease Detection using YOLOv8] via [https://doi.org/10.5281/zenodo.18895667].Open asset ↗zenodo · 10.5281/zenodo.18895667html-lines:284-295Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Rice and potatoes are major crops in Bangladesh, frequently affected by major disease outbreaks that challenge food security. Inaccurate disease identification often contributes to yield losses. Recently, machine learning garnered much attention in identifying crop diseases. The present study was conducted to develop a deep learning model-based image‑analysis system that automatically identifies key diseases of Bangladeshi rice and potato, and integrates it into a web app to provide farmers with rapid, accurate diagnoses. The system employs a convolutional neural network (CNN) implemented with TensorFlow’s Sequential API, featuring ReLU-activated hidden layers and a Softmax output layer. A dataset of 4,809 images, comprising both healthy and diseased, was collected and processed through pre-processing, feature extraction, and classification. A web-based application was deployed utilizing the Python Streamlit framework. This application integrates the proposed model to predict 2 rice diseases viz. blast ( Magnaporthe oryzae ), bacterial leaf blight ( Xanthomonas campestris ), and 2 potato diseases viz. Early blight (Alternaria solani) and Late blight ( Phytophthora infestans ) from uploaded images, providing a confidence score for the predictions with approximately 92.84% for all detected diseases. The proposed model achieved a training accuracy of 0.9357, a validation accuracy of 0.8983, and a test accuracy of 0.9333. The developed web application indicates strong diagnostic performance for four major diseases, offering Bangladeshi farmers an accessible tool to make timely management decisions.
Why it matches plant phenotyping methodsイネ・ジャガイモ葉の病徴を画像から分類するCNNと実用Webアプリを開発・評価しており、植物の病害状態推定が中心的な方法論的貢献である。
abstractdevelop a deep learning model-based image‑analysis system that automatically identifies key diseases of Bangladeshi rice and potato
Reproduction assets foundThe paper's rice/potato leaf disease image dataset partially comes from Kaggle, and the data availability statement points to PlantVillage for additional image data; both are public image assets used for the paper's CNN phenotyping/disease-classification analysis. No author analysis code, trained model checkpoints, or专Dataset · publicch, M.Y.H. analyzed the data, A.A.J.,
452
M.Y.H. and M.S. wrote this manuscript, M.R.I., F.M.A. and S.O.N. reviewed and edited the
453
manuscript. All authors have read and agreed to the published version of the manuscript.
454
Data availability statement
455
Some of the datasets used in this study was obtained from Kaggle
456
(https://www.kaggle.com/datasets). Additional datasets used and/or analyzed during the current
457
study are available from the corresponding author upon reasonable request. More image data can
458
be found at https://www.plantvillage.org/en/plant_images
459Open asset ↗Kagglepdf-raw-page:24 lines:1-57Dataset · publiche manuscript.
454
Data availability statement
455
Some of the datasets used in this study was obtained from Kaggle
456
(https://www.kaggle.com/datasets). Additional datasets used and/or analyzed during the current
457
study are available from the corresponding author upon reasonable request. More image data can
458
be found at https://www.plantvillage.org/en/plant_images
459Open asset ↗PlantVillagepdf-raw-page:24 lines:1-57Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Early and accurate disease detection is important for increasing the agricultural output, decreasing the financial costs, and ensuring food security. Traditional diagnostic procedures take much time and effort, involve the necessity of having deep expertise, and are not always suitable for large scale farming disease detection. For this purpose, the current research suggests developing an explainable lightweight CNN-based model for crop disease identification based on RGB leaf images. The model utilizes several innovative architectural solutions such as depth-wise separable convolution, SE blocks, skip connections, and guided attention-based feature learning that allow enhancing the effectiveness of features extraction and decreasing computation load. Moreover, Grad-CAM is used to visualize affected areas on a map and thus increase the interpretability of the model. The suggested solution was implemented and tested on the PlantVillage dataset containing 54,305 images for 38 crop diseases out of 14 crops. The results show that the training, validation, and testing accuracies equal 97.6%, 88.3%, and 97.63%, correspondingly, along with the Macro-F1 measure of 0.867 and Micro-ROC-AUC equal to 0.99. A comparative study reveals that the presented model performs comparably well in terms of classification with lightweight structure and built-in interpretability capabilities to be applied in the mobile and edge-enabled agriculture environment. The results show that the presented approach is capable of being used as an effective and interpretable tool for diagnosing plant diseases in real-time.
Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の開発・評価が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。
abstractthe current research suggests developing an explainable lightweight CNN-based model for crop disease identification based on RGB leaf images.
Reproduction assets foundThe paper's plant-phenotyping input is the public PlantVillage leaf-image dataset (54,305 RGB images, 38 crop-disease classes), explicitly declared in the Data availability statement with a Kaggle URL. No author code, trained model, or checkpoint is deposited.Dataset · publicThe data set analyzed during current study are available in https://www.kaggle.com/datasets/emmarex/plantdisease.Open asset ↗Kaggle · emmarex/plantdiseaselines:366-390Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Crop diseases play a significant role in food production globally; therefore, there is an urgent need to develop quick and accurate diagnostic techniques that are more effective than manual inspection methods. The proposed hybrid multimodal learning framework in this research provides a solution that integrates adaptive therapy suggestion, market price prediction, and image-based disease detection. This study also proposes a framework for pesticide recommendation and the treatment of plants. This study experiment on tomato and cotton crop leaf data for disease detection. Experimental results on a tomato crop disease detection dataset show that the proposed model shows high performance. EfficientNetB0 provides more stability and generalization capabilities in different scenarios compared to other models, such as YOLOv8, ResNet50, and a custom CNN model. The use of a knowledge-based decision support system provides sustainable pesticide recommendations based on environmental and symptom-specific parameters. Forecasting of pesticide prices through LSTM methods yields forecasts within 3.2% and 4.1% MAE, enabling improved decision-making by providing instant points of reference for potential price movements. Research uses SHAP and LIME to provide explainability to users, thus improving user buy-in through transparency. Overall, this modular system provides a data-driven decision-making model to improve the efficiency of managing crops.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する画像ベース手法を、複数モデルで比較評価しており、植物病害フェノタイピングがシステムの主要構成要素です。価格予測や農薬推薦も含みますが、病害検出の技術評価が明示されています。
abstractThe proposed hybrid multimodal learning framework in this research provides a solution that integrates adaptive therapy suggestion, market price prediction, and image-based disease detection.
Reproduction assets foundThe paper's disease-detection experiments use publicly available cotton and tomato leaf image datasets (Kaggle, IEEE DataPort, Roboflow), all cited with explicit public URLs in the references. No author analysis code or trained model checkpoints are stated as publicly available; the supplementary material is referencedDataset · publiccholar
View reference in article
19
Muppala C. Guruviah V. ( 2020 ). Machine vision detection of pests, diseases, and weeds: a review . J. Phytol. 12 , 9 – 19 . doi: 10.25081/jp.2020.v12.6145
CrossRef
Google Scholar
View reference in article
20
National College of Ireland ( 2025 ). “Cotton Disease Dataset.” Available online at: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset (Accessed May 19, 2025).
Google Scholar
View reference in article
21
Naveed Gul and Kaggle ( 2026 ). Tomato Leaf Disease . Kaggle. Available online at: https://www.kaggle.com/datasets/naveedgull/tomato-leaf-disease (Accessed March 29, 2026).
Google Scholar
View reference in article
22
Ngugi H. N. EzugOpen asset ↗Kagglelines:554-633Dataset · publicreference in article
20
National College of Ireland ( 2025 ). “Cotton Disease Dataset.” Available online at: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset (Accessed May 19, 2025).
Google Scholar
View reference in article
21
Naveed Gul and Kaggle ( 2026 ). Tomato Leaf Disease . Kaggle. Available online at: https://www.kaggle.com/datasets/naveedgull/tomato-leaf-disease (Accessed March 29, 2026).
Google Scholar
View reference in article
22
Ngugi H. N. Ezugwu A. E. Akinyelu A. A. Abualigah L. ( 2024 ). Revolutionizing crop disease detection with computational deep learning: a comprehensive review . Environ. Monit. Assess. 196 : 302 . doi: 10.1007/s10661-024-12454-z
Pubmed AOpen asset ↗Kagglelines:554-633Dataset · publicComputer Vision and Pattern Recognition (CVPR) ( Las Vegas, NV : IEEE ), 779 – 788 . doi: 10.1109/CVPR.2016.91
CrossRef
Google Scholar
View reference in article
29
Roboflow ( 2026a ). A Comprehensive Dataset of Cotton Plant Diseases for National Disease Identification and Treatment Guidance | IEEE DataPort. Available online at: https://ieee-dataport.org/documents/comprehensive-dataset-cotton-plant-diseases-national-disease-identification-and-treatment (Accessed March 29, 2026).
Google Scholar
View reference in article
30
Roboflow ( 2026b ). Cotton Plant Disease Prediction Object Detection Model by National College of Ireland . Available online at: https://universe.roboflow.com/national-colleOpen asset ↗IEEE DataPortlines:554-633Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially advanced automatic wheat spike detection and counting. However, precise spikelet-level phenotyping remains largely underexplored. This study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations. For spike detection, the pre-trained YOLOv11 achieved superior accuracy (mAP@0.5 = 95.8%, Pearson r = 0.993) with shorter training and inference times compared to YOLOv12. For spikelet detection, the non-pretrained YOLOv11 demonstrated higher accuracy (mAP@0.5 = 99.0%), while counting performance was comparable across models. These results establish OBB-based YOLO detection as a robust and scalable approach for AI-driven wheat phenotyping.
Why it matches plant phenotyping methods小麦の穂・小穂という収量関連形質の画像ベース検出・計数手法を比較評価し、大規模ベンチマークデータセットも構築しているため、フェノタイピング手法が中心である。
abstractThis study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations.
Reproduction assets foundThe paper openly states its supporting data (spike/spikelet imagery with OBB annotations) is available on Zenodo, and the underlying models are deployed on the authors' public WheatAI cloud platform.Dataset · publicData availability
The data supporting the findings of this study are openly available at: https://doi.org/10.5281/zenodo.20215489 .Open asset ↗zenodo · 10.5281/zenodo.20215489lines:219-266Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll measurements, it is destructive and temporally limited, whereas portable optical meters such as the CCM-300 enable rapid, non-destructive measurement of the chlorophyll fluorescence ratio (CFR) but require species- and season-specific calibration. This study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom. Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. RF regression achieved the highest predictive performance within the calibration dataset, although substantial uncertainty remained at the leaf level; a simple linear model was therefore adopted for cross-year projection due to its stability under extrapolation. Applying this calibration to daily 2022 CFR measurements generated a continuous "virtual acetone" trajectory, enabling qualitative comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines; however, senescence, defined as the initiation of sustained post-peak decline, occurred earlier during the warmer and drier 2022 season. Mixed-effects modelling identified positive effects of temperature and wind speed on CFR in 2022, while generalised additive modelling of the 2023 dataset revealed a non-linear seasonal decline under comparatively mild conditions. Because cross-year projections rely on a low-fit linear calibration, interannual differences are interpreted primarily in terms of relative seasonal trajectory shape and timing rather than absolute chlorophyll magnitude.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を破壊的測定と比較し、校正モデルの開発・性能評価と季節軌跡の再構築を行っており、植物表現型取得法が研究の中心である。
abstractThis study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotClassificationDisease symptoms / severity
This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique's parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process's accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task's MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system's predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.
Why it matches plant phenotyping methods植物画像に基づく病害分類モデルの開発・評価が研究の中心的技術貢献であり、感染植物の状態を直接推定しているため含める。
abstractThe study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics
Reproduction assets foundThe paper explicitly states that implementation code, trained models, and experimental configurations are publicly available in an authors' GitHub repository, and that the PlantifyDr plant disease dataset and IP02 pest detection dataset used in the study are available on Kaggle. These are paper-specific, public, and可直接Code · publicThe implementation code, trained models, and experimental configurations used are publicly available in: “ https://github.com/sumanthvmani/-Pest-Detection-and-Smart-Irrigation ”. The repository contains all necessary instructions and dependencies required to reproduce the reported experimental results.Open asset ↗https://github.com/sumanthvmani/-Pest-Detection-and-Smart-Irrigationlines:253-302Dataset · publicThe datasets generated and/or analyzed during the current study are available in the [PlantifyDr Dataset and Pest detection dataset] repository“ https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset ”Open asset ↗https://www.kaggle.com/datasets/lavaman151/plantifydr-datasetlines:400-436Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Testing for distinctness, uniformity, and stability (DUS) is a requirement for plant variety registration and based on phenotypic traits, which is time-consuming and sensitive to environmental variation. Advances in genomics allow to complement DUS testing with molecular markers, for which two models in DUS testing were proposed by the Union for the Protection of New Varieties of Plants (UPOV). A use cases was described for maize, but an implementation has been hindered by a lack of suitable markers and validated analytical frameworks. We address these challenges by integrating historical DUS characteristics scores from 352 European hybrid maize varieties with high-density genome-wide single nucleotide polymorphism (SNP) data. Using genome-wide association studies (GWAS), we identified 18 genomic regions and candidate genes associated with 12 DUS characteristics, enabling the development of diagnostic markers consistent with the UPOV model “Characteristic-Specific Molecular Markers”. Since most DUS traits are polygenic, we combined GWAS-informed marker selection with XG-Boost-based machine learning to predict notes of DUS characteristics. This approach achieved strong predictive performance across multiple traits (mean accuracy 0.67), demonstrating its potential for managing reference collections under UPOV model “Combining phenotypic and molecular distances in the management of variety collections”. Both approaches were validated for two characteristics using independent public USDA-NPGS maize datasets (>1,700 accessions) highlighting the value of public data for method validation. We also identify key limitations of historical DUS data, including imbalanced and sparse trait representation, and discuss mitigation strategies. Despite these constraints, our results demonstrate that molecular markers may improve maize DUS testing, enabling faster, more accurate variety registration and supporting accelerated crop improvement. Key message Historical DUS datasets can be used to identify marker-trait associations of DUS characteristics using genome-wide association study (GWAS) and to develop a genomic prediction framework for an accurate prediction of DUS character notes from marker data.
Why it matches plant phenotyping methodsGWASと機械学習によるDUS形質ノート予測フレームワークを開発し、独立データで検証しており、植物表現型評価の技術的手法が中心である。
abstractwe combined GWAS-informed marker selection with XG-Boost-based machine learning to predict notes of DUS characteristics
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicthe R scripts and computational pipelines used for the analysis of genetic and phenotypic variation in both the European maize hybrid panel and the USDA dataset have been deposited in the Zenodo repository (DOI: 10.5281/zenodo.20610279 )Open asset ↗Zenodo · 10.5281/zenodo.20610279lines:213-244Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Traditional apple maturity assessment methods are destructive and time- and labour-intensive, yielding only population-level approximations. Hyperspectral imaging provides a non-destructive alternative to assess individual fruit, but progress has been constrained by the lack of large, diverse datasets that support robust model generalisation. This study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness. Using this dataset, we adopt an iterative modelling framework to evaluate deep learning architectures, image resolutions, cultivar encoding, seasonal effects, and feature-specific models. Wavelength and spatial region importance were also analysed. The best predictive performance was achieved using Vision Transformer (ViT) models trained on edge-cropped 40 × 40 pixel images with explicit cultivar encoding, with Brix and firmness modelled independently. Although seasonal specificity was observed, models trained across all three seasons achieved the strongest overall performance. A 50% reduction in spectral wavebands did not compromise prediction accuracy. Key wavelength ranges contributing to Brix and firmness prediction were identified across the visible-near-infrared spectrum. Spatial regions were unimportant for Brix prediction but showed relevance for firmness. The optimised ViT model achieved firmness prediction performance comparable to previous studies (RMSE = 0.76 kgf, R[Formula: see text] = 0.63), while Brix prediction accuracy was lower (RMSE = 0.91 [Formula: see text]Brix, R[Formula: see text] = 0.75), likely reflecting increased biological and environmental variability captured in the dataset. Overall, this work demonstrates that hyperspectral imaging combined with deep learning and large, diverse datasets enables robust, non-destructive prediction of apple quality attributes across production conditions.
Why it matches plant phenotyping methodsリンゴ果実の硬度とBrixという植物器官形質を、ハイパースペクトル画像と深層学習で非破壊推定するデータセット・モデル・汎化性能評価が研究の中心である。
abstractThis study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness.
Reproduction assets foundThe paper explicitly states that the hyperspectral apple dataset (5756 apples, firmness/Brix/starch measurements) is deposited in the University of Essex research data repository and that the data cleaning, model training, and analysis code is on GitHub, both with public URLs.Dataset · publicThe datasets generated during and analysed during the current study are available in the University of Essex repository ( https://researchdata.essex.ac.uk/228/ )Open asset ↗researchdata.essex.ac.uk · 228lines:192-220Code · publicthe code used for data cleaning, model training and analysis are available on GitHub: ( https://github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaging.git )Open asset ↗github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaginglines:192-220Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Background Sorghum ( Sorghum bicolor ) is a versatile C4 crop used for food and feed and as biomass for bioproducts and energy. Improving nitrogen use efficiency (NUE) in sorghum is important because fertilizer is costly and excessive fertilizer use has negative environmental impacts. Leaf senescence mediates nutrient recycling, but its dynamic progression is difficult to quantify at scale. We evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum and link these phenotypes to gene expression. Sorghum Tx430 plants were grown under four N treatments (6, 9, 12, and 15 mM), imaged from vegetative growth through grain fill, and destructively sampled for RNA-seq at four developmental stages. Results A supervised support vector machine with a radial basis function kernel classified pixels from a hyperspectral image of sorghum plants grown under different N levels into green leaf, yellow leaf, dry leaf, stalk, panicle, and background classes with 0.93 accuracy. We defined the senescence ratio as the sum of yellow and dry leaf areas divided by the green leaf area and computed it across multiple growth stages and nitrogen levels. The senescence ratio did not differ among N treatments during vegetative growth, but it declined with increasing N during boot, anthesis, and grain fill, indicating earlier senescence under N limitation. Among the genes whose expression positively correlated with senescence ratio were 13 putative transcription factors, including SbiRTX430.02G247100, a WRKY1/ZAP1 homolog and a WRKY4 homolog. Gene regulatory network analysis of the top 1% of genes associated with SbiRTX430.02G247100 showed enrichment for processes associated with leaf senescence and chlorophyll catabolism. In contrast, the network associated with the WRKY4 homolog was enriched for autophagy-related terms. Conclusions Our study shows that automated hyperspectral imaging is highly effective for monitoring dynamic plant phenotypes, such as stress-induced senescence, that are difficult to visually score with the naked eye. Here, nitrogen deficiency served as the stress condition. Still, this approach supports large-scale phenotypic data collection for any such stressor and enables analyses with greater statistical power, yielding more robust conclusions and the potential for new insights that can be applied to engineering and breeding better crops.
Why it matches plant phenotyping methodsソルガムの動的な老化表現型を高スループットに取得する hyperspectral imaging と、SVMによる画像分類・senescence ratio算出が研究の中心であり、植物状態の定量化手法を実証している。
abstractWe evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum
Reproduction assets foundThe paper's availability statement points to a public GitHub repository containing the authors' image-processing, machine-learning classification, transcriptomic analysis, and figure-generation scripts. The 148 GB hyperspectral image data is only promised 'upon acceptance' (not yet public), and the RNA-seq deposit is aCode · publicle in the NCBI SRA repository,
552
under BioProject PRJNA1452908 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1452908)
553
(RNA-seq raw reads SRR38119224 to SRR38119282). Scripts used for image processing,
554
machine-learning classification, transcriptomic analyses, and figure generation will be
555
accessible through GitHub (https://github.com/belafif2/TX430_Senescence). Image data (148
556
GB) will be made available in a data repository upon acceptance. Other relevant processed data
557
files and supporting figures are available as supplementary data documents.
558
559
Competing interests
560
The authors declare that they have no competing interests.
561
Funding
562
This work was funded Open asset ↗belafif2/TX430_Senescencepdf-raw-page:22 lines:1-54Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Rubber tree powdery mildew is a major foliar disease that threatens the yield and quality of natural rubber. Its lesions are typically small, irregular, and embedded in complex backgrounds, making accurate automated detection difficult. Methods To address this challenge, we propose RubberFormer, an end-to-end detection framework based on a refined Transformer architecture for detecting small powdery mildew lesions in complex scenarios. RubberFormer adopts MobileNetV4 as a lightweight backbone, introduces the Hierarchical Attention with Local-global Optimization (HALO) module for multiscale local-global feature fusion, incorporates the Unified Cross-Attention Network (UCAN) to enhance multidimensional feature interaction, and applies Normalized Wasserstein Distance (NWD) Loss to improve small-object localization. Results Extensive experiments were conducted on PM-Dataset-Plus, which contains 9,765 images, and PD-40, a large-scale plant disease dataset containing 80,369 images across 40 disease categories and 8 crops. RubberFormer achieved superior detection accuracy and generalization performance compared with existing methods, while maintaining computational efficiency suitable for practical agricultural monitoring. Discussion These results demonstrate that RubberFormer is effective for detecting small and irregular rubber tree powdery mildew lesions under complex conditions. The framework has practical value for rubber tree disease monitoring and provides a transferable design strategy for agricultural vision tasks involving small objects and complex backgrounds.
Why it matches plant phenotyping methodsゴム樹の病斑という植物の病害状態を画像から検出するTransformer手法を開発し、複数データセットで性能検証しており、植物表現型取得が中心である。
abstractwe propose RubberFormer, an end-to-end detection framework based on a refined Transformer architecture for detecting small powdery mildew lesions in complex scenarios.
Reproduction assets foundThe paper's authors publicly release both plant disease image datasets used in this study: PM-Dataset-Plus (9,765 rubber tree powdery mildew images) and PD-40 (80,369 images, 40 categories, 8 crops), each with an explicit availability statement and GitHub URL matching the allowed URLs. No analysis code or trained modelDataset · publicPM-Dataset-Plus is available at https://github.com/wfcyliyuheng-dev/PM-Dataset-PlusOpen asset ↗wfcyliyuheng-dev/PM-Dataset-Pluslines:1199-1255Dataset · publicPD-40 is available at https://github.com/wfcyliyuheng-dev/PD40-DatasetOpen asset ↗wfcyliyuheng-dev/PD40-Datasetlines:1199-1255Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Clubroot disease, caused by Plasmodiophora brassicae , is one of the major constraints in rapeseed production. Breeding disease-resistant cultivars is the best way to control this devastating disease. However, breeding reliable resistant germplasm and genes is limited. Inactivation of susceptible genes has been shown to be a new and effective strategy for developing resistant crops. Therefore, we aimed to screen key candidate susceptible genes in this study. Firstly, we established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection. At 14 days post-inoculation (dpi), the earliest time point with a clear record of scorable root swelling, remarkable variations in the speed of gall formation were observed among 85 genotypes. Secondly, genome-wide association studies (GWAS) were performed to identify genes involved in gall development. Three and two consecutive significant peaks were detected at 14 and 21 dpi, respectively. Thirdly, comparative transcriptomic analysis was conducted between 2AF195 and 2AF058 at 7 and 14 dpi; these two materials exhibit contrasting speeds of gall development. Gene clustering analysis revealed two opposite expression patterns at 14 dpi. One pattern comprised 1,383 genes downregulated in 2AF195 but upregulated in 2AF058, which were significantly enriched in 10 KEGG pathways, including Environmental Information Processing and Plant-pathogen interaction, and involved core repressors JAZ8/10 in the jasmonic acid (JA) signaling pathway, as well as nucleotide-binding site (NBS) protein-encoding genes. The opposite pattern consisted of 79 genes upregulated in 2AF195 but downregulated in 2AF058, which were enriched in an additional 10 KEGG pathways, predominantly related to Carbohydrate Metabolism and the Ubiquitin System. These genes were functionally annotated mainly as pectin methylesterases, xyloglucan endotransglucosylase/hydrolases (XTHs), and lignin biosynthesis-related enzymes. These findings demonstrated that distinct regulatory networks exist in different susceptible rapeseed genotypes. Finally, through the combined analysis of haplotype and transcriptome data, we co-localized and identified the candidate gene BnaC08g46100D , a nodulin-related gene belonging to the MtN21 transporter family. These results provide a theoretical basis for developing novel disease-resistant materials by editing the key susceptibility genes involved in root gall formation. The candidate genes identified in this study are the most promising targets for this purpose.
Why it matches plant phenotyping methods根こぶ形成を高スループットに可視化・判定する方法の確立が明示され、感染植物の病徴を測定する手法として研究の主要な技術要素になっている。
abstractwe established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2
Disease incidence data of 85 rapeseed accessions at various time points following inoculation with the Xinmin strain.Open asset ↗lines:502-594Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping based on unmanned aerial vehicles still faces challenges regarding the direct correlation between spectral information with field-collected variables, due to the influence of environmental factors and the considerable variation among maize phenological stages. Therefore, the objectives of this research were: I) to evaluate the interaction of nitrogen doses and evaluation environments (phenological stages and growing seasons) and variance components for field variables and vegetation indices; II) to identify the most suitable indices according to the evaluation environments; and III) to predict field variables based on relevant vegetation indices identified through the proposed methodology. The study was conducted using a randomized complete block design with four repetitions, in which treatments consisted of six nitrogen (N) topdressing doses (0, 50, 100, 200, 300, and 400 kg ha−1) during the 2022/2023 and 2023/2024 growing seasons. Evaluations of agronomic variables and image acquisition were performed in five distinct phenological stages throughout the maize crop cycle. The data were analyzed using deviance analysis and variance components, principal component analysis (PCA), and multivariate linear modeling for the prediction of field variables. Our results demonstrated that all indices were affected by the interaction between N doses and evaluation environments (phenological stages and growing seasons). Additionally, the most reliable were EXGRaw, TGI, GNDVI, NDRE, CIRE, GVI, CVI, BNDVI, PanNDVI, SRNIRRe, SFDVI, RGBindex, NDVI, SAVI, MSAVI, and OSAVI, which showed clustering patterns according to growing season condition and phenological stage. Finally, the variables predicted using the proposed methodology achieved coefficients of determination above 0.80, except for shoot biomass and 100-grain weight. Therefore, it can be concluded that vegetation indices are influenced by the evaluated environment; however, the proposed framework based on the deduction of fixed and random effects enables the prediction of field variables with high accuracy using relatively simple models.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を選定し、農業形質を予測する方法論の開発・評価が研究の中心であり、植物形質の取得・推定に直接関与している。
titleMethodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.
Reproduction assets foundThe paper's supplementary file contains the REML-BLUP adjusted values for all vegetation indices and field variables, which directly reproduce the paper's phenotyping measurements and underpin its computational analysis. The raw UAV imagery and field data are only available on request, and the EstimateBreed R package (Dataset · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15121782/s1 , Table_Supplementary_1. This table contains all vegetation indices and field variables with values adjusted using the RELM-BLUP methodology.
Author Contributions
C.d.S.L.: Conceptualization, methodology, validation, visualization, writing—original draft, writing—review and editing. A.J.T.S.: Data collection and iOpen asset ↗lines:76-146Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Accurate crop disease detection supports precision agriculture, but field-deployable identification remains hindered by complex backgrounds, varying illumination, and heavy deep learning models. This work presents a lightweight visual detection approach for grape leaf diseases under unconstrained field conditions. Built on the YOLO11n backbone, the method integrates three customized modules: C3k2-UltraLightBlock for efficient feature representation, LeafRepFusionStem for low-level feature enhancement, and RCSA-HSFPN for refined multi-scale fusion with residual channel-spatial attention. A dedicated dataset with complex backgrounds is constructed via augmentation and background replacement. Experiments show the model achieves 92.0% precision, 92.9% recall, and 93.0% mAP@0.5, with only 2.9 GFLOPs and 1.73 M parameters, representing 54.7% and 33.2% reductions over the baseline. Heatmap visualization confirms improved lesion focusing and background suppression, while cross-crop tests validate strong generalization. This framework provides an efficient solution for real-time, edge-deployable plant disease monitoring, balancing accuracy and computational efficiency for practical agricultural visual computing applications.The implementation code for this study is available at:https://github.com/aitizc/Lightweight-Visual-Detection-Framework-for-Complex-Background-Grape-Leaf-Disease-Identification.git
Why it matches plant phenotyping methodsブドウ葉の病斑・病害状態を画像から推定する軽量な視覚検出手法を開発・評価しており、植物病害表現型の取得が中心である。
abstractThis work presents a lightweight visual detection approach for grape leaf diseases under unconstrained field conditions.
Reproduction assets foundThe authors explicitly state that the implementation code for this study is publicly available on their GitHub repository. The paper's grape leaf disease dataset itself is not stated as deposited (only the public PlantVillage source is cited), so only the authors' code qualifies as a paper-specific public asset.Code · publicThe implementation code for this study is avail-
able at:https://github.com/aitizc/Lightweight-Visual-Detection-Framework-for-
Complex-Background-Grape-Leaf-Disease-Identification.gitOpen asset ↗https://github.com/aitizc/Lightweight-Visual-Detection-Framework-for-pdf-page:2 lines:1-43Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Litchi is an important economic fruit in southern China, and its precision management relies on the rapid and accurate estimation of the Soil and Plant Analyzer Development (SPAD) values in leaves. Addressing the limitations of existing SPAD detection methods, such as limited rapid coverage, inadequate modeling of dynamic environmental interference, and shallow fusion of multi-source data, this study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features. A Long Short-Term Memory (LSTM) network model integrated with a feature level attention mechanism (MLSTM) was proposed to fuse IoT time-series data, vegetation index, and high dimensional texture features for dynamic SPAD value prediction. The results indicate that multi-source feature fusion significantly improves SPAD estimation accuracy. The MLSTM model achieved optimal performance under the all-features situation, with a coefficient of determination (R²) of 0.897 and a root mean square error (RMSE) of 2.638, outperforming other comparative models. The attention mechanism effectively enhanced the model's focus on key features, improving feature utilization efficiency and model interpretability. The multi-source data fusion method and MLSTM model proposed in this study enable high precision, dynamic estimation of SPAD values in litchi leaves, providing reliable data support for precision fertilization, stress diagnosis, and yield prediction in litchi orchards, as well as theoretical support for promoting the practical application of this technology in smart agriculture.
Why it matches plant phenotyping methodsIoT・UAVマルチスペクトル画像から葉のSPAD値を推定するデータ融合システムとMLSTMモデルを開発・評価しており、植物形質取得手法が研究の中心です。
abstractthis study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo repository containing the study's multi-source SPAD/IoT/multispectral dataset.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/18308090 .Open asset ↗zenodo · 18308090lines:427-441Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield ( r = 0.79-0.82, p r = 0.72 in hard spring, r = 0.53 in soft spring). These conditions also improved discrimination between varieties. Although heritability patterns were not clearly differentiated by stress clusters, environments with higher genetic control of yield also tended to show stronger NDVI heritability. Overall, NDVI reliably captured wheat grain yield, which is governed by the genotype × environment driven variability, with its predictive value strongest in stress-prone conditions. These findings underline NDVI's usability as a practical digital trait for improving variety testing and guiding breeding decisions in challenging environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを抽出し、複数環境・品種で収量との関係、予測性、遺伝率を評価しており、デジタル植物形質の測定・検証が中心である。
abstractThis study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicTrial data, including grain yield, variety, and market class information, were obtained from the Washington State University Extension Cereal Variety Selection and Testing Program ( https://smallgrains.wsu.edu/variety/ ).Open asset ↗lines:38-48Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafGrowth / time-series analysisGrowth / development / phenology
Disentangling genotype × environment (G×E) controls of flowering time requires phenotypes that link molecular regulation, developmental physiology and environment. Here, we integrated time-resolved measurements of apical development, final leaf number (FLN), and expression of the flowering-time genes VRN1, VRN2 and VRN3 across contrasting temperature and photoperiod regimes in six wheat genotypes spanning a wide range of developmental sensitivities. By combining controlled-environment phenotyping with concurrent gene-expression profiling, we show that environmentally driven variation in FLN is coherently explained by shifts in the timing of key apical transitions and associated VRN gene-expression dynamics. These integrated datasets were used to parameterise and interrogate the Cereal Anthesis Molecular Phenology (CAMP) model, enabling direct comparison between observed foliar gene-expression time courses and modelled gene activity. While overall developmental responses were well captured by the model, systematic differences between observed and modelled gene-expression patterns highlight the importance of distinguishing foliar expression from apical regulatory activity, as well as differences in temporal scaling. Building on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Why it matches plant phenotyping methodsFLN応答に基づくフェノタイピングプロトコルを提示し、温度・光周期処理下で遺伝的に解釈可能な発育表現型を取得する方法が中心的に扱われている。
abstractBuilding on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Reproduction assets foundThe paper's CAMP model code and the analysis scripts producing its figures are explicitly stated as publicly available on the authors' GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicwere also validated and the best-performing sets selected. A
348 description of each of the primers used in this study is given in the supplementary material
349 (Table SA1).
350 2.9 Verification of CAMP predictions
351 2.9.1 Model set-up and operation.
352 The CAMP model was coded into a Python script which is available at
353 https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
354 description of the code and parameterisation scheme is given in the supplementary material.
355 The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
356 derive the Vrn expression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicpression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
358 Vrn gene expression could be compared with those observed. The script running the CAMP
359 code and producing the graphs displayed in this paper can be viewed at
360 https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.
14
UNOFFICIALOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicnd testing of the model in
690 broader contexts. EW contributed substantially to the improvement of model concepts and the
691 manuscript and all authors provided final checking.
692 8. Data Availability
693 All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
694 publicly available at https://github.com/HamishBrownPFR/CAMP/
695 9. References
696 Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
697 response of wheat vernalization to environmental variables indicates that vernalization is not
698 a response to cold temperature. Journal of Experimental Botany 63: 847–857.
699 Baumont M, Parent B, Manceau L, Brown HE,Open asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:31 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published5 Jun 2026Proceedings of the National Academy of Sciences of the United States of AmericaCited by 0 · OpenAlex ↗
To improve crop yield and resilience, it is essential to identify the steps limiting [Formula: see text] assimilation rate in plant leaves. The combined effect of multiple traits can be resolved by mechanistic models of the underlying diffusion, biochemistry, and geometry. Yet the widely used simple serial resistance models overlook tissue geometry, and detailed anatomical models are computationally heavy and rely on parameters that are difficult to measure. Here, we develop a framework for systematic species and model comparison, and find that the necessary level of model resolution is species-specific. We apply a minimal reaction-diffusion model and reduce [Formula: see text] fixation in leaves to two key parameters. These parameters comprise a compact phase space in which three rate-limiting regimes emerge naturally: stomatal uptake, intercellular diffusion, and intracellular processes. Mapping diverse plant species into this phase space reveals: 1) dominant colimitations by stomatal and intracellular processes, 2) an equal partition between species that require spatially resolved leaf-scale models and species where intracellular models suffice. Taken together, we present a scalable path for interpreting complex trait data and bridging between models.
Why it matches plant phenotyping methods葉のCO2固定を機構モデルで2パラメータに縮約し、複数種の生理的制限状態と複雑な形質データを解釈・比較する計算フレームワークが中心であるため、植物生理形質の推定・解析手法として含める。
abstractHere, we develop a framework for systematic species and model comparison
Reproduction assets foundThe paper deposits its analysis code/scripts publicly on Zenodo (DOI 10.5281/zenodo.19087524) and GitHub (andreas-stillits/CarbonFixationModel), and uses the publicly deposited Knauer et al. leaf-trait/mesophyll-conductance dataset on Figshare (10.6084/m9.figshare.19681410) to map species into (τ, γ) space. All three, Code · publicCode and Scripts. All code is readily available at our github and at a public
repository (DOI: 10.5281/zenodo.19087524).Open asset ↗Zenodo · 10.5281/zenodo.19087524pdf-raw-page:8 lines:1-60Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Quantitative disease resistance in plants emerges from complex interactions between host tissues and pathogen growth dynamics, producing a spectrum of phenotypic responses. In plant-fungal interactions, disease is most visibly expressed through lesions that vary in number, size, shape, and color, collectively defining a lesion profile. For Cochliobolus heterostrophus, a fungus causing Southern Corn Leaf Blight of maize (Zea mays ssp. mays), we show that infection on different maize genotypes produces strikingly different lesion profiles. However, it remains unclear whether such macroscopic variation in lesion profiles corresponds to consistent differences in the three-dimensional organization of pathogen colonization within host tissue. We therefore examined variation in the three-dimensional structure of C. heterostrophus-infection networks across host genotypes representing four lesion-profile classes. Using light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue. In this dataset, network depth was similar across genotypes, whereas network morphology (shape and density), spatial association with vascular bundles, hyphal segment length, and branching frequency varied. Notably, genotypes with similar quantitative resistance levels sometimes exhibited distinct patterns of fungal colonization, suggesting that comparable resistance can arise from different underlying infection dynamics. These findings indicate that lesion profiles may not uniquely predict infection network structure and highlight the utility of three-dimensional network metrics for describing variation that likely reflects multiple underlying host and pathogen processes. This multi-scale framework provides tools for linking macroscopic disease phenotypes with microscopic infection processes in quantitative disease resistance.
Why it matches plant phenotyping methods植物病斑と病原菌感染ネットワークを対象に、ライトシート顕微鏡とトレーシング法を適応し、感染構造を定量化する指標を開発・適用しており、表現型取得法が研究の中心である。
abstractUsing light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue.
Reproduction assets foundThe paper's Data availability statement explicitly deposits metadata, data, and computer code as Supplementary Files accompanying the open-access publication (Supplementary Materials 1-5, including XLSX datasets and an untyped Supplementary Material 5 likely holding code). These are paper-specific phenotyping assets (eCode · publicMetadata, data, and computer code from this study are available in Supplementary Files included with the publication.Open asset ↗lines:147-204Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Genomic selection (GS) has revolutionized modern breeding by utilizing genome-wide single nucleotide polymorphisms (SNPs). While traditional models such as GBLUP and Bayesian approaches remain prevalent, several deep learning approaches have recently been introduced for plant GS, demonstrating superior predictive performance. Here, we introduce Gsformer, a novel deep learning framework designed to predict phenotypes by modeling complex genetic architectures. It features two distinct architectures: CSA, which combines convolutional neural networks (CNNs) with self-attention to capture local and long-range genomic dependencies, and NSA, which employs a native sparse attention mechanism to enhance computational efficiency by focusing on the most informative features. We evaluated Gsformer on six datasets spanning animal and plant species-pig, cattle, chicken, mouse, wheat, and maize-and compared its phenotypic prediction performance against five established GS methods: DNNGP, MLP, LightGBM, SVR, and GBLUP. Results Gsformer generally ranked among the top two models across six diverse animal and plant genomic prediction datasets. Specifically, Gsformer-CSA yielded notable improvements in predicting cattle fat percentage, while Gsformer-NSA was more accurate in predicting chicken first egg weight, pig age at 100 kg body weight, and mouse anxiety. With the topN hyperparameter set to 20%, Gsformer-NSA matched or marginally exceeded Gsformer-CSA for most traits-though it showed lower accuracy for a subset of traits. Adjusting the topN value further enhanced Gsformer-NSA's performance, allowing it to match that of Gsformer-CSA. Ablation studies confirmed the complementary roles of CNN and self-attention modules in the CSA architecture. To enhance interpretability, we applied SHAP (SHapley Additive exPlanations) to identify influential SNPs and annotate candidate genes associated with growth and body size traits in pigs. Functional enrichment analysis revealed biologically relevant pathways involved in nervous system development, glycolytic process regulation, and digestive tract morphogenesis. Conclusions In summary, Gsformer establishes a flexible and powerful framework for genomic prediction, demonstrating broad applicability across both animal and plant breeding. Owing to its lower computational cost, Gsformer-NSA is recommended over Gsformer-CSA in scenarios where the minor sacrifice in prediction accuracy is acceptable.
Why it matches plant phenotyping methods植物の表現型を予測する深層学習フレームワーク自体を開発し、コムギ・トウモロコシを含むデータセットで既存手法と比較検証しており、表現型推定法が中心である。
abstractHere, we introduce Gsformer, a novel deep learning framework designed to predict phenotypes by modeling complex genetic architectures.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe Gsformer software is available on GitHub at (https://github.com/hajudien/GSformer/tree/master).Open asset ↗https://github.com/hajudien/GSformer/tree/masterhtml-lines:256-299Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published1 Jun 2026International Journal of Electrical and Computer Engineering (IJECE)Cited by 0 · OpenAlex ↗
Plant diseases remain a critical challenge in agriculture, causing substantial yield losses and threatening food security. In this work, we propose a hybrid deep feature engineering framework that integrates deep learning-based feature extraction with classical machine learning for accurate plant disease detection. A pretrained vision transformer (ViT) model is employed to extract discriminative features from leaf images, effectively capturing complex spatial relationships. To address the curse of dimensionality, principal component analysis (PCA) is applied, retaining 98% of the variance while reducing feature space complexity. The refined features are then classified using a support vector machine (SVM) optimized through hyperparameter tuning. Experimental results on the bean leaf lesions dataset demonstrate strong performance, achieving 92% accuracy and a weighted F1-score of 0.92. The proposed ViT–PCA–SVM pipeline effectively balances accuracy, computational efficiency, and generalization, making it a promising solution for real-time smart farming applications.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定するViT–PCA–SVM解析パイプラインが研究の中心であり、植物表現型（病斑・病害状態）の画像ベース推定手法に該当する。
titleTransformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine
Reproduction assets foundThe paper's only qualifying asset is the public Bean Leaf Lesions dataset (leaf images used as phenotyping input for disease classification), explicitly declared in the DATA AVAILABILITY section with a Kaggle URL. No author analysis code, trained models, or checkpoints are released.Dataset · publicI R D O E Vi Su P Fu
Vijayalakshmi S. Abbigeri ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Geetha D. Devanagavi ✓ ✓
CONFLICT OF INTEREST STATEMENT
All authors declare that they have no conflicts of interest.
DATA AVAILABILITY
The data that support the findings of this study are openly available in Kaggle, "Bean leaf lesions
dataset," [Online] at https://www.kaggle.com/datasets/advayprasad/bean-leaf-lesions-dataset.
REFERENCES
[1] Food and Agriculture Organization (FAO), “Climate change fans spread of pests and threatens plants and crops, new FAO study,”
Food and Agriculture Organization (FAO), 2021. https://www.fao.org/newsroom/detail/Climate-change-fans-spread-of-pests-
and-threatens-plants-and-crops-new-FAOOpen asset ↗Kagglepdf-layout-page:7 lines:1-70Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Ear density ( ) and ear surface area in cereals are important traits for adaptation to low inputs and climate change. Here we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot. First, the YOLOv5 ear detection algorithm is applied to nadir RGB images to estimate . Second, an ear segmentation algorithm is applied to nadir and 45° RGB images to compute the ear gap fraction at different viewing angles. The Beer-Lambert law is then inverted to compute the ear area index (EAI) from the observed ear gap fraction. is finally derived as the ratio between EAI and . We applied the methodology to a panel of 10 commercial bread wheat varieties how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m -2 ) for and 18% (1.3 cm 2 ) for . For awned varieties, ground-truth observations of were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm 2 ). was strongly correlated with grain dry mass per ear at harvest ( r 2 = 0.80 across genotypes and environments, r 2 per genotype ranged between 0.80 and 0.95) and was strongly correlated with grain yield ( r 2 = 0.83). These results indicate that both EAI and can be interesting non-destructive proxies for yield and grain dry mass per ear.
Why it matches plant phenotyping methodsRGB画像と地上ロボット、物体検出・セグメンテーション・Beer–Lambert法を組み合わせ、コムギ穂の密度と表面積を推定・検証する手法が研究の中心であるため。
abstractHere we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot.
Reproduction assets foundThe authors publicly release their ear surface area estimation algorithm with an example dataset on an INRAE forge repository, and the Phenomobile-derived ear density/ear surface area estimations used in the multi-environment analysis are included as supplemental material with the open-access article. The YOLOv5 GWC_SoDataset · publicThe algorithm developed to estimate the EAI and the average ear surface using binary images from ear segmentation are publicly available in the repository https://forge.inrae.fr/raul.lopez-lozano/wheat-ear-surface , jointly with an example dataset from the Mauguio 2023 trial (4 treatments, 1 replicate). The Phenomobile estimations of ear surface area and ear density used in the multi-environmental mixed model presented in Section 2.5 are included as supplemental material.Open asset ↗lines:614-652Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Why it matches plant phenotyping methodsUAS画像からキャノピー高、穂形質、植生指数を抽出し、機械学習でソルガムのプロット収量を推定するパイプラインが研究の中心であり、形質取得・推定手法の評価も行っている。
abstractthis study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Mungbean (Vigna radiata (L.) R. Wilczek) is a vital source of digestible proteins and is well-suited for the plant-based protein industry. In this study, we analyzed pod morphological traits in the Iowa Mungbean Diversity (IMD) panel of 372 genotypes (2022-2023) using image-analysis-based phenotyping on 2,418 pod images. Pod morphological traits were extracted using deep learning image analysis, achieving excellent agreement with manual measurements (r > 0.96 for pod length (PL) and seed-per-pod (SPP)). Four complementary genome-wide association studies models identified 65 significant SNPs (-log10(P) ≥ 5.56) associated with pod curvature, length, width, and SPP traits. A significant SNP (5_35265704) on chromosome 4 was linked to pod dimensional traits, length, width, and curvature. A candidate gene, Virad04G0076900, located 15.6 kb from this SNP, is part of the GH3 gene family and has an Arabidopsis ortholog (AT4G27260) known for influencing organ elongation, pod, and seed development. Another SNP, 5_210437 on chromosome 6, has been found to be significantly associated with both PL and SPP. A candidate gene, Virad06G0002400 (36.5 kb from this SNP), encodes a potassium transporter and shares homology with the Arabidopsis gene HAK5 (AT4G13420), known to influence pod growth. Image-based measurements achieved genomic prediction accuracies ranging from 0.61 to 0.85 across various traits, demonstrating comparable accuracy to manual methods for linear traits and up to 22% improvement for complex shape traits. These results highlight the potential of deep learning-assisted phenomics integrated with genomic tools to accelerate selection for improved pod architecture in mungbean breeding programs across the Midwestern United States and globally.
Why it matches plant phenotyping methods深層学習による画像解析でマメ pod の形態形質を抽出し、手動測定との一致度を検証しており、画像ベース表現型取得が研究の中心です。
abstractusing image-analysis-based phenotyping on 2,418 pod images
Reproduction assets foundThe paper's image-based pod phenotyping and genomic analysis scripts are explicitly stated to be publicly available on the authors' GitHub repository. Raw phenotypic data (image-based and manual measurements, BLUEs, BLUPs, GP results) are only in supplementary files without a direct public URL, so they are not listed; Code · publicAll analysis scripts used in this study are publicly available at GitHub: https://github.com/vboddepalli89/Image-based-pod-phenotyping .Open asset ↗https://github.com/vboddepalli89/Image-based-pod-phenotypinglines:395-459Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
Modern plant phenotyping faces the challenge of interpreting complex, high-dimensional data. Traditional analytical tools often fail to capture the non-linear, hierarchical, and temporal relationships that define plant responses under multifactorial conditions. We present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space. Unlike conventional Euclidean approaches, HTDA-Mapper preserves the hierarchical structure of phenotypic traits, improves cluster resolution, and reveals hidden growth trajectories across treatments and time, offering a powerful means to explore latent phenoms. The pipeline supports both quantitative data and images. When integrated with unsupervised contrastive learning, HTDA-Mapper identifies similarities and differences in raw image data without requiring manual labelling or post hoc processing. We applied this framework to a high-throughput phenotyping (HTP) dataset of over 27,000 images of Arabidopsis thaliana seedlings exposed to varying nutrient levels and priming agents at different concentrations over seven days. Using cubical complexes, HTDA-Mapper mapped relationships between treatment variables, compound concentrations, and phenotypic outcomes. Furthermore, it reliably detected compound-specific effects, uncovered dynamic trait–environment interactions, revealed phenotypic trajectories not captured by conventional methods, and facilitated biologically meaningful interpretation of the complex dataset. By preserving the geometry and temporal evolution of plant development, HTDA-Mapper sets a new standard for HTP analysis. Beyond phenomics, it is a versatile tool for other omics, such as transcriptomics and metabolomics, where structured, high-dimensional data is prevalent. HTDA-Mapper can accelerate data-driven crop improvement by uncovering effective compounds, robust genotypes, and adaptive growth strategies that enhance plant resilience.
Why it matches plant phenotyping methods植物フェノミクスの高次元画像・形質データを解析するHTDA-Mapperアルゴリズムを開発し、27,000枚超の植物画像データで適用・評価しているため、解析手法が中心的である。
abstractWe present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicUpon acceptance, the codes and all material used in this research will be freely available at HYPERLINK: https://github.com/JZdrazilX/MML and data at ZENODO: 10.5281/zenodo.17952279.Open asset ↗JZdrazilX/MMLhtml-lines:222-260Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Urochloa grasses are among the most widely used forage grasses across the tropics. Spittlebugs (Hemiptera: Cercopidae) are major pests of tropical Urochloa (syn. Brachiaria) grass pastures, severely reducing forage productivity and quality. Understanding the genetic basis of host-plant resistance is essential for developing durable resistant cultivars. Here, we combined high-throughput image-based phenotyping and genome-wide association studies (GWAS) to dissect the genetic architecture of response to Aeneolamia varia nymphs in 339 interspecific F1 hybrids derived from crosses between resistant sexual and susceptible apomictic Urochloa parents. Digital image analysis using both unsupervised (DQU) and supervised (DTR) quantification pipelines enabled accurate estimation of plant damage, yielding moderate to high broad-sense heritability estimates (H2 = 0.49 to 0.66). In contrast, insect survival (NTS) exhibited low to moderate correlations with all damage traits and lower heritability estimates (H2 = 0.42). Using 57,051 high-quality SNPs aligned to the genome of the hybrid cultivar Basilisk, GWAS models identified 18 quantitative trait loci (QTLs) for plant damage traits, but none for insect survival (antibiosis). Six robust QTLs on chromosomes 1, 6, 7, 27, 29, and 36 were consistently detected across models and phenotyping methods, explaining up to 21.5% of phenotypic variance. Candidate gene analysis revealed proteins involved in hormone signaling, oxidative stress response, and cell wall modification, suggesting multifaceted plant-insect interaction mechanisms. These results provide a foundational set of molecular markers associated with spittlebug response in Urochloa grasses, useful for marker-assisted and genomic selection in the forage breeding program.
Why it matches plant phenotyping methods高スループット画像表現型解析と、植物損傷を推定する2つの画像解析パイプラインが研究の中心であり、異なる手法間の比較と形質推定性能も評価している。
abstractHere, we combined high-throughput image-based phenotyping and genome-wide association studies (GWAS) to dissect the genetic architecture of response to Aeneolamia varia nymphs
Reproduction assets foundThe paper's digital plant-damage images are publicly deposited in Harvard Dataverse (paper-specific phenotyping input). The RAD-Seq accession PRJEB109285 is a sequencing/omics deposit and is excluded per criteria. No author analysis code repository with explicit availability URL is stated.Dataset · publicThe digital images used for plant damage quantification are available in the Harvard Dataverse repository at the following identifier: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EGUVHA .Open asset ↗Harvard Dataverse · doi:10.7910/DVN/EGUVHAlines:387-414Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
As the demand for plant-based nutrition increases, improving the protein profile of legumes like cowpea has become a breeding priority. Cowpea, a multiuse legume and staple in many low-income regions, provides important dietary protein that can help meet the demand in our growing population. Our research used genome-wide association studies (GWAS) and phenomic tools to investigate the genetic architecture of seed protein content in cowpea and integrated 4 cohorts of undergraduate researchers through a USDA-AFRI REEU program. Using wet chemistry and near-infrared spectroscopy (NIRS), we assessed crude protein (CP) within the University of California Riverside Minicore collection, developed and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86), and performed GWAS with ∼41k single-nucleotide polymorphisms (SNPs). Significant SNPs associated with protein content were identified on chromosomes 1, 3, 7, 10, and 11, and candidate genes were linked to functions including nutrient transport, stress response, and seed storage protein regulation. These results provide a foundation for future marker validation and functional studies, and demonstrate the value of pairing trait discovery with undergraduate training.
Why it matches plant phenotyping methods種子タンパク質含量という植物形質の取得に用いるNIRS校正式を開発・検証しており、表現型測定法が研究の主要な技術的要素である。
abstractdeveloped and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86)
Reproduction assets foundThe paper's Data Availability statement deposits the phenotypic data (wet chemistry CP, NIRS-derived CP phenotypes used for calibration and GWAS) in Dryad. No author analysis code or trained NIRS model files are explicitly deposited; other URLs are generic tools or citations.Dataset · publicThe phenotypic data collected and used in this research are available in the Dryad Digital Repository under DOI: https://doi.org/10.5061/dryad.8cz8w9h72 .Open asset ↗Dryad Digital Repository · 10.5061/dryad.8cz8w9h72lines:305-345Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Spatially accurate estimates of forest above-ground biomass (AGB) are indispensable for carbon-stock accounting and sustainable silviculture. Existing mapping approaches face challenges in densely vegetated Coastal Plain forests because of seasonal optical variability, radar–optical saturation, and limited wall-to-wall structural information. We aimed to (i) develop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping, (ii) quantify the added value of seasonal optical data and photogrammetric canopy-height profiles, and (iii) interpret model drivers using explainable artificial intelligence (AI) to relate predictors to forest structure and composition. We mapped AGB across ~ 10,500 km 2 in southeastern North Carolina using wall-to-wall predictors from optical, radar, and photogrammetric sources. Forest Inventory and Analysis plot data (n = 305) were used to train and evaluate an ensemble of gradient-boosted tree models (CatBoost, LightGBM, XGBoost) and a neural network (RealMLP) via cross-validation. Model behavior was interpreted using feature importance and partial dependence analysis. Expanding Sentinel-2 temporal coverage from summer-only to four-season composites improved normalized RMSE by 8.7%. Incorporating canopy-height profiles from NAIP produced the largest accuracy gain, lowering nRMSE by 15.9–18.0% relative to the multisensor baseline, which underscores the critical value of structural information for AGB prediction. Three key predictors illustrated complementary ecological dimensions: the 10th percentile canopy height captured canopy openness, L-band polarimetric alpha indicated volume-scattering regime, and spring red-edge reflectance captured vegetation biochemistry. These findings show that fusing structure, polarimetry, and spectral phenology yields robust AGB maps and improves generalizability across heterogeneous landscapes. This transferable, broadly accessible framework integrating structural, polarimetric, and spectral phenology data enables landscape-scale AGB monitoring and supports targeted conservation planning, restoration tracking, and adaptive management for carbon sequestration. The incorporation of high-resolution wall-to-wall structural data is particularly valuable for improving the accuracy and usability of forest AGB maps, thereby informing more responsive decision-making.
Why it matches plant phenotyping methods森林の地上部バイオマスという植物群落形質を対象に、光学・レーダー・写真測量データを融合した推定フレームワークを開発・評価しており、形質推定手法が研究の中心である。
abstractdevelop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping
Reproduction assets foundThe authors explicitly state that the code reproducing all figures and analyses is archived in a GitHub repository and permanently preserved via Zenodo (doi 10.5281/zenodo.18688899). The GEDI-derived CHM25 product (Zenodo 11176727) is a cited prior-work dataset from Wang et al. (2025), not this paper's own asset, and FCode · publicGEDI data products are distributed by NASA’s Land Processes
Distributed Active Archive Center and are accessible through
Google Earth Engine. The code used to reproduce all figures
and analyses has been archived in a GitHub repository (https://
github.com/ChaoEcohydroRS/NC_SoutheastBiomass) and
permanently preserved via Zenodo (https://doi.org/10.5281/zenodo.18688899, submitted on 20 February 2026).
Declarations
Conflict of interest The authors declare no competing inter-
ests.
Disclaimer The findings and conclusions in this publication
are those of the author(s) and should not be construed to rep-
resent any official USDA or U.S. Government determination or
policy.
Open Access This articleOpen asset ↗Zenodo · 10.5281/zenodo.18688899pdf-raw-page:23 lines:1-89Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process and provide the means to identify factors limiting photosynthesis. However, the use of large-scale models is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型特異的な光合成動態パラメータを推定するニューラルネットワークであり、植物の生理形質の取得・推定手法の開発と検証が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper's Data Availability Statement provides a public GitHub repository with the authors' custom code for artificial dataset generation, neural network definition/training, and predicted maize genotype parameters. Zenodo datasets (gas exchange measurements and synthetic training data) are mentioned via DOIs but no Code · publicCustom code for the generation of the artificial dataset as well as code for neural model definition and training is available at https://github.com/pwendering/C4TUNE . This repository also contains the predicted parameters for the maize genotypes.Open asset ↗pwendering/C4TUNElines:223-270Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant leaf disease detection is a critical task in precision agriculture, where reliable diagnosis under real-world conditions is essential for reducing crop losses and supporting timely intervention. Although deep learning models have achieved high classification accuracy, their performance often degrades under domain shift between controlled laboratory datasets and real-field environments, while predictive uncertainty and confidence calibration remain largely unaddressed.This study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification. The framework integrates multi-scale feature learning with Monte Carlo Dropout-based predictive uncertainty estimation and temperature-based calibration to systematically analyze model behavior in terms of accuracy, reliability, and robustness. Experiments were conducted on two complementary datasets: the New Plant Diseases Dataset (controlled conditions) and the PlantDoc dataset (field conditions), enabling bidirectional cross-domain evaluation. Results demonstrate that the proposed framework achieves superior performance, attaining 97.8% accuracy on controlled data and 93.6% on field data, while significantly improving calibration with lower Expected Calibration Error (ECE = 0.032 / 0.041), reduced Negative Log-Likelihood, and lower Brier score compared to baseline CNN and transformer models. Furthermore, the framework exhibits improved robustness under domain shift, with reduced performance degradation and stable uncertainty behavior. Overall, this study highlights the importance of integrating uncertainty estimation and calibration within a hierarchical transformer-based framework, providing a more reliable and deployment-ready solution for real-world agricultural disease diagnosis.
Why it matches plant phenotyping methods植物葉の病害状態を直接推定する不確実性-aware分類フレームワークの開発・評価が中心であり、異なる条件のデータセット間で精度、校正、頑健性を検証している。
abstractThis study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification.
Reproduction assets foundThe paper's Data availability statement explicitly links the two public image datasets used for its cross-domain plant leaf disease classification experiments: the New Plant Diseases Dataset on Kaggle and the PlantDoc dataset on Dataset Ninja. No author analysis code, models, or checkpoints are reported as available.Dataset · publicThe New Plant Diseases Dataset can be obtained from Kaggle at [https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset]Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetlines:360-398Dataset · publicThe PlantDoc dataset is available for download at [https://datasetninja.com/plantdoc#download]Open asset ↗plantdoclines:360-398Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Cotton production is highly vulnerable to foliar diseases and pest-induced damage, which significantly reduce yield and compromise fiber quality. Rapid, reliable, and automated disease identification is therefore essential for supporting sustainable crop management. In this study, we propose a hybrid deep learning framework integrating a ResNet50 backbone with Squeeze-and-Excitation (SE) channel attention modules to enhance discriminative feature representation for cotton leaf disease classification. The model is trained on a publicly available disease dataset comprising six classes and optimized using Weighted CrossEntropyLoss, Adam optimization, ReduceLROnPlateau scheduling, and Early Stopping to ensure stable convergence and robust generalization. Experimental results demonstrate outstanding performance, achieving 99.72% training accuracy and 99.31% validation accuracy, with convergence at the 14th epoch. Visualization through Grad-CAM reveals that the model focuses on biologically relevant symptom regions, thereby enhancing interpretability and supporting expert validation. Comparative analysis with state-of-the-art methods shows that the proposed model surpasses existing CNN, transfer learning, and hybrid architectures in both accuracy and model transparency. These results indicate that the proposed SE-ResNet50 framework offers a highly accurate, interpretable, and computationally efficient solution suitable for real-world cotton disease monitoring and precision agriculture applications.Clinical trial registrationThis study is not a clinical trial; therefore, clinical trial registration is not applicable.
Why it matches plant phenotyping methods綿花葉の病徴を画像から分類する深層学習フレームワークの開発が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格です。
titleA hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification.
Reproduction assets foundThe paper's Data Availability statement points to the public Kaggle cotton plant disease dataset used for training the SE-ResNet50 model. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · public“Cotton plant disease.” Accessed: Nov. 21, 2025. [Online]. Available: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease.Open asset ↗Kaggle · dhamur/cotton-plant-diseasehtml-lines:458-493Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Plant diseases pose a major threat to global food security, significantly reducing agricultural yields. Therefore, timely diagnosis of plant diseases can help prevent food losses and support economic stability. This study explores the use of eight Convolutional Neural Networks and two Vision Transformers for apple leaf disease diagnosis. The feature extraction layers of each model were modified to incorporate DropBlock layers, while preserving pretrained weights from the ImageNet dataset. Images from the Plant Pathology 2021 dataset were used to fine-tune the models for multi-label classification, targeting five disease categories and a healthy label. Three experiments were conducted to evaluate model performance on the test set. First, the ResNet50 model was used to determine optimal Dropout and DropBlock probabilities. Second, these parameters were applied across all models to identify those with the best performance. Finally, twenty-three Swarm Optimization Algorithms were used to optimize classifier thresholds, improving accuracy and F1-scores. A DropBlock probability of 0.05 and a Dropout probability of 0.2 yielded superior results. Among the models, SwinV2T attained an accuracy of 90.7%, while SwinV2S achieved the highest F1-score of 91.7%, slightly outperforming the ConvNeXtT and ConvNeXtS architectures. The results demonstrated the effectiveness of DropBlock regularization and optimized classifier thresholds, highlighting the superior performance of recent architectures and optimization algorithms over their older counterparts. These findings suggest that such networks hold substantial promise for accurately identifying and diagnosing apple leaf diseases.
Why it matches plant phenotyping methodsリンゴ葉画像から病害状態を分類・診断する深層学習手法を比較評価し、正則化や閾値最適化による性能改善も検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study explores the use of eight Convolutional Neural Networks and two Vision Transformers for apple leaf disease diagnosis.
Reproduction assets foundThe paper's apple leaf disease phenotyping is based on the public Plant Pathology 2021 (FGVC8) Kaggle image dataset and an authors' reorganized multi-label version publicly deposited on GitHub; both are explicitly linked in the Data availability statement. No author analysis code or trained model checkpoints are statedDataset · publicFor this research, the dataset was reorganized and extended into a multi-label format. The complete modified dataset is publicly available at: https://github.com/soroushtou/Plant-Pathology-2021---MultiLabel-Dataset.Open asset ↗Plant-Pathology-2021---MultiLabel-Datasetlines:239-262Dataset · publicThe original dataset used in this study is the publicly available Plant Pathology 2021 dataset from the FGVC8 competition available at: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8.Open asset ↗lines:239-262Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
A comprehensive description of plant phenotypes of certain taxa is an important task when describing genera and species, as well as when setting their natural taxonomies. The development of modern technologies of effective phenotyping makes it possible to obtain a large amount of data with a quantitative and/or qualitative description of various traits in plants, mainly based on the analysis of their digital images. The study compared the results of the F2 hybrids assessment - visually and using machine learning methods - of two endemic tetraploid (2n = 4x = 28) wheat species which are Ethiopian wheat (Triticum aethiopicum Jakubz.) and Kartalian or Dika wheat (T. carthlicum Nevski). In the latter case, it is proposed to use the method of a mixture of Gaussian (normal) distributions in plant morphometry in order to identify groups that differ in character values. Most taxonomically important (species-specific) traits are controlled oligogenically and have a clear phenotypic manifestation, so hybridological analysis was an indispensable and basic type of analysis for subsequent detailed phenotyping of wheat spikes using machine-learning methods. According to a number of criteria, the estimates of patterns of inheritance obtained by different methods coincide. Based on the conducted research, we can state that the trait "tetraaristatum" (the presence of awns on both flower and spike glumes) is species-specific (taxonomically important) for T. carthlicum and it can be effectively used for taxonomic purposes both in carrying out hybridological analysis and in experiments using machine learning. Such a species-specific character is the "character (type) of awnedness" for T. aethiopicum. Our study demonstrates that a combination of automatic phenotyping methods and a model of a mixture of Gaussian distributions can, in principle, lead to an automatic analysis of the allocation of classes in F2 hybrids. It allows, in turn, to detect the presence of genes associated with species-specific traits of wheat plants. Further, the improvement of the applied artificial intelligence (AI) algorithms is required.
Why it matches plant phenotyping methodsコムギ穂の形態形質を対象に、画像に基づく機械学習フェノタイピングとガウス混合モデルを提案・適用しており、表現型の自動抽出・分類が研究の中心である。
abstractThe study compared the results of the F2 hybrids assessment - visually and using machine learning methods
Reproduction assets foundThe paper's supplementary materials (Supplementary Tables S1–S3 and Figure S1) contain the paper-specific phenotyping data: species-specific trait descriptions, the 19 spike morphometric characters per projection, and the Gaussian mixture model splitting results (means, variances, group sizes, χ² values). The full textSupplement · publicof these traits
are controlled by oligogenes and have a clear phenotypic
manifestation, the hybridological method was an indispensable
and primary type of analysis for subsequent detailed
phenotyping spikes of wheat species using machine learning
methods.
Supplementary Materials are available in the online version of the paper:
https://vavilov.elpub.ru/jour/manager/files/Suppl_Kruch_Engl_30_3.pdf
Plant material. The object of study was interspecific
hybrids obtained by crossing two endemic tetraploid
wheat species ♀T. aethiopicum Jakubz. (k-19301/2) with
♂T. carthlicum
Nevski (k-32496). The experiment was produced
in spring sowing in the greenhouses of the Breeding
and Genetics Complex (BGC)Open asset ↗lines:111-200Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Among carnivorous plants, the Venus flytrap (Dionaea muscipula) is known for its rapid (<1 s) trap closure. Although buckling instability, hydrostatic pressure, and hydroelastic coupling have all been proposed to be involved, the nature of this process and the relationship between trap size and curvature remain elusive. Here, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index. Based on these experimental data, we constructed a geometric model of the trap that takes leaf orientation into account. We found that leaf curvature is dependent on leaf size, a relationship we denote as a size-curvature constraint. We further propose a curvature design derived from differential deformations of a two-layer model of the leaf, which could be a powerful tool to control the curvatures of soft and bending surface structures in the field of biomimetics.
Why it matches plant phenotyping methodsマイクロCTと3D再構成で葉の閉鎖運動・曲率を定量化し、幾何モデルでサイズ–曲率関係を推定することが研究の中心であり、植物形態・運動状態のフェノタイピング手法に該当する。
abstractHere, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index.
Reproduction assets foundThe paper's Data Availability statement points to an authors' GitHub page hosting all data files and related rendering files for the Venus flytrap closure measurements and 3D reconstructions, matching an allowed URL.Dataset · publicAll data files and related rendering files are available from the github ( https://satorutsugawa.github.io/flytrap_geometric_model_datashare/) .Open asset ↗githublines:105-144Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Chlorophyll estimation is fundamental in plant physiology, crop management, and ecological studies; however, destructive and non-destructive methods are often interpreted interchangeably despite differing measurement principles. The present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions. Significant interspecific variation was observed for all methods. Correlation and regression analyses revealed generally weak relationships among methods, particularly between leaf-level (SPAD, solvent extraction) and canopy-level (GreenSeeker) measurements, reflecting scale-dependent behavior and methodological differences. Moderate associations were observed between SPAD and acetone-extracted chlorophyll for certain traits, whereas GreenSeeker showed poor agreement with solvent-based estimates. Differences between DMSO and acetone extraction further highlighted solvent-specific extraction efficiency. The results demonstrate that chlorophyll estimation methods are not directly interchangeable and should be selected based on study objectives, biological scale, and leaf anatomical characteristics. Species-specific calibration and integration of canopy structural parameters are required to improve cross-method interpretability.
Why it matches plant phenotyping methods複数の葉・キャノピーのクロロフィル推定法を作物種間で比較し、相関、回帰、スケール依存性、互換性を評価しており、植物表現型測定法の技術的検証が中心である。
abstractThe present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions.
Reproduction assets foundThe preprint declares that the datasets generated in this chlorophyll-method comparison study (SPAD, GreenSeeker, acetone and DMSO measurements across eight crop species) are publicly deposited in Figshare under DOI 10.6084/m9.figshare.31817989. This is a paper-specific, publicly actionable phenotype dataset. No authorDataset · publicThe datasets generated during the current study are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.31817989Open asset ↗Figshare · 10.6084/m9.figshare.31817989lines:163-185Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。
abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle lettuce growth dataset used for evaluation, matching an allowed URL. No author code or model checkpoints are disclosed.Dataset · publicThe datasets used and/or analyzed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/jurijsruko/lettuce/data.Open asset ↗Kaggle · jurijsruko/lettucehtml-lines:469-500Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100-350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Why it matches plant phenotyping methods葉レベルの植物揮発性物質排出と光合成パラメータを取得するタンデム計測系を開発・検証し、解析ソフトウェアも提供しているため、植物表現型取得法が中心である。
abstractWe developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs.
Reproduction assets foundThe paper's authors developed VAPOR, an open-access software tool used to post-process and analyze the paper's leaf VOC emission measurements, with explicit public availability at the authors' GitHub repository.Code · publicThe open-source code for VAPOR is accessible at https://github.com/INTERSECT-BESS/ORNL-VOC . In this study, VAPOR was used to post-process the VOC results generated from the offline collection of gases from poplars with different soil media.Open asset ↗INTERSECT-BESS/ORNL-VOClines:127-146Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Confidence calibration, selective prediction, out-of-distribution scoring, and deep ensembles are mature techniques in machine learning, yet their efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked. Methods Models trained on PlantVillage were evaluated on PlantDoc leaf-level crop images under a parent-image-aware split protocol, and a suite of standard mitigation techniques was applied to characterize the reliability gap. Analyses included temperature scaling and selective prediction for a fine-tuned ResNet-50, quantitative image-level shift analysis, Grad-CAM visualization, simple target-aware adaptation baselines, frozen-feature backbone comparisons, and ensemble baselines. Results In the primary case study, a fine-tuned ResNet-50 suffered a 67.7-percentage-point accuracy collapse upon cross-domain transfer, while mean predicted confidence remained at 79.76%. Post-hoc temperature scaling reduced calibrated ECE to 0.3645 but left selective risk at 80% coverage at 64.30%. Quantitative image-level shift analysis confirmed large-effect-size differences in saturation ( d = 3.90), border edge density ( d = 3.33), and foreground-occupancy proxy ( d = 2.48) between the two domains, while Grad-CAM visualizations showed that the model shifts attention from lesion-centered regions in PlantVillage to background-dominated areas in PlantDoc. Simple target-aware mitigations, including adaptive batch normalization and feature moment matching, improved accuracy from 0.321 to 0.343 and 0.366, respectively, whereas DANN-style adversarial adaptation degraded performance to 0.252. A frozen-feature backbone comparison across five backbones showed that, within the energy-scoring frozen-backbone comparison, DINOv2-S/14 achieved the highest unknown-detection AUROC (0.764) and the lowest selective risk at 80% coverage (0.520), with paired Wilcoxon tests confirming statistically significant accuracy and macro-F1 differences across backbones. Two ensemble baselines were evaluated: a warm-start end-to-end ResNet-50 ensemble reduced calibrated ECE to 0.063 but achieved only 0.666 AUROC, while a lightweight DINOv2 linear-probe ensemble achieved 0.779 AUROC after calibration but under limited epistemic diversity. Discussion Neither ensemble established deployment-grade reliability: the best selective risk at 80% coverage across all configurations remained above 0.51. The principal contribution is a reproducible, deployment-oriented reliability characterization showing that standard post-hoc and lightweight adaptation techniques reduce but do not eliminate the severe reliability gap under controlled-to-field transfer in agricultural computer vision.
Why it matches plant phenotyping methods植物病害画像分類の信頼性・ドメインシフト・校正・選択的予測を体系的にベンチマークしており、病害状態を画像から推定する方法の技術評価が中心である。
abstracttheir efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) was therefore constructed by normalizing all labels to a canonical Crop_Disease format and retaining only those categories for which an unambiguous semantic match existed in both datasets.Open asset ↗lines:335-337Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TissuePhysiological trait estimationWater status / transpiration
Process-based models that mechanistically represent water-carbon balances in the atmosphere-soil-plant continuum are an attractive tool for monitoring live fuel moisture content (LFMC) dynamics, a key variable when assessing fire danger. However, their application as operational tools to assess near-term wildfire danger at regional scale faces important challenges. Here, we explored key sources of prediction uncertainty in process-based modeling of LFMC. We applied the SurEau-ECOS model of plant hydraulics embedded within the MEDFATE modeling framework to assess how the accuracy of LFMC predictions was influenced by input data sources, by the availability of species-specific plant traits and by the level of mechanistic detail used to model water content of plant tissues. A lack of accurate data describing soil physical properties compromises the application of process-based models for predicting LFMC. Nonetheless, using global meteorological and vegetation data allows for successful regional-scale applications. Fully mechanistic approaches that model LFMC from plant water status using ecophysiological knowledge yield more accurate predictions. However, when reliable plant traits are lacking, semimechanistic approaches based on empirical equations offer a robust alternative. Overall, addressing the sources of uncertainty highlighted here could pave the way for developing operational tools to forecast near-term wildfire danger through process-based modeling of LFMC dynamics.
Why it matches plant phenotyping methods植物の生体燃料水分量（LFMC）という生理状態の推定モデルを対象に、入力データ、植物形質、機構的詳細度が予測精度へ与える影響と不確実性を評価しており、植物状態の取得・推定手法が中心である。
abstractHere, we explored key sources of prediction uncertainty in process-based modeling of LFMC.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the LFMC field data (Catalan and Reseau–Hydrique networks) and the analysis/figure code in a public GitHub repository, which directly reproduces this paper's phenotyping measurements (7203 LFMC values) and computational analysis. Supporting Information TablesSCode · publicof the ‘Severo Ochoa’ Centres of Excellence programme, Ref. CEX2023‐001340‐S, funded by MICIU/AEI/ https://doi.org/10.13039/501100011033 . Also it was supported by the Spanish Government project IMPROMED (grant no. PID2023‐152644NB‐I00).
Data availability
The data and code for analyses and figures are available through GitHub ( https://github.com/emf‐creaf/LFMC_FR_CAT ). Also, the data that support the findings of this study are available in the Supporting Information of this article, specifically in Tables S1–S3 .
References
Balaguer‐Romano
R
,
De Cáceres
M
,
Espelta
JM
. 2025 .
Second‐growth forests exhibit higher sensitivity to dry and wet years than long‐existing ones
. Ecosystems
28 : 6Open asset ↗emf‐creaf/LFMC_FR_CATlines:253-664Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The research proposes Cross Disease Similarity Awareness Learning (CDSAL), a robust multiclass tomato leaf disease detection framework based on high-quality and explainable deep learning. The approach solves the problem of superimposed patterns of disease especially Leaf Miner, Tomato Spotted Wilt Virus (TSWV), and nutrient deficiencies through the combination of multi-domain feature learning and inter-disease similarity modeling. In contrast to conventional metric learning or contrastive learning methods that function on pairwise or triplet sample associations, CDSAL develops a class-level Cross Disease Similarity Matrix that represents structured inter-disease proximity within the embedding space. Moreover, rather than employing episodic prototype construction typical of few-shot learning, the proposed system persistently updates centroid representations throughout supervised training and incorporates similarity-aware regularization directly into the loss function. This facilitates structural embedding reshaping specifically designed for visually overlapping illness categories, beyond traditional prototype-based learning methodologies. The input images are processed through HSV based green masking, morphological cleaning, extraction of leaf contours and resizing, and using a large amount of geometric and color-space augmentation to reduce the imbalance among the classes. DenseNet121 and EfficientNet-B0 are used to obtain feature representations and class-separated centroid of latent embedding's to form a Cross Disease Similarity Matrix, where similarity-aware optimization is possible during training. Grad-CAM on the target layers offers decipherable disease-specific activation signatures. The findings of the experiments show that classification accuracy at unseen samples is 99.77% with high resilience to visual confounding. The predictions, proximity of diseases that are similar and explainable features are provided by CDSAL, thereby facilitating reliable decision-making in agricultural diagnostics.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する深層学習手法を提案し、前処理・特徴抽出・類似度学習・説明可能性を技術的中心として評価しているため。
abstractThe research proposes Cross Disease Similarity Awareness Learning (CDSAL), a robust multiclass tomato leaf disease detection framework based on high-quality and explainable deep learning.
Reproduction assets foundThe paper's plant-phenotyping inputs are two publicly available Kaggle image datasets explicitly named in the Data Availability statement: PlantVillage (emmarex/plantdisease) used as the main dataset and TomatoVillage (mamtag/tomato-village) used for ablation/field-condition experiments. No author analysis code, modelsDataset · publicThe datasets analyzed during the current study are available in the Kaggle repository. [https://www.kaggle.com/datasets/emmarex/plantdisease]Open asset ↗Kaggle · emmarex/plantdiseasehtml-lines:605-624Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R2 rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m2·m−2. Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval.
Why it matches plant phenotyping methodsLiDAR点群からトウモロコシのLAIを推定する手法を、3D放射伝達モデル、PointNet++、転移学習で開発・検証しており、植物形態形質の取得・推定が研究の中心です。
abstractthis work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques.
Reproduction assets foundThe paper's field-measured LiDAR point cloud and LAI data (Yingke Oasis and Huazhaizi sites) come from a publicly accessible TPDC dataset with an explicit URL in the Data Availability Statement. No author analysis code, trained models, or synthetic dataset deposit is stated.Dataset · public2024WX06.
Data Availability Statement: The dataset used in this study was obtained from the National Tibetan
Plateau Data Center (TPDC, https://www.tpdc.ac.cn/ (accessed on 6 September 2025)), a publicly
accessible scientific data platform providing multi-source geoscientific datasets. The specific dataset
can be accessed via: https://www.tpdc.ac.cn/zh-hans/data/4d60d570-0aa9-417b-8a9d-c32b73b564
(accessed on 6 September 2025). The TPDC database integrates long-term observational and remote
sensing data with standardized quality control, ensuring the reliability and consistency of the datasets
for scientific research.
Acknowledgments: The authors would like to acknowledge the National TibetaOpen asset ↗4d60d570-0aa9-417b-8a9d-c32b73b564pdf-raw-page:19 lines:1-51Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Plant diseases are a serious danger to the world’s food security, because they lower agricultural output and increase economic losses. Due to subjectivity, fluctuating lighting, and environmental unpredictability, traditional visual examination techniques are frequently incorrect. The Excess Green (ExG) vegetation index and pseudo-thermal representations produced from RGB pictures are two synthetically developed complementary representations that are integrated with RGB imagery in this study’s lightweight multimodal deep learning system to address these issues. Histogram shifting and pseudo-infrared color mapping are used in a reproducible picture alteration pipeline to create the pseudo-thermal modality, which allows for extra visual signals without the need for specific thermal sensors. In order to classify plant diseases while preserving computational efficiency, the suggested framework uses MobileNetV3-Small backbones to extract modality-specific characteristics. This is followed by feature-level fusion. The publicly accessible Ginger Leaf Dataset, which includes RGB pictures of ginger leaves in four different conditions—Damage-Pest, Dehydrated, Healthy, and Leaf-blight—was used for the experiments. For training, validation, and testing, the dataset was split using a stratified 70:15:15 split. Python-based preprocessing procedures were used to create the extra modalities (ExG and pseudo-thermal representations) from the original RGB images. The experimental results show that the combination of the representations with RGB images can enhance the classification performance compared with the unimodal RGB-based models. Ablation experiments are also conducted to examine the contributions of different modalities to the overall categorization accuracy. The experimental results show that plant disease recognition can be improved with the help of efficient computing by combining lightweight convolutional neural networks with computationally generated visual representations.
Why it matches plant phenotyping methodsRGB画像からExG・疑似熱画像を生成し、植物葉の病害状態を分類するマルチモーダル手法が研究の中心であり、アブレーション評価も実施している。
titleHybrid deep learning-based multimodal framework for plant leaf disease classification using RGB, Excess Green (ExG), and pseudo-thermal representations with MobileNetV2
Reproduction assets foundThe paper's phenotyping experiments use the publicly available Ginger Leaf Dataset (RGB leaf images of four ginger leaf conditions), with a public GitHub repository and dataset website. The authors' derived ExG/pseudo-thermal representations and preprocessing scripts are only available upon request, so they do not yetDataset · publicor multispectral images
IEEE Geosci. Remote Sens. Lett. 2025
10.1109/LGRS.2025.XXXXXXX
Ulku, I., Tanriover, O. O. & Akagündüz, E. Cross-band correlation-aware interactive fusion for multispectral images. IEEE Geosci. Remote Sens. Lett.
10.1109/LGRS.2025.XXXXXXX
(2025).
10. Wong, J. Ginger Leaf Dataset. GitHub Repository (2023). https://github.com/wongjay1941/Ginger-Leaf-Dataset
11.
Bhakta I
A novel plant disease prediction model based on thermal images using modified deep convolutional neural network
Precis. Agric. 2023 24 23 39
10.1007/s11119-022-09927-x
Bhakta, I. et al. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precis.Open asset ↗https://github.com/wongjay1941/Ginger-Leaf-Datasetlines:580-681Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many countries greatly rely on agriculture as a means of livelihood and economic growth. Even the most industrialized countries need food, medicine, clothing, and shelter produced by crops. Rice is one of the most significant and widely grown crops worldwide. Nonetheless, the severely impacted crops in rice production are those of bacteria, fungi, and viruses, which decrease yield and quality. Manual disease detection is hectic, challenging, and, in most cases, inaccurate. Recent advances in deep learning and computer vision have demonstrated significant potential to improve the detection and classification of diseases. This study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases. This method consists of five key stages: image preprocessing, segmentation, augmentation, multi-feature extraction via adaptive fusion, and classification. There are five rice leaf diseases to discuss and recognize: Blight, brown spot, sheath blight, tungro, and leaf blast. The first step is global contrast enhancement, which improves image quality. After that, the segmentation is performed using Otsu's Thresholding to extract the leaf area. Then, the modified VGG16 and modified ResNet50 networks are used in parallel to extract features using a transfer-learning approach. The adaptive fusion technique combines these features to obtain a dominant, proper feature representation. Lastly, the classification is done using an adaptive fusion score technique. Experimental results show excellent performance, with class-wise Precision in the range of 95.5-100%, class-wise recall in the range of 97.4-100%, and overall test accuracy of 98.5%.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から自動検出・分類する深層学習ワークフローが研究の中心であり、葉領域抽出、特徴抽出、分類性能まで評価しているため、植物病害フェノタイピング手法に該当する。
abstractThis study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset employed in this study is accessible online at https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm.Open asset ↗Kaggle · rajeshbhattacharjee/rice-diseases-using-cnn-and-svmhtml-lines:929-951Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Today, the intelligent automation of agriculture has received much attention from researchers. One of the important factors for the success of this automation is the timely diagnosis of plant disease and making a decision appropriate to the existing conditions of the plant. Since the progress of the disease is a determining factor in the type of treatment method, the diagnosis of the severity of the disease is of particular importance. However, accurate diagnosis of plant disease progression depends on various factors, including the availability of appropriate and well-annotated training datasets for designing an efficient diagnostic system. On the other hand, the similarity of the complications of different diseases has made this work challenging. In this study, two tomato diseases, namely Bacterial Spot and Mosaic Virus, are investigated using images collected from the PlantVillage, Taiwan tomato leaves, Field-PlantVillage, and Syn-PlantVillage datasets. The disease severity levels are divided into six stages for Bacterial Spot and four stages for Mosaic Virus, and a specifically designed deep convolutional neural network is proposed for severity classification. Experimental results demonstrate that the proposed method achieves high accuracy under challenging field conditions and outperforms several state-of-the-art methods.
Why it matches plant phenotyping methodsトマト葉の病徴・病害重症度を画像から段階分類するCNN、背景除去、病斑セグメンテーションを開発しており、植物状態の取得・推定手法が中心である。
titleA parallel convolutional neural network with background removal and lesion segmentation for field plant disease severity classification
Reproduction assets foundThe paper's own severity-annotated datasets are explicitly restricted (available only on request), so no public paper-specific data asset qualifies. The authors do provide an explicit public code availability link for their proposed BaSPaC model. The Mendeley and Drive links are pre-existing external datasets cited as,Code · publicCode availability
https://github.com/m-hasheminejad/BaSPaC.Open asset ↗m-hasheminejad/BaSPaChtml-lines:878-908Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Colorimetric analysis of food using the CIELab/Ch colour space (i.e., from digital images of samples) is an accessible, non-destructive method for carotenoid and anthocyanin content prediction. Literature presents very well-fit, but rudimentary, models for pigment estimation (e.g., single/multiple linear regressions). However, standardised methods that statistically account for the high multicollinearity between CIELab/Ch colour parameters, varying light conditions and colour calibration, and samples with high genotypic variability are lacking. Methods An image analysis optimisation was developed for the prediction of carotenoid and anthocyanin content of 16 carrot genotypes of different colours. Samples were photographed under six light conditions with a digital camera and image colour was calibrated before analysis with the CIELab/Ch colour space. Total pigment contents and individual carotenoid contents were analysed chemically via spectrophotometry and high-performance liquid chromatography, respectively. Partial least squares (PLS) regressions were used to assess the colour-pigment relationships to correct for high multicollinearity amongst the independent variables (CIELab/Ch colour parameters). Results/discussion The PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions. The two models are suggested as robust approaches to total pigment prediction with multi-dimensional colour spaces, varying light conditions, and for a sample group of high genotypic variability. The carrot samples proved to have very high genetic diversity within each cultivar, resulting in unsatisfactory models for prediction of individual carotenoids ( ca. R 2 = 0.45) under the default light condition. However, all the results can be used to expand databases (towards artificial intelligence) and aid breeding programmes in search for higher concentrations of these interesting antioxidants for human health.
Why it matches plant phenotyping methodsニンジン試料の画像色解析を最適化し、化学分析値を用いてカロテノイド・アントシアニン含量を予測する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractThe PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions.
Reproduction assets foundThe authors deposited the paper's data and protocols in public repositories (DOI links in the Data availability statement). The anthocyanin quantification protocol is explicitly linked (10.34894/BTPTSV), and the other two DOIs (10.34894/P37WCL, 10.34894/OUURRH) are stated to hold the paper's data. No separate author's'Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/P37WCLlines:641-686Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/OUURRHlines:641-686Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Early disease diagnosis plays a key role in grape production for minimizing crop risk and maximizing yield. Downy Mildew, Powdery Mildew, and Bacterial Leaf Spot are some of the major diseases that threaten productivity and require timely and accurate diagnosis. This research introduces a new multi-model framework that integrates AI-based image segmentation triggered by Environmental Susceptibility Conditions to inform precision grape farming. The proposed method combines a soft-voting ensemble of the DeepLabV3+, U-Net, and FCN-8’s models for segmentation of diseased and healthy leaf areas with high accuracy, by understanding environment data to evaluate the risk of disease propagation. Major contributions of the study are the understanding of environmental conditions for context-aware disease propagation, an efficient ensemble segmentation method for accurate leaf disease segmentation and severity analysis, performed on a self-collected dataset from a grape farm in Nashik, Maharashtra, India. The system enables early warning and decision support mechanisms to promote sustainable disease management in grape cultivation, with potential implications for reducing unnecessary pesticide usage. Experimental results show the efficacy of the proposed method, with segmentation accuracy of 96.81% and precision of 99.09%, with a Dice score of 0.95 and a mean Intersection over Union (mIoU) of 0.91, demonstrating excellent robustness under noise conditions. Unlike existing studies either image or sensor-approaches, this work introduces the integration of image data and knowledge of environmental insights offers a scalable, reliable, and real-time disease monitoring solution aligned with the goals of smart and sustainable farming.
Why it matches plant phenotyping methodsブドウ葉の病斑領域を画像分割し、病害の重症度を推定する手法を開発・評価しており、植物の病害状態の取得が研究の中心です。
abstractThe proposed method combines a soft-voting ensemble of the DeepLabV3+, U-Net, and FCN-8’s models for segmentation of diseased and healthy leaf areas with high accuracy
Reproduction assets foundThe paper's grape leaf disease image dataset (NGLDD/NGLD) used for segmentation phenotyping is publicly deposited on Mendeley Data by the authors. No code or model checkpoints are reported as publicly available.Dataset · publicThe dataset used in this study is publicly available in the Mendeley Data repository as the Niphad Grape Leaf Disease
Dataset (NGLD) (DOI: https://doi.org/10.17632/8nnd2ypcv3.5).Open asset ↗Mendeley Data · 10.17632/8nnd2ypcv3.5pdf-page:25 lines:1-65Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Northern Corn Leaf Blight (NCLB; also, Turcicum Leaf Blight, TLB), caused by Exserohilum turcicum (teleomorph: Setosphaeria turcica), is one of the most destructive foliar diseases of maize worldwide, often causing severe yield losses under favorable conditions. We developed a maize-specific, web-based Decision Support System (DSS) for real-time NCLB detection and management ( https://maize-nclb.streamlit.app/ ), integrating advanced deep-learning for automated diagnosis and fungicide advisory. Among thirteen Machine-learning and deep-learning models evaluated for classification, the Visual Geometry Group 16-layer convolutional neural network (VGG16) outperformed all others, achieving 94.0% accuracy, with balanced precision, recall, and F1-score of 0.94, and an AUC-ROC of 0.93. Confusion matrix analysis revealed minimal misclassification, with only 12 errors out of 357 samples, confirming the model's high reliability in distinguishing healthy and infected plants, while Grad-CAM visualizations consistently highlighted biologically meaningful lesion regions, supporting the model's interpretability and alignment with plant pathological symptoms. Field validation of DSS-guided fungicide recommendations (Azoxystrobin 18.2% + Difenoconazole 11.4% w/w SC) demonstrated significant benefits, reducing disease incidence to 6.8% compared with 67.4% in controls, achieving 90% disease reduction, and enhancing grain yield by 35.4% (8.55 t/ha), with a favorable cost-benefit ratio of 1:2.49. Seasonal disease progression analysis further confirmed DSS effectiveness, with cumulative disease burden reduced by approximately 85% compared with untreated control. These results highlight the potential of integrating deep-learning with field-validated management strategies into a practical DSS, demonstrating its potential for precision disease management in maize.
Why it matches plant phenotyping methods葉の病斑を画像から分類・可視化する深層学習法を開発し、野外で検証した研究であり、植物病害状態のフェノタイピング手法が中心です。
abstractintegrating advanced deep-learning for automated diagnosis and fungicide advisory
Reproduction assets foundThe paper explicitly states that the complete implementation (model training, preprocessing, evaluation, Grad-CAM visualization) and the final trained VGG16 model are publicly available on GitHub, and the deployed Streamlit DSS is publicly accessible. The Scribd link is a cited prior-work bulletin, not a paper-specificCode · publicthe complete implementation, including model training, preprocessing, evaluation, and Grad-CAM visualization, along with deployment instructions, is publicly available at: https://github.com/anuragd02/NCLB-VGG16-Detection.Open asset ↗anuragd02/NCLB-VGG16-Detectionhtml-lines:133-143Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.
Why it matches plant phenotyping methods複雑形質の表現型性能を遺伝子型・環境情報から予測する新規計算手法を開発し、オオムギの収量・開花期で評価している。ウェブインターフェースも提供され、形質推定ワークフローが中心である。
abstractwe developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions.
Reproduction assets foundThe paper deposits its barley genotype, phenotype, and environmental datasets at three DOI repositories, and its analysis source code on GitHub, plus a public Shiny web tool.Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000010lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000003lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000011lines:31-42Code · publicAll the data and source codes have been uploaded to GitHub and can be accessed under the GNU Open License at: https://github.com/pwang2019/GxE_Model .Open asset ↗github.com/pwang2019/GxE_Modellines:196-205Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Plant disease detection using deep learning is essential for precision agriculture, enabling early and automated crop health monitoring. This study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset. The framework integrates data preprocessing, augmentation, and a VGG-16 backbone with a two-stage fine-tuning strategy. The proposed model is evaluated against CNN, DenseNet-121, Inception-V3, EfficientNetB0, and ResNet-50, achieving an accuracy of 0.93 with precision, recall, and F1-scores of 0.93, 0.90, and 0.92, respectively. These results demonstrate the effectiveness of transfer learning for fine-grained plant disease recognition. We further evaluate model robustness under adversarial cyber attacks to assess deployment reliability in agricultural systems. Under Fast Gradient Sign Method (FGSM) attacks ( ϵ = 0.01– 0.05), the model shows an accuracy drop of 1%–7.5%, while Projected Gradient Descent (PGD) attacks ( ϵ = 0.05, step size = 0.005, 10 iterations) produce similar degradation, highlighting the model’s vulnerability to adversarial perturbations. These findings highlight potential security and reliability risks in AI-based agricultural decision-making systems. Future work will focus on improving robustness and cyber-resilience and extending this framework to other crops for secure and context-aware deployment in resource-constrained environments.
Why it matches plant phenotyping methods植物葉の画像から病害・栄養欠乏状態を分類する深層学習パイプラインが研究の中心であり、複数モデルとの比較評価と敵対的攻撃下での頑健性検証も実施しているため。
abstractThis study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset.
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available Tomato-Village Variant-a dataset (4,525 tomato leaf images across 8 disease/deficiency classes), which the authors explicitly cite with a public Kaggle URL. No author analysis code, trained model checkpoints, or supplementary data deposits are mentioned. TheDataset · publicThis study uses the publicly available Tomato-Village
dataset [18], which is designed for real-world tomato disease
detection in agricultural environments.Open asset ↗pdf-raw-page:3 lines:1-59Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study developed a model to predict zeaxanthin content in peppers using multispectral imaging and chemical data. A one-dimensional convolutional neural network (1D CNN) model was identified as the optimal single-modal model after comparing four machine learning algorithms. On the prediction dataset, the model achieved a determination coefficient ( Rp 2 ) of 0.7639. Building upon the 1D CNN framework, a multimodal feature fusion model (MCSF) was constructed by integrating the chemical measurements of capsanthin and total carotenoid contents using a multilayer perceptron. This enhanced model demonstrated excellent predictive accuracy and robustness, with Rp 2 values of 0.9318 and 0.9211 across different spectral ranges. For high-throughput detection purposes, a simplified model that replaced measured capsanthin with a comprehensive red index still performed well, with an Rp 2 of 0.8912 and an RPD of 3.11. This strategy provides a new solution for the efficient spectral detection of plant chemicals affected by multicollinearity in their absorption spectra.
Why it matches plant phenotyping methodsマルチスペクトル画像と機械学習を用いて、トウガラシ果皮のゼアキサンチン含量という植物器官の形質を非破壊・高スループット推定する手法を開発・評価しており、フェノタイピング手法が中心である。
abstractThis study developed a model to predict zeaxanthin content in peppers using multispectral imaging and chemical data.
Reproduction assets foundThe paper's data availability statement explicitly states that the datasets (multispectral imaging and chemical trait measurements) and the main model code are publicly available in the authors' GitHub repository, which is an allowed URL.Dataset · publicThe datasets and the main model code are available online at https://github.com/liang-wei-tian/Chili-Peppers-Zeaxanthin.Open asset ↗liang-wei-tian/Chili-Peppers-Zeaxanthinhtml-lines:303-325Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate detection of such diseases is crucial for improving both crop yield and quality. While numerous deep learning approaches rely solely on image data for disease identification, they often overlook the complementary value of textual information in enhancing visual analysis. To address this limitation and effectively fuse features from different modalities, we propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition. Our approach utilizes the Zhipu.ai multi-modal model to generate comprehensive textual descriptions of diseased crop leaves, including global description, local lesion description, and color-texture description. These textual descriptions are then encoded into feature embeddings, while visual features are extracted using the ShuffleNet-v2 model as the image encoder. Subsequently, a cross-attention module aligns and fuses the two modalities, and a gated fusion module enables dynamic feature selection during the fusion process. Extensive evaluations on the Soybean Disease and PlantVillage datasets demonstrate that our method outperforms existing image-based models in terms of accuracy. Specifically, our model achieves recognition accuracies of 99.04% and 99.12% on the respective datasets, surpassing the ShuffleNet-V2 model by 1.09% and 2.53%, respectively. These results highlight the effectiveness of Cross-Model learning in integrating visual and textual cues for accurate and efficient disease recognition, offering a scalable solution for crop disease diagnosis.
Why it matches plant phenotyping methods植物葉の病徴を画像と言語情報から認識する融合フレームワークを開発し、複数データセットで既存手法と比較評価しているため、植物フェノタイピング手法が中心である。
abstractwe propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition.
Reproduction assets foundThe paper's crop disease recognition experiments use two openly available image datasets, both with explicit public availability statements in the Data Availability section: the Soybean Disease dataset (Dryad DOI) and the PlantVillage dataset (Kaggle). No author analysis code, trained models, or generated text-annotaitDataset · publicThe datasets utilized in this study are openly accessible. The soybean dataset is available at https://doi.org/10.5061/dryad.41ns1rnj3.Open asset ↗Dryad · 10.5061/dryad.41ns1rnj3html-lines:403-424Dataset · publicThe plantvillage dataset is available at https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset.Open asset ↗Kaggle · plantvillage-datasethtml-lines:403-424Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.
Why it matches plant phenotyping methods葉の病害症状を画像解析で定量化するワークフローを開発し、色空間比較と手動アノテーションによる検証を行っており、植物表現型取得法が中心である。
abstractwe present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously
Reproduction assets foundThe paper's authors explicitly state that all code implementing the leaf disease quantification workflow (SegLeaf, including the graphical interface and documentation) is hosted in a public GitHub repository. No separate public phenotype dataset or trained model checkpoint is described in the supplied blocks.Code · publicted by the Agence Nationale de
la Recherche (ANR) (project SCOOP, grant no. ANR-19-CE32-0011; and project MOBIDIV,
grant no. ANR-20-PCPA-0006).
Code and Data Availability
The method and associated scripts developed in this work are freely available to the re-
search community. All code is hosted in a public GitHub repository at https://github.com/titouanlegourrierec/SegLeaf, which includes the full implementation of the method includ-
ing the graphical interface and documentation to guide users through the analysis pipeline.
15
.
CC-BY 4.0 International license
made available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display tOpen asset ↗titouanlegourrierec/SegLeafpdf-raw-page:15 lines:1-39Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Quantifying the kinetics of net CO2 assimilation (A) and stomatal conductance (gs) under fluctuating light typically relies on gas exchange measurements, which are slow and thus unsuited for high-throughput phenotyping. As a result, faster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale. However, first the relationship between non-steady-state parameters must be examined in greater detail. In this study, we aimed to determine whether variations in non-steady-state values of chlorophyll fluorescence and leaf temperature reflect differences in key gas exchange traits under fluctuating light conditions. Here, the correlations between the times required for a change in non-steady-state A, gs, operating efficiency of PSII (ΦPSII), and leaf temperature (Tleaf) during stepwise changes in light intensity were evaluated across nine plant species. Both steady-state and non-steady-state photosynthetic traits varied significantly among species. Overall, we found significant positive correlations between non-steady-state A and ΦPSII for time to 50% and 90% of final steady-state values (t50; r2 = 0.70) and (t90; r2 = 0.33). The t90 of gs and that of Tleaf were also significantly correlated after both increases (r2 = 0.45) and decreases (r2 = 0.61) in light intensity. Our findings suggest that the times required for a change in ΦPSII (particularly t50) and Tleaf (particularly t90) can be used as indicators of dynamic A and gs, respectively, facilitating faster phenotyping of the complex processes of photosynthesis and stomatal conductance kinetics in the future.
Why it matches plant phenotyping methods非定常クロロフィル蛍光と葉温を用いて光合成・気孔コンダクタンス動態を推定する高速フェノタイピング手法を評価しており、相関検証が研究の中心である。
abstractfaster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale.
Reproduction assets foundThe paper's primary gas exchange, chlorophyll fluorescence, and leaf temperature phenotyping data are explicitly deposited in the WUR data repository (DOI 10.17887/WUR01-TMWYJN), stated in the Data availability section. No author analysis code repository is stated; the agricolae R package is a generic library, not a论文-Dataset · publicThe primary data and associated metadata are publicly available through the WUR data repository at https://doi.org/10.17887/WUR01-TMWYJN .Open asset ↗WUR data repository · 10.17887/WUR01-TMWYJNlines:406-446Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Why it matches plant phenotyping methods植物形質を分光情報から推定し、ハイパースペクトル画像へ展開して可燃性関連の植物状態を評価する手法が研究の中心であり、モデル性能も検証しているため。
abstractSpectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published12 May 2026Journal of Advanced College of Engineering and ManagementCited by 0 · OpenAlex ↗
Apple cultivation is a crucial agricultural activity in various mountainous regions, playing a vital role in supporting the local economy and sustaining the livelihoods of farmers. Several prominent mountain districts are known for leading apple production. However, apple orchards in these areas are often threatened by numerous diseases that reduce fruit yield and quality. In this research, we suggest a machine learning-based technique to automate the detection and classification of common apple diseases based on images of apple leaves collected from various regions. Through the use of Convolutional Neural Networks (CNN), the system can classify diseases with 97.36% precision. For post hoc explainability, Grad-CAM is used, which highlights the important regions that influenced CNN’s decision. The automated disease detection tool provides farmers in Nepal’s rural mountain areas with an affordable real time solution to monitor orchard health, minimize crop loss, and improve apple production. The dataset used in this study is originally derived from the United States based PlantVillage dataset, which is widely used for apple leaf disease classification research. Although the dataset is not collected from Nepal, the visual characteristics of apple leaf diseases remain largely consistent across regions due to similar biological infection patterns. Therefore, the model trained on this dataset is applicable to Nepali apple cultivation environments as well. At present, a publicly available or annotated Nepali specific apple leaf disease dataset is not available, which limits region-specific training and evaluation.
Why it matches plant phenotyping methodsリンゴ葉画像から病害状態を分類するCNNベースの手法とGrad-CAMによる解釈を中心に扱うため、植物病害フェノタイピング手法として該当する。
abstractwe suggest a machine learning-based technique to automate the detection and classification of common apple diseases based on images of apple leaves collected from various regions.
Reproduction assets foundThe paper's apple leaf disease image dataset (9,696 images, four classes) is publicly available on Kaggle and explicitly cited by the authors as the dataset used for training and evaluation. No author code, trained model, or other paper-specific assets are reported.Dataset · publicIn this study, the dataset used for apple leaf disease classification was obtained from
Kaggle [20]. The dataset contains a total of 9,696 images of apple leaves, which include
both diseased and healthy samples.Open asset ↗Kagglepdf-raw-page:4 lines:1-39Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Common beanRiceWheatLeafClassificationDisease symptoms / severity
In Bangladesh, crop leaf diseases create a serious risk to food security and production from agriculture. Timely identification of leaf diseases in rice, wheat, and bean crops is considered crucial for the implementation of effective disease detection and classification strategies. To address this challenge, a MobilenetV2-based disease identification and classification system is proposed in this research. Previous studies focus on classifying diseases of a single species, leaving the need to train models separately for each species. This research focuses on forming a single standard model to perform leaf disease classification for multiple crop species including rice, wheat, and beans. The approach makes use of transfer learning with the MobilenetV2 model, which is fine-tuned using a dataset of annotated crop leaf images specific to Bangladesh. Following a comprehensive evaluation, an overall accuracy of 97.87% was achieved in the classification of crop leaf diseases, which surpasses the accuracy of a number of previous studies focusing on leaf disease detection of a single crop. The system demonstrates the capability to rapidly diagnose diseases in real time by enabling the users to prompt intervention to mitigate potential crop losses, ultimately leading to amplified crop yield and food security. Overall, the research highlights the promise of AI-powered solutions in tackling crop leaf disease detection, which in turn encourages greater research and technology adoption to support sustainable farming methods especially in the crop disease classification domain in Bangladesh and throughout the world. Received: 24 May 2025 | Revised: 9 March 2026 | Accepted: 14 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Bangladeshi Crops Disease Dataset at https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset. Author Contribution Statement Md. Mahmudul Hasan: Conceptualization, Methodology, Visualization, Supervision. Md. Omar Faruq: Software, Validation, Writing – original draft. Mahadi Hasan Musa: Formal analysis, Investigation. Mohammad Mamunur Rashid: Resources, Data curation, Writing – review & editing. Khandaker Mohammad Mohi Uddin: Writing – review & editing, Project administration, Supervision.
Why it matches plant phenotyping methods葉画像から作物の病害状態を推定する深層学習手法を開発・評価しており、植物病害フェノタイピングが中心的な技術貢献である。
abstracta MobilenetV2-based disease identification and classification system is proposed in this research.
Reproduction assets foundThe paper's Data Availability Statement openly provides the Bean Disease Dataset on Kaggle, which is one of the two public image datasets used to train the multi-crop leaf disease classification model. The Bangladeshi Crops Disease Dataset URL is not among the allowed URLs, so only the bean dataset is reported. No codeDataset · publict
The authors declare that they have no conflicts of interest to
this work.
Data Availability Statement
The data that support the findings of this study are openly
available in the Bangladeshi Crops Disease Dataset at https://
www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-
dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset.Author Contribution Statement
Md. Mahmudul Hasan: Conceptualization, Methodology,
Visualization, Supervision. Md. Omar Faruq: Software, Valida-
tion, Writing – original draft. Mahadi Hasan Musa: Formal
analysis, Investigation. Mohammad Mamunur Rashid: Resources,
Data curation, Writing – review & editing.Open asset ↗Kaggle · therealoise/bean-disease-datasetpdf-raw-page:11 lines:1-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detection accuracy. This paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection. This model is composed of two network structures and a retentive feature aggregation module: (1) a Multi-scale Gradient-Aware Transfer Network (MGAT-Net) is designed to encode gradient information through the Sobel operator, thereby enhancing the localization stability for small and blurry lesions; (2) a Bidirectional Context Pyramid Network (BCPN) is proposed to enable bidirectional interactions among multi-level features through a top-down and a bottom-up pathway, thereby generating multi-scale lesion features and bridging cross-scale semantic gaps; and (3) a Retentive Feature Aggregation Module (RFAM) is used to suppress background noise and establish global feature correlations, thereby enhancing the overall representation capability for lesion recognition. Experiments on the Multi-scenario Tomato Leaf Disease (M-TLD) dataset show that GBR-DETR yields gains of 3.12, 4.88, and 3.41 percentage points in mAP 50-95 , mAP 50 , and mAP 75 , respectively, over the baseline RT-DETR, while also outperforming representative DETR-based and CNN-based detectors. The model demonstrates robust generalization on the PlantDoc cross-domain benchmark, achieving a 2.11% improvement in mAP 50 over the baseline. Deployed on the NVIDIA Jetson Orin Nano with TensorRT FP16, it achieves 54 ms latency, enabling real-time disease monitoring on edge devices. This solution provides effective technical support for real-time disease monitoring in smart agriculture.
Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から検出するモデルを開発し、複数データセットで比較検証、エッジデバイス実装まで評価しており、植物病害表現型の取得手法が中心である。
abstractThis paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection.
Reproduction assets foundThe paper's M-TLD tomato leaf disease dataset (2212 images, 6581 annotations) and the GBR-DETR implementation/training code are explicitly stated to be publicly available at the authors' GitHub repository.Dataset · publicThe M-TLD dataset and all annotation files are publicly available at https://github.com/zhuojiaxiong6/DETR (accessed on 29 April 2026) to facilitate reproducibility and future research.Open asset ↗zhuojiaxiong6/DETRlines:38-108Code · publicThe code and dataset used in this study are publicly available at the following GitHub repository: https://github.com/zhuojiaxiong6/DETR (accessed on 29 April 2026). This repository contains the implementation of GBR-DETR, a Detection Transformer variant developed for detecting tomato leaf diseases and pests. All relevant training scripts, configuration files, and instructions for dataset usage are provided in the repository.Open asset ↗zhuojiaxiong6/DETRlines:673-675Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Accurate plant disease segmentation is often constrained by the availability of large, finely annotated datasets, particularly for rare diseases. This work presents a synthetic data generation pipeline that combines 3D leaf modelling with diffusion-based disease synthesis to address this limitation. Procedurally-generated leaf geometries are built in the 3D modelling package Blender to provide exact ground-truth masks, after which style-transfer is applied using Stable Diffusion, fine-tuned with Low-Rank Adaptation (LoRA) and guided by ControlNet conditioning to both preserve leaf structure and enforce correct lesion placement. The approach is evaluated on apple leaf diseases using a deliberately restricted subset of the PlantVillage dataset, simulating a controlled low-data-resource environment. Downstream task effectiveness is measured through leaf disease segmentation. The results show that combining data from the pipeline with limited real data leads to consistent improvements in segmentation performance.
Why it matches plant phenotyping methods植物病斑の画像セグメンテーション性能向上を目的に、3D葉モデルと拡散モデルによる合成データ生成パイプラインを開発・評価しており、植物病害状態の画像ベース推定が中心である。
abstractThis work presents a synthetic data generation pipeline that combines 3D leaf modelling with diffusion-based disease synthesis to address this limitation.
Reproduction assets foundThe authors publicly deposited the paper's annotated PlantVillage subset (75 images with segmentation masks) plus 300 synthetic images with ground-truth masks on Zenodo, directly reproducing this paper's phenotyping/segmentation data.Dataset · publicThis annotated subset of PlantVillage is available at https://doi.org/10.5281/zenodo.18659728 . The repository contains the 75 images from the restricted dataset with the corresponding segmentation masks along with 100 synthetic images per disease generated using Blender and Stable Diffusion, each with corresponding ground truth masks.Open asset ↗zenodo · 10.5281/zenodo.18659728lines:314-325Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Latest imaging technologies play a vital role in the extraction of plant phenotypic traits in high ranges. Most existing analytical methods treat these traits as independent features, overlooking the complex interaction patterns that focus on plant responses to environmental stress. Proposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits plat information graph structured interaction networks leverages perceptual similarity learning to capture higher-order phenotypic patterns. In the PGK framework, traits extracted from RGB (Red, Green, Blue) and multispectral imagery are encoded as nodes, and biologically meaningful relationships amongst trait pairs are represented as weighted edges. Extracted trait values are continuously transformed into perceptual states to enhance biological interpretability, and a graph kernel is employed to measure similarity between trait graphs. Experiments performed in an agricultural field with a precision agriculture dataset for plant stress phenotyping demonstrated that the proposed PGK achieved 93.8% classification accuracy, improving performance by 5.3 percentage points over the CNN baseline. The outcome results clearly highlight the effectiveness of the perceptual graph model for plant phenotyping and provide a robust, interpretable computational framework for sustainable crop monitoring and decision-support in precision agriculture.
Why it matches plant phenotyping methods画像由来の植物形質を抽出・関係グラフ化し、ストレス表現型分類を行う計算手法が研究の中心であるため。
abstractProposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository (marathonengineer/Agriproject) containing the datasets used in this plant stress phenotyping study. The other allowed URL (PlantCV) is a generic phenotyping library, not a paper-specific asset.Dataset · publicThe datasets used in this study are available in publicly accessible online repositories. The repository can be accessed at: https://github.com/marathonengineer/Agriproject.Open asset ↗marathonengineer/Agriprojecthtml-lines:589-657Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. Timely disease detection remains challenging due to the scale of cultivation and reliance on manual monitoring. Developing reliable, ongoing computerized systems for plant health management is essential for efficient disease control. A deep learning approach is used as the core method to identify diseases in rice leaves. This methodology employs a range of advanced deep learning architectures to achieve top-tier feature extraction and classification. The publicly available rice leaf disease dataset on Zenodo supports research reproducibility and data transparency. We systematically process a balanced dataset of 1914 image samples using Python with TensorFlow and a GPU to enable high-speed computation for large-scale image processing. This study conducts a systematic comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L and MobileNetV2) trained independently. The base backbone models are then integrated with guided GrabCut segmentation with contour-detection method for interpretable disease localization. In this work, the methods of segmentation by GrabCut and contour detection are introduced to make the results of the study easier to interpret and explain the disease areas, but the final classification outcomes are obtained only on the basis of the underlying deep transfer learning models. As a result, infected leaf areas can be identified more effectively, allowing for better understanding and explainable of the disease.To enhance interpretability, GrabCut segmentation and contour detection are applied as post-hoc visualization techniques to highlight diseased regions corresponding to CNN predictions. These techniques do not influence the classification training process. All five models InceptionV3, DenseNet201,ResNet152V2,EfficientNetV2L and MobileNetV2 demonstrated their effectiveness in detecting rice diseases during training, validation, and testing phases, with models trained over 30 epochs. The training methods and accuracy rates of the models were compared during validation and final testing. InceptionV3 demonstrated the most moderate performance of 98.80% training, 98.44% validation, and 98.43% test accuracy, which means that it has strong generalization and consistent learning behavior. The performance of very high-density networks such as DenseNet201 (98.72% train, 98.43% val, 98.43% test), ResNet152V2 (99.02% train, 99.22% val, 97.39% test), EfficientNetV2L model accuracies (39.01% train, 48.70% val, 44.50% test) also showed competitive results, which validated the effectiveness of deep transfer learning in the classification of rice leaf disease, while MobileNetV2 model accuracies (98.09% train, 98.18% val, 96.87% test) indicate that a lightweight model can still achieve reliable classification performance with lower computational complexity. In general, the comparative analysis defines InceptionV3 as the most stable and efficient model in the framework proposed. These results illustrate InceptionV3 superior generalization ability, supported by explainable methods for improved feature localization, confirming the viability of transfer learning for accurate and practical rice disease detection using GrabCut segmentation and contour detection technique. The complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.
Why it matches plant phenotyping methodsイネ葉の病徴領域を画像から分類・局在化する深層学習ワークフローが研究の中心であり、GrabCut・輪郭検出と複数モデルの比較評価を含むため、植物病害状態の画像ベース表現型計測として採用。
abstractA deep learning approach is used as the core method to identify diseases in rice leaves.
Reproduction assets foundThe paper explicitly states that the complete implementation code and the rice leaf disease image dataset (1914 samples) used in this study are publicly available: code on the authors' GitHub repository and the dataset on Zenodo (DOI 10.5281/zenodo.15817084). Both are paper-specific, public, and actionable.Code · publicThe complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.Open asset ↗ummershakeel03/Rice-Leaf-Diseases-Classificationhtml-lines:1357-1368Dataset · publicThe dataset for this research study is available at: https://doi.org/10.5281/zenodo.15817084.Open asset ↗10.5281/zenodo.15817084html-lines:1357-1368Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Climate change-induced drought increasingly constrains water management in mixed-species urban gardens, requiring scalable and non-destructive approaches. This study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns. Ten garden plant species were evaluated under varying soil moisture conditions. Hierarchical cluster analysis integrating fluorescence parameters and RGB indices identified three physiologically defined response clusters, and their reproducibility using RGB indices alone was assessed. A total of 1,629 samples were augmented to 1,881 using the synthetic minority over-sampling technique (SMOTE) to address class imbalance. A support vector machine (SVM) model with a radial basis function kernel, using green leaf index (GLI), normalized green-red difference index (NGRDI), blue-green pigment index (BGI), and soil moisture (%) as predictors, achieved an accuracy of 0.91 and a Kappa coefficient of 0.84. In contrast, PLS-DA showed lower performance (accuracy 0.79, Kappa 0.65), indicating limited separability under linear assumptions. These results demonstrate that RGB indices combined with nonlinear models were able to reproduce physiologically defined drought response patterns under the given conditions. As a proof of concept, this study demonstrates the potential of the proposed framework; however, its generalizability is limited by the controlled greenhouse setting, the relatively small number of species, and the lack of external validation in heterogeneous field environments. The framework may provide a cost-effective approach for classifying plant drought responses and has the potential to support the grouping of plants with similar water requirements, which could contribute to improved irrigation management in mixed-species gardens under further validation.
Why it matches plant phenotyping methodsRGB画像指標と機械学習により、植物の干ばつ応答パターンという生理状態を分類し、蛍光測定との再現性を評価しているため、表現型取得・抽出手法が中心です。
abstractThis study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns.
Reproduction assets foundThe paper explicitly states that the authors' analysis code (data processing, feature extraction, SVM/PLS-DA modeling) is publicly deposited on Zenodo with a DOI matching an allowed URL. The phenotype datasets are only said to be in the manuscript/supplementary files, so the code deposit is the qualifying paperSpecificCode · publicThe code supporting the findings of this study, including data processing, feature extraction, and machine learning modeling is available at Zenodo: https://doi.org/10.5281/zenodo.19127295 .Open asset ↗Zenodo · 10.5281/zenodo.19127295lines:98-116Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsNDVIに基づくハイスループット植物フェノタイピングを主題としており、センサーによる植物形質取得が中心と明示されている。
titleAccelerating cassava genetic improvement through NDVI-based high-throughput phenotyping
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe data and scripts (R and SAS) that support the conclusions of this article can be freely and openly accessed at Zenodo: https://zenodo.org/records/18778974 [ 57 ].Open asset ↗Zenodolines:177-214Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Crop phenology is a critical determinant for yield prediction and germplasm evaluation. However, precise phenological monitoring in large-scale rice breeding trials faces significant challenges due to the inherent phenological asynchrony among hundreds of cultivars and the trade-off between spatial resolution and temporal continuity in unmanned aerial vehicle (UAV) remote sensing. To address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement. We introduce a Missing Aware Gated Fusion (MAGF) mechanism to dynamically integrate multi-resolution features on non-aligned timelines, enabling robust modeling under irregular sampling conditions. Validated on a massive dataset covering approximately 500 rice cultivars and over 100,000 images across 2023 and 2024 growing seasons, the proposed method significantly outperformed single-temporal-scale baselines despite multiple growth stages coexisting within the same dates. The integration of multi-spatial-scale fusion with LSTM temporal modeling yielded superior performance considering efficiency, achieving an Overall Accuracy (OA) and F1-score of 0.873, with a Kappa coefficient of 0.84. A hybrid sampling strategy (daily MR image combined with weekly HR image) demonstrates that weekly flight time can be reduced from 28 h to approximately 6 h while maintaining high accuracy. Notably, even when HR acquisition was reduced to a once every 14 days frequency, the fusion performance remained significantly superior to that of daily MR monitoring alone. The model exhibited strong generalization capabilities. When directly applying the model trained on 2024 data to the 2023 dataset, it maintained an OA of 0.774 and an F1-score of 0.738 under a 3-day error tolerance, with recall for the maturity stage consistently exceeding 0.96. This framework offers a flexible, scalable, and cost-effective solution for high-throughput phenotyping in precision breeding.
Why it matches plant phenotyping methodsUAVリモートセンシング画像と深層学習によるイネの生育ステージ（フェノロジー）推定手法を開発・検証し、大規模育種データで性能評価しているため、フェノタイピング手法が中心である。
abstractTo address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement.
Reproduction assets foundThe article explicitly states that the authors' source code and test samples for the rice phenology identification framework are publicly available on GitHub, matching an allowed URL. No public dataset deposit is stated; additional data is only on request.Code · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/Rice-phenology-identification-by-UAV . Additional data can be made available upon reasonable request.Open asset ↗https://github.com/gfjiyue/Rice-phenology-identification-by-UAVlines:601-709Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Multispectral / hyperspectralLeafRootMorphology / geometry measurementLeaf traitsRoot system architecture
Premise Selective breeding over thousands of years has prioritized aboveground yield, with little regard for changes belowground. Roots underpin plant growth and resilience, but our knowledge of these critical structures lags behind that of aboveground structures. Accurately phenotyping root traits is labor-intensive, expensive, and often destructive. High-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs. Methods We used American licorice (Glycyrrhiza lepidota Pursh.), a perennial legume with a rich ethnobotanical history, as a model to investigate root system phenotypes. We assessed root traits across multiple populations, analyzed relationships between above- and belowground phenotypes, and tested the use of multidimensional leaf traits, including spectral reflectance, in predicting root traits. Results Root traits of American licorice varied significantly across source populations. Root traits were strongly intercorrelated and each root trait correlated with an aboveground phenotype. Leaf spectral reflectance and elemental composition predicted belowground traits; however, interpretation of some trait-specific signals were complicated by isometric scaling between plant size and root traits. Conclusions These findings demonstrate the use of high-dimensional leaf traits as a proxy for root traits, with potential applications for understanding foundational questions in plant biology and in breeding programs targeting belowground structures of perennial herbaceous species. Further optimization and larger studies are needed to improve predictive models.
Why it matches plant phenotyping methods葉の高次元形質とスペクトル反射を用いて、測定困難な根形質を非破壊・高スループットに推定する方法が研究の中心である。
abstractHigh-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: raw root scans on Zenodo and a Figshare deposit containing RhizoVision Explorer output features, CropReporter data and metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses. NoDataset · publich Center Bioanalytical Chemistry Facility (RRID:SCR_001047). Finally, we thank the reviewers for their careful evaluation of our manuscript and constructive comments, which helped us clarify the conceptual framing and strengthen the overall quality of the work.
DATA AVAILABILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
KatOpen asset ↗Zenodo · 18852041lines:173-419Dataset · publicILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
Katsikis
,
Z.
Aldiss
,
S. M.
Brunner
,
S. V.
Meer
,
L.
Meijer
, et al. 2025 .
Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field
. Journal of Experimental Botany
76 : 5161 ‐ 5178 .
40580084
10.1093/jxb/eraf268
PMC1Open asset ↗Figshare · 10.6084/m9.figshare.28742870lines:173-419Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate detection of cocoa pod diseases is vital to reducing yield losses and supporting sustainable agriculture. Although deep learning models have shown promise in plant disease classification, their performance often varies between datasets due to limitations in feature extraction and generalisation. This study introduces a Learnable Gated Fusion Convolutional Block Attention Module (LGF-CBAM) integrated with a ResNetV2-101 backbone to improve discriminative feature learning and improve robustness in cocoa disease classification. Unlike the standard CBAM, which processes attention modules sequentially, LGF-CBAM adaptively balances the importance of spatial and channel cues through trainable gating parameters normalized with a softmax function. Incorporating LGF-CBAM provided outstanding results on the Cocoa_Pod_Disease_Gh dataset, achieving 98.95% accuracy along with F1 and PPV scores of 99.11%. The cross-dataset evaluation confirmed robustness, with accuracies of 98.53% on Cocoa Diseases (YOLOv4), 97.96% on Black and Borer Pod Rot, and 96.19% on Cacao Diseases in Davao. Although greater variability in the Coffee and Cocoa dataset reduced accuracy to 94.00%, the model still maintained strong adaptability under diverse conditions. These findings establish LGF-CBAM as a state-of-the-art framework that outperforms all other referenced systems, offering high accuracy, stability, and generalization. In general, this research contributes to a novel attention-based deep learning framework that can support early and reliable identification of cocoa pod diseases, providing a scalable solution for precision agriculture.
Why it matches plant phenotyping methodsカカオ果実の病害状態を画像から分類する深層学習手法を開発し、複数データセットで性能・頑健性を評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study introduces a Learnable Gated Fusion Convolutional Block Attention Module (LGF-CBAM) integrated with a ResNetV2-101 backbone to improve discriminative feature learning and improve robustness in cocoa disease classification.
Reproduction assets foundThe authors' primary plant-phenotyping asset is the Cocoa_Pod_Disease_Gh image dataset, publicly deposited on Figshare with an explicit Data Availability statement and DOI. The Kaggle/Roboflow datasets are cited prior external datasets used for cross-dataset evaluation, not paper-specific deposits, so they are excludedDataset · publicThe data that support the findings of this study is available at https://figshare.com/articles/dataset/Cocoa_Disease_Datasets/31294003. https://doi.org/10.6084/m9.figshare.31294003.Open asset ↗figshare · 10.6084/m9.figshare.31294003html-lines:1039-1062Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Disease prevention and water management are important to all the crops, particularly rice and sugarcane production in India. The article proposes a reinforcement learning (RL) based intelligent irrigation management system that is capable of optimising water consumption and crop nutrition in response to the changing agricultural climatic conditions. Decentralised reinforcement learning (RL) is used in a network of irrigation agents that utilise soil and microclimate sensor networks to set the terms of water allocation, water use efficiency (WUE) and crop health. At the same time, deep convolutional networks can be used to differentiate between plant stress/disease and leaf images and take applicable proactive actions. It is a framework that incorporates satellite-derived indices (NDVI, EVI, land surface temperature) with local sensor measurements and image-based health measurements through multimodal deep learning. Far-reaching simulations (including Indian climate and crop calendars) demonstrate that the multi-agent system lowers water consumption and preserves the yields and properly notifies stressed plants. The scores of disease detection with plantvillage-based fine-tuned on rice (120 (3 disease types) and 3829 (5 disease types) and sugarcane (2569 images for all disease types, Convolutional Neural Network (CNN) yield results of >98 % accuracy. Crop mapping (rice/sugarcane) Satellite/LSTM-based crop mapping (with Sentinel-1 / Sentinel-2) achieves more than 97 % accuracy. The suggested structure provides a data-driven, scalable system for precision agriculture to enhance the management of irrigation periods and crop health. Simulation experiments show that the RL-based controller can reduce water consumption while preserving optimal soil moisture levels when compared to rule-based irrigation strategies.
Why it matches plant phenotyping methods画像・衛星・センサーを統合して植物ストレス／病害状態を推定するマルチモーダル基盤が提案され、病害検出性能も評価されているため、植物表現型推定が実質的な構成要素である。
abstractdeep convolutional networks can be used to differentiate between plant stress/disease and leaf images
Reproduction assets foundThe paper reports simulation-based experiments using public leaf-image datasets. The only paper-specific public asset explicitly identified is the Kaggle rice leaf diseases dataset (vbookshelf/rice-leaf-diseases) cited as a data source for the rice disease fine-tuning set. No authors' code, trained models, or data dépDataset · publicConflict of interest: Authors do not have any conflict of interest 2026 Mar 31). Available from: https://www.kaggle.com/datasets/Open asset ↗Kagglepdf-page:16 lines:1-58Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Proper diagnosis of crop diseases and accurate measurement of fruit ripeness is essential in enhancing agricultural productivity, but conventional methods of diagnosis are time-consuming, error-prone, and inefficient. With the rapid development of AI, deep learning (DL), and IoT, there is increasing demand for combined solutions that jointly address plant health monitoring and harvest optimization in a reproducible and deployment-oriented manner. This study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages to optimize yield quality and minimize agricultural losses. The work explicitly targets improved classification reliability, broader class evaluation, rigorous validation and generation of decision-ready outputs for precision agriculture. AgroDualNet comprises two modules. The crop-disease prediction module integrates ResNet50 with a Convolutional Block Attention Module (CBAM), and a Sequential Minimal Optimization (SMO)-based SVM classifier to enhance feature learning and classification performance Several different architectural designs are benchmarked and the resultant model is tested on both a dedicated 3-class subset and a large multi-class model of the PlantVillage dataset with leakage safe protocol(augmentation applied only on training data), cross-validation, statistical significance testing as well as ablation. The fruit-ripeness module employs YOLOv8 for real-time fruit localization and MobileNetV2 for lightweight ripeness classification suitable for edge deployment and a prototype decision-support layer maps predictions to actionable recommendations. That is able to run on the edge. Experiments show that the hybrid CBAM + ResNet50 + SMO model achieves 99.6% accuracy for crop disease classification on a three-class configuration of the PlantVillage dataset and maintains consistently higher accuracy than strong baseline in a 38-class setting, with statistically significant results confirmed by McNemar's test (p < 0.001) outperforming baseline and intermediate architectures in accuracy, precision, Recall and F1-Score The fruit ripeness pipeline achieves 98.88% classification accuracy across four ripeness stages (unripe, semi-ripe, ripe, over-ripe) on a combined Kaggle and real-field apple dataset with low inference time, confirming its suitability for near real-time deployment on edge devices. Cross-validation, Statistical significance tests and ablation studies collectively validate the robustness and significance of these gains and the decision-support layer demonstrates the feasibility of converting raw predictions into interpretable, recommendation-oriented outputs. AgroDualNet provides an efficient and unified system for monitoring plant diseases and evaluating fruit ripeness, with statically validated performance across both focused and full multi-class settings, addressing two critical challenges in precision agriculture with a single extensible framework. The dual-module design of AgroDualNet, which combines disease prediction with ripeness analysis and a preliminary decision-support prototype offers a more comprehensive and practically relevant AI-driven monitoring solution than conventional single-task models. By emphasizing multi-class validation on PlantVillage, leakage-aware experimentation, statistical verification, and system-level integration, this works supports real-time, precise and automated guidance to reduce crop losses, improve harvest timing, and enable smarter farm-level decision making.
Why it matches plant phenotyping methods植物病害状態と果実成熟度を画像から推定する深層学習パイプラインの開発・比較検証が中心であり、PlantVillageおよび実圃場データで交差検証、アブレーション、統計検定を実施しているため、植物フェノタイピング手法として含める。
abstractThis study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages
Reproduction assets foundThe paper's Data availability statement names two public datasets used directly in the phenotyping experiments: the PlantVillage crop-disease dataset and a Kaggle apple fruit-ripeness dataset, both with explicit Kaggle URLs matching allowed_urls. The statement also mentions implementation files, trained weights, and a Dataset · public. and V.V. wrote the main manuscript text, and K.N. prepared figures. All authors reviewed the manuscript.
Funding
There is no funding received from any organization for this work.
Data availability
The datasets that have been used and analysed in this study are publicly available. PlantVillage crop disease data are on kaggle ( https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ) accessed March 2026). The dataset on the ripeness of apple fruits can be found in Kaggle ( https://www.kaggle.com/datasets/mdsagorahmed/fruit-image-dataset-22-classes ) accessed March 2026). The files used to run the implementation, trained model weights, class definitions and split metadata are opeOpen asset ↗Kaggle · plantvillage-datasetlines:583-665Dataset · publiction for this work.
Data availability
The datasets that have been used and analysed in this study are publicly available. PlantVillage crop disease data are on kaggle ( https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ) accessed March 2026). The dataset on the ripeness of apple fruits can be found in Kaggle ( https://www.kaggle.com/datasets/mdsagorahmed/fruit-image-dataset-22-classes ) accessed March 2026). The files used to run the implementation, trained model weights, class definitions and split metadata are openly available at: 10.5281/zenodo.19051520.
Declarations
Competing interests
The authors declare no competing interests.
References
1.
George R Thuseethan S RagelOpen asset ↗Kaggle · fruit-image-dataset-22-classeslines:583-665Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Objectives Nutritional deficiency in coffee is a major problem that compromises plant health, crop yield, and bean quality, directly threatening the economies of coffee-dependent regions. Traditional detection methods are primarily manual, time-consuming, and relied upon expert availability. Methods This study introduces a novel Deep Learning (DL)-based dual-track architecture designed for the efficient classification of nutritional deficiencies in coffee leaf. The first track utilizes a MobileNetV3 backbone integrated with a Multi-Convolutional Shape-Aware Kernel (MCSK) block to capture spatially adaptive features from leaf textures and vein patterns. The second track employs a Hierarchical Shuffled Group Attention Network (HSGAN), utilizing Efficient Channel Attention (ECA) and Local Group Attention (LGA) modules to balance fine-grained local variations with broad spatial dependencies. Finally, a Multidimensional Collaborative Attention (MCA) mechanism is applied to the fused features to enhance cross-channel interactions and feature extraction. Results The proposed model was evaluated using the CoLeaf dataset, where it achieved an accuracy score of 96.04%. This performance demonstrates an improvement over existing research and current state-of-the-art models, highlighting the architecture's ability to identify complex nutrient-related patterns in coffee leaves. Conclusion The performance of the proposed DL approach offer a solution for the automated monitoring of coffee plants. By providing a reliable alternative to manual inspection, this method presents the potential to help coffee production and support the agricultural regions worldwide.
Why it matches plant phenotyping methodsコーヒー葉の栄養欠乏という植物状態を画像から分類する深層学習手法を開発・評価しており、表現型取得・推定が研究の中心であるため。
abstractThis study introduces a novel Deep Learning (DL)-based dual-track architecture designed for the efficient classification of nutritional deficiencies in coffee leaf.
Reproduction assets foundThe paper's primary phenotyping asset is the CoLeaf coffee leaf nutrient-deficiency image dataset, which the authors state is publicly available via a Mendeley Data URL matching an allowed URL. No author analysis code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/brfgw46wzb/1 .Open asset ↗brfgw46wzb/1lines:733-764Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Purpose The primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix, and to assess whether integrating spectral, texture, soil, and fertilization information could improve prediction performance for precision sugarcane management. Methods Jilin-1 satellite imagery acquired at four growth stages, from seedling to maturity, was used to derive vegetation indices (VIs) and texture indices (TIs), including the normalized difference texture index (NDTI), enhanced vegetation texture index (EVTI), and double-difference ratio texture index (DDRTI). Soil chemical properties (SCPs) and fertilization information (FI) were further incorporated with the remotely sensed variables. Machine learning models were developed for plot-level prediction of sugarcane traits across plant cane and first ratoon cane, and texture window size was optimized to improve TI extraction and model performance. Results For yield, the combination of VIs and TIs outperformed VIs alone at the tillering stage (R 2 CV = 0.65, RMSECV = 15.06 t/ha, RPDCV = 1.68). Adding SCPs and FI further improved yield prediction across plant cane and first ratoon cane (R 2 CV = 0.70, RMSECV = 13.84 t/ha, RPDCV = 1.83). Millable stalk population was best predicted at the maturation stage by VIs and Tis, achieving the best performance (R 2 CV = 0.63, RMSECV = 6602 stalks/ha, RPDCV = 1.66). The best Brix model integrated VIs, TIs, SCPs, and FI at the maturation stage (R 2 CV = 0.44, RMSECV = 0.53 °Bx, RPDCV = 1.33). SHAP analysis identified VIs as the dominant features for sugarcane traits prediction. And, DDRTI contributed more than NDTI and EVTI in yield and Brix prediction. Conclusion It is concluded that integrating spectral, texture, soil, and fertilization information from high spatial resolution Jilin-1 imagery is a promising approach for improving plot-level prediction of key sugarcane traits.
Why it matches plant phenotyping methods衛星画像からサトウキビの収量、可販茎数、Brixを plot レベルで推定し、テクスチャ特徴抽出の最適化と機械学習性能評価を行っており、表現型取得・推定手法が中心である。
abstractThe primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the authors' model-training code and test data for the sugarcane trait prediction analysis. No public phenotype dataset or imagery deposit is stated; additional data are only available on request.Code · publicPart of the code and test data for model training are available at https://github.com/guangtaoxu08-dev/SPT_JL .Open asset ↗guangtaoxu08-dev/SPT_JLlines:228-248Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The decline in environmental quality caused by industrial pollution and climate change has weakened the natural resistance of rice plants (Oryza sativa), increasing their susceptibility to various diseases. Conventional disease identification methods that rely on manual observation are often limited by subjectivity and human visual constraints. This study proposes a deep learning–based system for automatic rice leaf disease classification using the You Only Look Once version 8 (YOLOv8) architecture. The model was trained using a publicly available rice leaf image dataset consisting of 6,889 images categorized into eight classes: Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Sheath Blight, Narrow Brown Leaf Spot, Rice Hispa, and Healthy Rice Leaf. The research methodology includes image pre-processing, data augmentation, dataset splitting, and training using the YOLOv8n-cls model for 50 epochs. Experimental results demonstrate high classification performance with an accuracy of 99.5%, precision of 99%, recall of 98%, and an F1-score of 0.99. The trained model was then deployed into a web-based application that allows users to upload rice leaf images and obtain real-time disease classification results. The proposed system provides a practical tool to support early detection of rice plant diseases and assist farmers in improving crop management in modern agriculture.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定するYOLOv8画像解析手法の開発と性能評価が中心であり、植物病害フェノタイピングに該当する。
abstractThis study proposes a deep learning–based system for automatic rice leaf disease classification using the You Only Look Once version 8 (YOLOv8) architecture.
Reproduction assets foundThe paper's rice leaf disease image dataset (6,889 images, eight classes) used for YOLOv8n-cls training is a publicly available Kaggle dataset cited by the authors with an explicit URL. No author code, trained model, or other paper-specific assets are reported.Dataset · publicThe primary dataset was obtained from a
publicly available dataset on Kaggle [16], which provides a
comprehensive collection of rice leaf disease images for
machine learning research.Open asset ↗Kagglepdf-raw-page:3 lines:1-102Code / dataset availability confirmedOpenAlex · Crossref · checked 5 Sept 2026
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Phenotyping plant traits using UAV-based multispectral imaging offers a robust and unbiased approach to assessing crop status. With approximately 70% of smallholder farmers in East and Southern Africa cultivating common beans as a key source of food and income, there is a critical need for accurate and timely measurements of crop health and yield to support data-driven management decisions and disease mitigation. Traditional phenotyping methods are labor-intensive, and existing remote sensing and machine learning approaches remain limited. This study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery. Data collected over three growing seasons (2022–2024) were used to extract canopy variables and vegetation indices (VIs) across phenological stages. For yield prediction, traditional machine learning models achieved a root mean squared error (RMSE) of 242.33 kg ha⁻¹ and an R² of 0.66 using an Extra Trees Regressor. A novel BY-GRU architecture improved performance, achieving an RMSE of 242.40 kg ha⁻¹ and an R² of 0.79. The analysis also identified 45–60 days after sowing as the optimal window for prediction. To address limitations in conventional plant health assessments, this study introduces a novel Health Index. Comparative analysis demonstrated its robustness across genotypes and stronger correlation with yield. Machine learning and deep learning models, including MaxViT, were applied to estimate the Health Index, achieving improved predictive performance. Overall, this work integrates UAV sensing and modelling to provide scalable tools for phenomics, crop management, and breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像から作物の健康状態、収量、キャノピー形質を推定するセンシング・機械学習フレームワークが研究の中心であり、植物フェノタイピング手法として適格です。
abstractThis study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery.
Reproduction assets foundThe preprint's DATA AVAILABILITY section states that all processed data required to reproduce the results are publicly available in a Google Drive repository, which qualifies as a paper-specific public phenotype dataset asset. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicCommon Bean Breeding Program for
facilitating field trials. We also thank the Phenomics team for their valuable assistance with UAV-based
data collection.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY
The datasets generated and/or analyzed during the current study are publicly available at:
https://drive.google.com/drive/folders/1fN3Q9n3bK_YoXFK8VFKZ3uEb13y9iRWj?usp=sharing. This
repository includes all processed data required to reproduce the results presented in this study.
SUPPLEMENTAL MATERIAL
Supp. Figure 1. Drone-based field view of the bean trial site at CIAT Palmira Research Station: A) RGB
image and B) NDVI image.
Supp. Figure 2. Drone Features Open asset ↗pdf-raw-page:40 lines:1-46Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Effective high-throughput phenotyping is crucial for modern plant breeding, yet the optimal image acquisition parameters for UAV-based systems in forage crops remain poorly defined. We optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height. Machine learning algorithms and mixed model analyses were applied to evaluate predictive power and heritability. Pixel count and Haralick's entropy showed strong correlations with conventional yield measurements, particularly in Environment 2, while most vegetative indices were poor predictors. Integrating machine learning substantially enhanced predictive power for green and dry matter yield (r > 0.80). For canopy height, machine learning models achieved correlations of 0.71 with ground truth measurements despite weak pairwise correlations. Mixed model analysis revealed high broad-sense heritability (0.7 < H 2 < 0.87) for yield traits, pixel count, and entropy, while vegetative indices and canopy height showed greater environmental susceptibility. Moderate GSD resolutions (0.5–1.0 cm) consistently outperformed both very high (0.27 cm) and very low (1.5 cm) resolutions. Coincidence index analysis demonstrated 80% correspondence between top genotypes ranked by pixel count and conventionally measured dry matter yield. This study provides an optimized framework for UAV-based phenotyping in M. maximus , demonstrating that combining advanced digital traits with machine learning accurately predicts key agronomic traits and significantly enhances genotype selection efficiency in forage breeding programs.
Why it matches plant phenotyping methodsUAV画像取得条件、RGBデジタル形質、機械学習による収量・草高推定を最適化・検証する研究であり、植物表現型取得法が中心的です。
abstractWe optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height.
Reproduction assets foundThe paper's data availability statement points to a public Mendeley Data repository containing the study's UAV-derived digital phenotyping and conventional trait datasets. No author analysis code repository is explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://data.mendeley.com/datasets/jrrb76x82h/1 .Open asset ↗jrrb76x82h/1lines:435-487Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.
Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。
abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters
Reproduction assets foundThe paper's digital phenotyping dose-response datasets are publicly deposited: the data availability statement names Ag Data Commons DOI 10.15482/USDA.ADC/29815082 and a figshare link, both paper-specific. No author analysis code repository is explicitly stated.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: 10.15482/USDA.ADC/29815082 or https://figshare.com/s/64d1bbac59a95c4721f1 .Open asset ↗figshare · 10.15482/USDA.ADC/29815082lines:548-573Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The quality of forest reproductive material is crucial for successful reforestation and afforestation. While physical seed properties like mass are known indicators of quality, the potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration. This study investigates the relationship between the seed coat color of individual Pinus sylvestris seeds, quantified in RGB (Red, Green, Blue) space using a flatbed scanner, and their subsequent germination in container nurseries. The resulting images were processed using ImageJ software to measure the mean pixel intensity (0–255) for the Red (R), Green (G), and Blue (B) channels from the segmented seed area, following the «seed–culture» passport methodology [Forestry Engineering Journal 14 | 55 (2024), 37–60]. From a population of individually tracked seeds, we compared the RGB values of germinated (N = 942) and non-germinated (N = 258) seeds after 30 days. Results from the Kolmogorov-Smirnov test showed that non-germinated seeds had significantly lower individual mass (p = 0.0045) and significantly higher pixel brightness values in the R-, G-, and B-channels (p < 0.0001) compared to germinated seeds. Normalized RGB indices also showed significant differences between groups. Our findings demonstrate that seeds with a lighter, more reflective epidermis – indicative of higher RGB brightness – are statistically associated with a lower probability of successful germination under container nursery conditions. This non-destructive, low-cost method shows significant promise for the rapid pre-sorting of Scots pine seeds. It offers a practical tool to improve the efficiency and predictability of seedling production in forest nurseries by increasing the proportion of viable seeds in sowing batches.
Why it matches plant phenotyping methods個別種子のRGB画像から種皮色を定量抽出し、発芽予測・事前選別に用いる非破壊的な表現型計測法が研究の中心である。
abstractthe potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration
Reproduction assets foundThe paper openly deposits its three core phenotyping datasets in Mendeley Data: morphometric seed data (Dataset 1), the raw VIS/RGB scanner images of individual Pinus sylvestris seeds (Dataset 2), and germination outcome data (Dataset 3). All three DOIs are listed in the Data Availability statement and match allowed URDataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/8g258nbgmf.1Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:133-160Dataset · publicThe original VIS image data of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/dt78jhyw2j.2Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:133-160Dataset · publicThe original germination data—Dataset 3—are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:133-160Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
FruitClassificationGrowth / development / phenology
Introduction Cashew apple is a nutrient-rich fruit containing abundant minerals, vitamins, and energy. However, its fleshy texture and delicate skin significantly limit its storage life and market value. Accurate maturity grading is therefore essential for improving post-harvest management and transportation efficiency. Methods This study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher. The distillation framework integrates response-based soft-label supervision, attention transfer, and token-level feature regression to enhance representation learning under limited data conditions. Auxiliary lightweight architectures, including MobileNet, ConvNeXt, and EdgeNeXt, were trained independently to provide complementary predictions, and a weighted fusion strategy was employed for ensemble evaluation. Results The proposed ensemble ViT-KD with EdgeNeXt achieved 90% accuracy under the evaluated test split. To ensure statistical reliability and address potential partition bias, a stratified fivefold cross-validation was conducted on the dataset, yielding a mean accuracy of 86.89% ± 2.89% with consistent F1 scores and recall. The relatively low variance across the folds indicates stable internal generalization. Comparative experiments with conventional convolutional neural network (CNN) baselines and lightweight CNN baselines such as MobileViT-S and ShuffleNetV2 were performed, with the proposed ensemble framework achieving improved accuracy while maintaining computational efficiency. Computational analysis indicates that the stand-alone distilled ViT maintains a real-time inference capability of 8.79 ms per image, which supports suitability for edge-oriented agricultural applications. Discussion These results highlight the effectiveness of knowledge-distilled lightweight transformers for data-efficient maturity grading of cashew apples.
Why it matches plant phenotyping methodsカシューナッツ果実の成熟度という植物器官の状態を画像から推定する手法を開発し、交差検証・比較実験・推論速度評価まで行っており、フェノタイピング手法が中心である。
abstractThis study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher.
Reproduction assets foundThe paper's cashew apple maturity grading experiments use a public image dataset from IEEE Dataport (Sawant, 2025), explicitly linked in the data availability statement. No author code or models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieee-dataport.org/documents/goa-cashew-apple-maturity-grading .Open asset ↗goa-cashew-apple-maturity-gradinglines:837-851Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Wood apple (Feronia limonia L.) is an underutilized perennial fruit tree with substantial ecological, nutritional, and economic potential, yet its phenotypic diversity and trait organization remain poorly characterized. Here, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors. Trait interrelationships were examined through the complementary use of Spearman’s rank correlation and Cramér’s V association analyses, capturing both directional rank-based dependencies and scale-independent categorical linkages. Hierarchical clustering based on Gower distance separated the genotypes into three distinct phenotypic clusters, with inter-cluster dissimilarities (0.92–1.18) consistently exceeding intra-cluster variation (0.42–0.55), indicating well-supported phenotypic stratification based on cluster validation. Multiple Correspondence Analysis (MCA) explained 23.30% of total inertia across the first two dimensions, with tree growth habit, branch angle, tree shape, and fruit color emerging as the principal drivers of phenotypic differentiation. Vegetative and leaf traits formed a tightly integrated module, whereas fruit-related traits displayed weaker monotonic but persistent categorical associations, reflecting partial phenotypic independence. The strong concordance among association analyses, clustering, and MCA indicates structured patterns of coordinated and partially independent trait associations in wood apple. Overall, this study demonstrates the effectiveness of mixed-scale multivariate approaches for resolving complex trait architecture in underutilized perennial fruit crops and provides a quantitative phenotypic framework to support germplasm conservation, parent selection, and ideotype-oriented improvement of wood apple.
Why it matches plant phenotyping methods混合尺度の多変量解析を用いて植物遺伝資源の表現型構造を定量化する手法が研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractHere, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll data generated or analyzed during this study are available in the article and the accompanying Supplementary Table S1.Open asset ↗lines:137-161Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
RootMorphology / geometry measurementSegmentationRoot system architecture
BACKGROUND: Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. Accurate segmentation is a prerequisite for extracting root traits relevant to plant physiology, breeding, and agronomy. While U-Net and other convolutional neural network (ConvNet) architectures have been applied to root segmentation, no systematic comparison of multiple Transformer and ConvNet architectures has been conducted across diverse root imaging conditions. RESULTS: We evaluated 21 segmentation architectures across nine diverse root image datasets, training 1511 models to assess all combinations of architecture, dataset, pre-training strategy, and learning rate, producing over 3 million segmentations for evaluation. Transformer-based models significantly outperformed ConvNets for Dice (mean Dice 0.679 vs 0.659; [Formula: see text]). Root-diameter and root-length correlation were also higher for Transformers, but the differences were not statistically significant ([Formula: see text] and [Formula: see text] respectively). Pre-training significantly improved mean Dice from 0.623 to 0.666 ([Formula: see text]), with Transformers benefiting more from pre-training than ConvNets (Dice improvement + 0.072 vs + 0.021; [Formula: see text]), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. MobileSAM achieved the highest Dice score (0.693) while maintaining computational efficiency. Both architecture families underestimated thin root length compared to manual annotations. Dataset choice explained 70.9% of performance variance, far exceeding model architecture (6.7%). PURPOSE: Transformer architectures significantly outperform ConvNets for root segmentation accuracy, and pre-training significantly improves performance, particularly for Transformers. Pre-trained MobileSAM offers the best accuracy at competitive computational cost. Dataset choice dominates performance variance, suggesting practitioners should prioritize data curation over architecture selection.
Why it matches plant phenotyping methods根の画像セグメンテーション手法を複数データセットで体系的に比較・検証し、根長・根径などの形質抽出性能も評価しているため、植物フェノタイピング手法が中心である。
abstractRoot segmentation is a fundamental yet challenging task in image-based plant phenotyping.
Reproduction assets foundThe paper's root image datasets (DeepRootLab, Grassland, Chicory, PRMI) are publicly available, and the authors' training code and modified RhizoVision Explorer trait-extraction fork are on GitHub with explicit availability statements.Dataset · publicImages are available from https://zenodo.org/records/15213661 .Open asset ↗Zenodo · 15213661lines:872-982Dataset · publicImages are available from https://figshare.com/ndownloader/articles/20440497/versions/2 .Open asset ↗Figshare · 20440497lines:872-982Dataset · publicImages are available from https://zenodo.org/records/3527713 .Open asset ↗Zenodo · 3527713lines:872-982Dataset · publicImages are available from https://gatorsense.github.io/PRMI/ .Open asset ↗lines:872-982Code · publicTraining code is available at https://github.com/sotlampr/seg .Open asset ↗GitHub · sotlampr/seglines:1183-1225Code · publicAll nine root image datasets used in this study are publicly available. DeepRootLab images are available from Zenodo (https://zenodo.org/records/15213661). Grassland images are available from Figshare (https://figshare.com/ndownloader/articles/20440497/versions/2). Chicory images are available from Zenodo (https://zenodo.org/records/3527713). The six PRMI datasets (Papaya, Peanut, Sesame, Sunflower, Cotton, Switchgrass) are available from https://gatorsense.github.io/PRMI/. Training code is available at https://github.com/sotlampr/seg. The modified RhizoVision Explorer fork used for trait extraction is available at https://github.com/sotlampr/RhizoVisionExplorer.Open asset ↗GitHub · sotlampr/RhizoVisionExplorerlines:1294-1347Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications.
Why it matches plant phenotyping methods植物表現型取得のためのUAV画像処理ソフトウェアを開発し、植物高・倒伏などの形質抽出、マルチセンサー統合、再現可能な解析ワークフローを中心的に提示している。
abstractWe present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports
Reproduction assets foundThe paper explicitly states that the code and data to reproduce its five case studies (thresholding, temporal vegetation indices, height analysis, multi-sensor index optimization, deep learning classification) are publicly available in the authors' GitHub repository, and the DRONE2REPORT software itself is released as Code · publicThe code and data to reproduce these case studies
can be found at https://github.com/ne1s0n/paper-drone2report (accessed on 13 April
2026).Open asset ↗ne1s0n/paper-drone2reportpdf-page:6 lines:1-59Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Accurate and efficient grain quality assessment is critical for making informed decisions throughout the grain value chain. Early detection of disease enables actions to mitigate spread and further damage, and optimal batch mixing to fulfill specified quality requirements allows for maximizing value and minimizing scrapping. Vision based machine learning and deep learning approaches are gaining attention in the agricultural sector and are useful for the development of automated grain quality assessment. These techniques can reduce the current manual inspection load and are key for objective and precise analysis. Yet, the majority of prior studies are constrained to small or controlled and curated datasets. Practical challenges associated with real-world deployment and reliability are rarely addressed. That is the focus of this work. We present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification. The objective is to determine a CNN model that ensures high and robust classification accuracy, while elucidating and explaining how different image dataset characteristics and training parameters affect performance and reliability. We use a commercial mirror-based imaging system that captures over 90% of each kernel's surface and contrast and compare model architectures, robustness, the effect on pre-processing and image resolution. Our results show similar and high overall performance for ResNet50V2 and EfficientNetV2B0 ([Formula: see text]% accuracy), but per-class analysis indicate that the smaller classes suffer from lack of representative examples, and that most classes benefit from pre-processing including downsampling whereas others benefit from higher resolution. Interactive visualizations reveal that another contributing factor is dubious annotation and multi-class belongingness. Thus, our step-by-step analysis of CNN performance underscores the need for representative data, proper pre-processing, and class-aware evaluation to ensure trustworthy deployment in wheat grain quality assessment.
Why it matches plant phenotyping methods小麦粒画像から品質・病害クラスを推定するCNN画像解析手法の性能、頑健性、前処理、解像度、データ特性を体系的に評価しており、フェノタイピング手法が中心的である。
abstractWe present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification.
Reproduction assets foundThe paper's wheat grain image dataset has a publicly available subset deposited on Zenodo (DOI 10.5281/zenodo.17397123), explicitly stated in the Data Availability statement. The full dataset is proprietary; code is only available upon request, so no qualifying code asset.Dataset · publicA publicly available subset of the segmented wheat grain images used in this study has been deposited in Zenodo to support transparency and reproducibility. The dataset includes representative samples per class collected from instrument and can be accessed at https://doi.org/10.5281/zenodo.17397123.Open asset ↗Zenodo · 10.5281/zenodo.17397123html-lines:337-368Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
RGB / grayscaleFlowerClassificationGrowth / development / phenology
This study presents a comprehensive BDFlower growth stage dataset designed to support research in precision agriculture and floriculture. The dataset encompasses eight common flower species found in Bangladesh: Bush Allamanda, Red Hibiscus, Yellow Bell, Pinwheel Flower, Pink Periwinkle, White Madagascar Periwinkle, Marvel of Peru, and White Hibiscus. Each species is represented across three growth stages-Early, Mid, and Full-resulting in 24 distinct classes. A total of 23,334 colour images are included, comprising 3889 original photographs and 19,445 augmented samples generated with five augmentation techniques. Bush Allamanda contains 499 images, Red Hibiscus contains 489 images, Yellow Bell contains 483 images, Pinwheel Flower contains 497 images, Pink Periwinkle contains 452 images, White Madagascar Periwinkle contains 472 images, Marvel of Peru contains 468 images and White Hibiscus contains 529 images. Each image was collected using smartphone camera at three-time intervals per day, spaced eight hours apart, to capture natural variations in lighting and appearance. The dataset is further organized into training, validation, and testing splits, enabling direct application to machine learning workflows. This is a publicly available dataset specifically curated for flower growth stage classification. In addition to dataset collection, we also conducted a simple experiment using a CNN model to evaluate its performance on this dataset. It is intended to facilitate the development of robust computer vision models that can monitor flower development, with potential applications in automated plant phenotyping, crop monitoring, and digital floriculture systems.
Why it matches plant phenotyping methods花の生育段階を画像で分類する公開データセットを構築し、CNN評価も行っており、植物表現型取得・解析が研究の中心である。
abstractThis is a publicly available dataset specifically curated for flower growth stage classification.
Reproduction assets foundThe paper's own flower growth-stage image dataset (BDFlower) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, directly reproducing the paper's phenotyping (flower growth stage) image measurements. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicRepository name: Data Mendeley
Data identification number: 10.17632/m8g2wynwyr.2
Direct URL to data: https://data.mendeley.com/datasets/m8g2wynwyr/2Open asset ↗10.17632/m8g2wynwyr.2html-lines:94-129Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
LeafStem / branchPhysiological trait estimationLeaf traitsWater status / transpiration
Leaf and hydraulic traits are key determinants of growth rates, and hence potentially exhibit significant associations with wood density (WD) and its intraspecific variation (ITV). However, the extent to which functional traits could improve WD prediction accuracy, and how ITV in WD correlates with functional traits remain incompletely understood. We investigated WD and its ITV across 10,218 plant species, mapped the global distribution of WD, and analyzed the association of ITV in WD with niche breadth and functional traits. Plant species with an acquisitive resource-use strategy, characterized by higher specific leaf area (SLA), leaf nitrogen concentration (LN), and leaf maximum stomatal conductance (g max ), exhibited lower WD. Associations of WD with hydraulic traits indicated species with greater hydraulic safety exhibited higher WD. Moreover, the integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%. Furthermore, resource-acquisitive species demonstrated higher ITV for WD. ITV was positively related to relative niche breadth concerning both climatic factors and soil properties. Overall, functional traits significantly improve WD prediction accuracy, and plant species with an acquisitive resource-use strategy exhibit lower WD but greater intraspecific variation.
Why it matches plant phenotyping methods木材密度という植物形質の予測モデルを構築し、機能形質・環境因子の統合による予測精度を検証しており、形質推定手法が中心的です。
abstractthe integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%.
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo deposit containing the authors' global wood density distribution data, which directly reproduces this paper's measurements. The TRY Plant Trait Database is a generic third-party database, not a paper-specific asset, and no author analysis code is stated.Dataset · publicData for the global distribution of wood density is available on Zenodo Repository https://sandbox.zenodo.org/records/425279.Open asset ↗Zenodo · 425279html-lines:405-429Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像を用いた高スループット圃場フェノタイピング枠組みを導入・検証し、植生被覆、草丈、NDVIから複数の植物形質・状態を抽出しており、フェノタイピング手法が中心的です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analysed (UAV-derived phenotyping measurements) in a public Zenodo repository with a DOI matching an allowed URL.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:197-235Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract. Large-scale mapping of plant biophysical and biochemical traits is essential for ecological and environmental applications. Given their finer spectral resolution and unprecedented data availability, hyperspectral data, in concert with machine and particularly deep learning models, have emerged as a promising, non-destructive tool for accurately retrieving these traits. However, when deploying these methods on a large scale, reliably quantifying the associated uncertainty remains a critical challenge, especially when models encounter out-of-domain (OOD) data, i.e., samples that differ substantially from those of the training data, such as unseen geographical regions, species, biomes, data acquisition modalities, or scene components (e.g., clouds and water bodies). Traditional uncertainty quantification methods for deep learning models, including deep ensembles (deterministic and probabilistic) and Monte Carlo dropout, rely on the variance of predictions but often fail to capture uncertainty in OOD scenarios, leading to overly optimistic and possibly misleading uncertainty estimates. To address this limitation, we propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty by measuring the dissimilarity in the predictor space (spectral inputs) and embedding space (features learned by the deep model) between the training and test data. Dis_UN leverages residuals as a proxy for uncertainty and employs dissimilarity indices in data manifolds to estimate worst-case errors via 95-quantile regression. We evaluate Dis_UN using a pretrained deep learning model to predict multiple plant traits from hyperspectral images, analyzing its performance across OOD data, such as pixels containing spectral variations from urban surfaces, bare ground, water, clouds, or open surface waters. In this study, we target six leaf and canopy traits: leaf mass per area, chlorophylls, carotenoids, nitrogen content, equivalent water thickness, and leaf area index. Compared to scaled variance-based methods, Dis_UN provides (1) a superior estimation of uncertainty in OOD scenarios, achieving 36 % higher contrast (KS distances: 0.648 vs. 0.475) between non-vegetation pixels, particularly under mixed-pixel conditions at medium resolution (30 m); (2) uncertainty quantification without requiring normality or symmetry assumptions, accommodating asymmetric error patterns; (3) enhanced interpretability of uncertainty sources, as uncertainty is directly linked to sample dissimilarity from the training data; and (4) computational efficiency at inference (2.6–7.7× faster), requiring only a single forward pass compared to multiple passes for ensemble-based methods. Challenges remain for traits that are affected by spectral saturation. These findings highlight the advantages of distance-aware uncertainty quantification methods and underscore the necessity of diverse training datasets to minimize sampling biases and enhance model robustness. The proposed framework improves the reliability of uncertainty estimation in vegetation monitoring and offers a promising approach for broader applications.
Why it matches plant phenotyping methods植物形質をハイパースペクトル画像から推定する深層学習について、OOD条件での不確実性推定手法Dis_UNを開発・評価しており、表現型取得・推定手法が中心である。
abstractwe propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty
Reproduction assets foundThe paper's authors publicly released their uncertainty-analysis code (two GitHub repositories) and the study data (Hugging Face dataset) with explicit availability statements and URLs. The EnMAP and NEON hyperspectral scenes are third-party public data sources, not paper-specific deposits, and the supplement is not anCode · publicThe code for this study is available at: https://github.com/echerif18/Multi_trait_Uncertainty/ (last access: 8 March 2026).Open asset ↗echerif18/Multi_trait_Uncertaintylines:449-456Dataset · publicThe data used in this study are available on Hugging Face: https://doi.org/10.57967/hf/7838 (Cherif et al., 2026).Open asset ↗Hugging Face · 10.57967/hf/7838lines:457-483Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Purpose Accurate estimation of crop transpiration is essential for optimizing irrigation management and improving water-use efficiency in precision agriculture. However, direct measurement of transpiration is often invasive, costly, and difficult to maintain at large scales. This study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques. Methods Field experiments were conducted during the 2023 and 2024 growing seasons in central Italy under irrigated silage maize. Meteorological variables, soil water content, and crop growth indicators were used as inputs, while sap flow measurements served as reference outputs. Several machine learning models were evaluated, including Linear Regression, Support Vector Regression (SVR), Decision Tree Regressor, and Multi-Layer Perceptron Regressor (MLPR), using both Point Estimation and Temporal Estimation strategies. Temporal approaches incorporated short-term historical information through feature concatenation and previous-average windows. Results Results demonstrate that non-linear models, particularly MLPR and SVR, consistently outperform linear and tree-based approaches. The inclusion of short temporal windows (45 minutes to 2 hours) significantly improves predictive accuracy, enhancing reconstruction of the diurnal transpiration pattern. Feature concatenation proved more effective than averaging strategies in capturing soil–plant–atmosphere interactions. Model performance remained robust across two contrasting growing seasons, confirming good generalization capability under interannual variability and data discontinuities. Conclusion The proposed framework provides a reliable and minimally invasive solution for real-time estimation of maize transpiration, supporting precision irrigation management. These findings highlight the potential of machine learning models as practical decision-support tools for sustainable agricultural water management.
Why it matches plant phenotyping methodsトウモロコシの蒸散・樹液流という生理形質を、気象・土壌水分データと機械学習で推定する手法を開発・比較し、複数年で性能検証しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques.
Reproduction assets foundThe paper's Data Availability statement says part of the datasets generated and analyzed (maize sap flow, climate, and soil moisture measurements) are publicly available on the authors' GitHub, while the analysis source code is only promised upon acceptance.Dataset · publicon; Datacuration; Formal
686
analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision;
687
Validation; Visualization; Writing – original draft; Writing – review and editing.
688
D t v il ility Part of the datasets generated and analyzed during the current study are
689
publicly available at https://github.com/isarlab-department-690
engineering/Agritech3.1.5FIWARE. The source code used for data processing and analysis will
691
be released upon acceptance of the paper in the GitHub repository https://github.com/isarlab-692
department-engineering/DD_Maize_Sap_Flow.
693
Funding This work was carried out within the framework of the project Agritech National ROpen asset ↗isarlab-department-690pdf-raw-page:31 lines:1-67Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract The global issue of water scarcity and climate change requires highly efficient and intelligent irrigation systems that are capable of optimizing water consumption with high crop productivity. The paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data. The paper analyzes 55,450 soybean data with 13 physiological and biochemical parameters to implement and compare six regression models for predicting the water stress index. After eliminating tautology by removing the direct water content parameter from the prediction model, LightGBM and XGBoost ensemble tree models achieved near-perfect accuracy for predicting crop water stress using regular plant parameters alone, with R² = 1.0 and RMSE = 1.57×10⁻⁸ to 5.04×10⁻⁵. The Random Forest classifier, which was implemented without any direct stress indicators, achieved perfect discrimination between low, moderate, and high stress classes with precision/recall equal to 1.0, and 5-fold cross-validation and noise tests confirmed its robustness. SHAP analysis of the results showed protein percentage (PPE) and seed yield per unit area (SYUA) to be key drivers of water stress, providing valuable insights for precision agriculture. The model for determining irrigation requirements based on crop evapotranspiration and stress level achieved R² = 1.0 with zero error, making it possible to translate trait values directly into irrigation requirements. The framework presented in this paper brings together machine learning and agronomic knowledge to provide real-time data-driven solutions for irrigation systems, which have 30–50% water savings potential while maintaining healthy crops. It lays the ground for the development of AI-assisted irrigation systems that are applicable to different crops and climatic conditions, particularly in water-scarce countries such as Iraq.
Why it matches plant phenotyping methods作物の水ストレス状態を生理・農学データから機械学習で推定し、複数モデルの比較、交差検証、ノイズ試験、解釈分析まで行う計算的フェノタイピング手法が中心である。灌漑最適化への応用を含むが、単なる日常的測定ではない。
abstractThe paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data.
Reproduction assets foundThe paper's soybean phenotyping dataset (55,450 records, 13 physiological/biochemical traits) is publicly available on Kaggle; the Data Availability statement points to it, though it ambiguously labels it as the code implementation location. No separate verified code repository is provided.Dataset · publicThe dataset used in this study (Advanced Soybean Agricultural Dataset) is available from the corresponding author upon reasonable request. The code implementation for all analyses is available at: https://www.kaggle.com/datasets/wisam1985/advanced-soybean-agricultural-dataset-2025 .Open asset ↗kaggle · wisam1985/advanced-soybean-agricultural-dataset-2025lines:372-406Code / dataset availability confirmedarXiv · checked 13 Sept 2026
Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model consistently benefits from the local context information, particularly during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.
Why it matches plant phenotyping methodsSentinel-2 NDVIを用いて森林キャノピーの季節変動から褐変・攪乱状態を推定する手法を開発し、適合度と独立参照データで検証しているため、単なる森林地図作成ではなく植物状態の取得・評価が中心である。
abstractwe present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2.
Reproduction assets foundThe paper explicitly states that its code and interactive content are publicly available in the authors' GitHub repository. Other URLs in the article are cited third-party data sources (swisstopo, EnviDat, GDAL, TauDEM, WhiteboxTools) rather than paper-specific assets.Code · publicThe code and interactive content are available at https://github.com/SamanthaBiegel/s2-forest-browning-monitoring .Open asset ↗SamanthaBiegel/s2-forest-browning-monitoringlines:51-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This dataset consists of a collection of high-resolution RGB images of grapevine leaves, designed to support research in plant pathology, precision viticulture, and computer vision. The images were collected in situ from experimental and commercial vineyards in the north of Portugal, covering different vineyard conditions and management practices. The dataset includes healthy leaves from three grapevine Portuguese cultivars Loureiro, Viosinho and Malvasia Fina, photographed under natural lighting conditions without artificial adjustments. It is organized into five categories: healthy leaves and leaves showing symptoms of downy mildew ( Plasmopara viticola ), powdery mildew ( Erysiphe necator ), Esca complex and Erineum Mite ( Colomerus vitis ). Images are provided in JPEG format with a resolution of 3000 × 3000 pixels and 1024 × 1024 pixels and arranged in folders by health status and disease type. This dataset can be used for machine learning and deep learning applications in disease detection/classification, cultivar identification, and can support other precision agriculture applications, as well as being used for agricultural robotics and educational purposes. An evaluation on three deep learning architectures demonstrated the suitability of the dataset into separating the five classes.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像化した再利用可能なデータセットで、植物の健康状態・病害状態の画像ベース推定を支えることが中心です。深層学習による5クラス分類評価も記載されています。
abstractThis dataset consists of a collection of high-resolution RGB images of grapevine leaves, designed to support research in plant pathology, precision viticulture, and computer vision.
Reproduction assets foundThe paper is a Data in Brief article describing a public Zenodo repository of RGB grapevine leaf images (healthy plus downy mildew, powdery mildew, Esca complex, erineum mite) collected for plant disease/phenotyping research, with explicit data accessibility details. No author analysis code or trained model checkpointsDataset · publicData accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.17343473
Direct URL to data: https://zenodo.org/records/17343473Open asset ↗Zenodo · 10.5281/zenodo.17343473html-lines:93-144Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Proper detection and treatment of plant leaf diseases are essential factors for achieving good crop yields and ensuring food security. Convolutional Neural Networks (CNNs) have shown significant potential for classifying diseases from leaf images. Instead, most current work focuses on image-level prediction and ignores the relationship between infected leaves. This limitation somewhat constrains their use in modelling disease spread. Also, it makes them less efficient in typical field situations where disease is transmitted from plant to plant by physical contact. Moreover, existing CNN architectures do not access inter-lobar contextual information, an essential factor for early detection and control. To tackle this, we propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass. The method uses the enhanced CNN model (LDDNet), with Atrous Spatial Pyramid Pooling (ASPP) and a Channel-Spatial Attention Module (CSAM), to achieve a more precise representation across multiple scales. These embeddings are then used to construct a similarity graph, enabling a GNN to infer likely disease transmission paths among leaves. We evaluate the PlantVillage dataset on the proposed model, and the results show that it outperforms state-of-the-art CNN-based methods, achieving 98.88% classification accuracy and 98.71% F1 Score. Additionally, we were able to predict disease spread with a high AUC-ROC of 0.942 and an MCC of 0.884 using our framework. These results confirm that AdjLeafGNN can accurately model both local and relational patterns. The approach we propose is scalable and interpretable, facilitating real-time monitoring and control of diseases in precision agriculture.
Why it matches plant phenotyping methods葉画像から植物病害を分類し、隣接葉間の病害拡大を推定する深層学習・GNN手法を提案・評価しており、植物の病害状態の取得・推定が研究の中心である。
abstractwe propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass.
Reproduction assets foundThe paper uses the public Kaggle PlantVillage leaf-image dataset as its phenotyping input and releases the complete AdjLeafGNN implementation (model, preprocessing, training, evaluation) on GitHub with a Zenodo-archived DOI.Dataset · publicthe dataset was obtained from the publicly available Kaggle distribution of the PlantVillage dataset: https://www.kaggle.com/datasets/mohitsingh1804/plantvillageTheOpen asset ↗html-lines:584-607Code · publicThe complete source code of the proposed AdjLeafGNN framework, including model implementation, training scripts, and evaluation pipeline, is publicly available. GitHub repository: https://github.com/surekhareddy123/AdjLeafGNN. A permanent archived version of the repository has been deposited in Zenodo and assigned the following DOI: 10.5281/zenodo.18862439.Open asset ↗https://github.com/surekhareddy123/AdjLeafGNN · 10.5281/zenodo.18862439html-lines:584-607Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Deep learning has improved automated plant disease detection by increasing recognition accuracy and robustness compared with traditional vision-based methods. Self-supervised learning (SSL) further reduces dependence on manual labels, but its transferability across heterogeneous agricultural datasets remains insufficiently characterized. Here, we evaluate a contrastive SSL pretraining and fine-tuning pipeline, termed PlantCLR, for plant disease classification under cross-dataset transfer with target-domain fine-tuning. PlantCLR combines SimCLR-style contrastive pretraining with a lightweight convolutional classifier to balance representation quality and deployment efficiency. Experiments on PlantVillage and Cassava Leaf Disease show strong performance, achieving 99.10% accuracy and 99.04% F1-score on PlantVillage, and 96.83% accuracy and 96.70% F1-score on Cassava. Feature embedding visualization using t-SNE and explanation maps using Grad-CAM indicate improved class separability and attention to disease-relevant regions. These results suggest that contrastive SSL can improve representation transfer while maintaining computational efficiency, supporting scalable plant disease diagnostics in practical agricultural settings. Code is available at GitHub .
Why it matches plant phenotyping methods植物病害を画像から分類するPlantCLR手法を開発し、異なるデータセット間で性能評価・検証しているため、植物の病害状態を推定するフェノタイピング手法が中心である。
abstractHere, we evaluate a contrastive SSL pretraining and fine-tuning pipeline, termed PlantCLR, for plant disease classification under cross-dataset transfer with target-domain fine-tuning.
Reproduction assets foundThe paper's plant disease detection experiments use two publicly available image datasets with explicit Kaggle URLs in the Data availability statement. The authors also state code is available at GitHub, but no concrete URL is provided, so no code asset is included.Dataset · publicThe Cassava Leaf Disease Classification dataset is available at https://www.kaggle.com/c/cassava-leaf-disease-classificationOpen asset ↗Kaggle · cassava-leaf-disease-classificationlines:232-268Dataset · publicthe PlantVillage dataset is available at https://www.kaggle.com/datasets/emmarex/plantdiseaseOpen asset ↗Kaggle · emmarex/plantdiseaselines:232-268Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Crop diseases significantly threaten global food security by directly affecting the crop yield and quality. The traditional diagnostic methods are labour intensive and human error prone. However, the existing deep learning solutions suffer with poor generalization due to the sharp loss landscapes. The proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM). This method minimizes both the training loss and loss landscape sharpness and enables the model to converge to a flatter-minima with improved generalization. The proposed work is evaluated on 60,000 corn leaf image samples for four classes with 15,000 balanced samples per class after augmentation. The optimized CNN model has achieved 99.66% test accuracy at 0.33% classification error rate and outperforms the conventional optimizers like Adam (98.44% accuracy) and the Stochastic Gradient Descent (SGD). The state-of-the-art analysis presents a 99% average precision rate along with 99.66% F1-score and 0.0013% mean squared error (MSE). The quantized model achieves an inference latency of 22.7 ms/image (≈44 FPS) on a Raspberry Pi 4 and reduces model overfitting and enhances feature discriminability. These results underscore the potential of SAM-based optimization in precision agriculture by driving a scalable automation of disease management. This work bridges the gap between theoretical advances in deep learning optimization and practical deployment in resource-constrained farming environments.
Why it matches plant phenotyping methodsトウモロコシ葉画像から病害を分類するCNNの最適化と性能評価が中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。
abstractThe proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM).
Reproduction assets foundThe paper's Data availability statement lists the public corn leaf image datasets used for its disease-classification experiments (Kaggle corn/maize leaf disease dataset, New Bangladeshi crop disease dataset, New Plant Diseases Dataset, and the MahindiNet maize leaf disease dataset on Science Data Bank). No author codeDataset · publicg.; Gireesh Kumar: Formal Analysis, Visualization, Writing – Review & Editing, Resources.
Funding
Open access funding provided by Manipal University Jaipur. Open access funding provided by Manipal University Jaipur, Jaipur. No external funding was received for this research.
Data availability
Corn or Maize Leaf Disease Dataset, https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset [ 33 ] New Bangladeshi crop disease dataset. https://www.kaggle.com/datasets/nafishamoin/new-bangladeshi-crop-disease [ 38 ] New Plant Diseases Dataset, https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset [ 46 ] Mohammed Abo-Zahhad et al. (2023). MahindiNet: Maize Leaf DOpen asset ↗Kagglelines:2568-2620Dataset · publicby Manipal University Jaipur. Open access funding provided by Manipal University Jaipur, Jaipur. No external funding was received for this research.
Data availability
Corn or Maize Leaf Disease Dataset, https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset [ 33 ] New Bangladeshi crop disease dataset. https://www.kaggle.com/datasets/nafishamoin/new-bangladeshi-crop-disease [ 38 ] New Plant Diseases Dataset, https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset [ 46 ] Mohammed Abo-Zahhad et al. (2023). MahindiNet: Maize Leaf Disease Dataset[DS/OL]. V1. Science Data Bank. https://cstr.cn/31253.11.sciencedb.12556 . CSTR:31,253.11.sciencedb.12556 [ 55 ] Open asset ↗Kagglelines:2568-2620Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published31 Mar 2026Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable ApplicationsCited by 0 · OpenAlex ↗
The aspect of crop monitoring takes into consideration the timely detection of crop stages, leaf disorders, and deficiencies to enhance crop yield and decrease losses in agriculture. However, most of the current methods are limited to either disease detection or nutrient evaluation and do not examine the conditions of crops at various stages of growth, even though several AI -IoT-based solutions have been suggested to be applied to crop health monitoring. In addition, the estimation of the severity of the diseases is neglected, and this restricts decision-making in favor of the farmers. To address these constraints, the paper presents a Parametrized Elliptical Cauchy Fuzzy Inference System (PECFIS) combined with a Deep Glorot Bessel Elliott Softplus Convolutional Neural Network (DGBESCNN), proposed as an AI-based solution for crop monitoring and IoT support. The IoT devices in the form of drones are used to get real-time field images, and they are preprocessed in terms of noise reduction, contrast enhancement by LHM-CLAHE, conversion to HSV color space, and feature discrimination by vegetation indexing, as well as C3MEK-Means. PECFIS is used to determine eight key stages of rice growth and the severity of leaf diseases, whereas DGBESCNN provides proper classification of leaf diseases and nutrient deficiencies at each growth stage. The evaluation of the proposed framework was conducted using publicly available datasets on rice leaf disease and nutrient deficiency. The results of the experiments show that the system achieves high classification performance, with an accuracy of 98.82, a precision of 98.65, a recall of 98.73, an F1-score of 98.59, and low error rates (MSE = 0.0135, RMSE = 0.116). The findings show that the developed AI-IoT system is superior to available approaches and can serve as a dependable, real-time, and scalable solution in precision agriculture and intelligent crop monitoring.
Why it matches plant phenotyping methodsドローン画像からイネの生育段階と葉病害の重症度を推定・分類するAI-IoT手法が研究の中心であり、植物状態の取得・抽出方法を技術的に評価している。
abstractThe IoT devices in the form of drones are used to get real-time field images
Reproduction assets foundThe paper evaluates its PECFIS-DGBESCNN crop monitoring framework on two publicly available Kaggle datasets (Nutrient Deficiency Symptoms in Rice, 1,156 images; Rice Leaf Diseases, 120 images), with explicit dataset links provided by the authors. No author code, models, or other paper-specific assets are shared.Dataset · publicn of the low-cost ground-based IoT and weather sensors and enhanced robustness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. SuOpen asset ↗Kaggle · guy007/nutrientdeficiencysymptomsinricepdf-raw-page:21 lines:1-50Dataset · publicstness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. Sustainability, 15(16), 12149.
https://doi.org/10.3390/su151612149
[2] AlfOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:21 lines:1-50Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk diseases (GTD) are a well-known example in viticulture that alter plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization. The pipeline integrates spatio-temporal anatomical alignment and rigid registration; a generalized cylindrical-coordinate transformation; supervised segmentation of water-depleted regions; and population-level statistical analyses, including population mean images, probabilistic atlases, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal wood pathogen, our approach enables in vivo time-lapse comparisons between cultivars and treatments. The results reveal reproducible early degradation signals across individuals and cultivar-dependent differences in lesion progression. Overall, this methodological innovation provides a new paradigm for internal plant phenotyping, enabling non-invasive quantification of disease development and comparative spatio-temporal assessment of host responses in woody plants, with strong potential to advance early diagnosis and management of GTDs and internal diseases.
Why it matches plant phenotyping methodsMRI画像と計算パイプラインにより、ブドウ樹内部の病変・組織劣化を非破壊かつ時空間的に定量化する手法を開発・適用しており、植物表現型取得が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization.
Reproduction assets foundThe paper's MRI phenotyping data (~160 GB raw, 1.4 TB processed) is only available upon request, but the authors' processing pipeline (scripts and parameters) is publicly deposited on Zenodo with an explicit URL.Code · publicThe processing pipeline (including scripts and parameters required to reproduce the processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369 .Open asset ↗Zenodo · 10.5281/zenodo.17944369lines:370-484Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
CoffeeField / plotFruitObject detectionGrowth / development / phenology
Introduction Coffee cherry ripeness assessment is critical for harvesting efficiency and product quality, yet traditional manual inspection methods suffer from subjectivity and low efficiency. Methods To address the challenges of detecting small, occluded, and densely distributed coffee cherries in complex field environments, this study proposes an Occlusion and Density Aware Network (ODANet). Built upon the YOLOv8 framework, ODANet integrates three innovative modules: (1) Condition-Guided Windowed Attention (CGWA), which incorporates occlusion and density maps as auxiliary guidance signals for efficient feature enhancement; (2) Attention-guided Space-Preserving Convolution (ASPC), which employs space-to-depth transformation with cascaded attention to preserve spatial information during downsampling; and (3) Dual-Adaptive Dynamic Upsampling (DADU), which achieves content-adaptive feature reconstruction through dual-branch offset prediction with learnable fusion weights. Results Comprehensive evaluation on a publicly available dataset demonstrates that ODANet achieves state-of-the-art performance among 17 diverse detection architectures, attaining 76.7% mAP@0.5 with a 6.3 percentage point improvement over baseline YOLOv8, while maintaining computational efficiency (8.1 GFLOPs, 30.4M parameters) suitable for real-time deployment. Ablation studies validate the contributions of each module: ASPC improves performance by 2.2%, DADU by 0.6%, and CGWA by 3.5%. Discussion The model demonstrates robust performance across varying lighting conditions, occlusion levels, and growth stages, making it particularly suitable for practical agricultural deployment. This research provides an efficient solution for small object detection in precision agriculture.
Why it matches plant phenotyping methodsコーヒーチェリーの成熟度という植物器官の状態を画像から推定する検出手法を開発し、複数モデル比較・アブレーションで技術的に検証しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes an Occlusion and Density Aware Network (ODANet)
Reproduction assets foundThe paper analyzes a publicly available coffee cherry dataset hosted on Kaggle, explicitly linked in the data availability statement. This is the paper-specific image dataset used for its coffee cherry ripeness detection experiments. No author code or model checkpoints are stated as available.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/harisyunanda/dataset-coffee-cherry/data .Open asset ↗Kaggle · harisyunanda/dataset-coffee-cherrylines:682-758Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published28 Mar 2026Metabolomics : Official journal of the Metabolomic SocietyCited by 0 · OpenAlex ↗
Introduction Pearl millet is a high nutritional cereal recognised for its agro-climatic resilience, making it relevant for food security under climate change scenarios. Phenotypic traits are indicative of crop performance, stability and adaptability, yet the potential of metabolomics to predict these traits has not been explored. Objectives This study aimed to identify metabolite-trait associations in the Brazilian germplasm core collection, comprising 203 pearl millet genotypes, by combining untargeted metabolomics with machine-learning models. Methods Grains metabolic profiles were obtained using untargeted UHPLC-LTQ-Orbitrap-HRMS. Phenotypic data were sourced from standardised evaluations conducted by Embrapa across different years and field trials within the Sete Lagoas experimental station (Minas Gerais, Brazil). Generalised linear modelling with penalisation (GLM) and Random Forest was applied to explore the correlation between metabolism and 21 phenotypic traits. Results GLM successfully predicted eight qualitative and seven quantitative traits. Prediction accuracy was higher for qualitative traits, reflecting their comparatively simpler genetic architecture, whereas quantitative traits also achieved satisfactory performance (R² ≥ 0.6). Key predictors included phenolic compounds, amino acids, fatty acids, and carbohydrates. Notably, several associations corresponded to metabolites involved in nitrogen metabolism and vegetative growth, underscoring biologically meaningful links between metabolic profiles and trait variation. Conclusions This exploratory study presents the first metabolome characterisation of a pearl millet germplasm bank, coupled with predictive modelling of phenotypic traits. However, our findings are constrained by the single-environment design and the absence of population-structure assessment. To establish the stability and biological relevance of these results, future work should incorporate multi-environment trials and pathway-level analyses accounting for population structure.
Why it matches plant phenotyping methodsメタボロームを入力として機械学習で植物の表現型形質を予測し、複数形質で予測精度を評価しているため、単なる生物学的測定ではなく形質推定手法の検証が中心です。
abstractThis study aimed to identify metabolite-trait associations in the Brazilian germplasm core collection, comprising 203 pearl millet genotypes, by combining untargeted metabolomics with machine-learning models.
Reproduction assets foundThe paper deposits its metabolomics and phenotypic metadata in a public repository (Recherche Data Gouv, DOI 10.57745/GU6WDG). No author analysis code or trained model deposit is stated; supplementary materials are not linked to a qualifying URL.Dataset · public.623/2023; 26/210.152/2023; 26/201.317/2022), National Council for Scientific and Technological Development (CNPq) (407350/2023-3; 314100/2023-7), Coordination for Improvement of Personnel with Higher Education (CAPES) (financial code 001).
Data availability
The metabolomics and metadata reported in this paper are available via https://doi.org/10.57745/GU6WDG.
Declarations
Competing interests
The authors declare no competing interests.
References
Alonso-Blanco C Méndez-Vigo B
Genetic architecture of naturally occurring quantitative traits in plants: An updated synthesis
Current Opinion in Plant Biology 2014 18 37 43
10.1016/j.pbi.2014.01.002
24565952
Alonso-Blanco, C., & Méndez-VigoOpen asset ↗10.57745 · GU6WDGlines:121-160Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Accurate plant health monitoring relies on hyperspectral imagery to extract vegetation spectral signatures and compute vegetation indices (VIs), which are critical for phenotyping and crop condition assessment. However, the requirement for high spectral resolution significantly increases the cost and complexity of data acquisition. In this study, we proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data. The aim was to reduce the dependency on high-resolution spectral imagery without compromising prediction accuracy. The framework integrated correlation-based feature selection with four regression models to identify and utilize the most informative spectral bands from coarsely sampled data. The system was trained and validated using a data set consisting of 555 spectral signatures collected from olive leaves at five stages of dehydration, with spectral resolutions ranging from 1 to 100 nm. A total of 25 vegetation indices, commonly used in the estimation of water stress, chlorophyll, and nitrogen, were predicted on various sampling scales. Experimental results show that even with 100 nm spectral resolution, the proposed framework achieves high prediction accuracy, with coefficients of determination reaching 0.99 for RVSI, VOPT, and SPADI indices. These findings demonstrate that accurate vegetation index estimation is achievable with significantly fewer spectral bands, offering a cost-effective solution for large-scale plant health monitoring. This framework lays the groundwork for the development of low-cost, data-efficient remote sensing systems for precision agriculture, especially in crops such as olives, where health dynamics are sensitive to water and nutrient status.
Why it matches plant phenotyping methodsオリーブ葉のハイパースペクトルデータから植物状態に関わる植生指数を推定する、低コストな機械学習・スペクトル測定フレームワークの開発と検証が中心である。
abstractwe proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data
Reproduction assets foundThe paper's Data Availability statement points to a Figshare deposit (DOI 10.6084/m9.figshare.26950660.v2), which per the statement hosts the study's data — the 555 olive-leaf hyperspectral signatures and vegetation index measurements underlying the phenotyping analysis. This is a paper-specific, publicly accessible,直接Dataset · publicnm.
(PDF)
S2 File
Inclusivity in global research questionnaire.
(PDF)
Acknowledgments
The authors thank the Advanced Center of Electric and Electronic Engineering - AC3E ANID. The authors acknowledge the support provided by Universidad Técnica Federico Santa María and the Direction of Post-Grade programs DDP.
Data Availability
https://doi.org/10.6084/m9.figshare.26950660.v2 .
Funding Statement
This work was funded by the ANID FB240002 basal center AC3E, and ANID national doctorate scholarship, folio N°21231129. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Ruiz-Carrasco B, Fernández-Lobato L, López-Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2lines:266-293Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
ABSTRACT Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1,423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (⍴=0.3, p 0.5, p<1x10 -16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.
Why it matches plant phenotyping methodsタワー型連続ハイパースペクトルセンシングを用いて多数の遺伝子型の生理・構造形質を時系列で取得し、表現型解析とGWASに substantively 適用しているため、フェノタイピング手法が中心的である。
abstractHyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales.
Reproduction assets foundThe paper's hyperspectral phenotype dataset (tower-based hyperspectral traits for 505 Populus trichocarpa genotypes) is explicitly stated to be publicly available through the Oak Ridge National Laboratory LabKey data portal with DOI 10.25983/CBI/3012775. This is a paper-specific, public, actionable phenotype dataset. ADataset · publicHyperspectral phenotype data are publicly available through the Oak Ridge National Laboratory LabKey data portal (DOI: 10.25983/CBI/3012775).Oak Ridge National Laboratory LabKey data portal · 10.25983/CBI/3012775lines:163-201Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This study addresses the challenge of detecting white grape clusters (Vitis vinifera L) in high-density vineyard canopies, a critical task for precision viticulture and yield estimation. Traditional statistical and image-processing methods have struggled with occlusion issues. In this work, over 100 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation, with various preprocessing strategies tested to enhance cluster visibility. Convolutional Neural Network (CNN) architectures were compared, highlighting YOLOv8 as superior to Mask R-CNN in both accuracy and efficiency. YOLOv8, trained for up to 100 epochs on equalized and augmented datasets, achieved outstanding performance: 84.9% precision, 72.6% recall, and mAP@0.5 of 83%, far surpassing Mask R-CNN (17% precision, 26% recall). The model successfully detected partially hidden clusters, including those invisible to human experts, better than previous studies that required controlled backgrounds or artificial lighting. Results confirm that combining RGB equalization with data augmentation optimizes detection. These findings underscore the potential of deep learning and low-cost RGB imaging systems to enable automated, scalable solutions for yield estimation and canopy analysis. In conclusion, YOLOv8 emerges as a promising tool for accurate grape bunch detection under field conditions, overcoming previous limitations.
Why it matches plant phenotyping methodsブドウ房を対象としたRGB画像とCNNによる検出手法を開発・比較し、精度を定量評価しているため、植物器官の表現型取得が中心である。
abstractIn this work, over 100 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation, with various preprocessing strategies tested to enhance cluster visibility.
Reproduction assets foundThe paper's Data Availability Statement points to the authors' public GitHub repository containing the original grape-cluster image dataset and annotations used in this study. The ultralytics repository is a generic third-party library, not a paper-specific asset.Dataset · publicData Availability Statement: The original data presented in the study are openly available at
[https://github.com/upmValeriano/racimosUva.git.]Open asset ↗upmValeriano/racimosUvapdf-page:13 lines:1-66Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The growing global demand for food production, coupled with the increasing threat of plant diseases, necessitates advanced and automated solutions for crop health monitoring. Among various crops, pome fruits such as apples and pears are widely cultivated yet highly susceptible to multiple diseases that can significantly reduce yield and quality. Existing approaches for disease detection and severity classification are often limited by their dependency on manual inspection and their inability to handle complex real-world imagery, especially when multiple diseases coexist on a single leaf. To address these limitations, this research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves. A fine-tuned MobileNetV2 backbone is employed to extract high-level discriminative features from a specialized pome leaf dataset annotated with multiple disease types and severity levels. The proposed system integrates a lightweight Lite-U-Net for semantic segmentation to isolate diseased regions and an enhanced Lite-YOLACT for instance segmentation using a linear combination of prototype masks and mask coefficients. Moreover, a new multi-disease severity scale is proposed to quantify the impact of multiple coexisting infections on a single leaf, an aspect not addressed in previous studies. To enhance interpretability, an improved Grad-CAM technique generates visual heatmaps highlighting the most influential regions in the model's decision-making process, providing transparency and validation for agricultural experts. Experimental evaluations demonstrate that the proposed framework achieves 95% accuracy in disease severity estimation, effectively identifying and grading multiple infections simultaneously. This study represents a significant step forward in precision agriculture, offering an efficient, interpretable, and scalable deep learning solution for real-world crop health monitoring and management. The source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .
Why it matches plant phenotyping methods果樹葉の病斑領域を画像から分割し、複数病害の重症度を定量推定する深層学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。
abstractthis research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves.
Reproduction assets foundThe paper's authors publicly release source code and trained models on GitHub, and the study analyzes two public Kaggle plant-image datasets (DiaMOS Plant and PlantVillage) used directly for the multi-disease severity phenotyping experiments.Code · publicThe source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .Open asset ↗https://github.com/mqasim0787/Multi-Disease-Severity · mqasim0787/Multi-Disease-Severitylines:1-23Dataset · publicThe datasets analyzed during the current study are available publicly in the Kaggle repository, DiaMOS dataset (1) and PlantVillage Dataset (2) 0.1. [https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset]Open asset ↗https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset · diamos-plant-datasetlines:964-977Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Precise detection of crop leaf damage is essential for real-time plant health monitoring and yield estimation. However, conventional deep learning models often exhibit poor generalization when deployed across varying species and complex, unstructured field environments. To address these limitations, we propose a new modeling paradigm that shifts from traditional task-specific training to foundation model adaptation. Specifically, we introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation. By incorporating a Spatial Prior Module (SPM) and a Projection Module, our approach effectively bridges the gap between general-purpose pre-training and domain-specific requirements. Experimental results on coffee and black gram datasets demonstrate that this paradigm consistently outperforms standard networks, including Unet, Unet++, and SwinUnet. On the coffee leaf dataset, the proposed model achieves an Intersection over Union (IoU) of 78.31% and a Pixel Accuracy of 88.00%, surpassing the baseline Unet by over 10.5% in IoU. Remarkably, the architecture reduces inference time by approximately 93.6% (from 63.41s to 4.07s), proving that high-parameter foundation models can be adapted for extreme computational efficiency in agricultural scenarios. To further validate scalability, we conduct additional experiments on a larger dataset, AMG HS . The proposed paradigm achieves the best overall detection performance while maintaining superior computational efficiency, confirming its robustness under increased data scale. Interpretability analysis reveals that the foundation model backbone effectively captures high-level semantic features of lesions, providing a clear explanation for its superior performance and cross-domain reliability. This research establishes a scalable, high-performance paradigm for intelligent crop protection, demonstrating that coupling customized encoders with foundation models is a superior strategy for cross-domain agricultural tasks.
Why it matches plant phenotyping methods植物葉の病変を画像からセグメンテーションし、葉の損傷状態を定量化する手法の開発・検証が研究の中心であるため。
abstractwe introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation.
Reproduction assets foundThe paper analyzes two publicly available plant image datasets hosted on Mendeley Data: a coffee leaf rust/leaf miner dataset and a black gram leaf disease dataset, both explicitly linked in the Data Availability Statement. No author analysis code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/vfxf4trtcg/5; https://data.mendeley.com/datasets/45djgf3p96/1.Open asset ↗html-lines:473-493Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/vfxf4trtcg/5; https://data.mendeley.com/datasets/45djgf3p96/1.Open asset ↗html-lines:473-493Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Early detection of maize leaf diseases is essential to prevent yield losses. Existing vision-based models face challenges in real-world environments due to data imbalance, lighting variations, and interpretability. This study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings. MaizeFormerX employs multi-scale patch embeddings and a Cross-Scale Attention Fusion (CSAF) module to capture both detailed lesion textures and larger disease patterns. The CSAF output is processed through a transformer encoder stack using multi-head self-attention to model long-range dependencies. Robust preprocessing and dataset-specific augmentations were applied to improve feature extraction and address class imbalances in the Dataverse, Tanzania, and Plagues Maiz datasets. For interpretability, Grad-CAM was used for pixel-level saliency mapping in an efficient web application. When benchmarked against MobileViT, EfficientFormer, TinyViT, and Swin Transformer, MaizeFormerX achieved 97.8% accuracy on Dataverse, 97.5% on Tanzania, and 96.9% on Plagues Maiz, outperforming Swin Transformer V2 by 2–3%. Cross-domain testing yielded 88.9% accuracy when trained on Dataverse and tested on Tanzania, surpassing baseline performance by 3–6%. Class-wise analysis revealed F1 scores over 98% for Healthy and MLB classes with 6× augmentation, and over 97% for MSV. Ablation studies highlighted the significance of the cross-scale attention module for high MCC during domain shifts. This study introduces a precise, explainable, and efficient image-based method for classifying maize diseases, which could aid in more targeted crop management, reduce unnecessary agrochemical use, and promote sustainable maize production in future decision-support environments.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から分類する手法の開発・ベンチマーク・交差ドメイン検証が中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractThis study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings.
Reproduction assets foundThe paper's Data Availability statement explicitly lists three public maize leaf image datasets used for its phenotyping/disease-classification experiments (Dataverse, Tanzania/Mendeley, Plagues Maiz/figshare) and an authors' GitHub repository containing all code, preprocessing pipelines, and experimental configs. All四Dataset · publicThe datasets used in this study are publicly available and sourced from Dataverse (https://doi.org/10.7910/DVN/LPGHKK)Open asset ↗Dataverse · 10.7910/DVN/LPGHKKhtml-lines:2304-2339Code · publicAll code, preprocessing pipelines, and experimental configurations used in this work are available at: https://github.com/rezaul-h/MaizeFormerX/.Open asset ↗github · rezaul-h/MaizeFormerXhtml-lines:2304-2339Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Many Artificial Intelligence and Machine Learning technologies have been applied to detect rice diseases. These approaches are either unable to identify the diseases or have a slow recognition speed. Therefore, an improved Faster-RCNN (Faster-RCNN-Pro) model is proposed to overcome these issues. First, SENet attention modules are embedded in the backbone of Faster-RCNN to enhance confidence of objects that are difficult to recognize by enhancing key image information and suppressing background information. Second, structure of the feature extraction network and RPN are improved by using multi-feature scale fusion to increase the utilization of micro-target features. Third, the quantization error introduced in the process of pooling the region of interest is then eliminated by ROI Align. Finally, a balanced L1 loss function is designed to effectively reduce the imbalance between samples with a large gradient that are difficult to learn, and samples with a small gradient that are easy to learn. The experiment results show that the improved model has a better detection accuracy and robustness in recognizing the fine features of rice leaf diseases. Therefore, the application of this model to the intelligent identification of rice leaf disease can significantly improve the accuracy and reduce the misjudgment rate.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から識別する改良Faster-RCNNを開発・評価しており、植物病害表現型の取得手法が研究の中心である。
abstractan improved Faster-RCNN (Faster-RCNN-Pro) model is proposed to overcome these issues.
Reproduction assets foundThe paper's rice leaf disease detection experiments were performed on a public Kaggle image dataset (rice blast, brown spot, hispa, healthy leaves), which the authors explicitly state is publicly available with a URL matching the allowed list. No author analysis code or trained model checkpoints are disclosed.Dataset · publical Internet of Things, aiming to recognize large-scale rice leaf diseases. Moreover, it is beneficial for the modernization of the agricultural industry.
Acknowledgments
The authors would like to thank the anonymous reviewers.
Data Availability
All relevant data for this study are publicly available from the Kaggle repository ( https://www.kaggle.com/minhhuy2810/rice-diseases-image-dataset ).
Funding Statement
This work is supported by the National Natural Science Foundation of China (62402308).
References
1. Mondal S, Ghosh S, Mukherjee A. Application of biochar and vermicompost against the rice root-knot nematode (Meloidogyne graminicola): an eco-friendly approach in nematode management. JOpen asset ↗Kaggle · minhhuy2810/rice-diseases-image-datasetlines:220-237Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Oranges, mandarins, bitter oranges, and lemons are examples of citrus fruits that make delicious meals and are highly nutritious. Citrus fruits suffer from a variety of infections that affect their yield. The Department of Agriculture wants to increase the production of oranges and lemons. On the other hand, several plant diseases and their advanced stages have impacted production. The quality of fruit influences market value and its financial effect. Therefore, accurate detection of ailments and their severity is crucial for improving the output and market value of oranges and lemons. To automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model. Python is used to create the ICNN model, and testing is performed using benchmark datasets from various repositories. The research presented here shows that ICNN performs better than traditional deep learning and machine learning models, such as the Convolutional Neural Network (CNN) and K-Nearest Neighbours (KNN). This illustrates how machine learning models require supplementary approaches to extract parameters from data that arrives in non-automated ways. Additionally, to improve the accuracy of their classification or prediction, deep learning models require pre-trained models. As a result, ICNN, an enhanced deep learning model that can automatically predict disease with higher accuracy than other models, represents an advancement over standard CNNs. Compared with KNN and CNN, ICNN achieves 99.69% accuracy.
Why it matches plant phenotyping methods柑橘の葉・果実の病害と重症度を画像から自動推定するCNN手法を開発し、ベンチマークデータセットで比較評価しており、植物フェノタイピング手法が中心である。
abstractTo automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model.
Reproduction assets foundThe paper's Data Availability statement explicitly lists three public Kaggle URLs as the datasets used and analysed in the study (citrus/plant leaf disease image datasets). These are paper-specific, publicly accessible image assets directly supporting the phenotyping/disease-classification analysis. The Mendeley URL (3Dataset · publicg agricultural specialists to properly understand and accept the model’s predictions.
Author contributions
Arunapriya.R – Problem Statements, Implementation and Testing Dr.S.P.Valli – Results, Conclusion, and Summary.
Data availability
The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://wwOpen asset ↗kagglelines:372-388Dataset · publicts, Conclusion, and Summary.
Data availability
The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ]. (https:/ www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ).
Declarations
Competing interOpen asset ↗kagglelines:372-388Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Reliable identification of maize leaf diseases is critical for mitigating crop losses, particularly in regions where farmers have limited access to experts. Although vision transformers (ViTs) have recently demonstrated strong performance in image recognition, their weak inductive bias and limited modeling of local texture patterns make them non-ideal for fine-grained maize leaf disease classification. To address these limitations, we propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks. The local and global features captured by the convolution and attention modules are concatenated along the embedding dimension and fused using a multilayer perceptron. This results in richer token representations without significantly increasing model size. Across three datasets, ConvDeiT-Tiny (6.9 M parameters) consistently outperformed DeiT-Ti, DeiT-Ti-Distilled, and DeiT-S (21.7 M parameters) when trained from scratch. With transfer learning, ConvDeiT-Tiny achieved an accuracy of 99.15%, 99.35%, and 98.60% on the CD&S, primary, and Kaggle datasets, respectively, surpassing many previous studies with far fewer parameters. For explainability, we present gradient-weighted transformer attribution visualizations showing the disease lesions driving model predictions. These results indicate that injecting local inductive bias in early transformer blocks is beneficial for accurate maize leaf disease classification.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴画像を対象に、病害分類のための新規Vision Transformerモデルを開発・比較しており、植物病害状態の画像ベース表現型推定が中心である。
abstractwe propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks.
Reproduction assets foundThe authors publicly release their analysis code and dataset splits (including their field-collected primary maize leaf image dataset) via a GitHub repository, and the paper's classification experiments use the public Kaggle Corn or Maize Leaf Disease Dataset (COMLDD). Both are paper-specific, public, and actionable.Code · publicThe program code and dataset splits for the three datasets used in this study, including our primary data, can be found at https://github.com/DamarisWaema/ConvDeiT-Tiny (accessed on 18 March 2026).Open asset ↗DamarisWaema/ConvDeiT-Tinylines:121-289Dataset · publicGhose S.
Corn or Maize Leaf Disease Dataset
Available online: https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset (accessed on 10 July 2025)Open asset ↗lines:474-623Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 5 Sept 2026
High-density planting is an effective strategy to increase maize yield but imposes greater demands on plant architectural adaptability. To elucidate the structural response mechanisms of maize under varying planting densities, we developed a high-throughput 3D phenotyping system tailored to complex field conditions. High-precision point clouds of field-sampled plants were obtained via multi-view 3D reconstruction. Using a deep learning network, stem and leaf organs were semantically segmented (95.6% accuracy), while leaves were individually separated via clustering (94.8% accuracy). From these data, 31 plant architectural traits and 14 ear-leaf traits were extracted, establishing a hierarchical trait characterization system. Results showed that increased planting density significantly influenced plant architecture reshaping and structural coordination, leading to more compact plant forms and ear height position centralization. Ear leaves exhibited heightened sensitivity to density variation, particularly in leaf area, vertical distribution, and leaf inclination angle, suggesting an early-response role. Principal component analysis and clustering further revealed patterns of structural differentiation and key traits driving these changes under density treatments. The integrated workflow-comprising data acquisition, modeling, segmentation, clustering, trait extraction, and analysis-offers a robust approach for structural phenotyping and intelligent breeding selection in maize and other tall crops. This pipeline provides valuable technical support and data resources for optimizing dense planting strategies and advancing digital agriculture.
Why it matches plant phenotyping methods高スループット3D表現型システムを開発し、点群再構成・器官分割・クラスタリングから多数の植物構造形質を抽出することが中心であるため。
abstractwe developed a high-throughput 3D phenotyping system tailored to complex field conditions
Reproduction assets foundThe paper's authors provide a public GitHub repository for the study's source code (segmentation/trait-extraction pipeline). The phenotype point-cloud dataset itself is only available on request from the corresponding author, so it is not a public asset.Code · publicThe code of this study will be made publicly available upon publication. The source code is available at https://github.com/CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responses .Open asset ↗CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responseslines:330-415Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recognizing lineages is a central challenge in plant systematics, making it essential to explore multiple analytical tools. In this context, this study investigates how frond shape can assist in discriminating against lineages within the Scaly clade of Microgramma (Polypodiaceae), and tests whether the integration of multiple lines of evidence enables a more consistent recognition of lineages than exclusively macromorphological approaches. We analyzed 271 specimens representing eight species, using Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA). Evolutionary relationships between spectral and morphometric data were assessed through phylogenetic generalized least squares (PGLS) regressions and phylogenetic partial least squares (Phylo-PLS) analyses. Dimorphic species exhibited higher discrimination capacity (average accuracy of 80–83%). Fertile and combined fronds yielded the highest accuracy values. Morphologically similar species, such as M. reptans and M. tobagensis, showed significant overlap, whereas M. percussa achieved the best performance (average accuracy of 80%). Morphometric-spectral integration showed a strong correlation (R² = 0.72; P = 0.003), and both the combined datasets (spectra and outline) and the individual datasets of spectral and shape features revealed a high phylogenetic signal (λ = 1–0.84), indicating partial coevolution between frond shape, chemical composition, and the evolutionary history of the group. Outline morphometry combined with infrared spectroscopy within a phylogenetic framework improves lineage discrimination, although overlap zones persist, reflecting complex evolutionary processes. Our study highlights the potential of integrative systematics to elucidate species boundaries in groups with high morphological disparity, as well as the need for broad sampling and multi-evidence approaches in future systematic reviews.
Why it matches plant phenotyping methodsフロンド形状をElliptical Fourier Analysisで定量化し、赤外分光との統合を用いて系統識別性能を評価しており、植物器官形質の取得・解析手法が研究の中心です。
abstractusing Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA).
Reproduction assets foundThe authors state that raw data, processed data, and R analysis code for the frond outline morphometrics are publicly available on GitHub (Microgramma-Outline), and the FT-NIR spectral data repository (Microgramma-FTNIR) is referenced in the methods. Both are paper-specific, public, and actionable.Code · publicSciELO Preprints - Este documento é um preprint e sua situação atual está disponível em: https://doi.org/10.1590/SciELOPreprints.15500
573 The raw data, processed data, and R analysis code are publicly available on GitHub:
574 https://github.com/labevofern/Microgramma-Outline.git.
575
576 REFERENCES
577 Ackerly D.D. (2004) Adaptation, Niche Conservatism, and Convergence: Comparative
578 Studies of Leaf Evolution in the California Chaparral. The American Naturalist, 163, 654–
579 671.
580 Adams D.C., Collyer M.L. (2018) Multivariate Phylogenetic Comparative Methods:
581 Evaluations, Comparisons, and RecoOpen asset ↗labevofern/Microgramma-Outline · Microgramma-Outlinepdf-layout-page:25 lines:1-48Dataset · publicbiting the highest
157 perpendicular distance from the line connecting the first and last bands in the R² × ranking
158 plot (Fig. S2). Following the methods described in Mendonça et al. (2026), spectral data were
159 acquired using a PerkinElmer Frontier™ near-infrared Fourier transform spectrometer (FT-
160 NIR) available at (https://github.com/labevofern/Microgramma-FTNIR).
161 Phylogenetic comparative analyses
162 To provide a phylogenetic framework for comparative morphometric and spectral analyses,
163 we used the pruned version of the Microgramma chloroplast phylogenetic inference from
164 Mendonça et al. (2026). This tree was based on the Bayesian phylogenetic tree published by
165 AOpen asset ↗labevofern/Microgramma-FTNIR · Microgramma-FTNIRpdf-layout-page:8 lines:1-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Peanut (Arachis hypogaea L.), a major legume crop valued for its high oil content, displays complex genotypic-phenotypic interactions shaped by environmental influences, yet these relationships remain poorly understood. We present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization. Using over 6500 pods collected across China, we identify a geographically distinct morphological signature and demonstrate accurate cultivar discrimination. This scalable approach establishes the foundation for a Large Geometric Model capable of predicting phenotypic traits and accelerating precision agriculture. Our pipeline offers a transformative tool for peanut breeding and sustainable crop improvement.
Why it matches plant phenotyping methodsデジタル顕微鏡・スマートフォン画像と多様体学習を統合し、ピーナッツ莢の形態を大規模に取得・解析する高スループット表現型解析フレームワークが中心である。
abstractWe present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization.
Reproduction assets foundThe authors state that the peanut pod image dataset, extracted phenotypic trait data, and the Orange Data Mining workflow (.ows) used for analysis are publicly available in their GitHub repository.Dataset · publicThe image dataset of peanut pods analyzed in this study and the extracted phenotypic trait data are publicly available in the GitHub repository: https://github.com/pengwengkung/Complex-geometry-peanut .Open asset ↗pengwengkung/Complex-geometry-peanutlines:169-192Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Lettuce ( Lactuca sativa ) is an important field crop, but our understanding of its phenotypic variation and underlying genetics under natural field conditions remains limited, posing challenges for identifying effective crop breeding targets. Longitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions. In this study, we used hyperspectral imaging to assess the phenotypic variation of almost 200 different field-grown lettuce varieties, following the same plants from just after seedling- to flowering-stage. With automated image processing, we extracted a wide range of spectral phenotypes related to metabolite content, growth efficiency, and environmental stress responses, creating a multi-dimensional time-resolved data set. Principal component analysis (PCA) revealed the major axes of spectral variation over time, and highlighted differences in spectral patterns among lettuce genotypes. Integrating on-site weather data, we modelled G×E interactions of reflectance, revealing regions of the lettuce vegetation spectrum that are primarily shaped by genotype and/or environment. We estimated phenotypic plasticity in response to time, temperature and rainfall using best linear unbiased predictions (BLUPs), capturing genotype-specific developmental trajectories and responses to the environment. We used genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs) of PC-based, single and BLUP-based phenotypes, disentangling the genetic architecture of spectral lettuce phenotypes from major axes of variation down to single wavelength spectral plasticity. These findings provide new insights into the genome-wide genetic regulation and dynamics of spectral phenotypes in field grown lettuce.
Why it matches plant phenotyping methods圃場レタスを対象に、縦断ハイパースペクトル画像と自動画像処理でスペクトル形質を抽出するフェノタイピング手法・データセットが研究の中心である。
abstractLongitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicScripts used in this study can be found at https://github.com/SnoekLab/Hyperspec_Mehrem_etal_2025.Open asset ↗SnoekLab/Hyperspec_Mehrem_etal_2025pdf-page:9 lines:1-31Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.
Reproduction assets foundThe paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lmeDataset · publicThe dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry
tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86.
Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein
A-K. 2025. Configuration of a multisensor platform for advanced plant phe
References notyping and disease detection: case study on cercospora leaf spot in sugar
Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart AgricultOpen asset ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFIpdf-layout-page:14 lines:52-72Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
1 Summary Zinc (Zn) deficiency limits rice productivity and poses a risk to human health, particularly in populations reliant on rice-based diets. Although rice germplasm exhibits wide variation in Zn-deficiency tolerance, the underlying physiological mechanisms remain poorly resolved. Evidence across the literature for Zn-deficiency–induced secretion of 2′-deoxymugineic acid (DMA) is inconsistent. This study clarifies the role of DMA secretion as a Zn-deficiency stress response. We developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates. Five rice genotypes with contrasting Zn-deficiency tolerance were grown hydroponically and DMA secretion measured. Zn-deficiency increased DMA exudation across all genotypes, with sensitive genotypes also showing higher secretion compared with control, supporting DMA’s role as a general response to Zn stress rather than being restricted to efficient genotypes. Fold-change responses exceeded previous studies, likely due to more severe stress exposure. Our results confirm that DMA secretion is induced under Zn-deficiency in rice as part of the micronutrient stress response. However, the lack of increased Zn uptake indicates that additional tolerance mechanisms are involved. These findings reconcile inconsistencies in the literature and position DMA secretion as an important, but not exclusive, component of Zn-deficiency adaptation in rice.
Why it matches plant phenotyping methodsイネ根滲出液中のDMAを選択的に検出するLC–MS法を開発・検証し、亜鉛欠乏応答という植物生理状態を測定しているため、化学分析が単なる付随測定ではなく中心的な方法貢献である。
abstractWe developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this study (DMA exudation and Zn uptake measurements in rice).Dataset · publicthe experiments, developed the
525
methods and analysed the results. The experimental data were collected by C.R. assisted by
526
G.L.M., C.T. and D.J.W. Data analysis and writing of paper by all authors.
527
528
Data availability
529
The data sets generated and/or analysed during the current study are available on Zenodo,
530
https://zenodo.org/uploads/18184803
531
532
533
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted March 18, 2026.
;
https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency.
Why it matches plant phenotyping methods自動化ハイスループット画像解析により根系形態・成長形質を抽出する表現型取得が研究の主要手段であり、根系構造の実質的な応用解析に該当する。
abstractWe evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15060935/s1 , Figure S1: Repeatability of image-derived shoot (a) and root traits (b) of the tested 65 maize inbred lines over time.Open asset ↗lines:68-215Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Cassava diseases such as Cassava Mosaic Disease (CMD), Cassava Brown Streak Disease (CBSD), and Cassava Bacterial Blight (CBB) pose serious threats to global food security, particularly in resource-limited regions where expert diagnosis is scarce. Although large vision–language models enable automated plant disease recognition, existing fine-tuning approaches struggle under extreme data scarcity. This paper proposes Progressive Layer Activation CLIP (PLA-CLIP), a curriculum-inspired fine-tuning framework for efficient few-shot classification of cassava diseases. PLA-CLIP progressively unfreezes transformer layers during training, stabilizing the optimization process while preserving pretrained vision–language alignment. Using only 43 images per class, PLA-CLIP achieves 78.25% accuracy and a 78.00% F1-weighted score on CD1, outperforming zero-shot CLIP by +15.98% and standard fine-tuning by +3.94%. Cross-dataset evaluations on CD2 and CD3 demonstrate robust generalization across varying conditions. Attention map visualizations confirm that the model focuses on disease-relevant regions, supporting interpretability. With a 2.65 ms inference time and moderate model size, PLA-CLIP offers an effective balance between efficiency and performance for practical plant health monitoring. The implementation and experimental code are publicly available at https://github.com/ mshafay5/PLA-CLIP.
Why it matches plant phenotyping methodsカッサバ葉画像から病害状態を推定するCLIPベースの画像解析手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis paper proposes Progressive Layer Activation CLIP (PLA-CLIP), a curriculum-inspired fine-tuning framework for efficient few-shot classification of cassava diseases.
Reproduction assets foundThe paper explicitly states that its implementation and experimental code for the PLA-CLIP cassava disease phenotyping/classification framework are publicly available on GitHub. The cassava image datasets (CD1/CD2/CD3) are cited third-party prior datasets, not paper-specific assets.Code · publicexperimental code are publicly available at https://github.com/Open asset ↗pdf-page:2 lines:1-62Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Background: A major challenge to agricultural productivity in the tea industry is disease. that affects the quantity and quality of tea leaves produced. The extensive development of computational methods for treating diseases has been widely used due to fast and accurate detection. Methods: The proposed method uses sequential Convolutional Neural Network (CNN) computations with many hidden layers to classify diseased and healthy tea leaves into multiple groups. By enhancing feature identification, this structure increases the criteria for accurate disease detection. The data having 5 diseased and one healthy category is obtained from the Kaggle database. After preprocessing the data, it is split into 80:20 ratios for training and testing steps. CNN is constructed using the Keras Sequential API in Jupiter notebook using Anaconda environment. Result: The total accuracy of the ML neural network training for classification was 98.52%. After 50 epochs of training, the model performed well, achieving high accuracy on training and validation datasets. The examination of the confusion matrix showed that several tea leaf diseases may be identified with high accuracy and few misclassifications. In general, the model demonstrated remarkable precision in differentiating between unhealthy and undamaged tea leaves.
Why it matches plant phenotyping methods茶葉画像から病害状態をCNNで分類する手法が研究の中心であり、植物の病徴・健全性という状態を直接推定しているため、植物フェノタイピング手法として含める。
abstractThe proposed method uses sequential Convolutional Neural Network (CNN) computations with many hidden layers to classify diseased and healthy tea leaves into multiple groups.
Reproduction assets foundThe paper's tea leaf disease image dataset is publicly available on Kaggle, with an explicit dataset link in the references. No author code or trained model is publicly deposited; other data are available only upon request.Dataset · publical tealeaf disease recognition
using a convolutional neural network model. Symmetry.
11(3): 343. https://doi.org/10.3390/sym11030343.Cho, O.H., Na, I.S. and Koh, J.G. (2024). Exploring advanced machine
learning techniques for swift legume disease detection.
Legume Research. 47(7): 1221-1227. doi: 10.18805/LRF-789.
Dataset Link: https://www.kaggle.com/datasets/shashwatwork/identifying-disease-in-tea-leafs?select=tea+sickness+
dataset. (Accessed on 06/05/2024).
Datta, S. and Gupta, N. (2023). A novel approach for the detection
of tea leaf disease using deep neural network. Procedia
Computer Science. 218: 2273-2286. https://doi.org/10.1016/j.procs.2023.01.203.Deka, N. and Goswami, K. (2020). EcOpen asset ↗Kaggle · shashwatwork/identifying-disease-in-tea-leafspdf-raw-page:8 lines:1-75Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published13 Mar 2026DMPedia Lecture Notes in Computer Science & EngineeringCited by 0 · OpenAlex ↗
This paper presents a practical pipeline for image-based plant disease detection that operates without pre-trained weights or internet access. A compact Efficient Net-like convolutional network is trained from random initialisation on the Plant Village dataset (∼ 41,000 snapshots, 15 classes) at 224 × 224 resolution. Adam, Intensive Augmentation, Reduce LROn Plateau, Early Stopping, and the sampling of the optimal checkpoint to stabilise convergence and avoid overfitting are used to optimise the geometry. Despite early validation oscillations typical of de novo training, the model converges reliably and attains 98.4%–98.9% accuracy on held-out data, with a highly diagonal confusion matrix and uniformly strong per-class precision/recall. These results show that carefully tuned schedules and lightweight regularization can substitute for transfer learning when bandwidth or policy constraints prevent downloading external backbones, enabling classroom, extension, and field deployments. The paper details dataset preparation, architecture and training choices, learning curve behavior, and common failure modes, and concludes with limitations and a roadmap for higher-resolution, field-domain generalization and efficient on-device inference.
Why it matches plant phenotyping methods植物病害状態を画像から推定する深層学習パイプラインが研究の中心であり、モデル、データ準備、学習、検証性能が具体的に記述されているため。
abstractThis paper presents a practical pipeline for image-based plant disease detection
Reproduction assets foundThe paper's phenotyping input is the public PlantVillage Kaggle dataset (15-class pepper/potato/tomato subset), explicitly cited with the allowed Kaggle URL. No author code or model checkpoints are released.Dataset · public[3] PlantVillage, “PlantVillage Dataset,” Kaggle, 2018. [Online]. Available:
https://www.kaggle.com/datasets/emmarex/plantdiseaseOpen asset ↗Kaggle · emmarex/plantdiseasepdf-page:10 lines:1-61Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice production is integral to the agricultural sector of India; over 65% of the populations are dependent on rice as their major staple. The cultivation of rice sustains this important agricultural sector; yet, there are many challenges encountered by rice producers, one of which is several types of disease that negatively impact yield and quality. Due to the fact that rice leaf smut, brown spot and bacterial leaf blights are among the most important types of diseases that can significantly reduce the yield and quality of rice, it is important to be diligent when identifying these diseases using accurate and speedy methods on an annual basis for successful and sustainable production of rice crops. As technology advances there continue to be emerging technologies such as Deep Learning (DL) as applied in agriculture to identify diseases and therefore reshape the agricultural paradigm so as to address agricultural disease challenges more readily. This research proposes a previously undemonstrated approach for identifying Rice Leaf Disease using EfficientNetV2; a Diffusion Bounded Attention method for disease detection. The quality of the input imagery has been greatly increased using a Preceding Noise Reduction (PNR) using the Guided Filopic Diffusion (GFD) technique, retaining important characteristics of Rice Leaves (Leaf Texture) which are critical for disease classification within agricultural imaging. To evaluate the performance of our model we utilized the Dice Similarity Coefficient (DSC). This coefficient measures how much the predicted image areas representing disease overlap with the actual affected areas of the image. Therefore, DSC is a reliable way to evaluate model segmentation capability. The Rice Leaf Diseases Dataset we used to identify and classify Rice Leaf Diseases was very comprehensive. Our model achieved an accuracy rate of 98.92% and also attained the best recall, precision and F1 score.
Why it matches plant phenotyping methodsイネ葉の病害症状を画像から検出・分類・セグメンテーションする手法が研究の中心であり、植物の病害状態を直接推定している。
abstractThis research proposes a previously undemonstrated approach for identifying Rice Leaf Disease using EfficientNetV2; a Diffusion Bounded Attention method for disease detection.
Reproduction assets foundThe paper's sole data asset is the public Kaggle Rice Leaf Diseases Dataset used for all experiments; no author code or models are deposited.Dataset · publicl analysis and data collection. N.K has done the initial drafting and statistical analysis. P.R. did the investigation. All the authors of the article have read and approved the final article.
Funding
Open access funding provided by Vellore Institute of Technology.
Data availability
The rice leaf disease data are assessed using https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Upadhyay N Gupta N
Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified ResNeXt approach
Environ. Monit. Assess. 2024 196 7 610
10.1007/s10661-024-12790-0
38862723
Upadhyay, N. & Open asset ↗Kaggle · vbookshelf/rice-leaf-diseaseslines:553-627Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate classification of maize yield potential is essential for food security and effective agricultural planning, particularly in regions characterized by environmental variability and socio-economic constraints. This study explores the binary classification of maize kernel weight into low ( n = 160). A Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy. The model's performance was benchmarked against standard classifiers including Logistic Regression, Random Forest, XGBoost, Decision Tree, Multi-Layer Perceptron (MLP), and Support Vector Classifier (SVC). The proposed HCA-DBN achieved a peak classification accuracy of 94%, demonstrating its potential to outperform conventional baselines even under small sample conditions. Rigorous validation, including bootstrapping and stratified 10-fold cross-validation, confirmed the statistical stability of the results. While these findings serve as a proof-of-concept given the dataset constraints, this study contributes a methodological benchmark for field-based maize yield classification and provides a scalable framework for future validation on larger, multi-season datasets.
Why it matches plant phenotyping methodsトウモロコシの収量ポテンシャル（kernel weight）を分類する計算手法を提案し、複数モデルとのベンチマークおよび交差検証で技術的に評価しているため、植物形質推定法が中心である。
abstractA Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy.
Reproduction assets foundThe paper's maize field phenotyping dataset (plant/ear traits, canopy temperature, chlorophyll from 160 tagged plants at VIT Sevur farm) is explicitly stated as publicly available via a Data in Brief DOI deposit, and the same dataset is cited in the references as a Mendeley Data deposit authored by the paper's authors.Dataset · publicPublicly available datasets were analysed in this study. This data can be found here: https://doi.org/10.1016/j.dib.2024.110367.Open asset ↗html-lines:851-875Dataset · publicRadhakrishnan S., Sandhya P., Venkatramana B., Pradeep Kumar T.
Analyzing various maize varieties grown organically: VIT Vellore’s phenotypic, yield, and canopy data. (2024) 1. Available online at: https://data.mendeley.com/datasets/6py9v57sf2/1Open asset ↗6py9v57sf2/1html-lines:900-924Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, Tennessee, USA. Two hundred transgenic switchgrass plants ({\it Panicum virgatum L.} `Performer') were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a maize ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian Stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high and low volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
Why it matches plant phenotyping methods固定・ドローン型サンプラーと蛍光測定、分散モデルを用いて植物由来花粉の放出量を推定し、サンプリング技術を評価することが中心であるため。
abstractPollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices
Reproduction assets foundThe authors state that all sampling data, modeling code, and simulation results from this switchgrass pollen dispersal study are publicly available in a Virginia Tech figshare repository. The GitHub 3D-printing files are cited prior work (Powers et al. 2018), not a paper-specific asset.Dataset · public737 Statements and Declarations
738 Data and code availability
739 All sampling data, modeling code, and simulation results are made available in the
740 Virginia Tech Data repository:
741 https://figshare.com/s/54a308163b60865d55bf.
742 Competing interests
743 The authors have no competing interests to declare.
744 Funding
745 This work is supported in part by the Biotechnology Risk Assessment Program, project
746 award no. 2019-33522-29989, from the U.S. Department of Agriculture’s National
747 Institute of Food and Agriculture.
748 References
749 AdamovOpen asset ↗figsharepdf-layout-page:28 lines:1-46Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Photosynthetic organisms have evolved multiple non-photochemical quenching (NPQ) processes, providing photoprotection by safely dissipating excess excitation energy. These processes involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized NPQ mutants of Nicotiana benthamiana , a vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching effectiveness of various xanthophylls and the contributions of six quenching components (qE V , qE A, qE Z, qE L, qZ, and qI) across different genotypes. It also suggests improved overall quenching efficiency at specific VDE:ZEP:PsbS overexpression stoichiometries, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.
Why it matches plant phenotyping methods葉の蛍光寿命測定を基盤に、NPQ成分を分離・定量するモデルを構築しており、植物の光防護状態を取得・抽出する方法が研究の中心です。
abstractBased on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes.
Reproduction assets foundThe paper's fluorescence lifetime/pigment phenotyping data and the NPQ model code are both publicly deposited on Zenodo (DOI 10.5281/zenodo.16755870), per explicit Data availability and Code availability statements.Dataset · publicThe data supporting the findings of this study are available within the article and at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code · publicThe codes for NPQ models used in this study are available at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To achieve agricultural automation, deep learning applications for early and accurate disease detection in tomato plants have been extensively developed. However, there is a fundamental trade-off between computational efficiency and diagnostic accuracy in resource-constrained agricultural edge environments. This paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential. Through evaluations of explainability, computational efficiency, and diagnostic performance, seven lightweight architectures (ShuffleNetV2, MobileNetV3-Small, SqueezeNet, MobilePlantViT, DenseNet121, ResNet50, and VGG16) are thoroughly examined. Three significant findings are derived from experiments conducted on a subset of tomato diseases in the PlantVillage dataset. First, the MobilePlantViT architecture accurately strikes the ideal balance between efficiency and performance. Second, in order to quantitatively assess the explainability of XAI models (Grad-CAM, SHAP, and LIME) and identify the best option for edge devices, we propose the perturbation stability score (PSS) metric. Third, we test CPU inference measurements to better reflect the actual scenario and find that the hybrid design effectively leverages parallel computing. According to these findings, MobilePlantViT is the ideal architecture for applications that require operation on edge devices with limited resources and achieve high diagnosis accuracy (above 99.5%).
Why it matches plant phenotyping methodsトマト病害という植物状態を画像から分類する深層学習手法について、複数モデルの精度・計算効率・説明可能性を体系的に比較評価しており、病害フェノタイピング手法の技術評価が中心である。
abstractThis paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential.
Reproduction assets foundThe authors publicly deposited their paper-specific tomato phenotype image subsets (derived from PlantVillage and expert-curated PlantDoc) on Kaggle via explicit Data Availability links. No author analysis code or trained model checkpoints are shared; ONNX Runtime is a generic library, not a paper-specific asset.Dataset · publicThe datasets are available at the following links: https://www.kaggle.com/datasets/cthngon/tomato-plantvillage-datasets, https://www.kaggle.com/datasets/cthngon/tomato-only.Open asset ↗Kaggle · cthngon/tomato-plantvillage-datasetshtml-lines:710-734Dataset · publicWe enhanced the quality of the PlantDoc dataset by collaborating with experts to identify and crop regions containing disease-specific symptoms, while eliminating irrelevant image content. For long-term preservation and ease of access, we have stored copies of the datasets in the published repository. The datasets are available at the following links: https://www.kaggle.com/datasets/cthngon/tomato-plantvillage-datasets, https://www.kaggle.com/datasets/cthngon/tomato-only.Open asset ↗Kaggle · cthngon/tomato-onlyhtml-lines:710-734Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Although the chasmophytic vegetation of Sicily has been examined previously, it remains insufficiently explored due to the formidable challenges associated with accessing vertical cliff habitats. This study employed drone-based surveys combined with Braun-Blanquet methodology to investigate cliff vegetation in the Peloritani and Madonie Mountains. High-resolution aerial imagery enabled species identification and cover estimation on inaccessible rock faces. Twenty-three new relevés were combined with 33 literature records for multivariate analysis. Cluster analysis and DCA revealed floristic differentiation between Peloritani and Madonie phytocoenoses, contrasting with communities from Apennines that we used as an outgroup. We describe Athamanto siculae-Saxifragetum australis for the calcareous cliffs of Rocca Salvatesta (Peloritani), characterized by Athamanta sicula , Hypochaeris laevigata , and Saxifraga callosa subsp. australis . Additionally, we propose to change the name Asperuletum gussonei to Cynanchicetum gussonei for the high-elevation vegetation of the Madonie dominated by Cynanchica gussonei . Drone methodology proved effective for documenting cliff vegetation, offering a safe and replicable approach for advancing phytosociological knowledge in extreme habitats. This research contributes to the syntaxonomic revision of Mediterranean chasmophytic vegetation within the alliance Saxifragion australis .
Why it matches plant phenotyping methodsドローン画像を用いてアクセス困難な崖面の植物種同定と被覆率推定を行う手法が、植生調査・分類の中心的手段として明示されているため。
abstractHigh-resolution aerial imagery enabled species identification and cover estimation on inaccessible rock faces.
Reproduction assets foundThe paper's drone-based phytosociological relevé dataset (the plant cover/trait measurements underlying the classification and DCA analysis) is published as Supplementary table S1 under an open license. Supplementary figure S1 is only an ordination diagram, and no author analysis code or raw drone imagery is stated to Dataset · publiclable under the Open
Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license
agreement intended to allow users to freely share, modify, and
use this Dataset while maintaining this same freedom for oth-
ers, provided that the original source and author(s) are credited.
Link: https://doi.org/10.3897/ved.182223.suppl1
Supplementary material 2
Supplementary figure S1
Authors: Gianmarco Tavilla, Pietro Minissale, Salvatore Cambria
Data type: docx
Explanation note: The supplementary file includes the DCA or-
dination diagram of species scores.
Copyright notice: This dataset is made available under the Open
Database License (http://opendatacommOpen asset ↗10.3897/ved.182223.suppl1pdf-raw-page:13 lines:1-46Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published5 Mar 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 2 · OpenAlex ↗
Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognized as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omics-omics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilizes this "proxy" to enhance the accuracy of the phenotype prediction. We compared a linear mixed model (Best Linear Unbiased Prediction, BLUP) and a nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.
Why it matches plant phenotyping methods植物の表現型予測を目的とする非線形マルチオミクス計算フレームワークが研究の中心であり、単なるオミクス測定や生物学的実験ではない。
abstractwe have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omics-omics nonlinearities.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub, and the metabolome data are publicly available via the RIKEN DropMet website (IDs DM0071, DM0072). Phenotype and other multi-omics data are only available from the corresponding author upon request.Code · publicAll source codes are available from the repository in GitHub: https://github.com/Yoska393/Twostep .Open asset ↗Yoska393/Twosteplines:306-317Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop's growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.
Why it matches plant phenotyping methods時系列点群から作物器官をセグメンテーションし、葉追跡、形態形質、成長量、光合成関連指標を抽出する手法が研究の中心であるため。
abstractA skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' public URLs for a subset of the analysis code (GitHub) and the complete time-series 3D crop point cloud dataset (Baidu pan), both directly supporting this paper's phenotyping measurements and analysis.Code · publicA subset of the code and dataset used in this study is publicly available on our GitHub repository: https://github.com/JiarenZhou/LTPCDCCM .Open asset ↗JiarenZhou/LTPCDCCMlines:578-686Dataset · publicThe complete time-series 3D crop point cloud dataset can be downloaded from https://pan.baidu.com/s/1mNSDz4F0ZjOwmqzMuXozSQ?pwd=1234 .Open asset ↗lines:578-686Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In agricultural automation, precise cotton segmentation is a key step for tasks such as intelligent harvesting and yield estimation. However, in complex field environments, factors such as background interference and irregular target shapes severely affect segmentation accuracy. Existing deep learning methods offer certain advantages but still generally suffer from limitations including insufficient accuracy, over-segmentation, and misidentification. To address these challenges, this study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet), which optimizes the ParaTransCNN architecture by incorporating the 2D Selective Scan (SS2D) module to replace the original Transformer branch, effectively balancing the extraction of local details and global semantic information while reducing computational burden. To enhance the model's perception of irregularly shaped cotton, a Deformable Convolutional Networks v1 (DCNv1) module is integrated into the Vision Mamba (VMamba) branch, further improving the delineation of target boundaries. Additionally, an Atrous Spatial Pyramid Pooling (ASPP) module is introduced at the end of the Convolutional Neural Network (CNN) branch to strengthen multi-scale feature representation. To optimize the fusion of channel and spatial information, the Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanism replaces the original module, enhancing feature modeling capability. Experimental results on an in-field cotton image dataset demonstrate that CMNet outperforms existing mainstream methods, achieving Dice, mIoU, and Accuracy of 91.06%, 84.18%, and 98.10%, respectively, while reducing parameter count and computational complexity, thus exhibiting excellent performance. Furthermore, generalization experiments on multiple other plant datasets also achieved outstanding results, validating the model's adaptability and potential for broader applications in multi-crop segmentation tasks, providing valuable insights for smart agriculture segmentation research. The source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.
Why it matches plant phenotyping methods綿花画像から植物領域を抽出する新規セグメンテーション手法を中心に開発・検証しており、植物表現型の画像取得・抽出ワークフローに該当する。
abstractthis study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet)
Reproduction assets foundThe authors explicitly state that the source code and dataset for CMNet are publicly available on GitHub. The paper also uses several public Roboflow plant image datasets in its generalization experiments, cited with public URLs in the references.Code · publicThe source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.Open asset ↗halidanmu/CMNethtml-lines:106-109Dataset · publicELTE (2023). Assignment 2 dataset. Available online at: https://universe.roboflow.com/elte-msgqy/assignment_2-mjhau (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLaola (2024). Defect banana dataset. Available online at: https://universe.roboflow.com/laola/defect-banana-qf4f6 (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLuffy24312 (2023). Cnn dataset. Available online at: https://universe.roboflow.com/luffy24312/cnn-myqtl.Open asset ↗html-lines:835-919Dataset · publicVyuha T. (2025). Rose dataset. Available online at: https://universe.roboflow.com/tech-vyuha/rose-kfpuf (Accessed November 4, 2025).Open asset ↗html-lines:835-919Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement are, however, laborious, destructive, and unsuitable for high-throughput online scenarios. To address this gap, this dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system. A total of 200 sweetpotato storage roots of the cultivar "Beauregard" were collected for constructing a 3-D multi-view imagery dataset. Each sample was imaged online using a short-range LiDAR camera (Intel RealSense™ L515) while traveling on a custom-built roller conveyor system that enables simultaneous translation and rotation for full-surface coverage. The curated dataset comprises raw color images (1280 × 720 pixels, .png format) and corresponding raw and segmented point clouds (1280 × 720 pixels, .laz format) for individual samples, alongside the reference volume measurements obtained using the standard water displacement method. In addition, to illustrate the modeling pipeline for volume prediction, the dataset provides the extracted geometric features derived from the segmented two-dimensional (2-D) masks and point clouds, and volume prediction results obtained through regression modeling. As the first publicly available LiDAR-based dataset for sweetpotato volume estimation, this dataset provides a valuable resource for developing and validating image processing pipelines, optimizing machine learning models, and advancing 3-D vision technologies for non-destructive, rapid measurement of the volume of irregularly shaped agricultural products.
Why it matches plant phenotyping methodsサツマイモ貯蔵根の体積という植物器官形質をLiDAR 3D画像から推定する公開データセットであり、取得系・参照測定・特徴抽出・予測結果を含むため、フェノタイピング手法とデータセットが中心です。
abstractthis dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system.
Reproduction assets foundThe paper's own LiDAR sweetpotato dataset (images, point clouds, ground-truth volumes, feature data, and Python modeling scripts) is publicly deposited on Zenodo with an explicit DOI. The librealsense GitHub link is a generic camera SDK, not a paper-specific asset.Dataset · publicDirect URL to data: https://doi.org/10.5281/zenodo.18378019Open asset ↗Zenodo · 10.5281/zenodo.18378019html-lines:90-113Code · publicThe complete Python modeling script and the associated feature datasets have been included in the public dataset repository [13] to facilitate reproducibility and provide a benchmark for future algorithm development.Open asset ↗html-lines:168-182Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Developing climate-resilient wheat varieties requires combining high yield with stability across diverse environments, especially under increasingly variable precipitation and rising temperatures. This study evaluated 64 post-Green Revolution durum wheat cultivars under irrigated and rainfed conditions at two contrasting Mediterranean sites in Spain. A classification framework was developed to support genotype selection based on yield and yield stability, estimated using linear mixed models and yield slopes across environments. Genotypes were classified by interquartile thresholds, and those showing either low yield or low stability were considered undesirable for selection. High-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data. VIs and TIR at anthesis and grain filling, and their differences (senescence proxies), were used to train Random Forests for yield and stability estimation including sequential feature selection. Environmental covariates (water input, reference evapotranspiration) were integrated in yield models, with strong outcomes (R 2 > 0.74; MAPE <23.6%). Stability predictions were based on VI stability and, though moderate (R 2 up to 0.56; MAPE <17.75%), outperformed previous studies. Selected features were used to evaluate seasonal reflectance phenotypes: “keep” genotypes (intermediate/high yield or/and stability) exhibited early-vigor but lower green retention by the end of grain filling, while “discard” genotypes (low yield or/and stability) showed reduced early vigor and “stay-green” behavior. This study highlights early-vigor and earlier senescence over “stay-green” for wheat selection, offering a cost-effective approach shifting the breeding focus from yield maximization to joint yield-stability evaluation, promoting sustainability.
Why it matches plant phenotyping methods高スループットの地上・UAVセンサーによる表現型取得と、機械学習による収量・安定性推定が研究の中心であり、育種選抜に用いる手法を実質的に評価・適用している。
abstractHigh-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data.
Reproduction assets foundThe authors explicitly state that the datasets and analysis scripts for all analyses (yield/stability modeling, VI extraction, Random Forest workflows) are publicly available in their Zenodo repository (DOI 10.5281/zenodo.17435708), referenced both in the statistical analysis section and the Data Availability statementCode · publicThe datasets and scripts for all the analyses conducted are available in our repository ( https://doi.org/10.5281/zenodo.17435708 ).Open asset ↗zenodo · 10.5281/zenodo.17435708lines:222-237Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Pea (Pisum sativum) production is challenged by drought stress. Traditional methods for assessing drought tolerance are limited, and high-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits. Herein, 180 Pisum spp. accessions were evaluated using an indoor HTP platform under two irrigation treatments, control (70% field capacity) and drought stress (30% field capacity), for 50 days. A combination of digital phenotyping via imaging and manual measurements was used to analyse biomass-related, architectural, and physiological traits. Drought conditions resulted in significant reductions in biomass-related traits including fresh weight (47%), total leaf area (43%), and dry weight (41%). In contrast, PSII photochemical efficiency, leaf weight ratio, and solidity showed negative sensitivity index values (ranging from -7% to -1%), indicating comparatively lower sensitivity to drought and suggesting relative stability of these traits under water-limited conditions. The high heritability value for water use efficiency (0.87) suggests that this parameter may be useful for distinguishing pea's responses to suboptimal soil moisture levels. Principal component analysis (PCA) highlighted patterns of trait variation and associations among biomass-related traits, such as fresh weight, dry weight, and leaf area, which were sensitive to drought conditions. This suggests that the plants may use a combination of strategies to cope with water limitations. Furthermore, studying the significant variation in drought response among the diverse Pisum species and subspecies revealed distinct adaptation strategies. These findings support the development of crops that are resilient to the negative effects of climate change.
Why it matches plant phenotyping methods屋内HTPプラットフォームと画像ベースのデジタルフェノタイピングを用いて、多数アクセッションの形態・生理形質を取得・解析しており、フェノタイピング手法の実質的な適用が研究の中心です。
abstracthigh-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe analysis software for the RGB side‐view imaging has been developed in Python by the NPEC data team, the source is published on Github, accessible via this link: https://github.com/NPEC‐NL/greenhouse_m5 .Open asset ↗NPEC‐NL/greenhouse_m5lines:68-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We used domestication as an in vivo replicated experiment to investigate how divergent selection has shaped the evolution of multivariate phenotypic spaces. We measured 11-57 qualitative and quantitative traits in 13 species, either unique or shared between species, and established a framework for cross-species comparisons. Our results revealed significant convergence that translated into a cross-species domestication syndrome. Most species exhibited a reduction of the multivariate phenotypic space during domestication. We brought evidence that Near-Infrared spectra measured on leaves reflect phenotypic evolution unrelated to domestication, enabling its use as a control for sampling effects across species. Building on this, we developed a multivariate phenotypic divergence index (mPDI) to rank species by the extent of phenotypic divergence under domestication. We found a high disjunction of wild and domestic phenotypic spaces in all species. Neither the mPDI nor the relative size of wild vs domestic multivariate phenotypic spaces was influenced by the domestication timing or mating system. Lastly, we observed a progressive decoupling of trait correlations with increasing time since domestication. In addition to introducing a new index that can be applied for cross-species comparisons, our study uncovers recurring patterns shared among species, pointing to general principles underlying plant domestication.
Why it matches plant phenotyping methods多変量形質空間を比較する枠組みと新しいmPDI指標を開発しており、植物形質の統合・比較手法が明示的な貢献であるため。
abstractestablished a framework for cross-species comparisons
Reproduction assets foundThe paper's phenotypic data, NIR spectra, and trait ontology are deposited at doi 10.57745/QWEKVK, and the authors' R analysis scripts are publicly available on INRAE Forge. Both are paper-specific, public, and actionable.Dataset · publicPhenotypic data and NIR spectra are available on https://doi.org/10.57745/QWEKVK .Open asset ↗10.57745/QWEKVK · 10.57745/QWEKVKlines:283-349Code · publicR scripts are available on the INRAE Forge at https://forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaces .Open asset ↗forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaceslines:283-349Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Diseases of tomato leaves are significant threats to the global food security and agricultural production. The old method of diagnosis is not reliable and is time consuming, and there is a demand to have effective and accurate automated systems. The paper uses transfer learning using Inception-V3 and Inception-ResNet-V2 network to detect tomato leaf diseases using an open dataset. To encourage generalizability, data augmentation and preprocessing techniques were used, whereas Grad-CAM was used to encourage visual interpretability. Experimentally, it has been demonstrated that Inception-ResNet-V2 and Inception-V3 performed with 92.33 and 89.33 accuracy, respectively, which is higher than the other existing methods. These results demonstrate the possibility of deep learning to improve precision agriculture and prepare further development of real-time and field-deployable systems of disease detection.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から深層学習で自動推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThe paper uses transfer learning using Inception-V3 and Inception-ResNet-V2 network to detect tomato leaf diseases using an open dataset.
Reproduction assets foundThe paper's Data Availability Statement explicitly identifies the public Kaggle tomato leaf image dataset used to train and evaluate the deep learning models, making it a paper-specific, publicly actionable asset. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe dataset used in this study is publicly available on Kaggle. It can be accessed at:
https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf
The dataset contains labeled images of healthy and diseased tomato leaves and was used for training
and evaluating the proposed deep learning model.Open asset ↗Kaggle · kaustubhb999/tomatoleafpdf-page:30 lines:1-9Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Maize is a vital global crop, but its productivity is often threatened by plant diseases, highlighting the need for precise and timely diagnostic methods. Traditional manual inspection is inefficient and prone to errors, motivating the development of automated solutions. Recent advances in computer vision and deep learning have enabled effective automated plant disease diagnosis. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown promise in plant disease classification, CNNs struggle to capture global contextual information, and ViTs require large datasets and high computational resources. Inspired by mixture-of-experts (MoE) architectures, we propose a lightweight hybrid model that integrates CNN and ViT components, adaptively emphasizing local or global features based on input characteristics. Evaluated on a novel, real-world dataset of full maize plant images, our approach achieves 99.90% classification accuracy, significantly outperforming state-of-the-art baselines such as MobileViT, PiT, EdgeNeXt, and DeiT. These results demonstrate that lightweight hybrid architectures can deliver high-performance disease diagnosis suitable for practical agricultural deployment. The code is available at: https://www.github.com/sabermehdipour/MXiT .
Why it matches plant phenotyping methodsトウモロコシ全身画像から病害状態を推定する軽量CNN-ViT手法を開発・評価しており、植物表現型取得・判定が中心的です。
abstractwe propose a lightweight hybrid model that integrates CNN and ViT components
Reproduction assets foundThe paper's authors' MXiT analysis code is publicly available via a GitHub URL stated in the abstract, and the PlantVillage image dataset used for evaluation is publicly available. The Plant Scanner maize dataset is paper-specific but only available upon request, so it is listed as request_only.Code · publicThe code is available at: https://www.github.com/sabermehdipour/MXiT.Open asset ↗sabermehdipour/MXiThtml-lines:1-77Dataset · publicThe PlantVillage dataset is publicly available (https://github.com/spMohanty/PlantVillage-Dataset).Open asset ↗spMohanty/PlantVillage-Datasethtml-lines:707-785Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Post-disturbance recovery is a central element of forest resilience against intensifying disturbance regimes. Although recovery signals are strong across Central European forests, the relative roles of different factors contributing to recovery remain incompletely understood. As climate change increasingly challenges recovery, elucidating these processes is essential to adapt forest management to changing climate and disturbance regimes. We extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany. We combined 23,036 ha of quality-filtered photogrammetric canopy height model data with a Landsat-based disturbance map, a forest ownership map and environmental covariates in a Bayesian modelling framework. Post-disturbance growth rates were governed primarily by forest type and site conditions, whereas management strongly influenced disturbance legacies, i.e. the remaining post-disturbance vegetation height structure on site. Legacies varied widely across management types: Federal and set-aside forests retained the highest level of disturbance legacies, while private forests had the lowest legacy levels. Despite marginally lower growth rates, set-aside areas had recovery trajectories that were comparable to managed forests. The median recovery time to 5 m mean canopy height was 14.3 years over all forest and management types. Set-aside areas exhibited the greatest variation in recovery trajectories. We here show that (i) management affects disturbance legacies more strongly than post-disturbance tree growth, (ii) set-aside areas do not differ in recovery speed from managed areas, and (iii) legacies are diversifying forest recovery trajectories, with potential implications for future forest resilience. Our results underline that the post-disturbance reorganization window is a crucial period for management to influence long-term forest development. The framework presented here provides a scalable approach to monitor structural recovery and guide adaptive forest policy and management under increasing disturbance. • Forest management in Central Europe affects post-disturbance recovery more via legacies than tree growth rates. • Set-aside forests recover their canopy height equally fast as managed forests in Central Europe. • Homogenizing and removing disturbance legacies can reduce forest canopy variation across forest stand development. • We combined a biological growth model with remote sensing data to assess forest canopy recovery.
Why it matches plant phenotyping methodsリモートセンシングによる林冠高構造の定量と生物学的成長モデルを組み合わせ、森林の構造回復をスケーラブルにモニタリングする枠組みが研究の中心である。
abstractWe extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the analysis data and code on Zenodo with a public DOI, which is a paper-specific, publicly actionable asset for reproducing the forest recovery analysis.Code · publicthank three anonymous reviewers for providing helpful
suggestions on an earlier version of the work.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.foreco.2026.123616.
Data availability
Data and code of the analysis are available at Zenodo: https://doi.org/10.5281/zenodo.17804070.References
Anderson-Teixeira, Kristina J., Miller, Adam D., Mohan, Jacqueline E., Hudiburg, Tara
W., Duval, Benjamin D., DeLucia, Evan H., 2013. Altered Dynamics of Forest
Recovery under a Changing Climate. Glob. Change Biol. 19 (7), 2001–2021. https://
doi.org/10.1111/gcb.12194.
Arano, Kathryn G., Munn, Ian A., 2006. Evaluating Open asset ↗Zenodo · 10.5281/zenodo.17804070pdf-raw-page:10 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Early identification of bean leaf diseases, particularly Angular Leaf Spot and Bean Rust, is vital for ensuring crop productivity and global food security, especially within smallholder farming systems where disease outbreaks can rapidly escalate and cause severe yield losses. Conventional disease identification through visual inspection is labor-intensive, subjective, and highly dependent on expert knowledge, making it impractical for large-scale agricultural monitoring. Although recent deep learning-based approaches have demonstrated impressive accuracy in plant disease classification, their inherent “black-box” nature significantly limits real-world adoption, as farmers and agronomists often lack the ability to understand, trust, or act upon unexplained predictions. To address these challenges, this study proposes an automated and explainable disease diagnostic framework based on a Vision Transformer (ViT-B/16) architecture optimized through transfer learning from ImageNet. Unlike traditional convolutional neural networks that primarily focus on localized features, the Vision Transformer processes images as a sequence of flattened patches and leverages self-attention mechanisms to capture long-range dependencies and global contextual patterns across the entire leaf surface. This global representation enables the model to detect subtle and spatially distributed disease symptoms that are often overlooked by CNN-based approaches. To further enhance transparency and interpretability, GradCAM + + is integrated into the framework as an explainable artificial intelligence (XAI) mechanism. This method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions, thereby establishing a human-interpretable validation loop for farmers, agronomists, and domain experts. The proposed framework was evaluated on the publicly available I-Bean dataset, achieving a validation accuracy of 97.52% along with strong precision, recall, and F1-score performance. The generated GradCAM + + visualizations consistently demonstrate the model’s sensitivity to true diseased regions, reinforcing both the reliability and trustworthiness of its predictions. By combining high-capacity global feature learning with visual explainability, the proposed approach offers a scalable, transparent, and practical solution for real-world precision agriculture. This framework not only enhances diagnostic accuracy but also bridges the critical gap between model performance and user trust, enabling informed decision-making and timely disease management in modern farming environments.
Why it matches plant phenotyping methods画像から豆葉の病害症状を分類・可視化する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格。
abstractThis method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions
Reproduction assets foundThe paper uses the publicly available I-Bean bean leaf disease image dataset (Healthy, Angular Leaf Spot, Bean Rust) and points to it via a Data availability DOI (10.21227/4k7y-vs03), which is an allowed URL. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicPSP and SNT: Problem Formulation and MethodologyAS and DS: Implementation and VisualizationMVV PK and KB: Original Draft and Supervision.
Funding
Open access funding provided by Symbiosis International (Deemed University). This research received no external funding.
Data availability
[https://dx.doi.org/10.21227/4k7y-vs03]
Declarations
Competing interests
The authors declare no competing interests.
The authors declare that they have no conflict of interest.
References
1.
Wang Y Wang Q Su Y Jing B Feng M
Detection of kidney bean leaf spot disease based on a hybrid deep learning model
Sci. Rep. 2025 15 1 11185
10.1038/s41598-025-93742-7
40169647
POpen asset ↗10.21227/4k7y-vs03lines:325-415Code / dataset availability confirmedbioRxiv · Europe PMC · checked 5 Sept 2026
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / tolerance
The Multiple Synthetic Derivatives (MSD) population is a unique hexaploid wheat resource that captures extensive genetic diversity from Aegilops tauschii and exhibits wide variation in agronomic traits. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype. A two-dimensional cultivation platform enabling continuous imaging of seedling root growth under controlled conditions was established to quantify RSA traits and their responses to high temperatures. MSD417 was compared with its recurrent parent, Norin 61 (N61). Under controlled conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61, indicating enhanced early root vigor. This genotype also exhibited a wider seminal root angle, suggesting improved horizontal soil exploration while maintaining root depth. High-temperature treatment reduced overall root growth and minimized genotypic differences, indicating that temperature stress constrains RSA expression. Microscopic observations further revealed a lower height-to-width ratio of coleorhiza tissue of MSD417, suggesting restricted downward expansion. Collectively, this study establishes a practical framework for RSA phenotyping and demonstrates the potential of Aegilops tauschii-derived germplasm to enhance wheat root-related adaptive traits.
Why it matches plant phenotyping methods根系構造を連続画像化して定量する2次元表現型解析プラットフォームを構築し、RSA形質の測定に実質的に適用しているため、方法が中心的である。
abstractHere, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype.
Reproduction assets foundThe paper deposits its paper-specific root images (N61 and MSD417) and coleorhiza microscopic images in Zenodo with explicit DOIs. The R analysis scripts are only in Supplementary Document S1 with no public URL, so they do not qualify as a public code asset.Dataset · publicThe microscopic images of coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131.Open asset ↗Zenodo · 10.5281/zenodo.18091131pdf-page:14 lines:1-71Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate and non-destructive assessment of fruit maturity is critical for sustainable agricultural practices. This study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification. A dataset of 443 strawberries spanning eight maturity stages was analyzed using six metaheuristic algorithms—Binary Grey Wolf Optimizer, Binary Particle Swarm Optimizer, Bee Colony Optimizer, Genetic Algorithm, Ant Colony Optimizer, and Gravitational Search Optimizer—integrated with four classifiers: Naïve Bayes, Decision Tree, Linear Discriminant Analysis, and Support Vector Machine. A new fitness function was designed to optimize classifier performance, and results were validated through Self-Organizing Map Neural Networks, cross-validation, and statistical significance testing. The Genetic Algorithm–Linear Discriminant Analysis combination achieved the highest and most stable accuracy (94.6–99%), outperforming existing image-based, deep learning, and conventional spectroscopic approaches while retaining interpretability. These findings demonstrate that metaheuristic-driven MIR analysis provides a robust, explainable, and efficient method for precise strawberry maturity assessment, offering significant potential for advancing eco-friendly and intelligent agricultural practices.
Why it matches plant phenotyping methodsイチゴ果実の成熟度という植物器官の状態を、MIR分光と特徴選択・分類器で非破壊推定する方法を開発し、交差検証や統計検定で性能評価しており、フェノタイピング手法が中心である。
abstractThis study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification.
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available at the authors' GitHub release URL. The spectral dataset itself is not public and is available only from the corresponding author on request.Code · publicCode availability
The code is available publicly on:
https://github.com/RabihAssaf89/RabihAssaf-codes/releases/tag/v1.0.Open asset ↗RabihAssaf89/RabihAssaf-codes · v1.0html-lines:822-851Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
1 Abstract Accurate quantification of plant disease is essential for resistance breeding, variety testing, and precision agriculture, yet visual ratings are limited by subjectivity, low precision, and restricted throughput. Image-based phenotyping can address these limitations, but field applications face substantial challenges due to spatial heterogeneity, symptom-level diagnostic requirements, and the need for very high-resolution imagery with limited spatial coverage. This introduces a fundamental trade-off: high-resolution images provide precise local measurements of disease, but spot-level estimates can be highly variable within experimental units. We analyzed a large image data set of wheat foliar diseases to characterize the distribution, spatial dependence, and aggregation behavior of spot-level severity estimates in plots. We combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area. Our results highlight focus bracketing as a promising approach for simultaneous diagnosis and quantification of disease in field plots. Autocorrelation in severity estimates both within focal image stacks and across plot positions was comparable, with 10 focal stack images or 10 positions per plot contributing approximately 2.5 independent observations each. Modeling plot-level severity as a latent Beta-distributed variable enabled robust estimation of mean severity and associated uncertainty. This supports both hypothesis testing and efficient sampling across the full range of disease severity associated with genotypic diversity and seasonality of developing epidemics. The proposed imaging approach is non-invasive and, in principle, transferrable to autonomous ground-based phenotyping platforms, offering the potential to shift the dominant source of uncertainty in estimating disease severity from measurement-related limitations toward biologically and environmentally driven variability in disease expression.
Why it matches plant phenotyping methods高解像度画像とフォーカスブラケティングを用いて植物病害の重症度を定量化し、圃場プロット単位の推定精度と不確実性を評価する手法が研究の中心であるため。
abstractWe combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area.
Reproduction assets foundThe paper states that R code to reproduce the full analysis (Beta-distribution modeling, autocorrelation/AR(1) mixed models, effective sample size estimation for wheat disease severity phenotyping) is publicly available on the authors' GitHub repository. The repository name appears truncated in the supplied text ('plotCode · publicR-code to reproduce the full analysis is available at https://github.com/and-jonas/plot-spot-Open asset ↗and-jonas/plot-spot-pdf-page:9 lines:1-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plants are known to generate various types of electrical signals, which have been observed ever since Darwin's times. We studied the electrical signals acquired in tomato plants infected with the fungal pathogen Oidium neolycopersici (On) , the causative agent of powdery mildew, and applied statistical analyses to detect the differences in electrical responses between healthy and infected plants, as reported in [1].•The underlying mechanism in the generation and transmission of electrical signals is not fully understood, yet it's generally accepted that they can be classified according to functional properties. Action potentials (APs) and slow wave potentials, in particular, are elicited by biotic and abiotic stimuli, thus are interesting as a hallmark of plant health status.•To analyse the application of these potentials in plant disease detection, voltages from electrodes inserted in plants were acquired periodically by a scanning multimeter and recorded under control of a dedicated custom Python program running on a Raspberry Pi board.•Here we describe the design of the experiment and analyse in some detail the solutions adopted for specific issues found in the measurements, such as electrode's material and placement; immunity to electromagnetic noise; data logging over long periods of time with intermediate monitoring of results.
Why it matches plant phenotyping methodsトマトの感染状態を電気シグナルから検出する測定・解析法が中心で、電極配置、ノイズ対策、長期データ記録などの技術設計と適用を扱っている。
titleMethod for the detection of powdery mildew in tomato from electrical signalling.
Reproduction assets foundThe paper deposits its electrical signalling measurements from tomato plants (infected and healthy controls) in a public Mendeley Data repository, explicitly listed in the specifications table's resource availability.Dataset · publicRepository name: Mendeley DataOpen asset ↗Mendeley Datahtml-lines:1-106Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Agriculture and global food security are critically dependent on accurate and timely identification of plant diseases and pests. Traditional approaches to disease identification rely heavily on visual inspection and expert knowledge, which frequently lack the accuracy, speed, and scalability needed to address growing agricultural challenges. Early and precise disease detection enables proactive interventions that can prevent widespread crop damage and reduce excessive pesticide use, thereby supporting sustainable agricultural practices. Artificial intelligence, particularly deep learning methods, has emerged as a transformative solution for automated plant disease diagnosis. Convolutional neural networks (CNNs) have demonstrated remarkable capabilities in image classification tasks, evolving from individual architectures to sophisticated ensembles and transferring learning models. However, existing CNN-based research on rice disease identification has typically focused on a limited number of disease classes, restricting their practical applicability in real-world agricultural settings. This study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification. We utilized a dataset comprising seven of the most common rice diseases, significantly expanding the scope beyond previous studies. The model employs transfer learning with pre-trained ImageNet weights and is optimized using the Adam optimizer with carefully tuned hyperparameters. The experimental evaluation on an independent test set demonstrates that our proposed model achieves an overall accuracy of 97.9%, with individual disease classification accuracy ranging from 94% to 99.67%. The model exhibits balanced performance across multiple metrics, including precision (96.2%), recall (97.97%), and F1-score (97%), confirming its robustness and generalizability. These results establish DenseNet121 as a highly effective framework for automated rice disease diagnosis, offering a practical tool for enhancing agricultural productivity and food security.
Why it matches plant phenotyping methodsイネ葉の画像から病害状態を分類する深層学習手法が研究の中心であり、独立テストセットによる性能評価も実施しているため、植物病害フェノタイピング手法として収載する。
abstractThis study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification.
Reproduction assets foundThe paper's rice disease classification experiments use the public Kaggle Paddy Disease Classification dataset (8030 images, 7 disease classes), explicitly cited and linked by the authors in the Data Availability Statement. No author code or trained model checkpoints are disclosed.Dataset · publicThe data presented in this study are available in Kaggle42.Open asset ↗Kagglehtml-lines:487-556Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Agricultural research increasingly relies on data-driven approaches for crop yield prediction that complement more established crop growth models, including machine learning techniques. However, these approaches rely on large training datasets. Here, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England on which a variety of crops are grown. In addition, the data also contains satellite-derived remote sensing data, weather data, and data on soil type, all aligned at a grid resolution of 10 km. Weather data is available at a daily temporal resolution, satellite data at 5-day resolution, while crop yield data is available at yearly resolution. This effort has been made possible through careful anonymisation of the yield data while preserving the alignment with remote sensing, weather, and soil data. This data will be useful both to train machine learning models of yield prediction as well as to parameterize mechanistic crop growth models. Furthermore, the anonymisation procedure itself will be of interest to the research community, as it represents a solution to a common problem on the interface of agricultural research and farming practice.
Why it matches plant phenotyping methods圃場単位の作物収量という植物形質を、衛星・気象・土壌情報と整合した再利用可能な大規模データセットとして構築しており、収量予測モデルの訓練・評価用データ基盤が中心です。
abstractHere, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England
Reproduction assets foundThe paper's authors provide public R code for merging/aligning climate, soil, and Sentinel-1 data and anonymising yield data in a GitHub repository. The CYCLeSS dataset itself is on figshare, but that URL is not in the allowed list, so only the code asset is reported.Code · publicnts of this repository. Researchers who are further interested in the underlying data should contact the authors affiliated with UKCEH.
Code availability
R code used to merge and align available UK climate, soil, and Sentinel-1 synthetic aperture radar data to the same 1 km 2 grid is provided in the following GitHub repository: https://github.com/alan-turing-institute/CYCLeSS-dataset-code . Dummy data and code needed to replicate the final process of merging climate, soil, and satellite data with UKCEH precision yield data and anonymisation of field locations is contained within the ‘CLYCESS_anonymisation.zip’ folder shared as part of this repository. R version 4.2.3 was used for the creatioOpen asset ↗https://github.com/alan-turing-institute/CYCLeSS-dataset-codelines:200-271Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Plant Leaf Diseases represent a significant risk to global agricultural production. Crops ranging from Peppers and Tomatoes to Potatoes are affected by these diseases. Traditional methods of identifying leaf diseases based primarily on visual inspection have historically been slow and relatively inaccurate. A deep learning-based solution for the automated identification of plant leaf diseases utilizes Convolutional Neural Networks (CNNs) as the primary methodology. To identify leaf images into 15 disease categories, both pre-trained models such as VGG16, EfficientNetB3, Inception V3 and a custom CNN model were used. Using pre-trained models to allow for the use of Transfer Learning helps to mitigate some of the issues associated with computational resource limitations and data limitations in providing faster convergence rates and higher accuracy when compared to training a model from scratch. The Plant Village image collection which contains over 20,000 images of different plant leaf diseases was utilized for training and testing purposes. Each model's performance was evaluated based on its accuracy, loss and generalization capabilities. Additionally, each model was fine-tuned through hyperparameter optimization. As a result, the model that achieved the highest validation accuracy rate of 95% was the EfficientNetB3 model while the second highest accuracy rate was achieved by the Inception V3 model at 92%. This methodology provides an excellent answer to addressing early disease detection, enabling farmers to take the necessary actions quickly to reduce their losses and maximize their harvest.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法を比較・評価しており、病害フェノタイピングと手法検証が研究の中心です。
abstractA deep learning-based solution for the automated identification of plant leaf diseases utilizes Convolutional Neural Networks (CNNs) as the primary methodology.
Reproduction assets foundThe paper's plant-phenotyping measurements (CNN classification of 15 leaf disease classes) were performed on the public PlantVillage-derived Kaggle 'Plant Disease Dataset' by E. Marrex, which the authors cite as their training/testing image source. No author analysis code, trained model checkpoints, or paper-specific衍生Dataset · publicD. E. Popescu, M. K. Chowdary, and J. Hemanth, "Deep learning-based leaf
disease detection in crops using images for agricultural applications," Agronomy, vol. 12, no. 10,
p. 2395, 2022. doi: 10.3390/agronomy12102395. Available:
https://doi.org/10.3390/agronomy12102395.11. E. Marrex, "Plant Disease Dataset," Kaggle, Available:
https://www.kaggle.com/datasets/emmarex/ plantdisease. [Accessed: 03- Apr-2025].Open asset ↗Kagglepdf-raw-page:12 lines:1-8Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tea leaf diseases seriously affect its yield and quality, and consequently there is an urgent need for intelligent detection methods with high precision and edge deployment capabilities. To address low detection accuracy in complex backgrounds, overfitting due to limited data, and redundant parameters for existing methods, this paper proposes an improved lightweight detection model FCHE-YOLO based on the YOLO11, which aims to achieve rapid and accurate identification of tea leaf disease combining low altitude remote sensing with unmanned aerial vehicle (UAV). The model has made three key optimizations in the structure: Introduce the self-developed lightweight backbone module FC_C3K2, which significantly reduces computation and parameter count while enhancing the robustness of the model to complex scenarios; construct an efficient feature fusion structure HSFPN, optimizing multi-scale information integration and compressing model volume; design the detection head Efficient Head, integrating group convolution and lightweight attention mechanism to improve detection accuracy and suppress overfitting. The experimental results from the self built tea gardens show that the FCHE-YOLO improves the average accuracy (mAP) from 94.1% to 98.1% compared to the benchmark model YOLO11, with an improvement of 4.0 percentage points. Meanwhile, the inference speed of the model increases from 43.3 FPS to 47.5 FPS, with an increase of 9.0%, meeting the real-time detection requirements. More importantly, by network structure optimization, the model's computational complexity is significantly reduced: The floating-point operations per second (FLOPs) decreases from 6.4 G to 4.2 G, with a decrease of 34.3%, and the parameter count decreases from 2.59 M to 1.46 M, with the compression rate reaching 38.9%, which makes the model more suitable for deployment on resource-constrained UAV edge devices. The final test show that the FCHE-YOLO significantly reduces the missed-detection rate, owns better detection accuracy and deployment practicality, and is suitable for real-time monitoring scenarios of tea leaf diseases with UAVs.
Why it matches plant phenotyping methods茶葉の病害状態をUAV画像から検出する軽量深層学習手法を開発・評価しており、植物病害表現型の取得が中心的な技術貢献である。
abstractthis paper proposes an improved lightweight detection model FCHE-YOLO based on the YOLO11, which aims to achieve rapid and accurate identification of tea leaf disease combining low altitude remote sensing with unmanned aerial vehicle (UAV).
Reproduction assets foundThe paper's Data Availability Statement points to a public figshare repository containing the study's relevant data (UAV tea leaf disease imagery/dataset). No separate author code deposit is stated.Dataset · publicAll relevant data for this study are publicly available from the figshare repository (https://figshare.com/s/316807b23895bc3ba3ae).Open asset ↗figsharehtml-lines:693-736Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高スループット画像フェノタイピングによる大規模データセットを提示し、画像取得基盤と作物成長動態の解析を中心に扱っているため、方法論文として適格です。
titleFIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper's canopy cover analysis code is publicly available on the authors' ETH GitLab repository. The FIP 1.0 soybean image/trait dataset itself is deposited in the ETH Research Collection and Hugging Face, but those URLs are not among the allowed URLs, so only the code asset qualifies.Code · publicCode availability
The code is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover. Users with similar data can use the implemented workflow to get canopy cover from their experiments.Open asset ↗gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycoverhtml-lines:207-226Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Plant diseases continue to pose a significant threat to worldwide food security, resulting in notable yield reductions and economic consequences. Automated disease diagnosis through machine learning has arisen as a potential solution; nevertheless, current methods frequently have difficulty in capturing both detailed local attributes and overarching contextual patterns found in plant leaf images. This study presents a thorough comparative examination of conventional and deep learning methods—such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), YOLO, Support Vector Machines (SVMs), and Random Forests—for the classification of multi-class plant diseases. To overcome the constraints of individual CNN and transformer models, a new hybrid framework that integrates EfficientNet-B7 for strong spatial feature extraction with a Vision Transformer (ViT-B16) for comprehensive contextual modeling is suggested. The system is assessed on an extensive dataset consisting of 21,534 images covering 38 classes of plant diseases and healthy specimens. Experimental findings show that the suggested hybrid model reaches an accuracy of 98.13%, surpassing standalone CNN baselines and other rival models, while consistently achieving high precision, recall, and F1-scores for all classes. The results emphasize the success of combining convolutional and transformer-based models for scalable and precise plant disease detection, aiding the creation of smart decision-support systems for precision farming.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類する新規ハイブリッド画像解析手法を提案し、複数手法との比較評価と大規模データセットでの検証を行っており、フェノタイピング手法が中心である。
abstractAutomated disease diagnosis through machine learning has arisen as a potential solution
Reproduction assets foundThe paper's plant disease image dataset (New Plant Diseases Dataset on Kaggle) and the authors' complete hybrid CNN–ViT implementation (GitHub repository with Zenodo DOI) are both publicly and explicitly available.Dataset · publicThe data that support the findings of this study are openly available in the New Plant Diseases Dataset at Kaggle [https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data].Open asset ↗Kaggle · new-plant-diseases-datasethtml-lines:296-327Code · publicThe source code, including model architecture, training scripts, evaluation routines, and Google Colab notebooks for inference, is hosted on GitHub at: https://github.com/mohdzunaidahmed15-ui/hybrid-cnn-vit-plant-disease-diagnosis.Open asset ↗GitHub · mohdzunaidahmed15-ui/hybrid-cnn-vit-plant-disease-diagnosishtml-lines:296-327Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Leaves are central indicators of photosynthesis and plant growth status, and their precise monitoring is crucial for smart agriculture. Dense leaf detection, as a foundation for leaf morphology analysis, must address challenges such as occlusion and overlap, directly enabling key tasks including phenotypic trait extraction, disease identification, and yield estimation. Leaves are the most important plant organs, and monitoring leaves is a crucial aspect of crop surveillance. Dense leaf detection plays an important role as a fundamental technology for leaf monitoring. Existing dense leaf detection methods rely on traditional modular detectors and generic feature extraction, lacking designs tailored to real-world dense leaf scenarios. The methods for dense leaf detection generally use traditional modular detectors and general feature extraction techniques, without designing methods specifically for dense leaves in reality. In detail, in complex field scenarios, it still faces challenges like incomplete individual feature extraction due to high leaf overlap and difficult network convergence caused by excessive leaf density. To this end, we propose the Leaf-DETR framework, which effectively addresses these challenges through the Progressive Feature Fusion Pyramid Network (P-FPN) and the Crowded Query Refinement Strategy (CQR). First, we construct the largest dense leaf detection dataset to date, containing 1696 images and 85,375 annotation boxes. Second, P-FPN alleviates the feature confusion problem of overlapping leaves through the multi-stage fusion of features and the Adaptive Feature Aggregation module (AFA), enhancing the interaction between low-level details and high-level semantics. Third, the CQR strategy significantly reduces the matching cost of crowded candidate boxes and improves the network convergence efficiency by culling a crowded query method and introducing a one-to-many matching mechanism. Finally, experimental results show that Leaf-DETR improves mAP@50 by 1% and AR@300 by 1.4% over the baseline model on our self-constructed dataset, outperforming existing detection methods. Furthermore, the model exhibits extremely fast training convergence and demonstrates strong generalization capability on both field-collected monitoring images and other staple crops, fully highlighting its practical value in complex agricultural scenarios. Finally, experiments show that Leaf-DETR outperforms existing detection methods on the self-built dataset and demonstrates good performance generalization in monitoring collected images, as well as for other staple food crops, which verifies its practicality in complex agricultural scenarios. The code and detailed information are available at http://leafdetr.samlab.cn.
Why it matches plant phenotyping methods葉の密集検出モデルとデータセットを開発・評価し、葉形態などの表現型抽出を可能にする画像ベース手法が研究の中心であるため。
abstractDense leaf detection, as a foundation for leaf morphology analysis, must address challenges such as occlusion and overlap, directly enabling key tasks including phenotypic trait extraction, disease identification, and yield estimation.
Reproduction assets foundThe paper's data availability statement explicitly points to an authors' public site (http://leafdetr.samlab.cn) hosting the Leaf-DETR code and detailed information, qualifying as a paper-specific public code asset. The self-constructed KiwiFruitLeaf dataset (1696 images, 85,375 annotation boxes) is described but its公开Code · publicThe code and detailed information are available at http://leafdetr.samlab.cn . For testing purposes, detailed instructions for running the model can be found in the repository's README file.Open asset ↗lines:504-529Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Sorghum ( Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Why it matches plant phenotyping methods穀粒の生化学的形質を少数の測定値から推定する機械学習モデルの開発・検証が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractreducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately
Reproduction assets foundThe article's data availability statement points to a Figshare deposit containing the study's datasets (agronomic, physiological, UAV-based, and grain composition data used for ML modeling). No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicith weather data acquisition.
Edited by: Filipe Matias , University of Wisconsin-Madison, United States
Reviewed by: Xiaolong Yang , Nantong University, China
David Mojaravscki , State University of Campinas, Brazil
Data availability statement
The datasets presented in this study can be found in online repositories, on Figshare https://figshare.com/s/2765f89c7ea840e5c6be?file=59367320 . The names of the repository/repositories and accessionnumber(s) can be found in the article/ Supplementary Material .
Author contributions
BG: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data Open asset ↗Figsharelines:471-515Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Potato leaf diseases, if left undetected, threaten food security in agricultural economies and cause substantial crop losses. To address this critical challenge, we developed an AI-based system called the Enhanced Single Shot Multibox Detector(EF-SSD), a variant of SSD that integrates multiscale feature fusion and Squeeze-and-Excitation attention to improve fine-grained lesion detection. The enhanced model processes high-resolution images (512×512 pixels) and analyzes leaves at ten magnification levels, enabling it to identify even minor signs of infection. The inclusion of Squeeze-and-Excitation filters allows the system to focus more effectively on characteristic disease patterns, increasing detection precision. After scanning the leaves, the system applies advanced image processing techniques to localize disease regions and assess their severity. We evaluated EF-SSD using 2,500 labeled potato leaf images representing healthy plants and cases of early and late blight. The proposed model achieved a mean Average Precision (mAP) of 97% at 0.5 IoU, an F1-score of 95%, and an Intersection over Union (IoU) of 89%, outperforming advanced detectors such as YOLOv5, YOLOv8, RetinaNet, and Faster R-CNN across all metrics. It also delivers real-time inference at 47 FPS, confirming its suitability for on-field deployment. An ablation study further demonstrates the effectiveness of SE blocks and extended feature hierarchies in enhancing detection accuracy. These outcomes highlight EF-SSD’s potential as a reliable, efficient, and scalable tool for smart agriculture and early crop disease management.
Why it matches plant phenotyping methodsジャガイモ葉の病斑を画像から検出・局在化し、病害の重症度を評価する深層学習手法を開発・検証しており、植物表現型取得が中心である。
abstractwe developed an AI-based system called the Enhanced Single Shot Multibox Detector(EF-SSD)
Reproduction assets foundThe paper's Data Availability statement explicitly provides a public GitHub repository with the authors' analysis/training code and a public Google Drive link to the custom 2,500-image potato leaf disease dataset with Pascal VOC annotations used in this study.Code · publicThe code implemented in this study is openly available at the following GitHub repository: https://github.com/bhavanisravan/potato-leaf-diseases-code.Open asset ↗bhavanisravan/potato-leaf-diseases-codehtml-lines:414-439Dataset · publicThe dataset used for training and evaluation can be accessed at: https://drive.google.com/drive/folders/1Yin9zp0gQKwqJ0LD3GWdLq_V_idLO2bG?usp=sharing.Open asset ↗html-lines:414-439Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Wheat emergence rate and emergence uniformity are key indicators for evaluating seed vigor and sowing quality, and they play an important role in wheat growth and yield formation. Traditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data. In this study, RGB images and a two-stage deep learning algorithm were used to extract and analyze seedling traits of 420 wheat varieties under two nitrogen levels, and the results were applied to genome wide association studies to elucidate the genetic basis. The two-stage algorithm integrates a Bidirectional Feature Pyramid Network, small object detection layer, large size image input, and FasterNet to improve detection and instance segmentation speed and accuracy. The proposed method achieved an emergence rate accuracy of 0.929, with R 2 = 0.914 and RMSE = 2.448 compared to manual measurements, and required less than 0.2 s per image for analysis. By employing this two-stage algorithm for processing and analysis, varieties (e.g., Gao8901 and ShiYou20) that consistently exhibited high emergence rates and uniformity under multiple nitrogen treatments were identified. Furthermore, genome-wide association study identified the major loci qEmergence rate-3A and qUniformity-6B governing seedling emergence rate and uniformity, which likely enhance wheat seedling traits by modulating energy supply or related signaling molecules. The emergence-rate and uniformity data generated by the two-stage algorithm significantly accelerated the discovery of relevant genes and enabled the identification of wheat varieties with high emergence rate and uniformity, providing valuable insights and practical references for high-quality breeding and gene mining.
Why it matches plant phenotyping methodsRGB画像と二段階深層学習による出芽率・均一性の自動取得手法を開発し、手動測定との精度比較および大規模品種適用を行っており、表現型取得法が研究の中心である。
abstractTraditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data.
Reproduction assets foundThe authors openly provide test code, base models, and sample test data for the WS-YOLO two-stage wheat seedling phenotyping pipeline in a public GitHub repository. Raw phenotype datasets are only available upon request, so they do not qualify as public assets.Code · publicThe test code, base models, and sample test data are openly available in the GitHub repository: https://github.com/AIWheatLab/WheatSeedling.Open asset ↗AIWheatLab/WheatSeedlinghtml-lines:375-402Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Estimating canopy structure - leaf inclination distribution (LIDFa), leaf area index (LAI), and fractional vegetation cover (FCover) - is vital for breeding, yet the added value of multi-angular UAV sensing over nadir-only baselines remains insufficiently quantified. This study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy. Using transfer learning across cultivars and dates, we compared the retrieval performance of multi-angle versus nadir-only baselines for LIDFa, LAI, and FCover. BRDF model simulations agreed well with airborne BRF (optimal R 2 > 0.80, RRMSE R 2 = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa ( R 2 = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy ( R 2 ≥ 0.73), with MA models providing marginal gains ( R 2 = 0.75). Methodologically, CNN-based transfer learning proved most effective for LAI and FCover, while a Random Forest model using raw multi-angle spectra yielded the best results for LIDFa. Optimal viewing configurations were trait-dependent, generally favoring forward scattering directions with zenith angles between 15° and 45°. These results indicate that kernel-driven BRDF modeling effectively captures spectral anisotropy in dense wheat canopies, and that multi-angular observations provide a distinct advantage for retrieving structural parameters with complex scattering behaviors, such as LAI and LIDFa.
Why it matches plant phenotyping methods小麦育種材料のキャノピー構造形質を対象に、UAVマルチアングルセンシング、BRDFモデル、CNN/RFによる推定フレームワークを開発・比較しており、形質取得手法が研究の中心である。
abstractThis study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy.
Reproduction assets foundThe paper's data availability statement explicitly deposits the complete source code for BRDF modeling and the transfer learning pipeline, plus a subset of preprocessed field data, in a public GitHub repository matching an allowed URL. Additional data are available only on request.Code · publicThe complete source code for BRDF modeling and the transfer learning pipeline, along with a subset of the preprocessed field data used in this study, are openly available in the GitHub repository at https://github.com/ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materials.git . Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request.Open asset ↗ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materialslines:451-474Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Medicinal plants including Ocimum tenuiflorum L. (Tulsi), Azadirachta indica A. Juss. (Neem), and Kalanchoe pinnata (Lam.) Pers. (Patharkuchi) are essential sources of bioactive compounds, yet leaf diseases threaten their yield and phytochemical integrity. This study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks, achieving 99.72% accuracy, 1.00 macro F1-score, and AUC = 1.00 across 12 disease classes (1,000 images/class post-augmentation) using only 9.38 M parameters and 2.50 GFLOPs. Five-fold cross-validation yielded 99.74% ± 0.14% accuracy, with rapid convergence and no overfitting. Explainable Artificial Intelligence (XAI) via Gradient-weighted Class Activation Mapping (Grad-CAM) (mean intensity: 0.1664-0.2702), Local Interpretable Model-agnostic Explanations (LIME), and t-distributed Stochastic Neighbor Embedding (t-SNE) (silhouette score: 0.87) confirmed biologically meaningful attention on pathological regions. External validation on the independent BD-MediLeaves dataset (8 classes, 8,000 samples) achieved 99.42% accuracy and 0.99 macro F1. With 6.98 ms/image inference latency and 35.81 MB memory, LSeTNet enables real-time, edge-based deployment. It significantly outperforms DenseNet169 (95.56%), ViT-B16 (95.61%), and LW-CNN+SE (95.39%) ([Formula: see text], paired t-tests), establishing a transparent, efficient, and generalizable benchmark for precision phytopathology and sustainable medicinal plant cultivation.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するCNN・Transformer手法を開発し、交差検証、外部データセット、既存モデルとの比較で検証しており、植物フェノタイピング手法が中心である。
abstractThis study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks
Reproduction assets foundThe paper publicly releases its primary medicinal leaf image dataset (MedicinalLeaf-12) on Mendeley Data, uses a public external validation dataset (BD-MediLeaves, also on Mendeley), and provides full training/evaluation code for LSeTNet on GitHub. All three are paper-specific, public, and actionable.Dataset · publicThe primary dataset used in this study is available in the Mendeley Data Repository: https://data.mendeley.com/datasets/ncg7kk3gwx/1 .Open asset ↗Mendeley Data · ncg7kk3gwx/1lines:712-750Code · publicThe full training and evaluation code for the proposed LSeTNet model is publicly available on GitHub at: https://github.com/mdtuhinkhan101/LSeTNet .Open asset ↗GitHub · mdtuhinkhan101/LSeTNetlines:712-750Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Ensuring the post-harvest quality and health of leafy vegetables is critical for minimizing economic loss, enhancing food security, and promoting sustainable agricultural practices. Spinach, a highly nutritious yet perishable crop, is particularly susceptible to rapid freshness degradation and foliar diseases. While computer vision and deep learning have shown promise for automated quality assessment, existing models often lack the robustness to handle the dual-task classification of both freshness and disease states across diverse local spinach varieties. To bridge this gap, this paper introduces a novel hybrid Convolutional Neural Network (CNN) architecture specifically designed for the multi-class detection of freshness and visual disease symptoms in local spinach leaves. The proposed model synergistically integrates a powerful feature extraction backbone with a tailored attention and fusion mechanism, enhancing its ability to capture discriminative spatial and textural features critical for fine-grained classification. It was trained and validated on a curated dataset comprising high-resolution images of three prominent local varieties (Malabar, Water, and Red spinach) in both fresh and non-fresh conditions. The proposed hybrid model achieved a classification accuracy of 98.36%, significantly outperforming benchmark state-of-the-art models including DenseNet121, ResNet50, and EfficientNetB0. Furthermore, explainable AI (XAI) techniques visually validated the model’s decision-making process, confirming its focus on biologically relevant leaf regions. The results demonstrate that the proposed hybrid framework offers a highly accurate, reliable, and interpretable tool for non-destructive, real-time quality monitoring. This work provides a significant contribution towards intelligent post-harvest management systems, capable of reducing waste and supporting the value chain for local spinach cultivation.
Why it matches plant phenotyping methods葉画像から鮮度および視覚的な病徴を分類するCNN手法の開発・検証が中心であり、植物の状態を直接推定している。
abstractthis paper introduces a novel hybrid Convolutional Neural Network (CNN) architecture specifically designed for the multi-class detection of freshness and visual disease symptoms in local spinach leaves.
Reproduction assets foundThe paper's Data Availability statement points to a public Mendeley Data deposit of the local spinach leaf image dataset used for the CNN freshness/disease classification, matching the paper's phenotyping inputs.Dataset · publicThe datasets analyzed during the current study are publicly available in the Mendeley Data repository at: [https://data.mendeley.com/datasets/skf6w2s2h2/2](https:/data.mendeley.com/datasets/skf6w2s2h2/2).Open asset ↗Mendeley Datahtml-lines:660-689Code / dataset availability confirmedCrossref · checked 14 Sept 2026
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotLeafObject detectionStress / disease detectionDisease symptoms / severity
Timely localization and diagnosis of crop lesions are critical for disease control and reducing pesticide use. However, in-field lesions often resemble leaf textures, vary widely in scale, and suffer from lighting and shadow interference-making simultaneous high accuracy and lightweight inference challenging. We propose WGA-YOLO, a lightweight YOLO variant for crop disease recognition. Central to our design is Wavelet Channel Recalibration (WCR), a DWT-based downsampling module: discrete wavelet transform naturally provides multi-resolution, time-frequency localized representations that explicitly separate low-frequency approximations from high-frequency edge/texture details. WCR fuses high- and low-frequency components and enhances feature representation through their frequency-domain complementarity, thereby preserving semantic and fine texture information during resolution reduction with negligible extra cost. We also introduce PS-C2f, which integrates Pinwheel-shaped convolutions into C2f to better capture tiny lesion details via multi-directional, irregular kernels, and replace SPPF with Dynamic Group Attention Pooling (DGAP) for efficient multi-scale context aggregation. On our PlantDoc_boost dataset, WGA-YOLO improves over YOLOv8n by 3.02 and 2.85% points, while reducing parameters and FLOPs by ~ 0.18 M and ~ 0.3G, demonstrating improved inference efficiency and deployment friendliness while maintaining strong detection performance in field scenarios.
Why it matches plant phenotyping methods植物葉の病斑を画像から検出・診断するYOLO改良手法の開発が中心であり、病害状態の画像ベース表現型計測に該当する。
abstractWe propose WGA-YOLO, a lightweight YOLO variant for crop disease recognition.
Reproduction assets foundThe paper's PlantDoc_boost dataset (the annotated crop-disease image dataset constructed and analyzed in this study) is explicitly stated to be publicly released on the authors' GitHub repository. No author analysis code or trained model checkpoints are stated as available. The Roboflow corn and tomato datasets are preDataset · publicThe PlantDoc_boost dataset used and analyzed in this study is publicly available from the project repository at http://github.com/YongChaoLiang/PlantDoc_boost/tree/master.Open asset ↗YongChaoLiang/PlantDoc_boosthtml-lines:693-708Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Soybean ( Glycine max L. ) performs an important position as a main resource of protein in Indonesia. Its quality and productivity can be assessed based on the characteristics of its seed. Accordingly, the identification process through the observation of soybean seed traits is a crucial step in plant breeding and quality assurance. Manual approaches rely on manual observation, which is subjective, prone to human error and time-consuming. With the improvement of artificial intelligence, automated seed identification has appeared as a potential solution. However, progress is constrained by the lack of open and standardized image datasets, especially for locally bred varieties in developing countries. To address this gap, we propose an open image dataset of Indonesian soybean seeds from three widely cultivated and plant-bred varieties: Anjasmoro, Grobogan, and DEGA-1. The dataset consists of high-resolution seed images captured with an Epson L360 flatbed scanner, with the optical resolution fixed at 800 dots per inch, yielding images of 6800 × 9359 pixels. All raw images are saved in JPG format. No manually segmentation masks are released in this version, instead of using Deeplab V3+ with MobileNet as backbone to enable the automated seed image segmentation. The curated dataset is intended to support a broad range of applications, including computer vision tasks such as image classification and segmentation, as well as research in plant breeding, seed quality assessment, and agricultural informatics. By providing a standardized and publicly accessible resource, this dataset contributes to the advancement of interdisciplinary studies at the intersection of agriculture and artificial intelligence.
Why it matches plant phenotyping methods大豆種子画像を標準化して公開するデータセット研究であり、種子形質の自動画像解析・セグメンテーションを支援する方法論的資源が中心です。
titleAn open image dataset of Indonesian soybean seed varieties (Anjasmoro, Grobogan, DEGA-1) for agricultural research and machine learning applications.
Reproduction assets foundThe paper is a data descriptor for a public Mendeley Data repository containing the authors' own soybean seed image dataset (raw scans and segmented seed images) used for seed phenotyping, with an explicit direct URL and DOI.Dataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c733bjz4m3.3
Direct URL to data: https://data.mendeley.com/datasets/c733bjz4m3/3Open asset ↗Mendeley Data · 10.17632/c733bjz4m3.3html-lines:115-142Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Common beanStem / branchObject detectionPhysiological trait estimationGrowth / development / phenology
Growing plants are remarkable at negotiating obstacles in their unstructured and changing environments. Measuring the mechanical interactions of growing plants with surrounding objects is a critical step towards deciphering thigmotropic responses underpinning complex growth strategies. Yet, available force measurement systems have limited capacity to capture weak forces in freely moving plant organs-such as the forces applied by a growing shoot pushing at an obstacle. We developed a measurement system based on the deflection of a pendulum by a freely moving shoot. Unlike many force measurement systems, the organ is not tethered to the device. Moreover, force is measured along two axes, as opposed to one axis in commonly used methods. Orthogonal cameras track the 3D position of the rod and shoot, yielding the rod deflection angle and, using a mechanical torque equilibrium equation, allowing extraction of the force applied by the plant over time. This system is relevant for measuring weak forces in macro-sized systems (e.g. growth or turgor pressures), and the force detection range can be tuned by altering rod mass and length. We demonstrate the system with Phaseolus vulgaris shoots, measuring the forces they apply on a candidate support during inherent circumnutation movements, prior to twining. Such measurements lay the foundations for deciphering how climbing plants assess whether to twine or not- an open question since Darwin's first observations.
Why it matches plant phenotyping methods自由に動く植物器官が発生する微弱な力を、カメラ追跡と力抽出により定量する測定システムを開発・実証しており、植物表現型の取得方法が研究の中心である。
abstractWe developed a measurement system based on the deflection of a pendulum by a freely moving shoot.
Reproduction assets foundThe authors deposited the full analysis workflow (data and code) for five example force-measurement trajectories on Zenodo, publicly accessible via DOI 10.5281/zenodo.15545548. This directly reproduces the paper's camera-based plant force phenotyping measurements and computational analysis. Other experimental data are仅Dataset · publicof interest
None declared.
Funding
YM acknowledges support from the Israel Science Foundation Research
Grant (ISF) no. 2307/22, and ERC grant GROWsmart 101165101. AO
acknowledges support from the Colton Foundation scholarship.
Data availability
We have put the full workflow for five example trajectories on a Zenodo
repository (https://doi.org/10.5281/zenodo.15545548; Ohad and
Meroz, 2025). Other experimental data are available upon request.
References
Autumn K, Liang YA, Tonia Hsieh S, Zesch W, Chan WP, Kenny TW,
Fearing R, Full RJ. 2000. Adhesive force of a single gecko foot-hair.
Nature 405, 681–685.
Backholm M, Bäumchen O. 2019. Micropipette force sensors for in vivo
force measurementsOpen asset ↗Zenodo · 10.5281/zenodo.15545548pdf-raw-page:9 lines:1-95Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation. To address these difficulties, we propose our solution approach LeafSightX . This dual-backbone architecture combines features from DenseNet201 and InceptionV3 using Multi-Head Self-Attention (MHSA) techniques, enhancing representational capability and spatial context reasoning. Our extensive procedure includes specialized preprocessing and limited data augmentation, improving model resilience in many scenarios. Furthermore, LeafSightX integrates explainable AI techniques with Grad-CAM visualizations to improve transparency. In assessments of a five-class apple leaf disease dataset featuring field and laboratory images, LeafSightX demonstrates exceptional performance, attaining a test accuracy of 99.64%, an F1-score of 0.9962, and AUC and PR-AUC scores of 1.000, far surpassing all baseline CNNs. Cross-validated Cohen's Kappa (mean = 0.9917, σ = 0.0020) and AUC (mean = 0.9998) indicate a significant level of predictive consistency. Despite its architectural complexity, the model offers real-time inference capabilities, ensuring per-sample latency suitable for edge device deployment. Additionally, the proposed LeafSightX framework was trained and evaluated on an additional independent apple leaf disease dataset, achieving a test accuracy of 99.69%, demonstrating its robustness and generalization. Our approach is a rigorously evaluated, clear, and highly accurate system for identifying plant diseases, providing a reproducible foundation for the actual application of AI in agriculture.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から識別するCNN手法を開発し、複数データセット・交差検証・ベースライン比較で性能を評価しており、植物病害表現型の取得・推定が中心である。
Reproduction assets foundThe paper uses two public Kaggle apple leaf disease image datasets as its phenotyping inputs; both are directly cited with public URLs. No author analysis code, trained model checkpoints, or supplementary code repository is deposited — the data availability statement only offers contact with corresponding authors.Dataset · publicThis research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal (Kaggle, 2025).Open asset ↗Kagglehtml-lines:128-184Dataset · publicDhar S. (2023). Apple leaf disease classification dataset. Available online at: https://www.kaggle.com/datasets/showravdhar/apple-disease-dataset (Accessed November 1, 2023).Open asset ↗Kaggle · showravdhar/apple-disease-datasethtml-lines:1449-1484Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Segmentation of hyperspectral image data is a well-established technique in remote sensing. While it is commonly applied to individual field crops, its use for individual trees is less prevalent. Conifers are crucial in forestry, and assessing physiological status, or genetic diversity is required for effective early-age treatment in nurseries and hyperspectral imaging (HSI) combined with high-throughput phenotyping (HTP) offers faster and non-destructive evaluation. NDVI-based thresholding is sufficient for detection of leaves with large projection areas, but needles of conifers present challenges due to spatial resolution constraints and increased proportion of border pixels. This study monitored the offspring of three locally adapted Scots pine (Pinus sylvestris L.) populations, representing distinct upland and lowland ecotypes. This study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings. Using a K-means algorithm, 23 hyperspectral centroids were successfully derived and subsequently classified into ten biologically distinct groups. Random forest classification model effectively differentiated Scots pine seedlings based on origin during water stress and recovery periods. This study highlights the potential of hyperspectral imaging and machine learning in evaluating the physiological state of conifer seedlings, demonstrating promising applications in forest tree physiology research and tree breeding.
Why it matches plant phenotyping methods個体のマツ苗を分離・セグメンテーションするハイパースペクトル画像処理パイプラインを開発し、機械学習で生理状態や由来を評価しており、表現型取得手法が中心である。
abstractThis study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings.
Reproduction assets foundThe paper's Data availability statement explicitly deposits demonstration hyperspectral sample data on Zenodo and the segmentation/classification scripts on GitHub; both are paper-specific, public, and actionable. Full experimental data is request-only and not listed as a public asset.Dataset · publicDemonstration sample data and their accompanying descriptions are available in the Zenodo repository (https://doi.org/10.5281/zenodo.17167809).Open asset ↗Zenodo · 10.5281/zenodo.17167809lines:161-192Code · publicThe scripts developed for this study are available on GitHub at: https://github.com/JCepl/Pine-hyperspectral-image-segmentaionCompleteOpen asset ↗GitHub · JCepl/Pine-hyperspectral-image-segmentaionCompletelines:161-192Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by available field surveys and trait measurements. Recent expansions in biodiversity data aggregation-including vegetation surveys, citizen science observations, and trait measurements-offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km 2 resolution. Our approach achieves correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance understanding of plant community properties and ecosystem functioning, while serving as tools for modeling global biogeochemical processes and informing conservation efforts. Our framework highlights the power of crowdsourced biodiversity data in addressing longstanding extrapolation challenges in global plant trait modeling, with continued advancements in data collection and remote sensing poised to further refine trait-based understanding of the biosphere.
Why it matches plant phenotyping methods地球観測データと多様な植物形質データを統合して植物形質を推定・検証する方法が研究の中心であり、単なる生態学的測定ではないため。
abstractcombining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km 2 resolution
Reproduction assets foundThe paper's own trait maps (Zenodo), source data (Zenodo), and analysis code (GitHub + Zenodo archive) are explicitly public. Core trait inputs (TRY, sPlot) are restricted-access and require requests; GBIF citizen-science occurrence datasets are public inputs.Code · publicThe code used to process data, train models, and generate trait maps in this study is available at https://github.com/dluks/cit-sci-trait-maps and archived on Zenodo at https://doi.org/10.5281/zenodo.18269445 .Open asset ↗GitHub · dluks/cit-sci-trait-mapslines:249-343Code · publicThe code used to process data, train models, and generate trait maps in this study is available at https://github.com/dluks/cit-sci-trait-maps and archived on Zenodo at https://doi.org/10.5281/zenodo.18269445 .Open asset ↗Zenodo · 10.5281/zenodo.18269445lines:249-343Dataset · publicSource data underlying the figures are available at https://doi.org/10.5281/zenodo.18108765 .Open asset ↗Zenodo · 10.5281/zenodo.18108765lines:240-248Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
The leaf blade and vasculature develop together within a shared morphological space. Despite shared molecular patterning pathways, it is unknown if developmental and evolutionary variation affect these tissues separately or together in a coordinated way. Grapevine leaves have a morphometric history and abundant data measuring the shape of the blade and vasculature together. Using a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature. Each tissue contains sufficient information to predict the other. We hypothesize that this is due to a one-to-one relationship between blade and vein. Using thin plate splines to swap and warp different combinations of blade and vein shapes, we show that a set of leaves with a many-to-one relationship of blade and vein are distinguishable from true leaves. We also swap blade and vein across the developmental series and between species and show that only reversing the developmental series disrupts the relationship between blade and vasculature. We end by discussing the evolutionary and developmental implications that there is a unique, one-to-one mapping between blade and vein that allows each to be predicted from the other. Author summary Leaves are made of two closely connected parts: the flat blade that captures light and the network of veins that transports water, nutrients, and developmental signals. Although these tissues grow together and share common molecular patterning pathways, it has remained unclear whether a particular blade shape is uniquely linked to a specific vein pattern. In this study, we use grapevine leaves as a model system and combine mathematical shape analysis with deep learning to examine this relationship. We show that the shape of the blade alone can accurately predict the vein network, and that the vein network can likewise predict the blade. This finding suggests a near one-to-one relationship between these two tissues. To test this idea, we created artificial leaves in which blade and vein shapes were deliberately mismatched. Although these synthetic leaves appeared realistic at a global level, a neural network was able to distinguish them from real leaves based on subtle differences. We further show that this tight coupling is maintained by the developmental sequence of leaf growth rather than by species identity, revealing a conserved constraint linking leaf form and internal structure.
Why it matches plant phenotyping methods葉身と葉脈の形状を深層学習による相互セグメンテーションと形状解析で抽出・予測する方法が研究の中心であり、植物形態表現型の方法開発に該当する。
abstractUsing a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature.
Reproduction assets foundThe Data Availability Statement explicitly lists three public Zenodo deposits containing data and code to reproduce the paper's analyses: reciprocal U-Net blade/vein prediction, the two-tower CNN 1:1 vein:blade relationship, and interspecies/intraspecies developmental series swaps. All URLs are in allowed_urls and the Code · public393
which the relationship between blade and vasculature is conserved or diversified across the
394
spectacular variety of leaf shapes remains to be seen.
395
Data Availability Statement
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the aOpen asset ↗zenodo · 16920155pdf-raw-page:22 lines:1-53Code · publicd across the
394
spectacular variety of leaf shapes remains to be seen.
395
Data Availability Statement
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuitOpen asset ↗zenodo · 17014105pdf-raw-page:22 lines:1-53Code · publicment
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 29, 2026.
;
https:Open asset ↗zenodo · 17013783pdf-raw-page:22 lines:1-53Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize. RESULTS: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies. CONCLUSION: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.
Why it matches plant phenotyping methods大規模RGB画像から67の画像形質を抽出し、発育段階ごとのトレイト動態を解析する高スループット植物表現型解析が研究の中核であるため、方法応用として収録する。
abstractleveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages
Reproduction assets foundThe paper's own phenotyping assets are publicly available: selected RGB plant images on Zenodo (record 18150504), and the image-analysis/i-trait extraction pipeline code on GitHub with a Zenodo mirror (record 18151471). The NCBI BioProject and MaizeGDB are prior-study/generic resources, not paper-specific.Code · publicThe image analysis and i-trait extraction pipeline and codes followed the previous procedure [ 18 ] without any modifications and has been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗GitHublines:195-202Code · publichas been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗Zenodolines:195-202Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Quinoa ( Chenopodium quinoa Willd.) is a genetically diverse Andean crop valued for its nutrition and adaptability to varied agro-climatic conditions with potential for cultivation in European and Mediterranean, particularly on marginal lands. Low temperatures during early sowing can impair germination, while delayed sowing increases the risk of poor maturation due to unfavorable autumn weather. To assess the adaptation of quinoa to low temperature conditions, that reflect cold stress, we evaluated germination and phenotypic variation in 60 accessions from highland and coastal ecotypes across three sowing dates in South-Western Germany: late winter (S1), early spring (S2), and spring (S3). Early sowing under low temperature conditions in S1 delayed seedling-emergence and reduced emergence percentages, yet these plants produced the highest average seed yield per plot (64 g) compared to S2 (46 g) and S3 (35 g). Highland accessions showed earlier seedling-emergence and with higher emergence percentages, while coastal types matured earlier and gave higher yields across sowing dates. A complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth. This confirmed the beneficial germination performance of highland accessions under low temperature conditions, with strong agreement between manual and automated scoring. Our findings suggest that quinoa demonstrates resilience to cold stress with highland quinoa exhibiting superior germination traits, and early sowing, despite reduced emergence, can lead to higher yields. We conclude that combining favorable traits such as faster maturity and higher yield of coastal ecotypes with superior germination traits of highland accessions is a promising avenue for breeding improved quinoa varieties for cold climatic regions.
Why it matches plant phenotyping methodsMask R-CNNによる発芽・幼植物成長の画像解析を手動評価と比較し、強い一致を検証しており、植物表現型取得法の技術的検証を含む。
abstractA complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth.
Reproduction assets foundThe paper states that all phenotypic data and R analysis scripts are available as supplementary material (publicly hosted with the bioRxiv preprint), while raw seed germination images are only available upon request. No separate repository or trained model checkpoint is named.Dataset · publicData availability: All phenotypic data and R scripts used for the analysis are available
as supplementary material.Open asset ↗pdf-page:1 lines:1-52Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
In real-world agriculture, healthy plant leaves are significantly more common than diseased ones. This natural class imbalance presents challenges in automated plant disease detection, as analyzing each leaf with computationally intensive deep-learning models is problematic, leading to inefficiency and increased resource consumption. To tackle this challenge and promote sustainable AI solutions, this study presents an iterative, hybrid AI approach that boosts computational efficiency, interpretability, and scalability for real-time disease detection. This hybrid system operates in two stages: first, a lightweight traditional machine learning classifier performs binary classification to quickly separate and exclude healthy leaves, followed by a deep learning model (ResNet, DenseNet, MobileNet, and EfficientNet) that classifies the specific disease in the smaller group of diseased leaves. This two-stage method minimizes computational load while maintaining high classification accuracy. Additionally, this study uses Explainable AI (XAI) methods, particularly Gradient-weighted Class Activation Mapping (Grad-CAM), to generate heatmaps. These heatmaps highlight the image areas that most significantly influence the model’s predictions, thereby improving transparency and refining the feature extraction process. The proposed hybrid model, comprising Logistic Regression and Mobilenetv3, offers up to 77.6% faster inference than conventional deep learning models with only about 3% accuracy loss. For a large-scale test of 1,227 images on an entry-level laptop, the hybrid model reduced the total inference time from 4,548 seconds to just 1,010.13 seconds, with minimal CPU load. By addressing class imbalance, optimizing inference efficiency, and incorporating explainable AI, this work contributes a scalable, sustainable, and trustworthy solution for plant disease detection in precision agriculture.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する二段階AI手法とXAIを開発・評価しており、病害の表現型取得が研究の中心である。
abstractThis two-stage method minimizes computational load while maintaining high classification accuracy.
Reproduction assets foundThe paper uses the public MangoLeafBD dataset (Mendeley Data) and the authors state they made their complete source code, trained model weights, and preprocessing scripts publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicwe have made the complete source code, trained model weights (for MobileNet and Random Forest), and preprocessing scripts publicly available on GitHub. The repository is accessible at:
3. GitHub - abkafi1234/Disease_Agnostic_Hybrid_classifierOpen asset ↗abkafi1234/Disease_Agnostic_Hybrid_classifierlines:832-873Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Hyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves. These data have been used to develop machine learning models for predicting diverse plant traits, yet key challenges remain. We collected hyperspectral reflectance data together with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines grown over three seasons. Using these data, we systematically (1) compare the performance of PLSR and SVR across a wide range of traits, including also slow fluorescence kinetics, (2) assess model generalizability and transferability, and (3) investigate how different aggregation strategies affect predictive accuracy. Based on a nested cross-validation framework, single cross-validation with MSE as metric performed comparably to repeated cross-validation or PRESS-based calibration. Optimal performance of trait-specific predictions was found to be dependent on the combination of model and data aggregation levels. Structural and biochemical traits showed the best generalizability and transferability, whereas physiological traits, particularly those derived from gas exchange and fluorescence kinetics, exhibited markedly reduced transferability. Together, these results provide a rigorous benchmark for evaluating machine learning models for trait prediction from hyperspectral reflectance data, and highlight both the opportunities and limitations for achieving robust generalization across diverse environments and genotypes.
Why it matches plant phenotyping methodsハイパースペクトル反射データから植物形質を予測する機械学習手法を、複数形質・環境・遺伝子型で系統的に比較し、一般化性と転移性を厳密にベンチマークしているため、方法論が中心である。
abstractHyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all code and raw hyperspectral/trait data in a public GitHub repository, matching an allowed URL.Code · publicAll code and raw data to ensure reproducibility of the results can be accessed at: [https://github.com/Rudan-X/HyperspectralML](https:/github.com/Rudan-X/HyperspectralML).Open asset ↗Rudan-X/HyperspectralMLlines:158-246Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract Mango diseases and pest infestations represent a major challenge to agricultural productivity, making early and accurate diagnosis crucial for reducing crop losses. This study presents a security-preserving ensemble convolutional neural network (CNN) framework for the automated identification and classification of mango leaf diseases using image-based analysis. The proposed system is designed to work with images captured under real field conditions, ensuring its suitability for practical agricultural applications. The dataset includes mango leaf images affected by various diseases and pests such as Gall Midge, Powdery Mildew, Sooty Mould, Die Back, Cutting Weevil, and Anthracnose, each characterized by distinct visual symptoms including discoloration, necrotic spots, fungal growth, leaf deformation, and edge damage. Traditional manual diagnosis of these conditions is often time-consuming, labor-intensive, and susceptible to human error. To overcome these limitations, the proposed framework employs an ensemble of transfer-learning-based CNN models to extract meaningful features related to texture, color distribution, shape, and lesion patterns. A security-preserving learning mechanism is integrated to ensure the safe handling of agricultural image data, minimizing data exposure risks while maintaining high model performance. Additionally, data augmentation techniques are utilized to improve model robustness, reduce overfitting, and address class imbalance commonly found in agricultural datasets. The system is capable of multi-class classification, reflecting real-world scenarios where multiple diseases may exhibit visually similar characteristics. Experimental results indicate that the ensemble CNN framework achieves high classification accuracy and demonstrates strong generalization across varying lighting conditions and complex backgrounds. By effectively capturing disease-specific visual features, the proposed approach enhances detection reliability in real-world field environments. Overall, this system offers a scalable, non-invasive, and security-aware solution for early mango leaf disease detection, contributing to precision agriculture and informed decision-making. The findings highlight the potential of deep learning and computer vision technologies in developing intelligent, secure, and efficient plant health monitoring systems.
Why it matches plant phenotyping methodsマンゴー葉の病徴を画像から分類するCNNフレームワークの開発が研究の中心であり、植物の病害状態を直接推定する画像ベース表現型解析に該当する。
abstractThis study presents a security-preserving ensemble convolutional neural network (CNN) framework for the automated identification and classification of mango leaf diseases using image-based analysis.
Reproduction assets foundThe paper's plant-phenotyping input is the public Kaggle Mango Leaf Disease Dataset of mango leaf images (Gall Midge, Powdery Mildew, Sooty Mould, Die Back, Cutting Weevil, Anthracnose, Healthy), explicitly declared as publicly available with a link. No author code, models, or checkpoints are shared.Dataset · publicdation.
Zahra Maryam handled data curation and resources.
Muhammad Haseeb Zia conducted the formal analysis. All
authors reviewed and approved the final manuscript for
submission.
Funding This research did not receive funding.
Data Availability The dataset used in this study is
publicly available on Kaggle. The dataset link is:
https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset.Declarations
Conflict of interest The authors declare that they
have no conflict of interest.
Ethical approval This study utilizes a publicly
available benchmark dataset from Kaggle (Mango
Leaf Disease Dataset:
https://www.kaggle.com/datasets/aryashah2k/mang
o-leaf-disease-dataset ). The dataset is Open asset ↗Kaggle · aryashah2k/mango-leaf-disease-datasetpdf-raw-page:11 lines:1-91Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Precision management in high-density orchards requires individual-tree, nondestructive monitoring of canopy nitrogen concentration (CNC), but hyperspectral applications are limited by two factors: unmodeled vertical stratification of CNC within 3D canopies and mixed-pixel effects near canopy boundaries. We develop a cross-modal framework that co-registers RGB-derived 3D point clouds with hyperspectral orthomosaics, enabling individual-tree localization in dense orchards. With this framework, we quantified layer-specific nitrogen-spectral relationships and assessed mixed-pixel effects across canopy positions. Stratified sampling, continuous wavelet transform (CWT), and partial least squares regression (PLSR) with variable importance in projection (VIP)-based band selection were used for spectral optimization, and K-means was applied to isolate representative canopy pixels. Field experiments over two consecutive years (2023-2024) revealed consistent CNC gradients, with the lower canopy exceeding the upper by 0.5-9.5 % across fertilization treatments. CWT-2 delivered the most accurate and robust performance across years. VIP-PLSR indicated layer-dependent CNC-informative wavelengths spanning the visible, red-edge, and near-infrared regions, with scale-dependent cross-layer overlap after CWT. Pixel clustering revealed distinct spatial structure: canopy-interior pixels exhibited characteristic vegetation spectra and achieved R 2 val of 0.69-0.76, substantially outperforming boundary-affected pixels with R 2 val of 0.48-0.57. These results demonstrate that coupling spectral feature optimization with layer-specific modeling and clustering-based pixel screening improves the accuracy of tree-level CNC estimation in complex canopies. The proposed framework provides a mechanistic and operational basis for robust biochemical retrieval in structurally complex orchard systems.
Why it matches plant phenotyping methodsUAVのRGB・ハイパースペクトルデータを統合し、個体樹の樹冠窒素濃度という植物形質を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心です。
abstractWe develop a cross-modal framework that co-registers RGB-derived 3D point clouds with hyperspectral orthomosaics, enabling individual-tree localization in dense orchards.
Reproduction assets foundThe paper's data availability statement explicitly deposits the apple canopy nitrogen concentration dataset and canopy original-reflectance validation dataset in a public GitHub repository, which is a paper-specific, publicly actionable phenotyping asset. No author analysis code or trained models are explicitly stated.Dataset · publicThe apple CNC dataset and the canopy OR independent validation dataset are available at https://github.com/Chenb94115/Plant-Phenomics . Additional supporting data are available from the corresponding author upon reasonable request.Open asset ↗Chenb94115/Plant-Phenomicslines:278-377Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Modern maize stems possess a well-developed vascular bundle system, which is critical for providing mechanical support and lodging resistance. However, characterization of the microanatomical features of vascular bundles and their functional implications in stem mechanics remains challenging, primarily due to technical limitations in high-throughput microanatomical analysis of stem tissues. We thus constructed data sets consisting of over 500,000 maize stem CT images from a maize diversity panel of 383 inbred lines. We evaluated 32 microanatomical phenotypes of maize basal internodes across two environments in different years. By incorporating engineering mechanics parameters, we calculated novel characteristics of the vascular bundles, including the moment of area (MOA) and the polar moment of inertia (PMOI). Through the high-density phenotypic data set, we identified multiple stem microanatomical phenotypes strongly associated with lodging resistance, particularly of vascular bundle mechanical traits. By integrating population genetic profiling, we discovered and confirmed that ZmLSM2 (U6 small nuclear ribonucleoprotein specific Sm-like 2) serves as a key regulator of stem mechanical strength, might function in RNA processing and maturation within vascular stem cells, identifying novel genetic targets for improving maize lodging resistance. This approach demonstrates the value of combining advanced phenotyping with multi-omics analyses for crop improvement. These discoveries will deepen the understanding of plant stem biomechanical principles and provide novel targets for enhancing lodging resistance in crop breeding programs.
Why it matches plant phenotyping methodsトウモロコシ茎のCT画像から微細構造形質を高スループットに抽出する表現型解析基盤とデータセットが研究の中心であり、単なる生物学的測定ではない。
abstracttechnical limitations in high-throughput microanatomical analysis of stem tissues
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicCT cross‐section images of the third internode from 383 maize inbred lines grown in Beijing and Sanya during two growing seasons can be downloaded via the link: https://pan.baidu.com/s/1CP2kkAmTvy1zi3QJGtKSWQ?pwd=JIPB . Extraction code: JIPB.Open asset ↗lines:204-306Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Agriculture is essential to human civilization, providing food and raw materials. Plant diseases significantly threaten agricultural productivity, making early and accurate detection essential. Despite Recent advances of deep learning in making automatic plant leaf diseases diagnosis systems, some of them depend on simple features fusion methods and lack an efficient method to exploit complementary information. Therefore, this paper proposes a system for diagnosing plant leaf diseases by fusing two powerful deep learning models: EfficientNetV2B0 and Swin Transformer via attention-based feature fusion that adaptively weights each model’s contribution with features. These models extract complementary features: EfficientNetV2B0 extracts fine-grained local features, and the Swin Transformer extracts global contextual information, producing highly and complementary expressive fused features. The high-dimensional fused features demand High-Performance Computing (HPC) resources for efficient parallel processing and accelerated training. Moreover, the Henry Gases Solubility Optimization (HGSO) metaheuristic is applied to select the most discriminative and related features. Unlike previous methods that diagnose diseases affecting only one plant, the proposed approach handles multiple plant species simultaneously, further increasing computational demand. Finally, RBF-kernel SVM is applied for a classification step. The system was implemented on a GPU-based high-performance computing environment using CUDA acceleration to enhance computational efficiency. Experimental evaluation on the PlantVillage benchmark dataset with seven classes achieved a high classification accuracy of 99.2%, outperforming other state-of-the-art methods. These results enhance the model’s practical capability for application in real-world agricultural decision-support systems.
Why it matches plant phenotyping methods葉画像から植物病害を診断する深層学習システムの開発とベンチマーク評価が研究の中心であり、植物の病害状態を直接推定しているため。
abstractTherefore, this paper proposes a system for diagnosing plant leaf diseases by fusing two powerful deep learning models: EfficientNetV2B0 and Swin Transformer via attention-based feature fusion
Reproduction assets foundThe paper's sole experimental input is the PlantVillage leaf-disease image dataset, which the authors explicitly state is publicly available online with a Kaggle URL in the Data availability statement. No author code, models, or other paper-specific assets are disclosed.Dataset · publicuthors have
read and agreed to the published version of the manuscript.
Funding Open access funding provided by The Science, Technology & Innovation Funding Authority
(STDF) in cooperation with The Egyptian Knowledge Bank (EKB). No funding.
Data availability The datasets used during the current research are available online at: https://www.kag-gle.com/datasets/mohitsingh1804/plantvillageOpen asset ↗kag-gle.com/datasets/mohitsingh1804/plantvillagepdf-raw-page:53 lines:1-44Code / dataset availability confirmedOpenAlex · arXiv · checked 13 Sept 2026
Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches based on manual scanning or fixed camera setups remain a major bottleneck, while active-mapping methods based solely on occupancy grids are too coarse for accurate trait estimation. To address this gap, we propose an active 3D reconstruction framework for horticultural environments using a mobile manipulator. The system integrates OctoMap with 3D Gaussian Splatting to enable accurate and efficient target-aware mapping. A low-resolution OctoMap provides probabilistic occupancy information for informative viewpoint selection and collision-free planning, while 3D Gaussian Splatting leverages geometric, photometric, and semantic information to optimize 3D Gaussians for high-fidelity scene reconstruction. We further introduce a robust mapping strategy that mitigates semantic segmentation and depth noise, together with a background pruning method that reduces memory and computational cost. We validate our framework across simulated, laboratory, and real greenhouse scenes, showing consistent improvements across three state-of-the-art Gaussian Splatting backbones. In simulation, where ground-truth geometry is available, our approach outperforms occupancy-based mapping in both reconstruction accuracy and runtime efficiency: compared with a 0.01m-resolution OctoMap, it doubles the fruit-level F1 score under noisy conditions while achieving up to a threefold reduction in runtime. Beyond simulation, novel-view synthesis quality also improves consistently in laboratory and real greenhouse environments, with PSNR and mIoU improving by up to 1.5 dB and 18%, respectively. Finally, the reconstructed semantic maps enable fruit counting and volume estimation with accuracies approaching 80%.
Why it matches plant phenotyping methods園芸ロボット向けの3D再構成・能動マッピング手法を開発し、果実の計数・体積推定という植物形質の取得に適用・検証しているため、フェノタイピング手法が中心的です。
titleOctoSplat: Hybrid OctoMap-Gaussian Splatting for Active Semantic Mapping and Phenotyping with Horticultural Robots
Reproduction assets foundThe paper's supplementary material is hosted on the authors' public project page (jrcuaranv.github.io/octosplat), and the authors state that all code and data are publicly available. The SimSense repository is a third-party depth-sensor simulator tool, not a paper-specific asset.Code · publicAll code and data are publicly available to facilitate reproducibility.Open asset ↗lines:59-163Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Wheat, being a major staple crop worldwide, is often attacked by rust diseases, which cause severe yield losses. The early detection and diagnosis of fungal infections, yellow rust, and brown rust are critical in minimizing their consequences. A web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases. The diseases that we examine in wheat plants are brown rust (BR) and yellow rust (YR), and healthy plants are classified in the third category. A dataset of labeled images of YR, BR, and healthy wheat plants was used to train the CNN. The model achieved a remarkable 96% classification accuracy. In addition to disease diagnosis, a recommendation module that gives advice on proper treatment based on disease names or symptoms is also provided. This twofold functionality allows for timely disease management and identification and facilitates the treatment of other wheat diseases besides rust diseases. Integrating the trained CNN model into an intuitive web application makes it user-friendly for end users, notably farmers, to have a practical tool in protecting wheat crops.
Why it matches plant phenotyping methods小麦植物画像から病害状態を分類するCNN手法を開発・評価しており、植物病害表現型の取得・推定が中心。治療推薦機能もあるが、画像ベース病害診断が主要な技術的貢献である。
abstractA web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases.
Reproduction assets foundThe paper's wheat disease image dataset (YR, BR, healthy; 3679 images) is a publicly available Kaggle dataset explicitly used for the CNN training, with an authors-provided URL matching an allowed URL.Dataset · publicThe images of YR and BR were taken from a Kaggle dataset, which is available at https://www.kaggle.com/datasets/sinadunk23/behzad‐safari‐jalal. The dataset includes 3679 images divided into three different categories, as shown in Table 2.Open asset ↗Kaggle · sinadunk23/behzad‐safari‐jalalhtml-lines:249-257Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Ensuring smarter and more sustainable farming practices is a critical challenge in modern agriculture. Agentic Artificial Intelligence (AAI), combined with Precision Agriculture (PA) and Federated Learning (FL), has the potential to enhance decision-making, optimize resource utilization, and reduce environmental impact. Methods This study proposes an AAI based framework for precision agriculture that integrates distributed sensing devices, intelligent agents, and federated learning to enable real time monitoring and decision support at the farm level. A practical deployment architecture is outlined, detailing inter-device communication and localized intelligence. The proposed model is evaluated across two distinct datasets tomato disease classification and weed detection. The model is designed to have DenseNet121, MobileNetV2, EfficientDet-D0, and YOLOv8 as local models within a federated learning environment. Results The federated global model achieved an accuracy of 96.4%, outperforming individual client models, with DenseNet121 and MobileNetV2 attaining accuracies of 95.0% and 93.9%, respectively. For weed species detection, EfficientDet-D0 demonstrated superior performance, achieving an mAP@0.5 of 0.978, average precision of 0.865, and an F1-score of 0.961, compared to YOLOv8 with an mAP@0.5 of 0.956 and an F1-score of 0.935. Discussion The results confirm the feasibility and effectiveness of integrating AAI with federated learning for intelligent precision agriculture. A SWOT analysis highlights the strengths of the proposed approach, along with deployment challenges and constraints. Overall, this study establishes a roadmap for future research, emphasizing sustainable intelligent farming systems.
Why it matches plant phenotyping methods植物病害分類を含む連合学習・エージェント型AI基盤を提案し、データセット上で性能評価しているため、植物の病害状態を推定する計算的フェノタイピング手法が中心である。雑草検出も含まれるが、病害分類の技術評価が明示されている。
abstractThis study proposes an AAI based framework for precision agriculture that integrates distributed sensing devices, intelligent agents, and federated learning to enable real time monitoring and decision support at the farm level.
Reproduction assets foundThe paper evaluates its federated learning phenotyping models on two publicly available Kaggle image datasets (tomato disease classification and weedcrop detection), explicitly linked in the data availability statement and references. No author code or models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: The datasets used in the current study are openly accessible at https://www.kaggle.com/datasets/ashishmotwani/tomato and https://www.kaggle.com/datasets/vinayakshanawad/weedcrop-image-dataset/data .Open asset ↗Kaggle · ashishmotwani/tomatolines:838-838Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: The datasets used in the current study are openly accessible at https://www.kaggle.com/datasets/ashishmotwani/tomato and https://www.kaggle.com/datasets/vinayakshanawad/weedcrop-image-dataset/data .Open asset ↗Kaggle · vinayakshanawad/weedcrop-image-datasetlines:838-838Code / dataset availability confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Background and aims Root system architecture (RSA) shapes biogeochemical concentration patterns in the rhizosphere. Root-soil studies are often conducted on plants cultivated in rectangular rhizotrons, including when using 2D hydrochemical analysis methods. However, roots naturally expand in three dimensions, with the rhizosphere extending accordingly. Three-dimensional neutron imaging can enhance interpretation of such studies, yet imaging flat, slab-shaped rhizotrons is technically challenging. This study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL, without requiring tilting of the rotation axis. Methods NT and NCL were applied to maize plants grown in rectangular rhizotrons. Imaging artifacts and their impact on root segmentation were assessed for two plants representing low and high soil moisture conditions suitable for neutron imaging. Results Both methods produced 3D tomograms of comparable quality across the tested moisture range, enabling effective segmentation of primary and seminal roots. Lateral root detection was more challenging and depended on soil moisture. NCL captured a greater number of horizontally oriented lateral roots while NT was more effective in resolving vertically oriented roots. Conclusions NCL is not required to resolve 3D RSA of maize plants in flat rhizotrons. Under high-flux neutron beam conditions, NT is preferable as it simplifies sample handling, reduces plant stress, avoids soil water redistribution and enables direct integration with timeseries of 2D chemical and neutron radiographic imaging.
Why it matches plant phenotyping methods3D中性子画像法を用いた根系構造の抽出を中心に、NTとNCLを比較検証しており、植物表現型取得手法が研究の主題である。
abstractThis study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL
Reproduction assets foundThe paper's neutron imaging datasets (NT and NCL scans of maize in slab rhizotrons) are stated to be publicly available on the ILL Data Portal under DOI 10.5291/ILL-DATA.UGA-111. No author analysis code or trained models are explicitly deposited.Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG project numbers 396368046 and 516672636).
Data availability The datasets used in this study were gener-
ated as part of a measurement campaign on the neutron imag-
ing instrument NeXT at the ILL and are available on the ILL
Data Portal at https://doi.org/10.5291/ILL-DATA.UGA-111.Declarations
Competing interests The authors have no relevant financial
or non-financial interests to disclose.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give Open asset ↗10.5291/ILL-DATA.UGA-111pdf-raw-page:16 lines:1-92Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10 d filtered timeseries: (1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and (2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: an inital estimate immediately after the 10 d reference period, four subsequent estimates every 10 d using new observations, and a final consolidated estimate 90 d later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsMODIS/VIIRSから植物キャノピー状態であるFPARを推定するNRTフィルタリング・相互較正手法とデータセットの開発、品質評価が中心であり、単なる農業モニタリングへの routine measurement ではない。
abstractThis paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers.
Reproduction assets foundThe paper describes its own global NRT 500 m 10 d filtered FPAR dataset (MODIS and intercalibrated VIIRS timeseries with QA layers), explicitly stated to be publicly and freely available via the JRC Data Catalogue DOI and directly downloadable from the ASAP server, with visualization in the ASAP Warning Explorer. This衍Dataset · publicThe NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 ( Seguini et al. , 2025 ) .Open asset ↗10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50lines:158-173Dataset · publicor can be directly downloaded from the following server https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR/ (last access: 30 September 2025).Open asset ↗lines:245-257Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published11 Jan 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 2 · OpenAlex ↗
Utilizing the diversity preserved in genebank collections is essential for accelerating crop improvement, yet information is often limited to selected core collections. Genome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation, with proven utility in practical pre-breeding contexts. In this study, we leveraged GWP to expand the German Federal ex situ barley core collection (core1000) with a focus on resistance to Puccinia hordei, Blumeria graminis hordei, and Rhynchosporium commune. Using the barley core1000 collection, which was originally selected to maximize molecular diversity, we trained genomic prediction models and imputed resistance scores for 20,458 genebank accessions based on sequence data encompassing 306,049 high-quality SNPs. To empirically validate prediction accuracy, we selected 300 spring and winter barley genotypes for field evaluation across four environments, resulting in moderate-to-strong correlations between predicted and observed resistance levels. Genome-wide association mapping in this set revealed five marker-trait associations that were not detected in the original core1000 collection. These results demonstrate that prediction-informed sampling can effectively expand trait-relevant genetic diversity and increase the frequency of resistance-associated alleles, thereby improving the power to detect loci that may be overlooked in conventional panels. Accordingly, GWP supports the targeted inclusion of accessions with trait-relevant variation and enhances the value of genebank resources for trait discovery and pre-breeding applications.
Why it matches plant phenotyping methodsゲノム情報から病害抵抗性という植物形質を大規模に推定・補完する方法を開発し、圃場評価で予測精度を検証しているため、方法論が中心である。
abstractGenome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation
Reproduction assets foundThe authors deposited the paper's raw disease-resistance phenotypic data, BLUEs for the spring/winter validation set, and the R script for curation/heritability/BLUE computation in the public e!DAL-PGP repository (Yuan 2025). The companion IPK/2024/7 dataset belongs to cited prior work (Yuan et al. 2025 training data).Dataset · publicThe raw phenotypic data described here as well as the ready-to-use phenotypic values (BLUEs) for spring and winter validation set, and the R script to import and curate the raw phenotypic data to compute heritability and BLUEs are available in the e!DAL-PGP Repository (Arend et al. 2016 ) and can be directly accessed here (Yuan 2025 ).Open asset ↗e!DAL-PGP Repositorylines:157-182Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear 'Bartlett,' which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D画像解析パイプラインと3D Gaussian Splattingを用いて果実の空間的成長を非破壊計測し、手動記録との精度比較で検証しているため、植物表現型取得法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery.
Reproduction assets foundThe authors deposited a subset of the 3DGS-reconstructed fruit models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data only on request. No author analysis code or raw imagery deposit is stated.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトルとガス交換・光合成形質を標準化して収録するデータベースを構築し、植物フェノタイピングモデルの開発・検証に供することが中心である。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper describes the GSTI database of paired leaf hyperspectral and gas-exchange measurements, with both the data and R processing/model-fitting code publicly available on GitHub and archived releases on ESS-DIVE.Code · publicThe GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti (last access: 4 January 2026)Open asset ↗https://github.com/plantphys/gstilines:537-549Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Multispectral / hyperspectralFlowerObject detectionStress response / tolerancePlant / canopy temperature
Hyperspectral imaging (HSI) is a noncontact camera-based technique that enables deep learning models to learn various plant conditions by detecting light reflectance under illumination. In this study, we investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses, which are vulnerable to abiotic stresses. Cut roses 'All For Love' and 'White Beauty' were used to compare cultivar-specific visible reflectance characteristics associated with contrasting petal pigmentation. HSI was performed at four time points, yielding 640 images per light source from 40 cut roses. The results revealed that the light source strongly affected both the image quality (mAP@0.5 60-80%) and VL (0-3 d) of cut roses. The HAL lamp produced high-quality spectral images across wavelengths (WL) ranging from 480 to 900 nm and yielded the highest object detection performance (ODP), reaching mAP@0.5 of 85% in 'All For Love' and 83% in 'White Beauty' with the YOLOv11x models. However, it increased petal temperature by 2.7-3 °C, thereby stimulating leaf transpiration and consequently shortening the VL of the flowers by 1-2.5 d. In contrast, INC produced unclear images with low spectral signals throughout the WL and consequently resulted in lower ODP, with mAP@0.5 of 74% and 69% in 'All For Love' and 'White Beauty', respectively. The INC only slightly increased petal temperature (1.2-1.3 °C) and shortened the VL by 1 d in the both cultivars. Although FLU and LED had only minor effects on petal temperature and VL, these illuminations generated transient spectral peaks in the WL range of 480-620 nm, resulting in decreased ODP (mAP@0.5 60-75%). Our results revealed that HAL provided reliable, high-quality spectral image data and high object detection accuracy, but simultaneously had negative effects on flower quality. Our findings suggest an alternative two-phase approach for illumination applications that uses HAL during the initial exploration of spectra corresponding to specific symptoms of interest, followed by LED for routine plant monitoring. Optimizing illumination in HSI will improve the accuracy of deep learning-based prediction and thereby contribute to the development of an automated quality sorting system that is urgently required in the cut flower industry.
Why it matches plant phenotyping methods切り花の状態評価に用いるHSIについて、照明条件が画像品質と検出精度に及ぼす影響を比較・検証し、実運用向けの照明戦略を提案しているため、植物フェノタイピング手法が中心である。
abstractwe investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S1: SNR of hyperspectral images acquired under different illumination sources in two cut rose cultivars (‘All For Love’ and ‘White Beauty’); Figure S1: Effect of light sources on hyperspectral image (HSi) quality in cut roses ‘All For Love’ and ‘White Beauty’.Open asset ↗lines:64-174Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
High-dimensional time series phenotypic data is becoming increasingly common within plant breeding programmes. However, analysing and integrating such data for genetic analysis and genomic prediction remains difficult. Here we show how factor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction. We use a subset of Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) elite yield wheat trial of 2014-2015, consisting of 1,033 genotypes. These were measured across three irrigation treatments at several timepoints during the season, using manned airplane flights with hyperspectral sensors capturing 62 bands in the spectrum of 385-850 nm. We perform multivariate genomic prediction using latent variables to improve within-trial genomic predictive ability (PA) of wheat grain yield within three distinct watering treatments. By integrating latent variables of the hyperspectral data in a multivariate genomic prediction model, we are able to achieve an absolute gain of .1 to .3 (on the correlation scale) in PA compared to univariate genomic prediction. Furthermore, we show which timepoints within a trial are important and how these relate to plant growth stages. This paper showcases how domain knowledge and data-driven approaches can be combined to increase PA and gain new insights from sensor data of high-throughput phenotyping platforms.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトルセンサーによる植物表現型時系列データから潜在特徴を抽出し、収量予測に統合する解析手法が研究の中心であるため。
abstractfactor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction
Reproduction assets foundThe paper's Data and code statement provides public GitHub repositories containing the authors' analysis scripts for the hyperspectral latent-factor/Procrustes workflow and the glfBLUP R package implementing the genomic prediction methodology. The hyperspectral phenotype dataset itself is only available upon request, iCode · publicy of secondary trait data and successful integration in multivariate genomic prediction.
As such, this method can contribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning mOpen asset ↗KunstJF/glfBLUP-Procrusteslines:388-492Code · publicntribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning methods for genomic prediction .
Springer , Cham, Switzerland .
External Links: ISBN 978-3-030-89009-4 978-3-030-8901Open asset ↗KillianMelsen/glfBLUPlines:388-492Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a specific sensor and corresponding reference values for developing novel methods. To this end, we present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset, which contains field data from various sensors collected over a growing season, covering six crop species. All data was georeferenced for alignment across sensors and dates. To collect our dataset, we deployed aerial and ground robotic platforms equipped with RGB cameras, LiDARs, and multispectral cameras, aiming to capture a wide variety of modalities and observations from different viewpoints. In addition to sensor data, we also provide manually collected leaf area index and biomass reference measurements. Our dataset enables the development of novel automatic phenotypic trait estimation methods, allows comparisons across different sensors, and generalizability across crop species.
Why it matches plant phenotyping methods複数センサー・ロボットプラットフォームによる圃場フェノタイピング用データセットを構築・提供し、形質推定法の開発、センサー比較、汎化評価を可能にすることが中心的な貢献である。
abstractwe present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset
Reproduction assets foundThe paper's MuST-C multi-sensor, multi-temporal crop phenotyping dataset (RGB/multispectral images, LiDAR point clouds, LAI and biomass reference measurements) is publicly available via the authors' project webpage, and the authors' custom Python processing/loading code is publicly available on GitHub.Dataset · publicThe MuST-C dataset is available via our project webpage https://www.ipb.uni-bonn.de/data/MuST-C/or directly via the bonndata public access repository 10.60507/FK2/OX9XTM34Open asset ↗html-lines:421-440Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye's parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates' "multi-scale parallelism-global modulation-long-range integration," multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network's backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems.
Why it matches plant phenotyping methods茶樹の干ばつストレス状態を画像から検出する改良YOLO手法を開発・検証しており、植物状態の取得・推定が研究の中心である。
abstractthis study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision.
Reproduction assets foundThe paper's Data Availability Statement states the original code is openly available in IEEE DataPort at the allowed DOI URL, making the authors' analysis code a paper-specific public asset.Code · publicThe original code presented in the study are openly available in IEEE DataPort at https://dx.doi.org/10.21227/v32y-mv49.Open asset ↗IEEE DataPort · 10.21227/v32y-mv49html-lines:829-851Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the datasets generated and analyzed (phenotype/trait and vegetation index data from the metribuzin tolerance phenotyping experiments) in the Washington State University Research Exchange repository with a public DOI. No author analysis code repository is URLDataset · public20-
67037-30671, 2022-67013-36426, and 2022-68013-36439.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L I B I L I T Y S TAT E M E N T
The datasets generated and analyzed for this study are avail-
able in the Washington State University Research Exchange
repository (https://doi.org/10.7273/000007507).O RC I D
Melinda Zubrod https://orcid.org/0000-0001-7024-8421
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Ahmadi, Z., Mehrabadi, M., Fazli, M., Khalesro, S., Abedi, R., &
Mokhtassi-Bidgoli, A. (2025). Enhancing tolerance of wheat culti-
vars to meOpen asset ↗Washington State University Research Exchange · 10.7273/000007507pdf-raw-page:12 lines:1-81Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.
Why it matches plant phenotyping methodsMRI画像と画像処理・統計アトラスを統合し、ブドウ樹内部の病変・組織劣化を3Dで定量化する植物フェノタイピング手法の開発が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization.
Reproduction assets foundThe paper's raw/processed MRI datasets are only available from the corresponding author upon reasonable request, but the authors' processing pipeline (scripts and parameters to reproduce processed outputs from raw data) is publicly deposited on Zenodo with an explicit DOI.Code · publiceer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
1 reasonable request. The processing pipeline (including scripts and parameters required to reproduce the
2 processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369.
3
Plant Phenomics Page 26 of 29Open asset ↗zenodo · 10.5281/zenodo.17944369pdf-layout-page:26 lines:1-14Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To accelerate the pace of wheat ( Triticum aestivum L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
Why it matches plant phenotyping methods自動マルチスペクトル画像と機械学習・コンピュータビジョンを統合し、個々の小麦種子の形態・スペクトル形質を高スループットに抽出するパイプラインが研究の中心である。
abstractwe first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds.
Reproduction assets foundThe paper's data availability statement names authors' public source code for the multispectral seed imaging analysis pipeline on GitHub (allowed URL), qualifying as a paper-specific public code asset. The multispectral imagery deposit (BioImage Archive S-BIAD2408, DOI 10.6019/S-BIAD2408) is also paper-specific and perCode · publicSource codes that support the results of this paper is available at https://github.com/The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipeline/releases .Open asset ↗The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipelinelines:562-570Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
AppleCottonPearField / plotNeRF / 3D Gaussian SplattingFruitCounting2D/3D reconstructionSegmentation
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
Why it matches plant phenotyping methodsNeRFと3Dインスタンスセグメンテーションを用いて作物個体・器官数を推定する画像ベース表現型計測手法を開発・検証しており、方法が研究の中心である。
abstractwe introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation.
Reproduction assets foundThe paper contributes a public infield cotton plant dataset (8 plants, ~150 iPhone images each, ground-truth boll counts, SAM instance masks) and states that source code, dataset, and multimedia are available at the authors' public project page, which is an allowed URL. The spectacularai GitHub URL is a generic third-pDataset · publicthat incorporates crop visibility
and mask consistency, enabling robustness against occlusions and annotation
discrepancies.
•
We release a public infield cotton plant dataset designed for 3D
rendering and cotton boll counting tasks.
The source code, dataset, and multimedia material associated with this project
can be found at
https://robotic-vision-lab.github.io/cropnerf .
II Related Work
II-A Image-Based Techniques
Image-based methods typically employ object detection to identify crops within
images. For example, Chen et al. [ 4 ] utilized multiple
convolutional neural networks (CNNs) to map input images to total fruit counts.
Similarly, Häni et al. [ 5 ] formulated crop counting as a
multOpen asset ↗lines:108-187Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
The distribution of spikelets significantly affects wheat (Triticum aestivum L.) spike architecture. However, traditional methods lack the precision to study spikelet distribution effectively. We developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images. In addition to traditional spikelet number per spike (SNS), rachis length (RL), and spikelet density (SD, SNS/RL), we introduced spikelet distribution traits based on rachis internode lengths, providing quantitative insights into spike architecture. RachisSeg showed high consistency with manual measurements for SNS and RL, with the R2 values of 0.975 and 0.998, respectively. Using RachisSeg, we analyzed spikelet distribution patterns across wheat germplasm and found that traits such as spikelet distribution index (SDI) and apical-to-basal spikelet number ratio (AVB_SNS) were moderately correlated with grain yield per spike (GYPS) (r = 0.57 and 0.53, respectively), while internode width (IW) showed a strong positive correlation with GYPS (r = 0.75). Specifically, a denser spikelet arrangement in the upper spike negatively impacted grain number and weight in that section. Furthermore, comparative analysis revealed distinct spikelet distribution patterns among landraces, American cultivars, and Chinese cultivars. In a recombinant inbred line population, we identified 46 quantitative trait loci (QTLs) associated with rachis traits. A major QTL controlling SDI was detected on chromosome 6B, explaining up to 24.8% of the phenotypic variance. Candidate gene analysis suggested TraesCS6B02G417000 as a potential gene, whose mutant exhibited significant changes in RL and SDI. RachisSeg is a powerful tool for quantifying spikelet distribution, facilitating wheat genetic analysis, gene discovery, and breeding.
Why it matches plant phenotyping methodsRachisSegは、スキャン画像からコムギ穂軸・小穂分布形質を自動抽出する深層学習フェノタイピング手法として開発・検証されており、方法が研究の中心です。
abstractWe developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images.
Reproduction assets foundThe paper's authors publicly released the RachisSeg phenotyping pipeline (deep learning node detection and internode segmentation code) together with sample rachis images via their GitHub repository, explicitly stated in the Implementation and Data availability sections.Dataset · publicRachisSeg and sample rachis images is freely available online ( https://github.com/Jiang-Phenomics-Lab/RachisSeg ).Open asset ↗Jiang-Phenomics-Lab/RachisSeglines:514-549Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Background High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs. Results The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs. Conclusion The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.
Why it matches plant phenotyping methods植物器官の形態・色・形状特性を抽出するオープンなRGB画像解析ワークフローを開発し、分割・再構成精度も検証しているため、植物フェノタイピング手法が中心です。
abstractwe have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs.
Reproduction assets foundThe authors publicly release their almond phenotyping workflow (AlmondCV) as Python/R notebooks on GitHub and as a registered WorkflowHub workflow, covering preprocessing, segmentation model development/deployment, morphology, and morphometric analyses used for this paper's measurements.Code · publiche manual process, which is challenging to automate because of variability in shell hardness and size. This extensive dataset will facilitate future studies aimed at dissecting quantitative traits and implementing genomic selection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied relateOpen asset ↗https://github.com/jorgemasgomez/almondcv2lines:222-243Code · publicselection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied related to quantitative almond morphological traits.
Supplementary Fig. S1 . Workflow description outlining the steps involved in developing the segmentation model (green) and deploying it (purple).
Supplementary Fig. S2 . YOpen asset ↗https://workflowhub.eu/workflows/1731lines:222-243Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Genotype-to-phenotype prediction remains a fundamental challenge in current genetic research. In recent years, it has become possible to construct different predictive models based on genomic data. However, in many horticultural crops, it is difficult to accurately verify genomic variations because of the complexity of their genome, making the application of these genome-based methods challenging. Gene expression reflects both genetic regulatory mechanisms and environmental stimuli, offering potential for predicting phenotypes in plants with complex genomes. Thus, in this paper, we tested the possibility for predicting adult plant phenotypes using the gene expression data from seedlings. By applying the transcriptional-based deep learning methods on cut chrysanthemums (Chrysanthemum spp.), which exhibits a complex genetic background characterized by high repetitiveness, heterozygosity, and genome size and is recognized as a segmental allopolyploid, we found that the method is robust and accurate for predicting continuous variables such as leaf vase life, as well as categorical variables such as flower types on the basis of gene expression data. Moreover, the power and performance of transcriptional-based deep learning methods for prediction was validated in rice (Oryza sativa). Our research shows the good performance of phenotype prediction based on gene expression, with potential applications in future gene chip-based breeding practices.
Why it matches plant phenotyping methods遺伝子発現データから成体の植物形質を予測する深層学習手法を開発・検証しており、形質予測が研究の中心である。
titlePredicting adult phenotypes from seedling transcriptional data using deep learning: a case study in chrysanthemum
Reproduction assets foundThe paper deposits its authors' analysis code publicly on GitHub and its raw RNA-seq data (used for the seedling-transcriptome phenotype prediction) in the Genome Sequence Archive with accession CRA022074. Both are paper-specific, public, and actionable.Code · publicn for
multiclass classification. For compiling each model, the RMSprop
optimization algorithm was used with a default initial learning rate
of 0.001, and categorical cross-entropy was selected as the loss func-
tion. The model was trained for 100 epochs with a default batch size
of 32.
The source codes are publicly available at https://github.com/lkwwang-ui/Deep-model-for-predicting-adult-traits-using-seedling-data-study.git
We used Weka 3.9.7 data mining software[23] and performed
machine learning analysis as described in our previously published
paper[24]. In brief, all 101 samples were used for training and testing
with 10-fold cross-validation, and the 20 samples from BGZ were
used for mOpen asset ↗https://github.com/lkwwang-ui/Deep-model-for-predicting-adult-traits-using-seedling-data-study.gitpdf-raw-page:3 lines:1-80Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely and accurate in-season estimation of aboveground biomass (AGB) and yield in winter wheat is crucial for optimizing resources and ensuring food security. Light use efficiency (LUE) models have proven effective in estimating crop gross primary productivity and yield across sites and years due to their strong physiological and ecological mechanisms. However, existing studies are limited to satellite applications and have not utilized unmanned aerial vehicle (UAV) imagery. This study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments. Subsequently, the scalability of the LUE-ML yield prediction approach was assessed in farmer's fields from five counties of Jiangsu Province, China. The results demonstrated that while the AGB for the heading stage was estimated by combining the retrieved LAI and 20-day accumulated meteorological features, the AGB during the post-heading period could be estimated accurately using the stage-skipping or stage-progressive strategy, with the latter ( R val 2 = 0.93) outperforming the former ( R val 2 = 0.84). The combination of one spectral index, LUE-derived AGB, and three 20-day accumulated relative meteorological features (Comb. #6) performed the best ( R cal 2 = 0.89; R val 2 ≥ 0.79) for yield prediction among all combinations. When extended to farmer-field yield prediction across the province, Comb. #6 also achieved acceptable performance. This study suggests the use of LUE-ML models represents a significant step forward towards mechanistic estimation of AGB and yield for cereal crops from UAV imagery.
Why it matches plant phenotyping methodsUAV画像から冬コムギの地上部バイオマスと収量を推定するLUE-ML手法を開発・評価し、圃場で性能検証しているため、植物形質取得・推定が研究の中心である。
abstractThis study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments.
Reproduction assets foundThe paper's Data Availability statement explicitly hosts the core code for the two UAV-LUE AGB estimation strategies and related test data in a public GitHub repository; other data are only available upon request.Code · publicThe core code for the two strategies and related test data in the UAV-LUE method for estimating wheat AGB are hosted in a public repository: https://github.com/qtaocheng/agb-estimation-uav-lue-two-strategies . Other data that support the findings of this study are available from the corresponding author (T.C.) upon reasonable request.Open asset ↗qtaocheng/agb-estimation-uav-lue-two-strategieslines:507-519Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI's superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.
Why it matches plant phenotyping methods植物ストレス状態を推定する動的植生指数と強化学習フレームワークを開発し、実圃場・ベンチマークデータで性能検証しているため、フェノタイピング手法が中心である。
abstractThis study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection.
Reproduction assets foundThe paper publicly releases its authors' field-captured mobile RGB rice canopy dataset on Kaggle and its full RL-VI analysis code (RL formulation, preprocessing, VI computation, training, evaluation) on GitHub. Sentinel-2 imagery and benchmark datasets are third-party public sources, not paper-specific deposits.Dataset · publicThe Mobile RGB dataset, consisting of field-captured rice canopy images collected by the authors at Polur, Tamil Nadu, India, is publicly available on Kaggle under a CC BY-NC 4.0 license (DOI: [https://doi.org/10.34740/kaggle/dsv/14105754](https:/doi.org/10.34740/kaggle/dsv/14105754)).Open asset ↗Kaggle · 10.34740/kaggle/dsv/14105754html-lines:616-683Code · publicAll custom code developed for this work including the RL-VI (Reinforcement Learning–based Vegetation Index) formulation algorithm, image preprocessing scripts, vegetation index computation modules, model training pipelines, and evaluation routines is openly accessible in a public GitHub repository. The code is available without restriction for non-commercial research use and fully available at Github Repository (https://github.com/Poornisrm/Vegetation-Index.git).Open asset ↗GitHub · Poornisrm/Vegetation-Indexhtml-lines:684-711Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Nature-based climate solutions, such as agroforestry, offer potential for carbon sequestration while providing co-benefits. However, the lack of scalable and low-cost measurement, reporting, and verification (MRV) systems limits smallholder participation in carbon markets. This study presents the DiameterAlgorithm, a non-contact method for tree diameter estimation using semantic segmentation and two-dimensional photogrammetry. The fine-tuned model achieved a mean intersection over union (mIoU) of 0.937. The algorithm was tested on image datasets from managed trees settings in Kenya (n = 142) and Pennsylvania, USA (n = 40), with regression analysis showing high accuracy (R² = 0.97, RMSE = 2.20–2.23 cm). Bias analysis showed slight overestimation for small to medium trees (5–35 cm DBH) and underestimation for larger trees (>36 cm DBH), with an overall mean bias of +0.68 cm. Coupled with allometric equations, the DiameterAlgorithm enables scalable, site-level biomass estimation for carbon markets.
Why it matches plant phenotyping methods樹木直径という植物形態形質を画像から推定する手法を開発し、複数地域のデータで精度・バイアスを検証しており、フェノタイピング手法が研究の中心である。
abstractThis study presents the DiameterAlgorithm, a non-contact method for tree diameter estimation using semantic segmentation and two-dimensional photogrammetry.
Reproduction assets foundThe paper publicly releases its tree image dataset (calibration/evaluation images from Kenya and Pennsylvania) on ScholarSphere and the containerized diameter estimation tool on Docker Hub, both explicitly stated in the data availability statement.Dataset · publicThe image dataset that was used to calibrate and evaluate the algorithm can be found on the ScholarSphere repository
of the Pennsylvania State University (https://scholarsphere.psu.edu/resources/08a985a4-d878-4fa9-b2f2-60601005Open asset ↗ScholarSphere · 08a985a4-d878-4fa9-b2f2-60601005pdf-page:13 lines:1-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely and precise detection of diseases on plants is crucial for minimizing losses during crop production in order to sustain food supply demands worldwide. In this work, deep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants using two publicly available datasets, the PlantVillage Potato Leaf Disease (2,152 images) dataset and the Kaggle Mango Leaf Disease dataset (4,000 images). Images were pre-processed, augmented, and split into training and testing datasets (80:20), to enable better model generalization. Four deep learning architectures, namely Convolutional Neural Networks (CNN), AlexNet, Residual Networks (ResNet), and EfficientNet, were evaluated in the context of multi-class disease classification. The baseline CNN achieved a training accuracy of 93.67% and a testing accuracy of 92.61%, with balanced precision and recall (92.5%), thus providing a very strong feature extraction and classification capability. AlexNet showed moderate performance (91.3% training, 90.2% validation), and a very small overfitting was observed. ResNet had an efficient convergence, and attained 96.7% validation accuracy in just a few epochs, thus pointing out the advantage of residual connections in the context of deeper learning. EfficientNet surpassed all the other architectures, since it reached a training accuracy of 98.2% and a validation accuracy of 97.8%, with very small loss (≈ 0.015) and no overfitting, thus proving to have the best generalization ability. The models demonstrated stability and discriminative ability with the support of confusion matrices and accuracy and loss plots produced on an epoch-wise basis. Therefore, the findings indicate that DL models can be adapted for real-time and accurate plant disease diagnosis, establishing a pathway for early remediation, and supporting precision agriculture. The research establishes the opportunity for EfficientNet to be considered a promising solution for scalable smart farming.
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法を開発・比較し、複数データセットで精度を検証しており、植物の病害状態の取得・推定が研究の中心である。
abstractdeep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (PlantVillage potato, mango leaf disease) and states that all code, preprocessing scripts, dataset splits, and model artifacts are publicly available in a GitHub repository (also archived on Zenodo). All three are paper-specific, public, and actionable.Dataset · publicThe datasets analyzed during the current study are available in (https://www.kaggle.com/datasets/aarishasifkhan/plantvillage-potato-disease-dataset)Open asset ↗html-lines:473-503Code · publicAll code, preprocessing scripts, dataset splits, and model artifacts used in this study are publicly available in the GitHub repository at: [https://github.com/logeswarig/PROJECT_1].Open asset ↗logeswarig/PROJECT_1html-lines:473-503Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralLeaf2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.
Why it matches plant phenotyping methods針葉樹シュートの3D構造をフォトグラメトリで取得し、光学特性とともに再利用可能なデータセットとして提供しているため、植物形態・構造の計測手法が中心です。
abstractThis paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the paper's own phenotyping measurements: 3D surface geometry models (.obj) of Scots pine and Norway spruce shoots, sample photos (.jpg), and needle/twig optical property spectra (HCRF/HCTF, .csv, 400-2000 nm). The repository, Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/h39f9t7fjg.1
Direct URL to data: https://data.mendeley.com/datasets/h39f9t7fjg/2Open asset ↗Mendeley · 10.17632/h39f9t7fjg.1lines:47-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice leaf diseases pose a significant and escalating threat to global food security. Timely and accurate detection, particularly in the critical early stages characterized by subtle lesions, is paramount for effective disease management. However, existing solutions often struggle with the complexities of real-world field environments (e.g., variable lighting, occlusions, complex backgrounds), computational constraints on edge devices, and limited generalizability across diverse disease types and plant species. To address these challenges, this study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection. Our key innovations include: (1) A Multi-branch Large-kernel Fusion Depthwise (MLFD) module enhancing multi-scale contextual feature extraction critical for identifying subtle early lesions; (2) A Multi-scale Dilated Transformer Attention (MDTA) module integrating spatial and channel attention mechanisms to improve feature representation under complex conditions; (3) A Lightweight Detection Head (Lo-Head) optimized with grouped and depthwise convolutions, drastically reducing model complexity without sacrificing accuracy. Crucially, extensive experiments demonstrate the framework's superior performance. On a dedicated rice leaf disease dataset, it achieves a mean Average Precision mAP@0.5:0.95 of 62.62%, outperforming state-of-the-art lightweight detectors including YOLOv5n (56.73%), YOLOv8n (57.41%), YOLOv10n (56.14%), and the baseline YOLOv11n (60.85%), while maintaining low computational demands (6.3 GFLOPs, 2.66M parameters). Significantly, rigorous generalization experiments validate the model's exceptional transferability. Evaluated on independent datasets encompassing potato and tomato leaf diseases, the proposed framework consistently surpasses comparable models in mAP@0.5:0.95, demonstrating its robust capability to detect diseases across different plant species. This combination of high accuracy, computational efficiency, and remarkable cross-crop generalizability positions our framework as a highly promising tool for practical deployment on resource-limited edge devices (e.g., drones, field sensors) in smart agriculture systems, enabling proactive disease surveillance and precision control strategies across diverse crops.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出する深層学習手法の開発と、独立データセットによる性能・汎化性検証が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractthis study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection.
Reproduction assets foundThe paper's rice leaf disease detection dataset was curated from three publicly available repositories (one Kaggle, two Roboflow), and the cross-species generalization used two additional public Roboflow datasets (tomato and potato leaf diseases). All five URLs are explicitly listed in the article as data sources. No作者Dataset · publicData Sources: The dataset utilized in this study was curated and screened from the following publicly available online repositories:.Open asset ↗html-lines:110-216Dataset · publicThe Tomato Leaf and Potato Leaf disease datasets were acquired from public domain resources. The dataset links are: Tomato Leaf Diseases: https://universe.roboflow.com/dyploma/tomato-leaf-diseases-4xa5iOpen asset ↗dyploma/tomato-leaf-diseases-4xa5ihtml-lines:747-783Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Aim Phenotypic characters have long been central to species diagnosis and delimitation and remain indispensable even in the age of genomics. However, phenotypic datasets are often complex— spanning dozens of traits of varying types and units, with correlated variables and unbalanced sampling—posing challenges for robust, reproducible analysis. Existing software solutions are fragmented, usually requiring labor-intensive workflows across multiple tools and manual steps, which undermines reproducibility and hinders comparisons across studies. To address these methodological and practical challenges, I introduce Orangutan, an R package designed to provide a flexible, easy-to-implement framework for comparing groups using mensural and meristic data. Innovation Orangutan provides a flexible and efficient framework for analyzing mensural and meristic data, supporting a full suite of statistical and visualization tools optimized for species delimitation and population comparisons. The package streamlines the identification of diagnostic, non-overlapping traits between species, while enabling rigorous assessment of both individual and multivariate trait differences. Core features include optional allometric correction to remove size effects, automated selection of appropriate univariate tests with post hoc comparisons, and integrated multivariate analyses. All outputs, including summary statistics and annotated publication-ready figures, are generated with minimal coding, ensuring accessibility and standardization. Main Conclusions Empirical validation with real-world datasets—including animal and plant species— demonstrates that Orangutan robustly identifies diagnostic traits, reveals both subtle and clear group differences, and achieves high classification accuracy with phenotypic data alone. By automating and unifying key analytical steps, Orangutan promotes reproducibility, transparency, and efficiency in phenotypic research. This package empowers researchers in taxonomy, ecology, and evolutionary biology to adopt quantitative best practices for species delimitation, facilitating comparative studies and advancing methodological standards in morphological data analysis. Orangutan is freely available with comprehensive documentation to support widespread adoption.
Why it matches plant phenotyping methods植物を含む形態形質データの解析・可視化を標準化するRパッケージの開発論文であり、植物種データでの検証も行っているため、表現型解析手法が中心です。
titleOrangutan: an R package for analyzing and visualizing phenotypic data in the context of ecology and systematics
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThe data to reproduce this work and software are freely and publicly available at
https://github.com/metalofis/Orangutan-R.Open asset ↗metalofis/Orangutan-Rpdf-page:15 lines:1-28Dataset · publicThe anole datasets can be downloaded from
https://github.com/metalofis/Orangutan-R/tree/main/example_datasets.Open asset ↗metalofis/Orangutan-R · example_datasetspdf-page:5 lines:1-51Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plants move chloroplasts in response to light, changing the optical properties of leaves. Low irradiance induces chloroplast accumulation, while high irradiance triggers chloroplast avoidance. Chloroplast movements may be monitored through changes in leaf transmittance and reflectance, typically in red light. We present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light. We show how to employ machine learning methods to classify leaves according to the chloroplast positioning. The convolutional network is a method of choice for the analysis of the reflectance spectra, as it allows low levels of misclassification. As a complementary approach, we propose a vegetation index, called the Chloroplast Movement Index (CMI), which is sensitive to chloroplast positioning. Our method offers a high-throughput, contactless way of chloroplast movement detection. Key features • Protocol for detached leaves handled in laboratory conditions. • Based on differential (dark-adapted versus irradiated) hyperspectral images of plant leaves. • Data analysis includes machine learning methods and the calculation of a vegetation index. • Requires irradiation equipment apart from the hyperspectral camera set.
Why it matches plant phenotyping methods葉の反射ハイパースペクトル画像から葉緑体位置を検出・分類する手法と指標を開発し、高スループット測定として提示しており、植物表現型取得が中心である。
abstractWe present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light.
Reproduction assets foundThe protocol explicitly deposits its authors' analysis code (HyperspectralImageProcessing.m, including the pretrained CNN classifier for chloroplast positioning) on GitHub and makes the original hyperspectral images of Arabidopsis and Nicotiana leaves used in the paper's figures available on figshare. Both are paper-‐Code · publicAll code has been deposited to GitHub: https://github.com/plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detection (access date, 08/18/2025)Open asset ↗plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detectionhtml-lines:104-130Dataset · publicOriginal files with hyperspectral images of Nicotiana benthamiana and Arabidopsis thaliana (WT and phot2) leaves, including recordings shown in Figure 3 and Figure 4 of this protocol, can be downloaded from https://figshare.com/articles/dataset/Hyperspectral_images_of_Arabidopsis_thaliana_and_Nicotiana_benthamiana_leaves/30402409?file=58898569Open asset ↗html-lines:104-130Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background The breeding process enables plants to inherit desirable traits, such as yield, flowering time, pest resistance, and cannabinoid and/or terpene content. As a result of these intensive genetic improvement practices, where genetically similar individuals or those from the same lineage are crossed, the expression of unfavorable recessive alleles may occur due to homozygosity. This can lead to less productive plants, increased susceptibility to diseases, and reduced quality. Despite the potential negative effects associated with inbreeding, self-pollination (a form of inbreeding) is a necessary cultivation technique used to obtain seeds that produce phenotypically female plants (feminized seeds) for commercialization and/or to fix desirable traits, albeit at the cost of reduced genetic variability. The Cannabis sativa L. seed market has grown significantly in recent decades, driven by the legalization and regulation of medicinal and recreational use. Self-pollinated feminized seeds are popular among growers and commercial seed banks because, in most cases, they guarantee that inflorescences will express the cannabinoid and terpene profile of the single parent plant. The objective of this work is to compare the morphological variation of seeds obtained from the reversal of female clones followed by self-pollination, and seeds obtained from crossing genetically distinct parental. To study seed shape and size, we employed 2D geometric morphometrics (GM) based on landmarks and semilandmarks, coupled with a supervised machine learning approach and multivariate statistical approach for analysis. Results No direct relationship was observed between size and seed type, although significant differences between varieties were detected. The shape of seeds from crosses between different parents (male and female) showed lower classification accuracy compared to feminized seeds. These results support the hypothesis that inbreeding reduces the variability, as feminized seeds from self-pollination were correctly identified at a high rate using a discriminant function. Conclusions Our research demonstrates that 2D geometric morphometrics can effectively distinguish and trace feminized and self-pollinated cannabis seeds. These seeds exhibit the least morphological variation, enabling accurate identification and providing a reliable foundation for practical applications. The Random Forest classifier's high performance confirms the effectiveness of using morphological traits for seed discrimination. These results open the door for advanced machine-learning techniques aimed to improve scalability and automation.
Why it matches plant phenotyping methods2D幾何形態計測と機械学習を用いて種子の形状・サイズを抽出し、種子タイプを識別する手法が研究の中心であるため。
abstractTo study seed shape and size, we employed 2D geometric morphometrics (GM) based on landmarks and semilandmarks, coupled with a supervised machine learning approach and multivariate statistical approach for analysis.
Reproduction assets foundThe authors publicly deposited the custom Python machine-learning code and the Procrustes coordinate dataset used for the paper's seed-shape classification in a GitHub repository, explicitly stated in the Data availability section.Code · publicTo ensure full reproducibility, the Procrustes coordinates and the custom Python code used for the machine learning classification are provided in a public GitHub repository: https://github.com/Francisco-ft/Self-pollinated-cannabis-seeds-lead-to-less-variation-in-shape.Open asset ↗Francisco-ft/Self-pollinated-cannabis-seeds-lead-to-less-variation-in-shapelines:115-151Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Cassava is one of the most widely cultivated crops worldwide, renowned for its rich natural ingredients and numerous nutritional benefits. However, the complex interdependencies among its features often pose challenges in image restoration and segmentation, particularly when identifying disease regions. In previous work, this manifested as higher false positives and misidentification of non-relevant areas, leading to a decline in precision and accuracy. Methods To address these issues, this study proposed an efficient artificial intelligence-powered image analysis system that leverages optimal feature selection with a HyperCapsInception-ResNet-V2-CNN model to enhance disease detection accuracy. Initially, the dataset was collected from the Kaggle repository, its name was Cassava Leaf Disease Classification, and it comprised 21,367 different images. Our approach began by normalizing cassava plant disease data using adaptive Gaussian Otsu thresholding. Histogram color evaluation and iterative clustering fragmentation were then applied to better isolate disease variations and improve precision. Subsequently, Cascaded Canny Edge Segmentation (CCES) was used to effectively segment the disease region. The disease variation properties were further evaluated using the Optimal Spider Swarm Intelligence Technique (OSSIT) to reduce irrelevant feature dimensions. For classification, the HyperCapsInception-ResNet-V2-CNN model was employed to categorize cassava diseases, including cassava bacterial blight (CBB), cassava mosaic disease (CMD), cassava green mite (CGM) disease, and cassava brown streak disease (CBSD), along with regular and abnormal leaf states. Results The proposed method's simulation results achieved 98.15% accuracy, a 97.22% F1-score, and 96.02% precision, outperforming other traditional methods such as EfficientNetB3, AlexNet, Faster-RCNN, and InceptionV3. Discussion Both optimized feature selection with OSSIT and hybrid HyperCapsInception-ResNet-V2-CNN architecture significantly enhanced the detection reluctance and the classification of the data. These findings indicate that the proposed system is effective in the automated detection of cassava disease and has a high potential of being practical in agricultural practices especially in precision farming and early detection of diseases.
Why it matches plant phenotyping methodsカッサバ葉画像から病変領域を分割・抽出し、病害状態を分類する画像解析手法の開発が研究の中心であるため、植物表現型手法として採用。
abstractCascaded Canny Edge Segmentation (CCES) was used to effectively segment the disease region.
Reproduction assets foundThe paper uses the public Kaggle 'Cassava Leaf Disease Classification' dataset (21,367 cassava leaf images) as its phenotyping input; the dataset is publicly downloadable at the authors' stated URL, which matches an allowed URL.Dataset · publicThe Cassava Leaf Disease Classification dataset is available on Kaggle and comprises 21,367 images. The images have an average resolution of 512 × 512 pixels. The data are split into training and test sets, enabling machine learning algorithms to be trained and tested to accurately detect diseases. The data are available for download from Kaggle: https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classification .Open asset ↗Kaggle · cassava-leaf-disease-classificationlines:523-601Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice leaf diseases significantly reduce productivity and cause economic losses, highlighting the need for early detection to enable effective management and improve yields. This study proposes Artificial Neural Network (ANN)-based image-processing techniques for timely classification and recognition of rice diseases. Despite the prevailing approach of directly inputting images of rice leaves into ANNs, there is a noticeable absence of thorough comparative analysis between the Feature Analysis Detection Model (FADM) and the Direct Image-Centric Detection Model (DICDM), specifically when it comes to evaluating the effectiveness of Feature Extraction Algorithms (FEAs). Hence, this research presents initial experiments on the Feature Analysis Detection Model, utilizing various image Feature Extraction Algorithms, Dimensionality Reduction Algorithms (DRAs), Feature Selection Algorithms (FSAs), and Extreme Learning Machine (ELM). The experiments are carried out on datasets encompassing 3829 original rice leaf images across six classes (bacterial leaf blight, brown spot, leaf blast, leaf scald, sheath blight rot, and healthy leaf). A Direct Image-Centric Detection Model is established without the utilization of any FEA, and the evaluation of classification performance relies on different metrics. Ultimately, an exhaustive contrast is performed between the achievements of the Feature Analysis Detection Model and the Direct Image-Centric Detection Model in classifying rice leaf diseases. The results reveal that the highest performance is attained using the Feature Analysis Detection Model. We have also applied Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretability of the model's predictions. The adoption of the proposed Feature Analysis Detection Model for detecting rice leaf diseases holds excellent potential for improving crop health, minimizing yield losses, and enhancing the overall productivity and sustainability of rice farming.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する手法を中心に、特徴抽出モデルと直接画像モデルを比較・評価しており、植物病害フェノタイピング手法の開発・検証に該当する。
abstractThis study proposes Artificial Neural Network (ANN)-based image-processing techniques for timely classification and recognition of rice diseases.
Reproduction assets foundThe paper's rice leaf disease classification experiments are built on a publicly available Kaggle image dataset (3829 rice leaf images across six classes), explicitly linked in the Data Availability Statement. Datasets produced during the study are only available upon request from the corresponding author.Dataset · publicThe dataset is publicly available at https://www.kaggle.com/datasets/vbookshelf/riceleafdiseases.Open asset ↗Kaggle · vbookshelf/riceleafdiseaseshtml-lines:883-951Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust ( Puccinia graminis f. sp. tritici Eriks. and E. Henn.) of wheat ( Triticum aestivum L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.
Why it matches plant phenotyping methods小麦茎锈病の無症状感染を対象に、ハイパースペクトル画像の前処理パイプラインを開発・評価し、植物病害状態の早期推定性能を検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection
Reproduction assets foundThe paper's data availability statement points to a public Google Drive repository containing the study's hyperspectral datasets used for wheat stem rust early detection. No separate author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:616-634Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Developing an effective machine vision system is crucial to successfully deploying robotic inspection in open field conditions and controlled environments like greenhouses. Robotic arms with vision-based deep learning models offer an efficient, real-time, non-invasive crop monitoring solution. In agricultural settings, they enable consistent, automated inspection under varying conditions, reduce labor dependency, and support early disease detection, enhancing productivity and sustainability in precision farming. Although considerable progress has been made in computer vision-based approaches, significant challenges persist in developing models that reliably perform under the diverse and variable conditions encountered in real-world agricultural settings. Method Within the domain of precision agriculture, we introduce an advanced robotic system for the detection of plant diseases, utilizing an innovative model based on deep learning principles. This system introduces an algorithm for real-time analysis, called as Strawberry Leaf Disease Inspection (SLDI). The algorithm integrates the use of Receptive Guided Channel Attention (RGCA) alongside a Deep Context Aggregator (DCA), designed to significantly improve the characterization and representation of feature sets, thereby enhancing the overall accuracy and efficiency of disease identification. To optimize the system performance and preserve real-time performance, a Multi-Scale Feature Fusion Module (MSFF) is proposed that facilitates a comprehensive multi-level representation, enabling the model to capture disease symptoms promptly. The SLDI algorithm is deployed on a robotic platform equipped with an RGB camera, enabling real-time, in-field inspection of strawberry crops. Results The proposed system is trained on two publicly available datasets, PlantDoc and PlantVillage. It attains a precision of 91.10% and a recall of 88.50%, while maintaining a real-time processing speed of 76.50 frames per second (fps). Experimental field inspection of strawberry studies demonstrates that the proposed model significantly outperforms existing approaches in accuracy and efficiency.
Why it matches plant phenotyping methodsイチゴ葉の病徴をRGB画像と深層学習でリアルタイム検出するアルゴリズムおよびロボットプラットフォームを開発・評価しており、植物病害状態の表現型取得が中心である。
abstractwe introduce an advanced robotic system for the detection of plant diseases, utilizing an innovative model based on deep learning principles.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe publicly avail- able datasets can be accessed at [ www.plantvillage.org ] and [ https://github.com/pratikkayal/PlantDoc-Dataset ].Open asset ↗pratikkayal/PlantDoc-Datasetlines:271-381Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from imagery. Such advancements have led to phenotypic digitization and made rapid yield forecasting possible. Yield predictions are critical to assess the merit of genotypes to propel cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 m above using a DJI M300 drone at 90° nadir and 45° oblique angles. This research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN in detecting sorghum panicles, achieving a mean average precision at 50 % IoU (mAP@0.50) scores of 0.92-0.98, compared to 0.61-0.89 for Faster R-CNN. Panicle detection from field imagery showed a linear correlation of 0.86 with ground truth field panicle counts. Lab imagery analyses measured panicle area, seed counts, and seed area with correlation coefficients of 0.79, 0.94, and 0.25 with respective ground truth observations. Support Vector Regression (SVR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) were used to predict yield with correlation coefficients of 0.74, 0.71, and 0.78, respectively, and SHapley Additive exPlanation (SHAP) analysis revealed panicle seed count as the primary driver of yield prediction. We observed YOLO models are well-suited for extracting yield-predictive features from pertinent images. Such features can then be incorporated into ML regression models to predict yield per se performance with greater accuracy. The GitHub link is provided in the Data availability section.
Why it matches plant phenotyping methodsUAS・実験室画像から穂数、穂面積、種子数・面積などの植物形質を深層学習で抽出し、検出精度を検証して収量予測へ利用する方法が研究の中心である。
abstractThis research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images.
Reproduction assets foundThe authors explicitly state that scripts, fine-tuned models, datasets, and sample images for this sorghum yield-forecasting study are publicly available on GitHub, matching the allowed URL exactly.Code · publicThe scripts, fine-tuned models, datasets, and sample images pertinent to this manuscript are available on GitHub at https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.git .Open asset ↗https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.gitlines:244-299Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In this study, we present a combined image dataset created from two distinct plant species: Hibiscus and Tea leaf. The dataset consists of high-resolution images of leaves from both species, captured using a SONY α7 II DSLR camera and a OnePlus 7T lubricant Tea Leaf dataset includes images categorized into five disease classes: Algal Leaf Spot, Brown Blight, Grey Blight, Red Leaf Spot, and Healthy, while the Hibiscus Leaf dataset includes images labeled across eight conditions, including citrus spot, fungal infection, mild edge damage, and healthy foliage. To ensure balanced representation and address class imbalances, extensive data augmentation techniques-such as flipping, rotation, zooming, shifting, noise addition, and brightness adjustment-were applied, resulting in a total of 1,413 combined original images and 13,000 augmented images. The ConvNextTiny deep learning model was fine-tuned on this combined dataset to classify the various leaf conditions, achieving an overall accuracy of 96%. This demonstrates the model's robust performance and high discriminatory power across the diverse set of leaf diseases and conditions. This experiment highlights the utility of combining multiple plant species into a single dataset and utilizing a lightweight yet effective model like ConvNextTiny for plant disease classification. The resulting dataset, along with the model and training scripts, is publicly available to facilitate further research in plant pathology, computer vision, and smart farming applications, enabling more accurate and efficient early-stage disease detection for both Hibiscus and Tea plants.
Why it matches plant phenotyping methods植物葉の病害・健全状態を画像から分類するデータセットを構築し、分類モデルで性能評価しているため、植物フェノタイピング手法・ベンチマークが中心です。
abstractwe present a combined image dataset created from two distinct plant species: Hibiscus and Tea leaf
Reproduction assets foundThe paper's combined Hibiscus and Tea leaf disease image dataset is publicly deposited on Mendeley Data (DOI 10.17632/5bzy89brkv.4), and the authors' augmentation/training scripts are on a public GitHub repository; both are paper-specific, public, and directly actionable.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/5bzy89brkv.4
Direct URL to data: https://data.mendeley.com/datasets/5bzy89brkv/4Open asset ↗Mendeley Data · 10.17632/5bzy89brkv.4lines:1-46