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 confirmedOpenAlex · arXiv · checked 5 Sept 2026
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Code / 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings. Here, we propose Lite U-net FiLM (LUF-net), a lightweight U-net framework integrated with feature-wise linear modulation (FiLM) layers, which incorporates both multispectral-derived irradiance and camera exposure parameters as auxiliary inputs. This metadata-aware design enables dynamic modulation of intermediate image features, allowing the network to disentangle unified canopy color from environmental artifacts. Experiments conducted across diverse crop types (soybean, rice, maize), flight altitudes (6m, 12m, 25m, 40m), and illumination conditions (overcast skies, cloudy, sunny, early morning) demonstrate that the LUF-net model substantially reduces the mean absolute percentage error to below 5.5%, outperforming conventional Gray-world, U-net, AlexNet-MLP, and SIDBlock-MLP methods. Ablation experiments further show that both irradiance and exposure metadata provide complementary information, while FiLM-based conditioning effectively integrates these acquisition parameters into feature learning, jointly contributing to improved color reconstruction performance. Moreover, correlation analysis indicates that the model's color reconstruction errors are weakly dependent on irradiance and exposure settings, confirming that LUF-net reduces sensitivity to external imaging conditions while maintaining physically meaningful and robust corrections.
Why it matches plant phenotyping methodsUAV画像の色校正手法を開発し、複数作物・撮影条件で性能検証しており、作物フェノタイピングの画像取得・補正が中心である。
abstractAccurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings.
Reproduction assets foundThe paper's data and code availability statement explicitly points to a public GitHub repository containing training/evaluation/inference code, pretrained model weights, and example data for the LUF-net color calibration method.Code · publicThe data and source code for model training, evaluation, and inference, together with pretrained model weights, example data, and detailed usage instructions, is publicly available at: https://github.com/wangchufeng3652/color-correction.Open asset ↗wangchufeng3652/color-correctionhtml-lines:278-299Code / 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 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 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 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Code / 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 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 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 · 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
Machine learning has become an increasingly important tool for overcoming agricultural challenges by enabling efficient and consistent classification of crop-related data. Training such supervised models requires high quality labeled datasets. This work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species on California's Central Coast: annual sowthistle ( Sonchus oleraceus ) and little mallow ( Malva parviflora ). Hyperspectral imaging provides rich spectral-spatial data cubes that can support the development of deep learning models and autonomous technology for precision weed management. Plants were grown in a greenhouse under five conditions: standard, drought, overwatering, excess fertilizer, and no fertilizer. Custom MATLAB scripts were utilized for preprocessing, including k-means clustering to define regions of interest (ROIs), and extraction of spectral metrics. Data visualization was performed using Wolfram language and MATLAB. The dataset includes both raw and ENVI-formatted hyperspectral cubes and pre-processed MATLAB outputs, supporting spectral feature engineering, benchmark development, and exploratory machine learning workflows for controlled environment stress classification.
Why it matches plant phenotyping methods植物のストレス状態を対象とするハイパースペクトル画像データセットで、ROI抽出・スペクトル指標化と機械学習ベンチマークを中心的に提供しているため、植物フェノタイピング手法・データセットとして適格。
abstractThis work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species
Reproduction assets foundThe paper is a Data in Brief article describing its own hyperspectral imaging dataset of annual sowthistle and little mallow under five abiotic stress treatments, deposited publicly on Zenodo (record 17398082). The dataset includes raw ENVI-format hyperspectral cubes, preprocessed MATLAB outputs (ROI masks, extracted植被Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: zenodo.17398082
Direct URL to data: https://doi.org/10.5281/zenodo.17398082Open asset ↗Zenodo · zenodo.17398082html-lines:92-120Code / 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 confirmedOpenAlex · arXiv · checked 15 Sept 2026
Multispectral / hyperspectralLeafCalibration / preprocessingSegmentationVisualization / data management
Hyperspectral imaging (HSI) allows researchers to study plant traits non-destructively. By capturing hundreds of narrow spectral bands per pixel, it reveals details about plant biochemistry and stress that standard cameras miss. However, processing this data is often challenging. Many labs still rely on loosely organized collections of lab-specific MATLAB or Python scripts, which makes workflows difficult to share and results difficult to reproduce. MVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data. The software handles everything from calibrating raw ENVI files to detecting and clipping individual leaves based on multiple vegetation indices (NDVI, CIRedEdge and GCI). It also includes tools for data augmentation to create training-time variations for machine learning and utilities to visualize spectral profiles. MVOS_HSI can be used as an importable Python library or run directly from the command line. The code and documentation are available on GitHub. By consolidating these common tasks into a single package, MVOS_HSI helps researchers produce consistent and reproducible results in plant phenotyping
Why it matches plant phenotyping methods葉レベルHSIの校正・葉検出・切り出しを含む再現可能な植物表現型解析用ソフトウェアであり、手法が中心。
abstractMVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data.
Reproduction assets foundThis is a software paper describing MVOS_HSI, the authors' open-source Python library for hyperspectral plant-phenotyping preprocessing (calibration, leaf segmentation/clipping, augmentation, spectral plotting). The authors' code is explicitly and publicly available on GitHub at the allowed URL, making it a paper-phenyCode · publicyping.
K eywords Hyperspectral imaging ⋅ \cdot
Plant phenotyping ⋅ \cdot
Data preprocessing ⋅ \cdot
Vegetation indices ⋅ \cdot
Data augmentation ⋅ \cdot
Python
Table 1: Code Metadata for MVOS_HSI
Nr.
Code metadata description
Metadata
C1
Current code version
v0.2.1
C2
Permanent link to code/repository used for this code version
https://github.com/MVOSlab-sdstate/mvos_hsi
C3
Permanent link to Reproducible Capsule
N/A
C4
Legal Code License
MIT License
C5
Code versioning system used
git
C6
Software code languages, tools, and services used
Python 3.x; NumPy, SciPy, Matplotlib
C7
Compilation requirements, operating environments & dependencies
Standard scientific Python environment on Windows, LinOpen asset ↗MVOSlab-sdstate/mvos_hsi · mvos_hsilines:1-122Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Abstract Background: Next-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support Results: Using our integrated, co-located methods, we produced hierarchically-scaled datasets detailing the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available at within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C). In this paper, we detail methods and the repository structure. Conclusions: The study was designed to evaluate and advance methods for 3D fuel characterization and to provide consistently scaled and labelled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and structure-from-motion photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically-scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.
Why it matches plant phenotyping methodsALS、TLS、SfMと現地観測を統合して植物群落の3D構造・燃料特性を取得し、手法の評価・改良と公開データセット構築を主目的としているため、植物形質計測法が中心である。
abstractThe objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support
Reproduction assets foundThe paper's hierarchically scaled ALS/TLS/SfM point clouds, field fuel measurements, and analysis scripts are explicitly stated to be open source and archived in the Wildland Fire Science Initiative data repository (DOI 10.60594/W4859C), a paper-specific public asset directly reproducing this study's phenotyping/fuel-3Dataset · publicThe datasets and analysis scripts for this study are open source and are being archived with the Wildland Fire Science Initiative data repository (doi.org/10.60594/W4859C), including project metadata, methods documentation and data libraries (Prichard and Rowell 2025).Open asset ↗Wildland Fire Science Initiative data repository · 10.60594/W4859Clines:384-403Code / 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 confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
None
1.
Value of the Data
•
The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published15 Feb 2026Journal of Artificial Intelligence and Engineering Applications (JAIEA)Cited by 0 · OpenAlex ↗
Monitoring plant health is an important factor in maintaining agricultural productivity. Manual identification of leaf diseases requires expert knowledge and is prone to errors due to visual similarities among disease symptoms. This study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach. The dataset consists of plant leaf images representing three disease classes: Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot. Prior to model training, the images undergo preprocessing steps including image resizing and pixel normalization. The performance of the CNN model is evaluated using a testing dataset that is not involved in the training process, employing accuracy, confusion matrix, precision, recall, and F1-score as evaluation metrics. Experimental results show that the proposed model achieves a test accuracy of 95.56%, with balanced performance across all disease classes. In addition to quantitative evaluation, the trained model is implemented in a Streamlit-based application, allowing users to upload plant leaf images and obtain disease classification results interactively. The findings indicate that the CNN-based approach is effective for plant leaf disease classification and has potential application as an early decision-support system for plant health monitoring.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するCNN分類法を開発し、独立テストデータ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 study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach.
Reproduction assets foundThe paper's phenotyping input is a publicly available PlantVillage image dataset (900 leaf images across three disease classes) obtained from Kaggle, with an explicit authors' URL. No author analysis code or trained model is publicly deposited.Dataset · publicleaf disease images obtained from the PlantVillage Dataset, which is publicly available through the Kaggle platform [17]. The dataset is
organized using a folder-based class structure, where each folder represents a specific leaf disease category. In this study, three disease
classes are used—Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot—with 300 images per class, resulting in a total of
900 images.Open asset ↗Kagglepdf-page:3 lines:1-51Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published14 Feb 2026International Journal of Engineering Trends and TechnologyCited by 0 · OpenAlex ↗
The study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data, focusing on the ability to withstand salt-and-pepper and Gaussian noises. The image datasets used in agriculture are usually affected by noise because of changes in light, sensor defects, and environmental conditions, which may lead to lower diagnostic accuracy. In order to address this, the proposed system incorporates high noise reduction methods whereby a median filter and a Gaussian filter are used to restore the image quality without compromising on the important leaf texture information. After processing, colour, texture, and shape are used to extract features, which are effective in extracting disease-specific visual representations. These fine features are then trained on various optimized machine learning models, such as Light Gradient Boosted Machine (LGBM), Quantum Support Vector Machine (QSVM), a Modified Random Forest (MRF) with adaptive weighted features, and a Multi-SVM classifier with a custom kernel to map nonlinear features. Through experimental analyses, the proposed ensemble framework is shown to be highly accurate, robust, and noise-tolerant as opposed to the traditional frameworks. The hybrid method is effective in recognizing the significant cucumber diseases and grapes, including powdery mildew, downy mildew, and anthracnose, which will be utilized in the noisy agricultural conditions in the real world. In general, this system offers a noise-resistant, reliable, and computationally efficient system to detect early signs of plant diseases, which can be used in sustainable crop monitoring and precision farming.
Why it matches plant phenotyping methods植物葉の画像から病徴を推定する画像処理・特徴抽出・機械学習システムの設計開発が中心であり、植物病害状態のフェノタイピング手法に該当する。
abstractThe study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (cucumber and grape) as its phenotyping inputs; both are publicly accessible with URLs given in the references. No author code or model checkpoints are reported.Dataset · publicGrape Disease Dataset, which was collected on Kaggle
[17], is an extensive collection of images created for the
classification and analysis of different diseases in grape
leaves.Open asset ↗Kagglepdf-raw-page:7 lines:1-64Code / 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 · checked 5 Sept 2026
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Code / dataset availability confirmedCrossref · checked 5 Sept 2026
ABSTRACT Sample preparation is an important first step to obtain high quality mass spectrometry imaging (MSI) data. Preparing plant tissues is especially challenging for MSI of thin tissues along the lateral dimensions. The unique challenges involved with plant tissues, such as fragile cell walls, hydrophobic barriers, and specific tissue structures, often lead to inefficiency and difficulties in sample preparation. Imprinting plant tissues onto porous polytetrafluoroethylene (pPTFE) sheet has been widely used to extract internal metabolites in leaves and petals while keeping spatial resolution for MSI. However, pressure applications were typically made manually using a vise or pliers leading to low reproducibility and resolution in MS images. In this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting. To evaluate the performance of the new device, Lemna minor fronds, Arabidopsis thaliana , and Bacopa monnieri leaves were imprinted onto the pPTFE with PNP, vise, or pliers, and matrix‐assisted laser desorption/ionization (MALDI) MSI was obtained on the imprints. The PNP showed dramatic improvements in reproducibility and image quality compared to manual pressure application tools.
Why it matches plant phenotyping methods植物組織の空間的な代謝物情報を再現性よく取得するための空気圧式インプリンティング装置を開発し、手動法と性能比較している。植物表現型取得に関わる試料調製・イメージング手法が中心である。
abstractIn this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting.
Reproduction assets foundThe paper's MALDI-MSI data (imzML files of imprinted Lemna minor, Arabidopsis, and Bacopa tissues) are openly deposited in a paper-specific METASPACE project, as stated in the Data Availability Statement. No author analysis code or trained models are disclosed.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in METASPACE (https://metaspace2020.eu/project/pnp_ptfe_imprinting_plant).Open asset ↗METASPACE · pnp_ptfe_imprinting_planthtml-lines:230-307Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput plant phenotyping with hyperspectral imaging (HSI) is pivotal for accelerating crop improvement to address global food security. Adoption is limited by a data-processing bottleneck, forcing a trade-off between costly, inflexible commercial software and programming-intensive open-source libraries. To overcome this barrier, we developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface. The platform introduces spectrally guided segmentation strategies (Range Averaging, Difference Enhancement) and a spectral Fractional-Order Differencing (FOD) preprocessor to enhance extraction of subtle, physiologically relevant features. Across diverse in-house and public datasets, FOD-preprocessed spectra improved model performance over conventional pipelines, yielding 87.35% accuracy for tomato maturity and R 2 = 0.878 for fruit firmness. In cross-software benchmarks, PlantSpecLab matched the accuracy of ENVI and code-based Python pipelines while reducing end-to-end workflow time by >90% (>80 min to ∼8 min). PlantSpecLab provides a transparent, efficient analytical environment that lowers the technical barrier to HSI analysis. This enables researchers to prioritize biological interpretation while minimizing computational overhead.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から表現型特徴を抽出・モデル化するオープンソース基盤を開発し、既存ソフトウェアとの性能・処理時間を比較検証しているため、フェノタイピング手法が中心である。
abstractwe developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface.
Reproduction assets foundThe authors explicitly state the PlantSpecLab source code (the platform used for all phenotyping analyses in the paper) is publicly available on GitHub under an MIT license, with a versioned release archived alongside the data.Code · publicsis. Jingye Liu: Data curation. Chu Zhang: Supervision, Writing—review & editing. Wei Xu: Supervision, Funding acquisition, Writing—review & editing.
Data and code availability
All data and code that support the findings of this study will be made publicly available upon publication. The PlantSpecLab source code is available at https://github.com/Another-Train/PlantSpecLab (MIT License), with a versioned release archived alongside the data.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62265015 and 32360750), the Xinjiang Uygur Autonomous Region Key R&D Program (Grant No. 2023B02028-3), and the Finance Plan Project of the 8th Division of the Open asset ↗Another-Train/PlantSpecLablines:458-487Code / 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 confirmedCrossref · checked 15 Sept 2026
The farming of citrus is a crucial component of Pakistan’s fruit-based agricultural economy. But, the foliar diseases citrus canker, black spot, and greening have been posing a constant threat on citrus’s productivity. An optimal solution is an early and accurate detection of these diseases to improve the productivity. Therefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs). The proposed solution has five stages: image acquisition (dataset), preprocessing, data augmentation, deep feature extraction and optimization, and disease classification. Firstly, the images are obtained from a public dataset downloaded from Kaggle. Secondly, preprocessing techniques are used to improve the image quality and shape, thirdly the data augmentation techniques are used to enhance the model generalization, fourthly pre-trained models DenseNet-121, MobileNet, and InceptionV3 with transfer learning technique to extract deep features, and finally Adam optimizer and categorical cross-entropy loss function are used to fine tune the pre-trained models for classifications. The proposed model is evaluated on accuracy, precision, recall, and F1-score metrics. All the models demonstrated robust performance while DenseNet-121 achieved the best performance. The evaluation results assured the robustness of the use of transfer learning-based DCNN in citrus leaf disease detection.
Why it matches plant phenotyping methods柑橘葉の病害状態を画像から直接検出・分類する深層学習ワークフローが研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractTherefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs).
Reproduction assets foundThe paper's phenotyping input is a public Kaggle citrus leaf image dataset (654 RGB images of healthy, blackspot, canker, and greening leaves) explicitly cited with a URL matching an allowed URL. No author code or trained models are reported as publicly available.Dataset · publictaset is essential. Additionally, the dataset must be prepared
so that our model can fully comprehend the data. The model will then be able to effectively
use that dataset for learning. A random sample of infected and healthy leaves images from the
datasets shown in Figure 1. The details of the images are provided in Table 1.
1
https://www.kaggle.com/datasets/sourabh2001/citrus-leaves-dataset?resource=downloadOpen asset ↗Kaggle · sourabh2001/citrus-leaves-datasetpdf-raw-page:3 lines:1-48Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
MultimodalLiDAR / point cloudAnnotation / quality controlClassificationObject detectionCalibration / preprocessingSegmentationTracking
Plant phenomics, the comprehensive study of plant phenotypes, has gained prominence as a vital tool for understanding the intricate relationships between genotypes and the environment. Image-based plant phenomics has progressed rapidly, and three-dimensional (3D) phenotyping is a valuable extension of traditional 2D phenomics. However, the increased data dimensionality poses challenges to feature extraction and phenotyping. In recent decades, deep learning has led to remarkable progress in revolutionizing 3D phenotyping. Therefore, this review highlights the importance of using deep learning in 3D plant phenomics. It systematically overviews the capabilities of deep learning for 3D computer vision, covering 3D representation, classification, detection and tracking, semantic segmentation, instance segmentation, and generation. Additionally, deep learning techniques for 3D point preprocessing (e.g., annotation, downsampling, and dataset organization) and various plant phenotyping tasks are discussed. Finally, the challenges and perspectives associated with deep learning in 3D plant phenomics are summarized, including (1) benchmark dataset construction by using synthetic datasets and methods such as generative artificial intelligence and unsupervised or weakly supervised learning; (2) accurate and efficient 3D point cloud analysis by leveraging multitask learning, lightweight models, and self-supervised learning; and (3) deep learning for 3D plant phenomics by exploring interpretability, extensibility, and multimodal data utilization. The exploration of deep learning in 3D plant phenomics is poised to spur breakthroughs in a new dimension of plant science.
Why it matches plant phenotyping methods3D植物フェノミクスにおける深層学習手法を体系的にレビューしており、植物形質の抽出・推定手法が中心である。
abstractTherefore, this review highlights the importance of using deep learning in 3D plant phenomics.
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 can be downloaded from https://github.com/Jinlab-AiPhenomics/Mazie3D.Open asset ↗Jinlab-AiPhenomics/Mazie3Dhtml-lines:332-336Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotMultimodalMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / fieldCalibration / preprocessingGrowth / development / phenology
This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400-700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility. The dataset integrates several components: (i) multispectral images that provide spatial information on canopy and structural characteristics, (ii) field spectral signatures with detailed reflectance values for each sampled plant, and (iii) metadata describing phenological stage, acquisition date and time, environmental conditions, and equipment settings. For subsequent analysis, data was preprocessed and normalized to enable reliable comparisons between growth stages and across acquisition sessions, resulting in a clean, structured resource ready for computational analysis. In this regard, this dataset has been organized to facilitate its direct application across multiple research and development contexts. Specifically, potential applications include the training and validation of machine learning and computer vision models for automated phenological stage classification, harvest time estimation, and development of species-specific vegetation indices. Moreover, owing to its standardized design, the resource can serve as a benchmark for comparing methods, validating algorithms, and supporting reproducible workflows in precision agriculture and remote sensing. Beyond Stenocereus queretaroensis, the documented acquisition and preprocessing methodology can be replicated or adapted to generate similar multimodal datasets for other climate-resilient crops, particularly those cultivated in arid and semi-arid regions. This could enable comparative analyses across species and provide a reference for extending multimodal sensing approaches to underrepresented plants of ecological and economic importance.
Why it matches plant phenotyping methods植物の生育段階・生理・構造特性を対象に、標準化されたマルチスペクトル画像とフィールド分光データを収集・前処理した再利用可能なデータセットであり、ベンチマークやアルゴリズム検証を目的とするため、フェノタイピング手法が中心です。
abstractThis data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo)
Reproduction assets foundThe paper's own multimodal phenotyping dataset (multispectral/RGB images, spectral signatures, NDVI products, metadata, and example MATLAB scripts) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public) at ∼1750 m a.s.l., under semi-arid temperate conditions with spring temperatures ranging 20–33°C. The data were collected from the Unit Academic of Electrical Engineering Plantel Jalpa.
Data accessibility
Repository name: Multimodal_Cactaceae_Dataset_25
Data identification number: doi:10.17632/skw8tjc82f.1
Direct URL to data: https://data.mendeley.com/datasets/skw8tjc82f/1
Instructions for accessing these data: click on the direct URL to obtain the multimodal data from Mendeley Dataset Repository.
Related research article
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1.
Value of the Data
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These data provide a unique, non-invasive resource for studying Stenocereus spp. physiology. The integrated collection of high-resolution multOpen asset ↗Mendeley Data · doi:10.17632/skw8tjc82f.1lines:32-58Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.
Why it matches plant phenotyping methods植物フェノタイピングプラットフォームで取得した3D点群と器官レベル注釈を提供するデータセットで、再利用可能な画像解析・AI開発基盤が中心です。
abstractThis data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India).
Reproduction assets foundThe paper's own annotated 3D point cloud dataset (223 scans of legumes with organ-level segmentation annotations), raw scanner data, MIAPPE metadata, and preprocessing/cuboid-generation/baseline-evaluation code are publicly deposited on Figshare and mirrored on GitHub.Code · publicinto this software. All the code and data are also available as the GitHub (https://github.com/kit-pef-czu-czOpen asset ↗GitHubpdf-page:2 lines:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Diagnosis of banana plant disease is a crucial aspect of sustaining the harvest of crops and their quality. Visual inspection of certain diseases like Black Sigatoka, Panama disease, and aphids is not easy and can lead to misjudgments. Generally, traditional deep learning approaches have been previously used but they have not performed well in addressing issues of class imbalance, sensitive disease differentiation and noisy images obtained in the field. Furthermore, most models are based on a collection of predetermined preprocessing methods and single-path networks that limit their ability to generalize to a wide variety of environments. Current methods of deep learning tend to achieve reasonable overall performance but fail to perform well on key performance indicators such as recall and F1-score when considering underrepresented and overlapping classes, such as Yellow and Black Sigatoka. Such constraints impede efficient field implementation, as diseases of minority classes are often falsely classified. To overcome these deficiencies, we develop a novel Duel-Path Attention Fusion Network (DPAFNet) that is trained utilizing adaptive quantum monarch butterfly optimization (AQMBO). The concept behind the proposed model is to feed MaxViT and HorNet-S two feature extractors to deliver global contextual details and minute-scale textural features. The traditional filters which do a reasonable job in handling dynamic noise and contrast are replaced by a learnable preprocessing unit. The cross-layer fusion attention encourages interclass discriminative learning of diseased plants. The suggested model has been trained and tested on an open-source dataset of Mendeley banana disease, which includes 5,170 images in 7 disease categories and 1 control condition. The accuracy, F1-score and MCC of 98.6% and 0.93 and 0.87 respectively (achieved experimentally) demonstrate the superiority of DPAFNet over baseline models such as EfficientNetB0 (accuracy 95.0%), DenseNet121 and ResNet50 (accuracy 93.50% and 92.0% respectively). As can be seen, the model had a 0.26-0.48 increase in F1-score in the challenging Panama disease category. These results prove that the proposed architecture can be successfully used to achieve high-accuracy disease classification in smart agriculture that is robust and prepared for field implementation.
Why it matches plant phenotyping methodsバナナ植物の病徴画像から病害状態を推定する深層学習手法を開発し、データセットとベースラインで性能検証しており、植物フェノタイピング手法が中心である。
abstractTo overcome these deficiencies, we develop a novel Duel-Path Attention Fusion Network (DPAFNet) that is trained utilizing adaptive quantum monarch butterfly optimization (AQMBO).
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 suggested model has been trained and tested
on an open-source dataset of Mendeley banana disease, which includes 5,170 images in 7
disease categories and 1 control condition.Open asset ↗pdf-page:1 lines:1-55Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Color accuracy and consistency in remote sensing imagery are crucial for reliable plant health monitoring, precise growth stage identification, and stress detection. However, without effective color correction, variations in lighting and sensor sensitivity often cause color distortions between images, compromising data quality and analysis. This study introduces a novel in-flight color correction approach for RGB imagery using cooperative dual unmanned aerial vehicle (UAV) flights integrated with a color chart (CoF-CC). The method employs a master UAV equipped with an RGB camera for image acquisition and a synchronized secondary UAV carrying a ColorChecker (X-Rite) chart, ensuring persistent visibility of the chart within the imaging field of the master UAV for the calculation of a color correction matrix (CCM) for in-flight image correction. Field experiments validated the method by analyzing cross-sensor color consistency, assessing color measurement accuracy on field-grown rice leaves, and demonstrating its practical applications using rice maturity estimation as an example. The results indicated that the CCM significantly enhanced color accuracy, with a 66.1 % reduction in the average CIE 2000 color difference (ΔE), and improved color consistency among the six RGB sensors, with a 70.2 % increase in the intracluster distance. CoF-CC subsequently reduced ΔE from 18.2 to 5.0 between the corrected rice leaf color and ground-truth measurements, indicating that the color differences were nearly perceptible to the human eye. Moreover, the corrected imagery significantly enhanced the rice maturity prediction accuracy, improving the R 2 from 0.28 to 0.67. In summary, the CoF-CC method standardizes RGB images across diverse lighting conditions and sensors, demonstrating robust performance in color analysis and interpretation under open-field conditions.
Why it matches plant phenotyping methods植物葉の色および成熟度を推定するためのUAV画像色補正法を開発し、圃場で精度検証と成熟度推定への適用を行っており、フェノタイピング手法が中心である。
abstractThis study introduces a novel in-flight color correction approach for RGB imagery using cooperative dual unmanned aerial vehicle (UAV) flights integrated with a color chart (CoF-CC).
Reproduction assets foundThe paper's UAV image datasets are explicitly deposited on GitHub (RiceUAVImageData); labelme is a generic third-party tool, not a paper-specific asset.Dataset · publicThe UAV image datasets used in this study are openly available in the GitHub repository at https://github.com/GaryLXQ/RiceUAVImageData .Open asset ↗GaryLXQ/RiceUAVImageData · GaryLXQ/RiceUAVImageDatalines:179-198Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published31 Jul 2025International Journal for Research in Applied Science and Engineering TechnologyCited by 3 · OpenAlex ↗
The early and accurate detection of plant diseases plays a pivotal role in enhancing crop health and ensuring food security in modern agriculture. Traditional disease diagnosis techniques often rely on visual inspection by experts, which may be subjective, time-consuming, and inaccessible to farmers in remote regions. To overcome these limitations, this project introduces an intelligent plant disease detection system that leverages deep learning and image analysis to identify symptoms directly from leaf images. The proposed methodology begins with the collection of a curated dataset comprising various plant species exhibiting both healthy and diseased conditions. The images undergo preprocessing to enhance quality and ensure consistency, followed by feature extraction using Convolutional Neural Networks (CNNs). Transfer learning is applied to improve model generalization and reduce the training time by utilizing pre-trained models. The system is integrated into a Flask-based web application, enabling users to upload leaf images and receive instant disease diagnoses along with treatment suggestions and suitable fertilizers. Evaluation of the model has shown high classification accuracy across multiple disease classes, affirming its potential to support precision agriculture. The solution is designed to be lightweight, user-friendly, and deployable in real-world agricultural settings, aiming to assist farmers with timely and informed interventions to mitigate crop loss and promote sustainable farming practices.
Why it matches plant phenotyping methods葉画像から植物の病徴を深層学習で抽出・分類するシステム開発が中心であり、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 project introduces an intelligent plant disease detection system that leverages deep learning and image analysis to identify symptoms directly from leaf images.
Reproduction assets foundThe paper's only qualifying paper-specific asset is the PlantVillage leaf-image dataset, which the authors explicitly state they used for training and evaluation. No author analysis code, trained model checkpoints, or supplementary data deposit is mentioned with an availability statement or URL.Dataset · publicThis project uses the PlantVillage dataset, a
well-known open-source repository that contains labeled images of healthy and diseased plant leaves.Open asset ↗PlantVillagepdf-page:3 lines:1-55Code / dataset availability confirmedCrossref · checked 15 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-day 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: a inital estimate immediately after the 10-day reference period, four subsequent estimates every 10 days using new observations, and a final consolidated estimate 90 days 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://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsFPARという作物・植生キャノピーの明示的な状態量を対象に、MODIS/VIIRSデータのNRTフィルタリング、相互校正、品質評価を開発・記述しており、単なる農業利用ではなく再利用可能な測定データセットと抽出手法が中心である。
abstractwe present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications.
Reproduction assets foundThe paper's own filtered and intercalibrated MODIS/VIIRS FPAR dataset (the paper's core output) is explicitly described as open and freely available in near real time via the JRC Data Catalogue and the ASAP website, both of which appear in allowed_urls. No author analysis code is mentioned.Dataset · publicec.europa.eu/, last
access: 30 September 2025) early warning system. The
FPAR dataset is accompanied by associated quality layers
and has a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR Open asset ↗1aac79d8-0d68-4f1c-a40f-b6e362264e50pdf-raw-page:3 lines:1-86Dataset · publichas a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR data; (ii) to present the intercalibration performed
between the filtered MODIS-FPAR and the filtered VIIRS-
FPAR; (iii) to evaluate tOpen asset ↗ASAP websitepdf-raw-page:3 lines:1-86Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate diagnosis of apple diseases is vital for tree health, yield improvement, and minimizing economic losses. This study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases. The approach begins with enhancing the CycleGAN-M network using a multi-scale attention mechanism to generate synthetic samples, improving model robustness and generalization by mitigating imbalances in disease-type representation. Next, an improved YOLOv8s-KEF model is introduced to overcome limitations in feature extraction, particularly for small lesions and complex textures in natural environments. The model's backbone replaces the standard C2f structure with C2f-KanConv, significantly enhancing disease recognition capabilities. Additionally, we optimize the detection head with Efficient Multi-Scale Convolution (EMS-Conv), improving the model's ability to detect small targets while maintaining robustness and generalization across diverse disease types and conditions. Incorporating Focal-EIoU further reduces missed and false detections, enhancing overall accuracy. The experiment results demonstrate that the YOLOv8s-KEF model achieves 95.0% in accuracy, 93.1% in recall, 95.8% in precision, and an F1-score of 94.5%. Compared to the original YOLOv8s model, the proposed model improves accuracy by 7.2%, precision by 6.5%, and F1-score by 5.0%, with only a modest 6MB increase in model size. Furthermore, compared to Faster RCNN, ResNet50, SSD, YOLOv3-tiny, YOLOv6, YOLOv9s, and YOLOv10m, our model demonstrates substantial improvements, with up to 30.2% higher precision and 18.0% greater accuracy. This study used CycleGAN-M and YOLOv8s-KEF methods to enhance the detection capability of apple leaf diseases.
Why it matches plant phenotyping methodsリンゴ葉の病斑・病害を画像から検出する深層学習モデルの改良と性能比較が研究の中心であり、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 introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits a portion of the apple leaf disease dataset and source code in a public GitHub repository, which directly supports this paper's CycleGAN-M augmentation and YOLOv8s-KEF detection experiments.Code · publicA portion of the dataset and source code is available on GitHub at https://github.com/Lijun-Gao/Apple-Leaf-Disease-Detection .Open asset ↗https://github.com/Lijun-Gao/Apple-Leaf-Disease-Detection · Lijun-Gao/Apple-Leaf-Disease-Detectionlines:158-169Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Background: Agriculture is very important for human existence since time immemorial. Approximately sixty percent of the world’s population is dependent on agriculture and its allied activities. Due to plant diseases every year farmers bear heavy economic loss which can be reduced by having an early detection system for plant disease. Conventional plant disease detection techniques are laborious and based on chemical and analytical testing. In this work, we suggest a deep learning method to precisely detect plant diseases by applying the VGG19 convolutional neural network. Methods: Modifications specifically designed for the classification of plant diseases were applied to the VGG19 model. By first freezing the bottom layers of the pre-trained VGG19 model-which had been trained on the ImageNet dataset-and then fine-tuning the upper layers to better fit the PlantVillage dataset, transfer learning was used. Resizing, standardization and data augmentation are a few of the image preprocessing approaches that were used to increase the variety of the dataset and boost model performance. The efficacy of the model was assessed through the use of metrics like F1-score, recall, accuracy and precision. Result: The constructed model performed well in the classification of plant diseases, with over 95% accuracy on the test set. The success of the model in generalizing across different plant disease categories was largely attributed to the application of transfer learning and data augmentation. The findings show that deep learning techniques-in particular, the use of VGG19-can significantly enhance agricultural practices and decision-making processes by helping to quickly and accurately identify plant illnesses. VGG9-based image processing offers an accurate and automated solution for plant disease identification, achieving 98% accuracy in classifying healthy and diseased leaves. By integrating mobile applications, drones and smart farming cameras, farmers can detect diseases early and take timely action, improving crop health and yield. Future advancements in dataset expansion and real-time processing will further enhance its effectiveness in precision agriculture.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するVGG19ベースの画像解析手法を開発・評価しており、病害表現型の取得が中心です。
abstractIn this work, we suggest a deep learning method to precisely detect plant diseases by applying the VGG19 convolutional neural network.
Reproduction assets foundThe paper's plant disease detection experiments are performed entirely on the public PlantVillage leaf image dataset (14,103 images of apple, grape, potato, tomato used here), which is openly available via the Hughes & Salathé arXiv reference. No author-specific code, trained model, or supplement is disclosed.Dataset · publicWe have gathered plant images from an open-source
database named PlantVillage. The PlantVillage dataset
comprises 54,303 photos and 38 classes representing
14 distinct plant species, of which 12 are healthy and 26
are diseased (Hughes and Salathe, 2015).Open asset ↗PlantVillagepdf-raw-page:3 lines:1-58Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物形質データを含むマルチオミクス統合用ツール panomiX を開発・提示し、画像ベース表現型データを統合解析する再利用可能な計算ワークフローを示しているため、表現型取得そのものより解析ツールが中心的な方法論的貢献である。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration
Reproduction assets foundThe paper's computational analysis assets are publicly available: the panomiX toolbox source code (GitHub) and its deployed Shiny app, plus the authors' rnaseq-mapper pipeline used to process this study's RNA-seq data. No public deposit of the paper-specific phenotype/FTIR/RNA-seq datasets is stated in the supplied.Code · publicThe source
code for the platform is available on GitHub: https://github.com/NAMlab/panomiX-tool. The
repository contains all the necessary R scripts for data processing, visualization, and machine
learning prediction.Open asset ↗NAMlab/panomiX-toolpdf-page:4 lines:1-42Code · publicThe source code is managed with a GitHub repository connected to
the Shinyapps.io via ‘rsconnect’ [53]: https://szymanskilab.shinyapps.io/panomiX/.Open asset ↗pdf-page:4 lines:1-42Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Common beanLeafMorphology / geometry measurementCalibration / preprocessingSegmentationLeaf traits
Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves ( Phaseolus vulgaris ). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.
Why it matches plant phenotyping methods葉面積・周長・長さ・幅の画像ベース計測用データセットを提供し、セグメンテーション、マーカー姿勢、カメラ校正も含むため、植物表現型取得手法の基盤として中心的です。
abstractWe captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width).
Reproduction assets foundThe paper is itself a data descriptor for the LSID-Beans bean leaf image dataset (6981 images, 612 leaves, with leaf dimension annotations, segmentation masks, area maps, and camera calibration). The dataset is publicly deposited on Mendeley Data (DOI 10.17632/f42hwwrpgn.2), and the authors' data-processing scripts areDataset · publicstakes and improved the data quality.
Data source location
The images were collected in the city of Ouro Branco, Minas Gerais, Latitude −20.535912, Longitude −43.711031, Brazil.
Data accessibility
Repository name: Leaf on Stem Image Dataset Beans (LSID-Beans)
Data identification number: 10.17632/f42hwwrpgn.2
Direct URL to data: https://data.mendeley.com/datasets/f42hwwrpgn/2
1
Value of the Data
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The dataset images are useful for developing deep learning methods for non-destructive leaf dimension estimation. We provide each leaf's known area, perimeter, width, and length, which can be used to train supervised machine learning algorithms.
•
Methods developed using the dataset can help to moniOpen asset ↗10.17632/f42hwwrpgn.2lines:1-50Code · publicfor that split. Section Cross-validation protocol definition details our proposed cross-validation protocol.
4
Experimental Design, Materials and Methods
Fig. 3 shows the steps performed to build our dataset. We describe each step in the next sections. The source codes used to process the data are available in this repository: https://github.com/gcg-ufjf/LSID-Beans-Scripts . Fig. 3
Steps of the dataset construction.
Fig 3
4.1
Plant cultivation
We selected black bean seeds and carried out planting in April 2022. On average, 3 seeds were sown in each pit, made with the aid of a hoe, along 9 rows of 30 plants. The soil used had never been cultivated and had rejects of construction material on tOpen asset ↗githublines:66-146Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: The reconstruction of 3-dimensional (3D) plant models can offer advantages over traditional 2-dimensional approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction techniques often produce sparse or noisy representations of plants using software or are expensive to capture in hardware. Recently, view synthesis models have been developed that can generate detailed 3D scenes, and even 3D models, from only RGB images and camera poses. These models offer unparalleled accuracy but are currently data hungry, requiring large numbers of views with very accurate camera calibration. RESULTS: In this study, we present a view synthesis dataset comprising 20 individual wheat plants captured across 6 different time frames over a 15-week growth period. We develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework. We trained each plant instance using two recent view synthesis models: 3D Gaussian splatting (3DGS) and neural radiance fields (NeRF). Our results show that both 3DGS and NeRF produce high-fidelity reconstructed images of a plant subject from views not captured in the initial training sets. We also show that these approaches can be used to generate accurate 3D representations of these plants as point clouds, with 0.74-mm and 1.43-mm average accuracy compared with a handheld scanner for 3DGS and NeRF, respectively. CONCLUSION: We believe that these new methods will be transformative in the field of 3D plant phenotyping, plant reconstruction, and active vision. To further this cause, we release all robot configuration and control software, alongside our extensive multiview dataset. We also release all scripts necessary to train both 3DGS and NeRF, all trained models data, and final 3D point cloud representations. Our dataset can be accessed via https://plantimages.nottingham.ac.uk/ or https://https://doi.org/10.5524/102661. Our software can be accessed via https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis.
Why it matches plant phenotyping methods3D植物表現型取得のための撮影システム、再構成手法、データセットを開発し、スキャナとの精度比較で検証しているため、方法が中心的である。
abstractWe develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework.
Reproduction assets foundThe paper releases its wheat plant multiview image dataset (via plantimages.nottingham.ac.uk and GigaDB DOI 10.5524/102661), its authors' analysis/capture codebase on GitHub (3D-Plant-View-Synthesis), a Software Heritage archive of that code, and a DOME-ML registry annotation. All are paper-specific, public, and have作者Code · publicruction output across all plants. We hope that our study will provide opportunities for researchers exploring new and improved 3D phenotyping algorithms, 3D reconstruction and view synthesis research, and active vision systems.
Availability of Source Code and Requirements
Project name: 3D Plant View Synthesis:
Project homepage: https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis [ 13 ]
Operating system(s): Windows, Ubuntu
Programming language: Python (>=3.8)
License: Apache 2.0
Any restrictions to use by nonacademics: None
Our code has also been archived in Software Heritage [ 66 ]. Functionality, such as Robotic View Capturing, 3DGS to Point Cloud, and our UR5 Configs files, are storOpen asset ↗GitHub · Lewis-Stuart-11/3D-Plant-View-Synthesislines:663-695Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert’s experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases. Moreover, more information can be extracted from the input images due to different feature extraction techniques. This paper proposes a new system for detecting and classifying rice plant leaf diseases by fusing different features, including color texture with Local Binary Pattern (LBP) and color features with Color Correlogram (CC). The proposed system consists of five stages. First, input images acquire RGB images of rice plants. Second, image preprocessing applies data augmentation to solve imbalanced problems, and logarithmic transformation enhancement to handle illumination problems has been applied. Third, the features extraction stage is responsible for extracting color features using CC and color texture features using multi-level multi-channel local binary pattern (MCLBP). Fourth, the feature fusion stage provides complementary and discriminative information by concatenating the two types of features. Finally, the rice image classification stage has been applied using a one-against-all support vector machine (SVM). The proposed system has been evaluated on three benchmark datasets with six classes: Blast (BL), Bacterial Leaf Blight (BLB), Brown Spot (BS), Tungro (TU), Sheath Blight (SB), and Leaf Smut (LS) have been used. Rice Leaf Diseases First Dataset, Second Dataset, and Third Dataset achieved maximum accuracy of 99.53%, 99.4%, and 99.14%, respectively, with processing time from $$100(\pm 17)ms$$ . Hence, the proposed system has achieved promising results compared to other state-of-the-art approaches.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類する特徴抽出および分類システム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に該当する。
abstractThis paper proposes a new system for detecting and classifying rice plant leaf diseases by fusing different features, including color texture with Local Binary Pattern (LBP) and color features with Color Correlogram (CC).
Reproduction assets foundThe paper evaluates its rice leaf disease classification system on three publicly available image datasets, each with explicit public URLs in the Data Availability statement. No author analysis code or trained models are shared.Dataset · publichors 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).
Data availibility
This research study was tested using three datasets which are publicly available in: https://data.mendeley.com/datasets/fwcj7stb8r/1 . https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm . https://data.mendeley.com/datasets/dwtn3c6w6p/1 .
Declarations
Competing interests
Te authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in publishedOpen asset ↗fwcj7stb8rlines:2657-2679Dataset · publicof the manuscript.
Funding
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Data availibility
This research study was tested using three datasets which are publicly available in: https://data.mendeley.com/datasets/fwcj7stb8r/1 . https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm . https://data.mendeley.com/datasets/dwtn3c6w6p/1 .
Declarations
Competing interests
Te authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1.Open asset ↗Kaggle · rajeshbhattacharjee/rice-diseases-using-cnn-and-svmlines:2657-2679Dataset · publicInnovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Data availibility
This research study was tested using three datasets which are publicly available in: https://data.mendeley.com/datasets/fwcj7stb8r/1 . https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm . https://data.mendeley.com/datasets/dwtn3c6w6p/1 .
Declarations
Competing interests
Te authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Viana, C. M., Freire, D., Abrantes, P., Rocha, J. & Pereira, P. Agricultural land syOpen asset ↗dwtn3c6w6plines:2657-2679Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.
Why it matches plant phenotyping methods森林の機能形質を推定するための大規模ハイパースペクトルLUTデータセットを構築しており、形質取得・推定基盤そのものが研究の中心である。
abstractwe present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests.
Reproduction assets foundThe paper is a Data in Brief article describing a public hyperspectral LUT dataset (3.5 million trait/structural combinations) deposited in the Czech National Repository, including the authors' processing codes (merge_images.m, LUT_processing.py) within the deposit. The direct repository URL is given in the text and isDataset · publiceaf pixels.
Data source location
Institutions: Institute of Computer Science, Masaryk University; Global Change Research Institute of the Czech Academy of Sciences
City: Brno
Country: Czech Republic
Data accessibility
Repository name: National Repository
Data identification number: 10.48700/datst.bcnpf-47q73
Direct URL to data: https://data.narodni-repozitar.cz/general/datasets/4y0sy-qh735
1.
Value of the Data
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Look-Up Tables (LUT) are considered important training datasets for machine learning models to predict leaf traits.
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To date, only a limited number of LUT datasets have been developed for forest sites, particularly for Central European temperate broadleaf forests. Most of them are lOpen asset ↗National Repository · 10.48700/datst.bcnpf-47q73lines:36-69Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published31 Oct 2024International Journal for Research in Applied Science and Engineering TechnologyCited by 1 · OpenAlex ↗
In this project, we have developed a plant disease detection model using a machine learning approach on a dataset named “Plant Disease Expert” which is available on Kaggle. The dataset contains 199,611 images across 58 subdirectories. The splitting of the dataset in training, validation and testing is done in ratio 90:5:5. The data preprocessing included resizing the images, normalizing pixel values and applying augmentation techniques. We have used different libraries throughout the project such as NumPy, Pandas, Operating system, Time, Matplotlib, OpenCV, Shutil, TensorFlow, Keras, Seaborn, etc. EfficientNetB3 is used as the base model. EfficientNetB3 is a convolutional neural network architecture developed by Google Brain researchers [1]. It is known for its efficiency and effectiveness in image classification tasks. Additional custom layers such as max pooling, batch normalization, dense layers, and activation functions are used to optimize the performance. The adamax optimizer and categorical cross-entropy were used during training. The model was trained for 10 epochs with a batch size of 20 and a learning rate of 0.01. For evaluation, classification report, accuracy, precision, recall, F1-score and support were used. The model achieved an accuracy of 98.76% [1].
Why it matches plant phenotyping methods植物画像から病害状態を推定する機械学習モデルの開発・評価が研究の中心であり、植物病害の画像ベース表現型解析に該当します。
abstractwe have developed a plant disease detection model using a machine learning approach
Reproduction assets foundThe paper's plant-phenotyping analysis is built entirely on a public image dataset, 'Plant Disease Expert' by S. S. Mahi, hosted on Kaggle (199,611 leaf images across 58 subdirectories), which the authors explicitly state is publicly available. No author analysis code, trained model checkpoint, or supplement with code/Dataset · publicwe have developed a plant disease detection model using a machine learning approach on a dataset
named “Plant Disease Expert” which is available on KaggleOpen asset ↗Kaggle · Plant Disease Expertpdf-page:2 lines:1-57Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
PoplarField / plotRootCalibration / preprocessingRoot system architecture
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.
Why it matches plant phenotyping methods複数の根スキャン画像から根形質を統合する画像連結・統計集約法を開発し、比較検証した研究であり、根形質取得ワークフローが中心です。
abstractHere, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation.
Reproduction assets foundThe paper's Data availability statement deposits the root scan imageset, the Python image-concatenation script, and the R statistical-aggregation/figure code on Zenodo with explicit DOIs, making the paper-specific phenotyping images and analysis code publicly actionable. Since the Zenodo deposit URLs are not among the,Dataset · publicThe imageset is available at doi: 10.5281/zenodo.12667583Open asset ↗Zenodo · 10.5281/zenodo.12667583pdf-raw-page:7 lines:1-85Code · publicthe R code for statistical aggregation along
with the figures and statistics presented here are available at doi:
10.5281/zenodo.12668177Open asset ↗Zenodo · 10.5281/zenodo.12668177pdf-raw-page:7 lines:1-85Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.
Why it matches plant phenotyping methodsMODIS反射率から作物のFVCを推定するリモートセンシング手法について、NBAR補正の時期効果を比較・検証しており、植物形質推定が研究の中心である。
abstractThe PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product.
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 · publicwhile the GEOV3 FVC data can be downloaded from:
https://land.copernicus.eu/global/products/fcoverOpen asset ↗pdf-page:17 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Structural complexity refers to the three-dimensional arrangement and variability of both biotic and abiotic components of an ecosystem. Metrics that characterize structural complexity are often used to manage various aspects of ecosystem function, such as light transmittance, wildlife habitat, and biological diversity. Additionally, these metrics aid in evaluating resilience to disturbance events, including hurricanes, bark-beetle outbreaks, and wildfire. Recent advances in wildland fire modelling have facilitated the integration of forest structural complexity metrics into the QUIC-Fire model, enabling real-time prediction of fire spread and behaviour by simulating interactions between fire, weather, topography, and forest structure. While QUIC-Fire is designed to be highly adaptable, model performance depends on the availability and accuracy of local data inputs. Expanding the model's usability across different regions can be facilitated by the availability of more comprehensive and high-quality data. Thus, the primary goal behind the data products we developed was to establish a basis for collaborative research across various disciplines, particularly within the focal areas of the Southern Research Station, such as forestry, wildland fire, hydrology, soil science, and cultural resources at Bent Creek, Coweeta, Escambia, and Hitchiti Experimental Forests (EFs). Airborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season to minimize interference from foliage. Subsequent processing of the raw lidar data involved outlier detection and filtering, ground and non-ground classification, and the computation of a variety of metrics representing various aspects of topography and forest structure at both the pixel-level and the tree-level. Pixel-level topographic data products include: digital elevation model (DEM), slope, aspect, topographic position index (TPI), topographic roughness index (TRI), roughness, and flow direction. Forest structural-complexity metrics include canopy height, foliar height diversity (FHD), vertical distribution ratio (VDR), canopy rugosity, crown relief ratio (CRR), understory complexity index (UCI), vertical complexity index (VCI), canopy cover, mean vegetation height, and the standard deviation of vegetation height. Tree-level data products were computed from the point cloud using multiple algorithms to perform individual tree detection (ITD) and individual tree segmentation (ITS). The datasets have been harmonized and are openly accessible through the USDA Forest Service Research Data Archive.
Why it matches plant phenotyping methods航空レーザースキャンから樹冠高、植生高、樹冠構造、個体樹木を抽出した再利用可能なデータセットであり、植物の構造形質取得と処理が中心です。
abstractAirborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season
Reproduction assets foundThis Data in Brief article describes its own openly archived dataset: lidar-derived forest structural-complexity metrics (raster and vector products, including tree detection/segmentation outputs) for four experimental forests, deposited in the USFS Research Data Archive (RDS-2024-0019) with R processing code in theSupDataset · public−83.450054
Coweeta Experimental Forests: 31.007539, −87.078571
Escambia Experimental Forests: 33.057078, −83.679620
Hitchiti Experimental Forest: 35.484250, −82.633346
Data accessibility
Repository name: US Forest Service Research Data Archive
Data identification number:
https://doi.org/10.2737/RDS-2024-0019
Direct URL to data: https://www.fs.usda.gov/rds/archive/catalog/RDS-2024-0019
Raw ALS point-cloud data are located at https://app.box.com/s/4s3412g8mtky0hb6wb63a44epv08c22o
Related research article
none.
1.
Value of the Data
•Open asset ↗US Forest Service Research Data Archive · RDS-2024-0019lines:1-50Dataset · publicarch Station.
Bent Creek Experimental Forests: 35.050580, −83.450054
Coweeta Experimental Forests: 31.007539, −87.078571
Escambia Experimental Forests: 33.057078, −83.679620
Hitchiti Experimental Forest: 35.484250, −82.633346
Data accessibility
Repository name: US Forest Service Research Data Archive
Data identification number:
https://doi.org/10.2737/RDS-2024-0019
Direct URL to data: https://www.fs.usda.gov/rds/archive/catalog/RDS-2024-0019
Raw ALS point-cloud data are located at https://app.box.com/s/4s3412g8mtky0hb6wb63a44epv08c22o
Related research article
none.
1.
Value of the Data
•Open asset ↗US Forest Service Research Data Archive · RDS-2024-0019lines:1-50Dataset · publicean crown diameter. Additionally, the generalized additive model (GAM) that was developed from the inventory data was used to predict bole height at the tree-level.
Limitations
The size of the raw point-cloud data precluded storage on the USFS Research Data Archive. Therefore, this data is accessible for download from box.com ( https://app.box.com/s/4s3412g8mtky0hb6wb63a44epv08c22o ). Additionally, the volume of the point-cloud data may pose computational limitations.
Ethics Statement
The authors have read and follow the ethical requirements for publication in Data in Brief and confirm that the current work does not involve human subjects, animal experiments, or any data collected from sociaOpen asset ↗lines:420-434Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Pollen is becoming an increasingly important subject for molecular researchers in genetic engineering, plant breeding, and environmental monitoring. To broaden the scope of these studies, it is essential to develop accessible methods for scientists who are not specialized in palynology. The article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM). The protocol is convenient for any molecular laboratory due to its small set of reagents, ease of execution, low cost, does not require special equipment, and takes only one hour to complete. The high penetrating ability of formaldehyde and the final delicate dehydration using hexamethyldisilazane (HMDS) instead of critical point drying allow for sufficient preservation of the architecture of the aperture, which is considered a gateway for the passage of biomolecules. The method was successfully applied to pollen grains of representatives of dicotyledons (beetroot, petunia, radish, tomato and tobacco) and monocotyledons (lily, onion, corn, rye and wheat). Species studied included insect-pollinated (entomophilous) and wind-pollinated (anemophilous) species. A comparative analysis of the sizes of fresh living pollen grains under a light microscope and those prepared for SEM showed some shrinkage. Quantitative analysis of the degree of pollen grain shrinkage showed that this process depends on the initial shape of dry pollen grains, and the number and structure of apertures. The results support the theoretical model of the folding/unfolding pathways of pollen grains.
Why it matches plant phenotyping methods植物花粉のSEM観察用試料調製法そのものを開発し、複数植物で適用・比較検証しているため、形態計測に関する中心的な方法論研究である。
abstractThe article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM).
Reproduction assets foundThe paper's quantitative pollen shrinkage measurements (Table S1) and light microscopy images (Figures S3–S4) are contained in the publicly downloadable MDPI Supplementary Materials, which directly reproduce this paper's phenotyping measurements. No author analysis code or trained models are mentioned.Supplement · publicoly Bogdanov—at the department of electron microscopy, Lomonosov Moscow State University.
Abbreviations
The following abbreviations are used in this manuscript:
SEM
Scanning Electron Microscopy
HMDS
Hexamethyldisilazane
SA
Short axis
LA
Long axis
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13152140/s1 , Figure S1: The order of steps for pollen preparation according to the developed protocol; Figure S2: Scheme of measured pollen grain diameters; Figure S3: Light microscopy of pollen grains of insect-pollinated species; Figure S4: Light microscopy of pollen grains of wind-pollinated species; Table S1: Comparison Open asset ↗lines:98-127Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agricultural image dehazing method used for extracting such information is limited due to unclear texture details and color representation in the images. To address these limitations, we propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping. The algorithm incorporates an atmospheric scattering model to improve the discriminator model and employs a whole-detail consistent discrimination approach to enhance discriminator efficiency, thereby accelerating convergence towards Nash equilibrium state within the adversarial network. Finally, by training with network adversarial loss + cycle consistent loss, clear images are obtained after dehazing process. Experimental evaluations and comparative analysis were conducted to assess this algorithm's performance, demonstrating improved accuracy in dehazing agricultural images while preserving detailed texture information and mitigating color deviation issues.
Why it matches plant phenotyping methods農業植物フェノタイピング画像から特徴情報を抽出するための画像デヘイズ手法を開発し、性能比較・評価しており、フェノタイプ取得前処理が研究の中心である。
abstractwe propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping.
Reproduction assets foundThe paper's Data availability statement explicitly commits the authors' cucumber hazy/haze-free image dataset and AgriGAN analysis code to a public GitHub repository (HZSUZJ/DLDF), which is listed in the allowed URLs. The dataset (407 haze-free / 479 hazy training images, 51/49 test images) and TensorFlow code are the纸Code · publicOur dataset and code will be publicly available at https://github.com/HZSUZJ/DLDF.Open asset ↗pdf-page:10 lines:1-65Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improving model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-of-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, with the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, with the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets related to this study are available at Github.
Why it matches plant phenotyping methods植物画像向け超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子の計数性能を評価しており、表現型取得・抽出法が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper's authors publicly released their analysis code (PlantSR super-resolution model and experiments) on GitHub, and also deposited the PlantSR dataset and HR soybean images on figshare; however, only the GitHub URL is among the allowed URLs, so only the code asset is reported.Code · publicThe source code is available at https://github.com/SkyCol/PlantSR (accessed on 28 November 2023).Open asset ↗SkyCol/PlantSRlines:94-293Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.
Why it matches plant phenotyping methods植物画像の超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子のカウント性能を改善する手法を検証しており、植物器官数の画像ベース推定が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the PlantSR plant image dataset (figshare), and the high-resolution soybean seed images used for transfer learning (figshare). The P2PNet-Soy repository is cited prior work, not a paper-specific asset.Code · publicThe source codes and associated
datasets are available at https://github.com/SkyCol/PlantSR.Open asset ↗SkyCol/PlantSRpdf-page:2 lines:1-63Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Abstract Artificially extracted agricultural phenotype information has high subjectivity and low accuracy, and the use of image extraction information is easily disturbed by haze. Moreover, the agricultural image dehazing method used to extract such information is ineffective, as the images often contain unclear texture information and image colors. To address these shortcomings, we propose unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype (AgriGAN). The algorithm improves the dehazing performance of the network by adding the atmospheric scattering model, which improves the discriminator model, and uses the whole-detail consistent discrimination method to improve the efficiency of the discriminator so that the adversarial network can accelerate the convergence to the Nashi equilibrium state. Finally, the dehazed images are obtained by training with network adversarial loss + cycle consistent loss. Experiments and a comparative analysis were conducted to evaluate the algorithm, and the results show that it improved the dehazing accuracy of agricultural images, retained detailed texture information, and mitigated the problem of color deviation. In turn, useful information was obtained, such as crop height, chlorophyll and nitrogen content, and the presence and extent of disease. The algorithm's object identification and information extraction can be useful in crop growth monitoring and yield and quality estimation.
Why it matches plant phenotyping methods植物表現型画像から情報を抽出するための画像デヘイズ手法を開発・評価しており、表現型取得の技術が中心である。
abstractwe propose unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype (AgriGAN).
Reproduction assets foundThe paper's Data availability section states that the authors' dataset (hazy/haze-free agricultural plant images) and code will be publicly available at the GitHub repository HZSUZJ/DLDF, which matches an allowed URL. This is a paper-specific, publicly actionable asset for the dehazing/phenotyping analysis.Code · publicOur dataset and code will be publicly available at https://github.com/HZSUZJ/DLDF.Open asset ↗HZSUZJ/DLDFpdf-page:20 lines:1-34Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.
Why it matches plant phenotyping methods植物画像と画像由来形質を収録する高スループット表現型データアーカイブであり、データセット、事前学習モデル、オンライン解析ツールを提供することが中心的な方法論的貢献である。
abstractHere, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms.
Reproduction assets foundThe paper describes OPIA, an open archive of plant images, i-traits, and pre-trained models, freely available online with explicit download and trait pages. The archive itself is the paper-specific public asset containing the phenotyping images, i-trait values, and downloadable datasets.Dataset · publicdifferent types of imaging sensors (e.g. visible light, near-infrared, depth camera and chlorophyll fluorescence sensors) can be submitted via opia@big.ac.cn . Users can also submit a compiled dataset with relevant metadata ( Supplementary Figure S3 ). All image datasets can be freely downloaded in a compressed zip format from https://ngdc.cncb.ac.cn/opia/downloads , which contains label records of image data in diverse formats (e.g. RSML ( 30 ), JSON, TXT, XML, MAT, CSV or H5). Collectively, these online tools and data services are invaluable for plant phenotyping research and application.
Potential applications of datasets and i-traits
To highlight the potential applications of the in-hOpen asset ↗OPIAlines:148-156Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisTissueClassificationCalibration / preprocessingGrowth / time-series analysisGrowth / development / phenology
Transcriptomic data can be used to predict environmentally impacted phenotypic traits. This type of prediction is particularly useful for monitoring difficult-to-measure phenotypic traits and has become increasingly popular for monitoring high-value agricultural crops and in precision medicine. Despite this increase in popularity, little research has been done on how many samples are required for these models to be accurate, and which normalization should be used. Here we create a massive RNA-seq dataset from publicly available Arabidopsis thaliana data with corresponding measurements for age and tissue type. We use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required. We find that Median Ratios Normalization significantly increases performance when predicting age. We also find that in the case of our dataset, only a few hundred samples are required to predict tissue types, and only a few thousand samples are necessary to accurately predict age. Researchers should consider these results when choosing the number of samples in a transcriptomic experiment and during data-processing. Author Summary Large datasets have become ubiquitous in both research and industry, with thousands and sometimes millions of samples being collected for a single project. In biology a prominent new technology is RNA-seq, which can be used to measure the expression level of thousands of genes for a single sample. These measurements are used for a variety of downstream applications, including predicting phenotypic traits (i.e. height, disease, etc.). A number of experiments have attempted to use RNA-seq data to make phenotype predictions with varying success. This is partially due to the small sample size of their experiments. RNA-seq datasets are currently relatively small--only a dozen to a few hundred samples--due to the cost per sample. This is expected to change as the cost of sequencing decreases. In this paper we create a massive conglomerate RNA-seq dataset from publicly available Arabidopsis thaliana RNA-seq data. We use this dataset to determine how many samples are required to accurately predict plant age and tissue type using machine learning models. We also explore the best way to normalize large datasets. Our results show the potential of massive RNA-seq datasets, and can be used to inform experimental design for phenotype prediction.
Why it matches plant phenotyping methodsRNA-seqデータから植物の年齢・組織型を予測する機械学習について、必要サンプル数と正規化法を大規模Arabidopsisデータで評価しており、表現型推定手法の検証が中心である。
abstractWe use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required.
Reproduction assets foundThe paper's normalized gene expression matrices, curated phenotype annotation datasets, and intermediary files are publicly deposited on Zenodo, and all analysis code is publicly available on GitLab. These directly reproduce the paper's plant-phenotyping measurements (Arabidopsis age/tissue annotations) and modeling/MLDataset · public(NoNo). TMM normalization [24,29] and MRN normalization [25] were performed using
the Python “conorm” package 1.2.0 [30]. TPM and NoNo normalization values were an
output of Kallisto [27]. How these normalizations impacted sample count is visualized as
S2 Figure. We have made these GEMs publicly available on Zenodo at the link
https://zenodo.org/records/10183151
Sample Phenotype Annotations Pre-Processing
Sample phenotype annotations were retrieved from the NCBI BioProject database
[16,17] using BioSampleParser which was slightly modified to check for successful data
retrieval [31]. Phenotype annotations were retrieved for 48696 NCBI BioSamples,
representing data from 2643 BioProjects.Open asset ↗Zenodopdf-raw-page:9 lines:1-55Dataset · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. BostanciOpen asset ↗Zenodo · 10.5281/zenodo.10183150pdf-raw-page:43 lines:1-51Code · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning
Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Open asset ↗GitLabpdf-raw-page:43 lines:1-51Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Introduction The challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions. Methods In this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications. Results Quantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN’s average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint’s FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61). Discussion This study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field.
Why it matches plant phenotyping methods植物病害画像を対象に拡散モデルとGANを比較し、病害画像データ拡張の性能をFID・KIDで検証する研究であり、植物病害状態の画像ベース評価を支える方法が中心である。
titleHarnessing the power of diffusion models for plant disease image augmentation
Reproduction assets foundThe paper's experiments use a subset of the public PlantVillage image dataset, which the authors explicitly link in the data availability statement. No author analysis code, trained models, or generated-image deposits are mentioned.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/mohitsingh1804/plantvillage .Open asset ↗Kaggle · mohitsingh1804/plantvillagelines:841-873Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.
Why it matches plant phenotyping methodsTLSによる個体樹木の体積という植物形質の推定手法を、破壊的実測データと比較検証しており、測定・推定法が研究の中心である。
abstractTerrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees
Reproduction assets foundThe paper's Data availability statement points to a public EnviDat deposit (DOI 10.16904/envidat.403) containing the data associated with this TLS-based tree volume estimation study, including the TLS point clouds and destructive reference measurements collected in the SwissBiomass project. No author analysis code URL,Dataset · publicvolume by integrating them into new allometric models.
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Data associated with this article is available at http://dx.doi.org/10.16904/envidat.403.Acknowledgements
The authors would like to thank everyone involved in the WSL
project ‘‘SwissBiomass’’, in the course of which the data used in this
study were collected. In particular, we thank Marina Beck for project
coordination, as well as all those who helped with the field and lab-
oratory measurements. We also thank DanielOpen asset ↗envidat · 10.16904/envidat.403pdf-raw-page:11 lines:1-93Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
The devastating effect of plant disease infestation on crop production poses a significant threat to the attainment of the United Nations' Sustainable Development Goal 2 (SDG2) of food security, especially in Sub-Saharan Africa. This has been further exacerbated by the lack of effective and accessible plant disease detection technologies. Farmers' inability to quickly and accurately diagnose plant diseases leads to crop destruction and reduced productivity. The diverse range of existing plant diseases further complicates detection for farmers without the right technologies, hindering efforts to combat food insecurity in the region. This study presents a web-based plant diagnosis application, referred to as mobile-enabled Plant Diagnosis-Application (mPD-App). First, a publicly available image dataset, containing a diverse range of plant diseases, was acquired from Kaggle for the purpose of training the detection system. The image dataset was, then, made to undergo the preprocessing stage which included processes such as image-to-array conversion, image reshaping, and data augmentation. The training phase leverages the vast computational ability of the convolutional neural network (CNN) to effectively classify image datasets. The CNN model architecture featured six convolutional layers (including the fully connected layer) with phases, such as normalization layer, rectified linear unit (RELU), max pooling layer, and dropout layer. The training process was carefully managed to prevent underfitting and overfitting of the model, ensuring accurate predictions. The mPD-App demonstrated excellent performance in diagnosing plant diseases, achieving an overall accuracy of 93.91%. The model was able to classify 14 different types of plant diseases with high precision and recall values. The ROC curve showed a promising area under the curve (AUC) value of 0.946, indicating the model's reliability in detecting diseases. The web-based mPD-App offers a valuable tool for farmers and agricultural stakeholders in Sub-Saharan Africa, to detect and diagnose plant diseases effectively and efficiently. To further improve the application's performance, ongoing efforts should focus on expanding the dataset and refining the model's architecture. Agricultural authorities and policymakers should consider promoting and integrating such technologies into existing agricultural extension services to maximize their impact and benefit the farming community.
Why it matches plant phenotyping methods植物病害画像から病害状態を分類するCNNとモバイル診断アプリの開発・性能評価が研究の中心であり、植物病害フェノタイプの取得・推定に該当する。
abstractThis study presents a web-based plant diagnosis application, referred to as mobile-enabled Plant Diagnosis-Application (mPD-App).
Reproduction assets foundThe paper's CNN plant-disease diagnosis model was trained on the publicly available PlantVillage image dataset, which the authors explicitly retrieved from a public GitHub repository with a URL matching the allowed list. This is the paper-specific image input used for its phenotyping measurements. No author analysis代码,Dataset · publicThe implementation procedure of the mPD-App is further repository https://github.com/spMohanty/PlantVillage-DatasetOpen asset ↗spMohanty/PlantVillage-Datasetpdf-page:5 lines:1-49Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops.
Why it matches plant phenotyping methods大豆植物の3D形態取得を目的に、画像セグメンテーション前処理を開発・比較し、再構成精度と処理効率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。
abstractThe investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition.
Reproduction assets foundThe paper publicly releases its semantic segmentation dataset of 500 annotated soybean plant images, the 3D reconstruction model data from both preprocessing methods, and the authors' four segmentation network implementations (DeepLabv3+, Unet, PSPnet, HRnet) via Baidu pan links and GitHub repositories.Dataset · publicent research, LabelMe was used to annotate 500 images of soybean plants during
the vegetative period. The soybean plants and the calibration pad were labeled as a whole
and marked as “soybean”. The training set and testing set were divided in an 8:2 ratio. A
dataset was created for semantic segmentation, and the dataset link is
https://pan.baidu.com/s/13qpZsOl3bgmAgua2D441UQ (accessed on 4 August 2023). Ex-
tract code: dr2v.
Four deep-learning-based semantic segmentation models were selected as follows:
DeepLabv3+ [19], Unet [20], PSPnet [21] and HRnet [22]. These models were used to sep-
arate the soybean plants and the calibration pad from the background. Figure 3 shows the
network architeOpen asset ↗pdf-raw-page:5 lines:1-36Dataset · publichis study is pub-
licly available. These data can be found at: https://pan.baidu.com/s/13qpZsOl3bgmAgua2D441UQ
(accessed on 4 August 2023). Extract code: dr2v. Meanwhile, 3D reconstruction of soybean plant
images obtained using two image preprocessing methods was conducted, and the constructed
model data were linked as follows: https://pan.baidu.com/s/1UIBAts1dbjIiLvBv6YVpPA (accessed
on 4 August 2023). Extract code: 65xf.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
(a) (b)
(c) (d)
Figure A1. The confusion matrix diagram of the true value and the predicted value of the training
set. (a) DeepLabv3+; (b) Unet; (c) PSPnet; (d) HRNet.
(a)Open asset ↗pdf-layout-page:25 lines:1-32Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2-17 m, meeting the accuracy requirements of forest survey.
Why it matches plant phenotyping methodsスマートフォン画像・深度情報と3D再構成を用いて樹高という植物構造形質を自動測定する手法を開発し、誤差評価とAndroidアプリ化まで行っているため、植物フェノタイピング手法が中心である。
abstractWe propose a tree height measurement method based on three-dimensional (3D) reconstruction.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides two paper-specific public assets: the source code of the TreeHeight prototype app on GitHub and the authors' annotated tree image dataset (300 annotated images augmented to 1000 pairs of color images, relative depth maps, and tree masks) on Google Drive. Both,Code · publicThe source code of the prototype app is publicly available on GitHub at https://github.com/LisaShen0509/Tree_Height_Measurement (accessed on 27 July 2023).Open asset ↗LisaShen0509/Tree_Height_Measurementlines:432-615Dataset · publicTree image dataset is available at https://drive.google.com/file/d/1kG6LWMOAiA2KvGF_suZ5cG_4C-udUV0m/view?usp=sharing (accessed on 27 July 2023).Open asset ↗lines:432-615Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
MicroscopyTissueCalibration / preprocessingVisualization / data management
Motivation Quantitative descriptions of multi-cellular structures from optical microscopy imaging are prime to understand the variety of three-dimensional (3D) shapes in living organisms. Experimental models of vertebrates, invertebrates and plants, such as zebrafish, killifish, Drosophila or Marchantia, mainly comprise multilayer tissues, and even if microscopes can reach the needed depth, their geometry hinders the selection and subsequent analysis of the optical volumes of interest. Computational tools to "peel" tissues by removing specific layers and reducing 3D volume into planar images, can critically improve visualization and analysis. Results We developed VolumePeeler, a versatile FIJI plugin for virtual 3D "peeling" of image stacks. The plugin implements spherical and spline surface projections. We applied VolumePeeler to perform peeling in 3D images of spherical embryos, as well as non-spherical tissue layers. The produced images improve the 3D volume visualization and enable analysis and quantification of geometrically challenging microscopy datasets. Availability ImageJ/FIJI software, source code, examples, and tutorials are openly available in https://cimt.uchile.cl/mcerda.
Why it matches plant phenotyping methods植物を含む3D組織画像の層構造を仮想的に展開し、可視化・定量化するFIJIプラグインの開発研究であり、植物組織形態の画像解析に再利用可能な手法が中心である。
abstractWe developed VolumePeeler, a versatile FIJI plugin for virtual 3D "peeling" of image stacks.
Reproduction assets foundThis is a software paper for VolumePeeler, a FIJI plugin for 3D volume peeling applied to zebrafish, killifish, and the plant model Marchantia. The authors' plugin source code and example data/tutorials are explicitly and publicly available, covering the paper's computational analysis including the Marchantia (plant) 3Code · publicSource code is available from https://github.com/busmangit/volume-peeler . Examples and video tutorials are available under Creative Commons license (CC BY-NC).Open asset ↗busmangit/volume-peelerlines:556-587Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.
Why it matches plant phenotyping methods植物のさび病画像から病斑をセグメンテーションするための適応的画像拡張法をDRLで開発・評価しており、植物病害状態の画像ベース表現型取得が中心である。
abstractThis study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system.
Reproduction assets foundThe paper's authors publicly released their DRL adaptive image augmentation analysis code on GitHub, and the plant rust leaf image dataset used for phenotyping/segmentation is publicly available on Baidu AI Studio per the data availability statement.Code · publicctical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git .
adaptive image augmentation deep reinforcement learning deep Q-learning automated optical inspection semantic segmentation Department of Education of Guangdong Province
10.13039/501100010226 2021KTSCX005 Natural Science Foundation of Guangdong Province
10.13039/501100003453 2022A1515240061, 2023A1515012975 Open asset ↗lynnkobe/Adaptive-Image-Augmentationlines:1-51Dataset · publicwork should consider more advanced image augmentation methods, segmentation targets, and a more flexible and efficient DRL framework to provide more effective detection schemes for complex AOI application scenarios.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://aistudio.baidu.com/aistudio/datasetdetail/11591 .
Author contributions
SW, AK, YL, ZJ, HT, SA, MS and UB were responsible for question formulation, method, experimental design, and manuscript writing. YL, ZJ, HT, SA, MS and UB contributed to the issue investigation. HT contributed to the data analysis and AK funded the research. All authors listed have made a Open asset ↗11591lines:642-707Code / dataset availability confirmedOpenAlex · arXiv · checked 7 Sept 2026
The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide the first deep investigation of the predictive potential of machine learning models to understand the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant phenotypes from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. We show that prediction is improved when incorporating environmental features like soil physicochemical properties and microbial population density into the models, in addition to the microbiome information. Exploring various data preprocessing strategies confirms the significant impact of human decisions on predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is not the optimal strategy to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level or model characteristics. In cases where humans are unable to classify samples accurately, machine learning model performance is limited. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Our work is accompanied by open source reproducible scripts (https://github.com/solislemuslab/soil-microbiome-nn) for maximum outreach among the microbiome research community.
Why it matches plant phenotyping methods土壌マイクロバイオーム等から植物表現型を予測する機械学習フレームワークを中心に、モデル、前処理、ラベル精度、性能最適化を評価しており、植物表現型推定法として方法論的に重要である。
abstractWe investigate an integrative framework performing accurate machine learning-based prediction of plant phenotypes from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network.
Reproduction assets foundThe paper's reproducible analysis scripts (RF and Bayesian NN prediction of potato phenotypes from soil microbiome data) are publicly available on the authors' GitHub repository. The underlying soil/phenotype dataset is not public and must be requested from the authors.Code · publicCode Availability
All reproducible scripts are open source and publicly available in https://github.com/solislemuslab/soil-microbiome-nn .Open asset ↗solislemuslab/soil-microbiome-nnlines:465-608Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .
Why it matches plant phenotyping methods作物病害画像から病害状態を推定する画像拡張手法そのものを提案・評価しており、植物病害フェノタイピング手法が中心である。
abstractthis paper proposes a supervised image augmentation method Negative Contrast
Reproduction assets foundThe authors explicitly state that their Plant Real-World crop disease dataset (Kaggle) and their analysis/augmentation code (GitHub) are publicly available.Dataset · publiccy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows 9
that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, 10
and thus reflects better generalization ability in real-world disease classification. Our dataset and 11
codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean 12
and https://github.com/hiter0/contrastaug . 13
Keywords: Crop Disease Classification; Crop Disease Dataset; Image Augmentation 14
1. Introduction 15
Since AlexNet[1] first used deep learning to win the ImageNet[2] competition in 16
2012, deep learning-based approaches have comprehensively outperformOpen asset ↗kaggle.com/datasets/w970704112/corn-wheat-rice-soybean · corn-wheat-rice-soybeanpdf-raw-page:1 lines:1-66Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Field / plotMultispectral / hyperspectralCalibration / preprocessing
Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.
Why it matches plant phenotyping methods植物フェノタイピング用の近接アクティブ反射センサーについて、温度影響を評価し、正規化・安定化を含む運用方法を提案する技術研究であり、測定法が中心的です。
abstractThe purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers.
Reproduction assets foundThe paper's Data Availability Statement points to public USDA Ag Data Commons datasets containing the authors' proximal-sensing/phenotyping raw data (high-throughput phenotyping data from the proximal sensing cart and the Bronson Files field datasets), which directly underpin this paper's ACS-470 NDVI/temperature phenyDataset · publicRelated raw datasets are available from the USDA Ag Data Commons. https://data.nal.usda.gov/dataset/high-throughput-phenotyping-data-proximal-sensing-cartOpen asset ↗USDA Ag Data Commonslines:100-326Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 7 Sept 2026
Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.
Why it matches plant phenotyping methodsドローン画像とRTK-GNSSを用いた植物高のリアルタイム測定法を開発し、既存法との精度・処理時間比較で検証しており、植物表現型取得が研究の中心である。
abstractA new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed.
Reproduction assets foundThe paper's Data availability statement points to a public supplementary materials zip on the authors' site (nobuharaken.com) containing the datasets (drone images with GNSS data) generated and analysed in this plant-height measurement study. No separate analysis code repository is stated.Dataset · publicThe datasets generated and/or analysed during the current study are available in the https://nobuharaken.com/Open asset ↗nobuharaken.compdf-page:14 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).
Why it matches plant phenotyping methods植物フェノミクスのマルチモーダル画像・点群から形質を抽出する、スケーラブルで再現可能な処理パイプラインの開発と反復性評価が中心である。
abstractwe developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds.
Reproduction assets foundThe paper's Code and Data Availability statements provide explicit public URLs for the authors' PhytoOracle processing code, ML training-data preparation scripts, trained model training code, and the season-10 lettuce benchmarking dataset (raw RGB/thermal/PSII images and point clouds) hosted on CyVerse.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://datacommons.cyverse.org/browse/iplant/home/shared/phytooracle/season_10_lettuce_yr_2020Open asset ↗iplant/home/shared/phytooracle/season_10_lettuce_yr_2020lines:640-662Code · publicThe automation script and data processing repositories can be accessed at: http://github.com/phytooracleOpen asset ↗github.com/phytooraclelines:640-662Code · publicThe Python scripts used to prepare RGB training data can be accessed here: http://github.com/phytooracle/automation/blob/main/ml/collect_rgb_data.pyOpen asset ↗github.com/phytooracle/automationlines:640-662Code · publicThe Python script used to prepare thermal training data can be accessed here: http://github.com/phytooracle/automation/blob/main/ml/collect_flir_data.pyOpen asset ↗github.com/phytooracle/automationlines:640-662Code · publicThe Python script used to prepare 3D-derived images can be found here: http://github.com/phytooracle/3d_heat_map/blob/main/3d_heat_map.pyOpen asset ↗github.com/phytooracle/3d_heat_maplines:640-662Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.
Why it matches plant phenotyping methods植物の熱画像・マルチスペクトル画像を用いた表現型取得と解析のためのオープンソース計算パイプラインを開発しており、画像補正・共登録・解析が中心的な方法論的貢献である。
abstractThis study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors.
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 · publicAll codes were written in MATLAB to produce a library package which is available at https://github.com/SmartSense-iHub/Thermal-and-Multispectral-Image-Analysis-Processing-Pipeline.git (accessed on 12 November 2022).Open asset ↗SmartSense-iHub/Thermal-and-Multispectral-Image-Analysis-Processing-Pipelinelines:35-43Dataset · publicThe data is freely shared in google drive and can be accessed from the following link. https://drive.google.com/file/d/1VSqRu5CUZhyd3MF23kdRjqrtRke7sbJU/view?usp=share_link .Open asset ↗lines:91-241Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and 'n' rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.
Why it matches plant phenotyping methodsオリーブ果実を対象とした圃場ハイパースペクトル画像データセットであり、画像取得、正規化、アルゴリズムによるスペクトル特徴抽出、データ保存が中心的に記述されているため、植物フェノタイピング手法・データセットとして収録する。
abstractThis study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September.
Reproduction assets foundThe paper is a data descriptor whose hyperspectral olive dataset (raw/normalised HSIs and processed spectral signatures) is publicly deposited in Mendeley Data with DOI and direct URL given in the article.Dataset · publicolive field in a city on the north-west side of Seville in the south of Spain.
• City/Town/Region: Espartinas, Seville province
• Country: Spain
• Latitude and longitude: 37.394327, -6.121881
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/8xvhcsdvst.1
Direct URL to data: https://data.mendeley.com/datasets/8xvhcsdvst/1
Value of the Data
•
In smart agro applications, there are technological approaches that use artificial intelligence or traditional statistical methods such as ANOVA or PLS [1] , [2] , [3] , which require the use of data. In this regard, data are essential for both artificial intelligence and stochastic approaches. There Open asset ↗Mendeley Data · 10.17632/8xvhcsdvst.1lines:1-52Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
Conventional crop height measurements performed using aerial drone images require the 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation times and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths during flight by linking the RTK-GNSS and aerial image capture points. A new calibration method is proposed to further improve the accuracy and stereo matching speed. Throught the comparison between the proposed method and conventional methods in natural world environments, wherein it reduced the error rates by 62.2% and 69.4%, at flight altitudes of 10 and 20 m. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the errors were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.
Why it matches plant phenotyping methodsドローン画像とRTK-GNSSを用いた植物高のリアルタイム測定法を開発し、既存法との精度・速度比較で検証しており、表現型取得手法が中心である。
abstractThrought the comparison between the proposed method and conventional methods in natural world environments, wherein it reduced the error rates by 62.2% and 69.4%
Reproduction assets foundThe preprint declares that the datasets generated/analysed (drone images, GNSS data, and phenotyping measurements) are publicly available in the authors' supplementary materials zip archive. The Middlebury stereo dataset is a generic external resource, not paper-specific.Dataset · publicAvailability of data and material : The datasets generated and/or analysed during the current study are available in the https://nobuharaken.com/NatSciRep/supplementary_materials.zip repository.Open asset ↗nobuharaken.com/NatSciRep/supplementary_materials.ziplines:266-284Code / dataset availability confirmedarXiv · OpenAlex · checked 14 Sept 2026
We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).
Why it matches plant phenotyping methods植物の多視点画像取得、SfM再構成、質量推定を中核とするロボット型フェノタイピング手法の開発・実証であり、単なる生物学的測定ではない。
abstractWe describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each.
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.
Why it matches plant phenotyping methods3D LiDARとRGBによる苗のスキャン基盤を開発し、植物フェノタイピング用データベースを構築・検証しているため、取得手法と再利用可能なデータセットが中心である。
abstractthis paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research.
Reproduction assets foundThe paper's maize seedling LiDAR phenotyping database (362 plants, 7 campaigns, raw/processed point clouds with stem/leaf labels, RGB images, rosbags) is publicly released on OSF across seven campaign-specific repositories, explicitly stated in the Data Availability Statement and Table 3. The GitHub links (sick_scan, uDataset · publicOur generated dataset is available online at: 1st campaign: https://osf.io/fcgwk/ ;Open asset ↗osf · fcgwklines:435-442Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.
Why it matches plant phenotyping methods植物の動的な生理状態を画像から定量化するFRET画像処理ツールの開発・ベンチマークが中心であり、花粉管内カルシウム分布の抽出に実質的に応用されている。
abstractFRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python.
Reproduction assets foundThe paper's authors publicly released the FRET-IBRA analysis toolkit (Python source code, test images, example configuration files, and tutorial) under a BSD license on GitHub. The test images include the FRET microscopy image stacks of growing Arabidopsis pollen tubes used in the paper's calcium-distribution phenotypiCode · publicThe source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.Open asset ↗github.com/gmunglani/fret-ibralines:113-128Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 8 Sept 2026
Abstract Background Color distortion is an inherent problem in image-based phenotyping systems that are illuminated by artificial light. This distortion is problematic when examining plants because it can cause data to be incorrectly interpreted. One of the leading causes of color distortion is the non-uniform spectral and spatial distribution of artificial light. However, color correction algorithms currently used in plant phenotyping assume that a single and uniform illuminant causes color distortion. These algorithms are consequently inadequate to correct the local color distortion caused by multiple illuminants common in plant phenotyping systems, such as fluorescent tubes and LED light arrays. We describe here a color constancy algorithm, ColorBayes, based on Bayesian inference that corrects local color distortions. The algorithm estimates the local illuminants using the Bayes’ rule, the maximum a posteriori, the observed image data, and prior illuminant information. The prior is obtained from light measurements and Macbeth ColorChecker charts located on the scene. Results The ColorBayes algorithm improved the accuracy of plant color on images taken by an indoor plant phenotyping system. Compared with existing approaches, it gave the most accurate metric results when correcting images from a dataset of Arabidopsis thaliana images. The software is available at https://github.com/diloc/Color_correction.git .
Why it matches plant phenotyping methods植物フェノタイピング画像の局所的な色歪みを補正するアルゴリズムを開発し、既存手法およびArabidopsis画像データセットで精度を検証しているため、方法が中心的である。
abstractWe describe here a color constancy algorithm, ColorBayes, based on Bayesian inference that corrects local color distortions.
Reproduction assets foundThe paper's ColorBayes color-correction algorithm code is explicitly stated as publicly available on the authors' GitHub repository. The green fabric ground-truth image dataset and Arabidopsis plant image datasets are described but no public deposit is stated for them.Code · publicThe code of the color correction algorithm is available for reuse at https://github.com/diloc/Color_correction.git.Open asset ↗diloc/Color_correctionpdf-page:15 lines:1-82Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceAnnotation / quality controlCalibration / preprocessingVisualization / data management
Background Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators. Results We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline . Conclusion The analysis workflow and document presented are not limited to IRRI's RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI's RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way.
Why it matches plant phenotyping methods作物育種における表現型データの前処理・品質管理・統計解析・可視化を一貫して行う、再現可能なオープンソース解析パイプラインが中心である。
abstractHere, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline
Reproduction assets foundThe paper's authors publicly release their phenotypic data analysis pipeline source codes, sample HTML workflow documents, and demo phenotypic dataset on GitHub, directly reproducing this paper's computational analysis.Code · publicThe analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:1-75Dataset · publicAll the instructions, R source codes, examples, and the data sets are freely available in the GitHub repository at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:80-91Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Field / plotLeafClassificationCalibration / preprocessingSegmentationDisease symptoms / severity
Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, apart from the region of interest, contain unwanted parts of plants, soil, rocks, and/or human body parts. Segmentation helps isolate the target region and a deep convolutional neural network classifies images precisely. Therefore, we combined edge and morphological based segmentation, background subtraction, and the convolutional neural network to help improve accuracy on image sets with images containing clean and cluttered backgrounds. In the proposed system, segmentation was applied to first extract leaf images in the foreground. Several images contained a leaf of interest interposed between unfavorable foregrounds and backgrounds. Background subtraction was implemented to remove the foreground image followed by segmentation to obtain the region of interest. Finally, the images were classified by a pre-trained classification network. The experimental results on two, four, and eight classes of datasets show that the proposed method achieves 98.7%, 96.7%, and 93.57% accuracy by fine-tuned DenseNet121, InceptionV3, and DenseNet121 models, respectively, on a clean dataset. For two class datasets, the accuracy obtained was about 12% higher for a dataset with images taken in the homogeneous background compared to that of a dataset with testing images with a cluttered background. Results also suggest that image sets with clean backgrounds tend to start training with higher accuracy and converge faster.
Why it matches plant phenotyping methods植物葉の病害分類に向けて、背景除去・セグメンテーションとCNNを組み合わせた画像解析手法を開発し、異なるデータセットで精度を評価しているため、病害状態のフェノタイピング手法が中心である。
abstractTherefore, we combined edge and morphological based segmentation, background subtraction, and the convolutional neural network to help improve accuracy on image sets with images containing clean and cluttered backgrounds.
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 Statement: The data used in this study can be found at kamalchhetri24/Plant_dis-
ease_classification: Dataset for 8 classes of plant diseases (github.com).Open asset ↗pdf-page:15 lines:1-58Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. The FRET-IBRA package has been extensively used in quantifying the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra . Author Summary FRET is a fundamental imaging technique used to generate fluorescence signals sensitive to molecular conformations and interactions. Despite its wide use and the large body of literature on the theoretical steps required to process images generated from this procedure, we were unable to locate a tool that contained the entire processing workflow, whilst allowing the user the flexibility to adjust parameters for maximum accuracy and runtime efficiency. FRET-IBRA was thus created to be an all-in-one, open-source, parallel solution to process FRET images, while eliminating complications arising from repeated image format conversions. Besides enhancing the background subtraction algorithm for FRET images, several additional options were implemented for the user to extract the cleanest signal possible for their specific use case. FRET-IBRA is primarily built for flexibility when handling large image stacks by supporting sequences of image frames to be treated independently, greatly reducing time spent on splitting and concatenating image stacks. In accuracy and speed benchmarks against more general background subtraction packages, FRET-IBRA was able to provide the cleanest results with a fast runtime, leading to reliable analysis without additional tuning.
Why it matches plant phenotyping methodsFRET画像の背景補正・補正処理・比画像構築を一体化したソフトウェアを開発し、既存ツールとのベンチマークで検証している。花粉管成長中のカルシウム分布という植物生理状態の抽出にも適用され、方法が中心である。
abstractFRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python.
Reproduction assets foundThe paper's authors publicly release the FRET-IBRA analysis toolkit (source code, test images, example configuration files, and tutorial) on GitHub, directly supporting the paper's FRET image processing and ratiometric analysis of pollen tube calcium imaging.Code · publicThe source code for Linux and Mac operating systems is released under the
BSD license and, along with installation instructions, test images, example configuration
files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.Open asset ↗github.com/gmunglani/fret-ibrapdf-page:1 lines:1-66Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotPhotogrammetry / SfM / MVSCalibration / preprocessing
Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.
Why it matches plant phenotyping methodsUAV植物フェノタイピングの中間データ処理を担うオープンソースPythonパッケージを開発しており、ROI抽出・逆投影などの画像解析ワークフローが中心である。
abstractThe functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images.
Reproduction assets foundThe paper's own analysis software, the EasyIDP Python package implementing the ROI cropping and reversing modules used for the UAV phenotyping measurements, is explicitly stated to be publicly downloadable under an MIT license from the authors' GitHub repository.Code · publicest
IoU Euclidean distance raw image was selected to mark the manual reference, and the
overall trend of the indicators was simply analyzed.
2.6. Implementation
The cropping and reversing modules mentioned above were implemented into a Py-
thon package called EasyIDP using the MIT license. The source code can be downloaded
from https://github.com/HowcanoeWang/EasyIDP. For specific package documentation,
please refer to https://github.com/HowcanoeWang/EasyIDP/wiki. Although the source
codes were cross-platform owing to the characteristics of the Python language, they were
programmed and tested on a Windows 10 64-bit platform and an Intel CPU with a math
kernel library (MKL). More than 8 GB ROpen asset ↗HowcanoeWang/EasyIDPpdf-raw-page:8 lines:1-38Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R 2 p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs. CORE IDEAS A low-cost, handheld near-infrared spectrometer was tested for phenotyping of cassava roots Plant breeding-relevant cross-validation schemes were used for predictions High prediction accuracies were achieved for cassava root dry matter content A spectral region predictive of plant water content was identified as important
Why it matches plant phenotyping methodsカッサバ根の乾物含量という植物形質を対象に、低コスト携帯型NIR分光計と予測モデルを開発・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。
abstractwe investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava.
Reproduction assets foundThe paper's raw RDMC and NIR spectral data (21,044 SCiO scans paired with oven-dry RDMC from 10 Nigerian field trials) are publicly deposited on Cyverse under the GoreLab shared directory, as stated in the Data Availability section. The analysis R code on GitHub (GoreLab/CassavaNIRS) is paper-specific but its URL is anDataset · publicRaw RDMC and spectral data are available for download on Cyverse atOpen asset ↗Cyversepdf-page:20 lines:1-54Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Field / plotRaman / spectroscopyLeafWhole plant / canopy / plot / fieldObject detectionCalibration / preprocessingGrowth / development / phenologyLeaf traitsWater status / transpiration
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica . We first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over time (2.7 ± 1.7%), or between individuals (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual measured within one week. However, measurements of variation within an individual at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Highlights We partition biological variation from measurement uncertainty for leaf spectra. Measurement uncertainty represents ca. 3% of spectral variation among beech trees. Biological variation within an individual increases by 80% as leaves mature. Maxima of uncertainty correspond to maxima of biological variation (water content). We recommend procedures to quantify biological variation in spectral measurements.
Why it matches plant phenotyping methods葉の反射分光測定における不確実性を定量化し、植物形質変異の検出能力と標準化プロトコルを評価する研究であり、フェノタイピング手法が中心です。
abstractWe first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation.
Reproduction assets foundThe paper deposits its analysis scripts and source data in Dryad and its FieldSpec leaf/fabric reflectance spectra in the SPECCHIO spectral database, both publicly accessible with explicit identifiers.Dataset · publicumption. The number of
390
replicates is indicated for each dataset. Data processing and statistical analyses were all
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performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
392
individual wavelengths; see Results and figure captions for details. Scripts and source data
393
are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
394
spectroradiometer data are also deposited in SPECCHIO
395
(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
396
be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
397
Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatOpen asset ↗Dryad · 10.5061/dryad.gtht76hkxpdf-raw-page:17 lines:1-49Dataset · publicall
391
performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
392
individual wavelengths; see Results and figure captions for details. Scripts and source data
393
are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
394
spectroradiometer data are also deposited in SPECCHIO
395
(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
396
be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
397
Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatica La Massane’ (dataset C),
398
‘Field spectroscopy F. sylvatica SwissForest’ (dataset D). Visualization and descriOpen asset ↗SPECCHIOpdf-raw-page:17 lines:1-49Code / dataset availability confirmedCrossref · OpenAlex · checked 9 Sept 2026
The use of 3D sensors combined with appropriate data processing and analysis has provided tools to optimise agricultural management through the application of precision agriculture. The recent development of low-cost RGB-Depth cameras has presented an opportunity to introduce 3D sensors into the agricultural community. However, due to the sensitivity of these sensors to highly illuminated environments, it is necessary to know under which conditions RGB-D sensors are capable of operating. This work presents a methodology to evaluate the performance of RGB-D sensors under different lighting and distance conditions, considering both geometrical and spectral (colour and NIR) features. The methodology was applied to evaluate the performance of the Microsoft Kinect v2 sensor in an apple orchard. The results show that sensor resolution and precision decreased significantly under middle to high ambient illuminance (>2000 lx). However, this effect was minimised when measurements were conducted closer to the target. In contrast, illuminance levels below 50 lx affected the quality of colour data and may require the use of artificial lighting. The methodology was useful for characterizing sensor performance throughout the full range of ambient conditions in commercial orchards. Although Kinect v2 was originally developed for indoor conditions, it performed well under a range of outdoor conditions.
Why it matches plant phenotyping methodsRGB-Dセンサーの性能を、果樹キャノピーの3D形状・色・NIR特徴の取得という植物フェノタイピング用途で、照明・距離条件下で評価する方法論が中心である。
abstractThis work presents a methodology to evaluate the performance of RGB-D sensors under different lighting and distance conditions, considering both geometrical and spectral (colour and NIR) features.
Reproduction assets foundThe paper's Kinect Evaluation in Orchard conditions (KEvOr) dataset of RGB/NIR/point-cloud captures from an apple orchard is publicly deposited on Zenodo, and the authors' MATLAB analysis code for the sensor evaluation is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicA MATLAB® (R2020a, Math Works Inc., Natick, MA, USA) code was developed to analyse all
the data and provide the sensor evaluation results. This code has been made publicly available at
https://github.com/GRAP-UdL-AT/RGBD_sensors_evaluation_in_Orchards [35].Open asset ↗GitHub · GRAP-UdL-AT/RGBD_sensors_evaluation_in_Orchardspdf-page:6 lines:1-60Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.
Why it matches plant phenotyping methods植物HTPデータから形質を抽出・補正する解析パイプラインを開発し、実データとシミュレーションで検証しているため、方法が中心的です。
abstractwe developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment.
Reproduction assets foundThe paper explicitly provides two public GitHub repositories: the SpaTemHTP R pipeline package and a validation repository containing all data, scripts, and functions needed to reproduce the paper's phenotyping analyses. Raw phenotypic data itself is only available on request.Code · publicThe R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP .Open asset ↗ICRISAT-GEMS/SpaTemHTPlines:316-319Code · publicAll data, scripts, and functions required to reproduce the results can be found at: https://github.com/ICRISAT-GEMS/SpaTemHTP_Validation .Open asset ↗ICRISAT-GEMS/SpaTemHTP_Validationlines:457-479Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Plant interactions are as important belowground as aboveground. Belowground plant interactions are however inherently difficult to quantify, as roots of different species are difficult to disentangle. Although for a couple of decades molecular techniques have been successfully applied to quantify root abundance, root identification and quantification in multispecies plant communities remains particularly challenging. Here we present a novel methodology, multispecies genotyping by sequencing (msGBS), as a next step to tackle this challenge. First, a multispecies meta‐reference database containing thousands of gDNA clusters per species is created from GBS derived High Throughput Sequencing (HTS) reads. Second, GBS derived HTS reads from multispecies root samples are mapped to this meta‐reference which, after a filter procedure to increase the taxonomic resolution, allows the parallel quantification of multiple species. The msGBS signal of 111 mock‐mixture root samples, with up to 8 plant species per sample, was used to calculate the within‐species abundance. Optional subsequent calibration yielded the across‐species abundance. The within‐ and across‐species abundances highly correlated ( R 2 range 0.72–0.94 and 0.85–0.98, respectively) to the biomass‐based species abundance. Compared to a qPCR based method which was previously used to analyse the same set of samples, msGBS provided similar results. Additional data on 11 congener species groups within 105 natural field root samples showed high taxonomic resolution of the method. msGBS is highly scalable in terms of sensitivity and species numbers within samples, which is a major advantage compared to the qPCR method and advances our tools to reveal hidden belowground interactions.
Why it matches plant phenotyping methods根サンプル中の各植物種の相対 abundance（バイオマスに対応する植物状態）を定量する分子ベースの測定法を開発し、模擬試料および自然試料で検証しているため、方法中心の植物フェノタイピング研究と判断します。
abstractHere we present a novel methodology, multispecies genotyping by sequencing (msGBS), as a next step to tackle this challenge.
Reproduction assets foundThe paper's msGBS analysis scripts are publicly available on GitHub, and metadata underlying the study are deposited on Dryad; both are explicitly stated in the Data Availability Statement with authors' public URLs.Code · publicAll scripts used were made available on GitHub ( https://github.com/NielsWagemaker/scripts_msGBS/tree/msGBS-1.0 ).Open asset ↗GitHub · NielsWagemaker/scripts_msGBSlines:804-868Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
BACKGROUND: (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. RESULTS: The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. CONCLUSIONS: DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しているため、方法開発が中心である。
abstractWe developed the Digital Adjustment of Plant Development (DAPD) normalization method.
Reproduction assets foundThe authors explicitly state their DAPD normalization and segmentation code is publicly available on GitHub.Code · publicOur code and a Python notebook come with a friendly user manual detailing how to use it, and they are available at https://github.com/diloc/DAPD_Normalization.git .Open asset ↗diloc/DAPD_Normalizationlines:73-78Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
PREMISE: High-resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above-ground plant attributes. However, the acquisition of high-resolution images of plant roots is more challenging than above-ground data collection. An effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. METHODS: We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non-plant-root images, (ii) training with plant-root images, and (iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. The architectures of the SR models were based on two state-of-the-art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. RESULTS: In our experiments, we observed that the SR models improved the quality of low-resolution images of plant roots in an unseen data set in terms of the signal-to-noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non-root data sets. DISCUSSION: The incorporation of a deep learning-based SR model in the imaging process enhances the quality of low-resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal-to-noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.
Why it matches plant phenotyping methods植物根の低解像度画像を高解像度化する深層学習手法を開発・比較し、根画像の分割性能への効果も検証しており、表現型取得・抽出法が中心である。
abstractAn effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors
Reproduction assets foundThe paper's authors explicitly state that their source code and pre-trained SR models are publicly available on GitHub and Zenodo, and they used five publicly available image datasets (DIV2K, 91-Image, Arabidopsis thaliana root data, wheat seedling roots, 3D MRI barley roots) plus a SegRoot soybean test set as phenotypCode · publicbidopsis thaliana data set ( https://zenodo.org/record/50831#.XjIAPVNKhQI ), wheat seedling data set ( http://gigadb.org/dataset/100346 ), and barley data set ( https://www.quantitative‐plant.org/dataset/3d‐magnetic‐resonance‐images‐of‐barley‐roots ). The source code and pre‐trained SR models are available at GitHub and Zenodo (https://github.com/GatorSense/SRrootimaging; https://doi.org/10.5281/zenodo.3940562 ; Ruiz‐Munoz, 2020 ).
LITERATURE CITED
Akinnifesi , F. K.
,
B. T.
Kang
, and
D. O.
Ladipo
. 1998
Structural root form and fine root distribution of some woody species evaluated for agroforestry systems
. Agroforestry Systems
42 : 121 – 138 .
Araus , J. L.
, and
J. E.
Cairns
. 2014
FielOpen asset ↗GatorSense/SRrootimaginglines:155-276Dataset · publicWinRHIZO images of the barley roots.
In our experiments, we grouped the three plant‐root data sets into a single data set named “Roots.” Figure 3 shows examples of the plant‐root data sets used for training the SR model. To test the performance of the SR models, we used a data set of 65 soybean ( Glycine max (L.) Merr.) roots ( https://github.com/wtwtwt0330/SegRoot [accessed 11 June 2020]) (Wang et al., 2019 ).
SR model training
Many CNN architectures that enable the mapping of LR images into SR images can be found in the machine learning literature. In this study, we used two state‐of‐the‐art CNN‐based models, FSRCNN and SRGAN, to convert LR root images to SR images. FSRCNN is a model thOpen asset ↗wtwtwt0330/SegRootlines:93-102Dataset · publicIn this study, we used five publicly available data sets to train the SR models. We used two non‐plant‐root data sets, DIV2K ( https://data.vision.ee.ethz.ch/cvl/DIV2K/ [accessed 11 June 2020]) and 91‐Image ( https://www.kaggle.com/ll01dm/t91‐image‐dataset [accessed 11 June 2020]). DIV2K is a data set of natural images that has been used by others to train and test SR algorithms (Timofte et al., 2017 ). We trained our models on the grayscale version of this training data set (800 images). TheOpen asset ↗lines:93-102Dataset · publicIn this study, we used five publicly available data sets to train the SR models. We used two non‐plant‐root data sets, DIV2K ( https://data.vision.ee.ethz.ch/cvl/DIV2K/ [accessed 11 June 2020]) and 91‐Image ( https://www.kaggle.com/ll01dm/t91‐image‐dataset [accessed 11 June 2020]). DIV2K is a data set of natural images that has been used by others to train and test SR algorithms (Timofte et al., 2017 ). We trained our models on the grayscale version of this training data set (800 images). The 91‐Image information is a classical data set commonly used in SR studies. We also used tOpen asset ↗lines:93-102Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.
Why it matches plant phenotyping methodsUAV画像とCNNを用いて rapeseed の葉を認識し、植物体数（stand count）を自動推定する手法の開発・性能評価が研究の中心であるため、植物フェノタイピング方法論に該当します。
abstractA CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves.
Reproduction assets foundThe paper's data availability statement explicitly deposits the 'Rapeseed_seedling_counting' data (supporting the UAV imagery-based stand count findings) in a public GitHub repository with an authors' URL, qualifying as a paper-specific public asset.Dataset · publicThe “Rapeseed_seedling_counting” data that support the findings of this study are available in “LARSC-Lab/Rapeseed_seedling_counting” in GitHub, which can be found at https://github.com/LARSC-Lab/Rapeseed_seedling_counting .Open asset ↗LARSC-Lab/Rapeseed_seedling_countinglines:590-664Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 9 Sept 2026
Abstract Background Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. Results The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.
Why it matches plant phenotyping methods植物表現型ハイスループット測定の時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しており、方法が研究の中心である。
abstractWe developed the Digital Adjustment of Plant Development (DAPD) normalization method.
Reproduction assets foundThe paper's DAPD normalization and segmentation analysis code is explicitly stated to be publicly available on the authors' GitHub repository, matching an allowed URL. No phenotype dataset or image deposit is stated; the in-house dataset is not publicly shared.Code · publicOur code is available for reuse at https://github.com/diloc/DAPD_Normalization.git.Open asset ↗diloc/DAPD_Normalizationpdf-page:14 lines:1-67Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Published10 Mar 2020Environmental modelling & software : with environment data newsCited by 104 · OpenAlex ↗
Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regular or irregular satellite time series. This work describes the main features of DATimeS, and provides a demonstration case using Sentinel-2 Leaf Area Index time series data over a Spanish site. GPR resulted as an optimum fitting algorithm with most accurate gap-filling performance and associated uncertainties. DATimeS further quantified LAI fluctuations among multiple crop seasons and provided phenological indicators for specific crop types.
Why it matches plant phenotyping methods植物のLAI時系列から雲のない合成画像、季節動態、フェノロジー指標を抽出するソフトウェアを開発・提示しており、形質推定ワークフローが中心である。
abstractThe here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns.
Reproduction assets foundThe paper's own analysis software, DATimeS, is explicitly made freely available for download from the ARTMO web page, and the MLRA source code used within it is stated to be freely available on the authors' IPL-UV GitHub repository. GPy and GDAL are generic third-party libraries cited in references and excluded.Code · publicTheir source code is freely available and can be found at: https://github.com/IPL-UV/simpleR .Open asset ↗IPL-UV/simpleRlines:122-166Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background Seed analysis is currently a bottleneck in phenotypic analysis of seeds. Measurements are slow and imprecise with potential for bias to be introduced when gathered manually. New acquisition tools were requested to improve phenotyping efficacy with an emphasis on obtaining colour information. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically and individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help breeders to develop more productive cultivars. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を自動取得・抽出する撮像システムと解析ソフトウェアの開発が中心であり、植物フェノタイピング手法に該当する。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper's phenoSEED image-analysis script (used for seed shape, size, colour, and clustering phenotyping) is explicitly stated to be publicly available on the authors' GitLab repository. The BELT image datasets themselves are only available on request, so they do not qualify as public assets.Code · publicde and hard-
ware plans available for academic and non-commercial
use. It is hoped that this will support the collection
of more easily cross-comparable data and encourage
other research groups to contribute to further de-
velopment of the project. At the time of publica-
tion, a version of the processing script is available at
https://gitlab.com/usask-speclab/phenoseed. Further
information on a comprehensive hardware and soft-
ware bundle will be made available as it is packaged
for distribution.
Methods
BELT System Design and Description
BELT (Figure 1) was designed around a 150 mm wide
conveyor with at white, low-gloss belt (Mini-Mover
Conveyors, Volcano CA) mounted on an audio-visual
carOpen asset ↗usask-speclab/phenoseedpdf-raw-page:8 lines:1-99Code / dataset availability confirmedarXiv · OpenAlex · checked 14 Sept 2026
Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The method is based on calibrating the camera using information available from the EXIF tags from the image, as well as visual information from the pattern. Code is provided to implement the method, as well as a dataset for testing. We include steps to verify protocol correctness by imaging an artifact. The use of this protocol for two-dimensional plant phenotyping will allow data capture from different cameras and environments, with comparison on the same physical scale. We abbreviate this method as CASS, for CAmera aS Scanner. Code and data is available at http://doi.org/10.5281/zenodo.3677473.
Why it matches plant phenotyping methods植物部位の2次元形質をカメラで測定する手法を開発し、校正・検証手順、コード、テストデータを提供しており、植物フェノタイピング手法が中心である。
abstractThis chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern.
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 code and test datasets are provided in [ 16 ] . Within [ 16 ] , are some example data and two programs:
aruco-pattern-write and camera-as-scanner . To prepare for the experiments, download the example data and install the code (C++ code as well as a Docker image are provided).Open asset ↗lines:54-78Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 10 Sept 2026
High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
Why it matches plant phenotyping methods植物画像の色・明るさ補正法を開発し、形態計測の精度向上と高スループット解析パイプラインへの実装を示しており、フェノタイピング手法が中心である。
abstractHere, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized.
Reproduction assets foundThe paper's image standardization C++ code is publicly deposited on GitHub, and the paper-specific phenotyping assets (original and corrected plant images, R scripts, and numerical data) are publicly downloadable from the Danforth Center phenotyping site; the method is also implemented in PlantCV (GitHub and Zenodo-achCode · publicThe code is deposited at GitHub https://github.com/jberry47/phenotypercv .Open asset ↗https://github.com/jberry47/phenotypercvlines:189-238Dataset · publicOriginal images, corrected images, R scripts and all numerical data are available to download at https://bioinformatics.danforthcenter.org/phenotyping/ .Open asset ↗lines:189-238Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO₂ flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.
Why it matches plant phenotyping methodsFluoSpec 2は植物キャノピーのSIF・光学特性を継続取得する自動センサーシステムであり、装置設計、校正、試験が論文の中心です。植物の光合成・生理状態を測定するフェノタイピング基盤に該当します。
abstractHere we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance
Reproduction assets foundThe paper's acknowledgments explicitly state that source codes used to control the FluoSpec 2 system and for postprocessing are publicly available at two GitHub repositories (persl/SeaBreeze and zhangyaonju/seabreeze_control), both of which are in the allowed URL list. These are authors' public code assets directly支撑该仪Code · publicDong Yan, Xian Wang, and Matt Dannenberg for the help with the installation of FluoSpec 2. We also thank Christian Frankenberg, Ari Kornfeld, Joe Berry, Troy Magney, Lianhong Gu, and Jochen Stutz for providing important and useful feedbacks. Source codes that are used to control the system and for postprocesing can be found in https://github.com/persl/SeaBreeze and https://github.com/zhangyaonju/seabreeze_control .
Author Contributions
X.Y. designed the FluoSpec 2 system. X.Y., H.S., A.S. designed all the tests of FluoSpec 2 and installed three FluoSpec 2. All authors contributed to the improvement of FluoSpec 2 and the writing and editing of the manuscript.
FundingOpen asset ↗persl/SeaBreezelines:139-146Code · publicrg for the help with the installation of FluoSpec 2. We also thank Christian Frankenberg, Ari Kornfeld, Joe Berry, Troy Magney, Lianhong Gu, and Jochen Stutz for providing important and useful feedbacks. Source codes that are used to control the system and for postprocesing can be found in https://github.com/persl/SeaBreeze and https://github.com/zhangyaonju/seabreeze_control .
Author Contributions
X.Y. designed the FluoSpec 2 system. X.Y., H.S., A.S. designed all the tests of FluoSpec 2 and installed three FluoSpec 2. All authors contributed to the improvement of FluoSpec 2 and the writing and editing of the manuscript.
FundingOpen asset ↗zhangyaonju/seabreeze_controllines:139-146Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution-canopy shoot area as a function of height-which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an R2 value of 0.92), and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopies than can be captured by a simple trait measure such as overall canopy height.
Why it matches plant phenotyping methodsステレオ画像による作物キャノピー構造・高さ分布の自動取得法を開発し、手動測定と比較検証しているため、植物表現型取得が中心である。
abstractwe report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging
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 sub-dataset for land-based crop phenotyping using stereo images can be downloaded from https://sourceforge.net/projects/land-based-crop-phenotyping/ , which includes images of all 60 plots and their depth maps on 23 Sept 2016 and 11 Oct 2016. These data are sufficient to validate the results presented in this paper.Open asset ↗sourceforge · land-based-crop-phenotypinglines:135-142Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).
Why it matches plant phenotyping methods複数作物の植物パラメータ、表現型、ハイパースペクトル反射を体系的に取得した地上基準データベースであり、取得設計と各パラメータの測定法を詳細に記述して、リモートセンシングモデルの校正・検証に用いる点が中心的です。
abstractwe describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations
Reproduction assets foundThis is a data descriptor paper whose plant-phenotyping measurements (biophysical/biochemical plant parameters, phenology, hyperspectral reflectance, FVC/PSM, soil moisture, photos, survey data) are explicitly deposited as public PANGAEA datasets with DOIs listed in the text. Multiple paper-specific public assets are直接Dataset · publicThe database is freely available at PANGAEA
(https://doi.org/10.1594/PANGAEA.874251).Open asset ↗PANGAEA · 10.1594/PANGAEA.874251pdf-page:1 lines:1-54Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 10 Sept 2026
O_LIPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.\nC_LIO_LIMethods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.\nC_LIO_LIConclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.\nC_LI
Why it matches plant phenotyping methods低コストの画像取得プラットフォームを開発・記述し、植物形質の定量化への適用性を示すことが中心である。
abstractTo make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.
Reproduction assets foundThe paper's PlantCV image-analysis scripts for its Raspberry Pi phenotyping examples are publicly available in the authors' danforthcenter/apps-phenotyping repository. The outreach and gphoto URLs are not paper-specific analysis assets.Code · publicAn example image has been p rocessed with PlantCV (Fahlgren et al., 2015), and
the analysis script is available at https://github.com/danforthcenter/apps-phenotypingOpen asset ↗danforthcenter/apps-phenotypingpdf-page:8 lines:1-52Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
RootMorphology / geometry measurementCalibration / preprocessingRoot system architecture
Background There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration. Results Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database. Conclusions Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.
Why it matches plant phenotyping methods根系表現型取得パイプラインを開発し、カスタムソフトウェア、画像からの形質抽出、処理時間と精度のベンチマークを中心に評価しているため、方法論が中心である。
abstractHere we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration.
Reproduction assets foundThe paper's ArchiPhen software, analysis scripts, and supporting root phenotyping data are publicly available at archiroot.org.uk and mirrored on the authors' GitHub repository (linked to Zenodo).Code · publicSoftware files are also stored on Github repository https://github.com/LionelDupuy/ARCHI_PHEN and linked to Zenodo (DOI: 10.5281/zenodo.399222).Open asset ↗https://github.com/LionelDupuy/ARCHI_PHEN · ARCHI_PHENlines:126-268Code · publicThe source code is freely available at www.archiroot.org.uk .Open asset ↗lines:99-104Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Within the HarvestPlus program there are many collaborators currently using X-Ray Fluorescence (XRF) spectroscopy to measure Fe and Zn in their target crops. In India, five HarvestPlus wheat collaborators have laboratories that conduct this analysis and their throughput has increased significantly. The benefits of using XRF are its ease of use, minimal sample preparation and high throughput analysis. The lack of commercially available calibration standards has led to a need for alternative calibration arrangements for many of the instruments. Consequently, the majority of instruments have either been installed with an electronic transfer of an original grain calibration set developed by a preferred lab, or a locally supplied calibration. Unfortunately, neither of these methods has been entirely successful. The electronic transfer is unable to account for small variations between the instruments, whereas the use of a locally provided calibration set is heavily reliant on the accuracy of the reference analysis method, which is particularly difficult to achieve when analyzing low levels of micronutrient. Consequently, we have developed a calibration method that uses non-matrix matched glass disks. Here we present the validation of this method and show this calibration approach can improve the reproducibility and accuracy of whole grain wheat analysis on 5 different XRF instruments across the HarvestPlus breeding program.
Why it matches plant phenotyping methods小麦粒のFe・Zn濃度という植物形質を測定するXRF校正法を開発し、5台の装置で再現性と精度を検証しており、測定法が中心的です。
abstractConsequently, we have developed a calibration method that uses non-matrix matched glass disks.
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 material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.00784
Click here for additional data file.Open asset ↗lines:369-504