RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Rice panicle blast detection is an important task in plant disease phenotyping. Field-based detection remains challenging because infected spike regions are often small, sparse, elongated, and affected by overlapping panicles, complex backgrounds, and variable illumination. In this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection. The model uses a Lightweight Ghost Backbone (LGB) to reduce redundant computation. It uses Anisotropic Axial Stripe Attention (A2SA) to represent elongated panicle structures. It also uses Focal Multi-Scale Attention (FMSA) for multi-scale feature refinement and Adaptive Geometric Shape IoU (AGS-IoU) for geometry-aware localization. The model was trained and evaluated on a rice panicle image dataset containing 1,055 training images, 69 validation images, and 169 test images. On the test set, RPB-YOLO11 achieved 76.09% mAP50, 45.44% mAP50-95, 73.98% precision, and 72.75% recall with 6.21 GFLOPs. Compared with the YOLO11n baseline, it improved mAP50, mAP50-95, precision, and recall by 2.73, 2.12, 1.64, and 2.11 percentage points, respectively. An Android-oriented inference application supports local image inference, detection visualization, class counting, and diseased-panicle incidence estimation. These results suggest that RPB-YOLO11 provides a practical approach for image-based rice panicle blast survey.
Why it matches plant phenotyping methodsイネ穂いもちの画像検出モデルを開発・比較検証し、罹病穂率を推定する実用アプリまで構築しており、植物病害状態の画像ベース表現型取得が中心である。
abstractIn this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection.
Reproduction assets foundThe paper links a public Hugging Face dataset used to establish the rice panicle blast detection dataset and a public GitHub release (data availability statement) containing the study's datasets/models.Dataset · publicsites, cultivars, growth stages, imaging conditions, and disease severities are still needed to evaluate generalization more fully.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/XuzheYang2Doc/RPB-YOLO11/releases/tag/rpb-yolo11 .
Author contributions
XY: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. XZ: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. CX: Formal analysisOpen asset ↗XuzheYang2Doc/RPB-YOLO11 · rpb-yolo11lines:639-658Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Rice leaf diseases pose a significant threat to global food security by reducing crop productivity and causing substantial economic losses. The traditional diagnosis method is manual method, which is low in efficiency, subjective and not suitable for large-scale agricultural monitoring. Despite the advances in automated disease detection using deep learning methods like CNNs, GANs, and transfer learning models, these techniques remain highly computational, not very flexible, and struggle to perform well in different imaging scenarios. Considering these drawbacks, this paper introduces an Explainable Deep Q-Learning based CNN framework which employs CNN-based feature extraction and Deep Q-learning-based adaptive policy optimization for rice leaf disease classification. The proposed model continuously refines the classification actions through reward-based learning, which makes the model more robust in various agricultural imaging environments, in contrast to traditional supervised CNN models that have static classification decisions. The proposed model achieved 98.5% accuracy, 98.52% precision, 98.50% recall, and a 98.51% F1-score, outperforming existing CNN, GAN, reinforcement learning, and transformer-based methods. It also offers a high computational efficiency of 14.2 GFLOPs, 248 MB memory consumption, ~ 48 min of training time, and 6.8 ms inference time per image suitable for resource constrained applications in agriculture. The results demonstrate the effectiveness, scalability, and practical applicability of the proposed framework. The proposed framework performs well on benchmark datasets but more research in the deployment of the edge-devices under different real-world agricultural settings will be investigated in future work.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する深層学習手法の開発・評価が研究の中心であり、植物の病害表現型を直接推定しているため。
abstractthis paper introduces an Explainable Deep Q-Learning based CNN framework which employs CNN-based feature extraction and Deep Q-learning-based adaptive policy optimization for rice leaf disease classification.
Reproduction assets foundThe paper trains its Deep Q-CNN rice leaf disease classifier on public Kaggle rice leaf image datasets, which are cited with explicit public URLs and qualify as paper-specific phenotyping image inputs. The authors' own derived data/analysis artifacts are only available upon request, so no authors' code or trained modelDataset · publicSoni Gautam. Rice Leaf Bacterial and Fungal Disease Dataset. Kaggle. Available:Open asset ↗Kagglepdf-page:24 lines:1-94Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
The inflorescence is a key yield-determining organ, yet its complex morphology makes manual pixel-level annotation time-consuming, leading to a scarcity of high-quality segmentation datasets. To address this bottleneck, we propose ZCAT (Zero-shot Cross-crop Annotation Transfer), a novel paradigm for zero-annotation cross-crop pseudo-mask screening. ZCAT completely eliminates pixel-level manual annotation of the target crop, requiring only holistic quality assessment of model-generated pseudo-masks (5-10 s per image). Specifically, we train a SegFormer model on public rice panicle datasets (CVRP and RiceSEG) and transfer it across crops to the wheat spike segmentation task. The key innovation is the introduction of a human-defined quality function Q, which circumvents the fundamental challenge in self-learning algorithms: the inability of computers to autonomously distinguish good masks from bad ones. Through iterative human-in-the-loop pseudo-label screening with a curriculum learning strategy, each round adds only a few high-quality pseudo-masks to the training set, continuously improving model performance. After four iterations, ZCAT produces pseudo-masks with an average Spike IoU of 0.7003, evaluated against the GWFSS manual annotations as ground truth. Moreover, the pseudo-mask dataset exhibited higher benchmark performance than the GWFSS manual annotations (Spike IoU 0.7612 vs. 0.7027; mIoU 0.8627 vs. 0.8247), suggesting stronger self-consistency. A generalization test on a strictly held-out set of 100 manually annotated wheat spike images showed that the model trained on ZCAT-generated pseudo-masks achieved marginally better performance than that trained on the GWFSS manual annotations (Spike IoU: 0.5112 vs. 0.4927; mIoU: 0.5627 vs. 0.5247). The time budget of the ZCAT pipeline was substantially lower than that of manual annotation. ZCAT opens a new pathway for rapid annotation of plant reproductive structures or other organs and significantly reduces data preparation costs in plant phenomics. The generated wheat spike pseudo-mask dataset and the mask quality screening tool (Mask Quality Screener) are open-sourced.
Why it matches plant phenotyping methods植物器官セグメンテーションのためのゼロショット転移、擬似マスク品質評価、反復学習パイプラインを開発・検証しており、表現型取得基盤が中心である。
abstractThe key innovation is the introduction of a human-defined quality function Q
Reproduction assets foundThe paper explicitly open-sources two paper-specific assets: the ZCAT-generated wheat spike pseudo-mask dataset and the Mask Quality Screener tool, both with public GitHub URLs in the Data availability statement.Dataset · publicThe wheat spike pseudo-mask dataset and Mask Quality Screener are available at https://github.com/zyxyes1/MaskQualityScreener and https://github.com/zyxyes1/Wheat-Spike-Semantic-Segmentation , respectively.Open asset ↗Wheat-Spike-Semantic-Segmentationlines:415-440Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Sustainable agriculture has substantial share on improvement of food security and optimization of resources utilization particularly for high value crops like rice leaf. Rice varieties should be properly classified in order to benefit the harvest management, reduced loss after harvest and improved agriculture methods. The traditional classification method usually brings the low precision and the traditional classification method is also subjected to human error, which is difficult to bring about reliable output. This study introduces an optimized multi-class rice leaf disease classification system utilizing “Rice Feature Selection” (RiceFS) and ensemble machine learning approaches. RiceFS is realized based on a feature selection mechanism based on Recursive Feature Elimination. Selected classifiers such as KNN, Random Forest, Gradient Boosting, Ensemble Learning and Optimized SVM are analyzed based on the extracted subset of features and the proposed system is used to classify the seven classes of rice leaf disease. The experimental results show that the Optimized SVM has the best classification results among the different classifiers with accuracy of 92.10%, Precision of 92.20%, balanced Recall and F1 Score, which shows that Optimized SVM is very effective in multi-class rice leaf disease classification. The performance can be improved by feature reduction, generalization capability and computational complexity reduction, which are realized with the help of RiceFS. The proposed framework is designed to provide an intelligent decision support system for timely intervention, loss minimization and sustainable agriculture. Results indicate that these algorithms are applicable for rice leaf disease classification since they are accurate, reliable and scalable.
Why it matches plant phenotyping methodsイネ葉の病害状態を観察データから分類する計算手法が研究の中心であり、RiceFSと複数の機械学習器を用いた分類フレームワークを開発・評価しているため。
abstractThis study introduces an optimized multi-class rice leaf disease classification system utilizing “Rice Feature Selection” (RiceFS) and ensemble machine learning approaches.
Reproduction assets foundThe paper's RiceFS phenotyping/classification experiments are built on two public Kaggle rice leaf disease image datasets, explicitly cited with URLs and a data availability statement. No author code or models are deposited.Dataset · publicThe RiceFS framework proposed initially
performs a feature selection, followed by training
several classifiers: K-Nearest Neighbors (KNN),
Random Forest (RF), Gradient Boosting (GB),
Ensemble Learning, and Optimized Support
Vector Machine (Optimized SVM). The data is
published on the Kaggle website. The data is
open-source at:
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases [44].
This data consists of 120 jpgs of disease infected
rice leaves. The photos are divided into 3
categories according to the kind of disease. There
are 40 images in each class.
Classes
• Leaf smut
• Brown spot
• Bacterial leaf blight
The datasets are preprocessed by eliminating
redundant information, normOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:11 lines:1-103Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Rice stem internal structure is a critical micro-phenotype influencing lodging resistance and yield; however, its analysis remains constrained by labor-intensive manual methods. Here, we present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated. Five deep learning architectures were systematically evaluated, among which UNet-VGG16 achieved the best performance with a mean intersection over union (mIoU) of 87.4% (82.49% for LVBs and 74.41% for SVBs). An improved model, UNet-ECA-Bio, further raised mIoU to 89.32% and SVB IoU to 78.97% by integrating Efficient Channel Attention (ECA) and biologically informed class weighting using an image-level dataset. Leveraging these high-accuracy phenotypic predictions, genome-wide association studies (GWAS) indicated concordance between annotated and predicted traits, with SNP overlap rates of 96% (LVB count: 1,217/1,262), 43% (SVB count: 6/14), 98% (stem area: 122/124), and 100% (cavity area: 3/3) at −log10(p) ≥ 6. Meanwhile, compared with manual annotation (estimated 10–30 minutes per image), the proposed approach processed all 686 images within 10 minutes, representing a >600-fold increase in throughput. We further developed a user-friendly software tool, “Rice_Stem_Pre_V1.1.exe,” for automated phenotyping of 14 stem traits, providing a cost-effective platform for genetic studies of lodging resistance and yield improvement.
Why it matches plant phenotyping methodsイネ茎維管束の画像から複数の表現型形質を自動抽出する深層学習モデル、データセット、検証、ソフトウェアを中心的に開発しており、明確な植物フェノタイピング手法研究である。
abstractwe present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated.
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 6
Description of annotated and predicted stem internal structural traits.Open asset ↗lines:510-582Code / 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 confirmedEurope PMC · checked 5 Sept 2026
With climate change and global population growth, accelerating the breeding of superior crop varieties is essential for food security. Genomic prediction, which uses genome-wide genetic markers to predict crop traits, plays an important role in intelligent crop breeding. However, existing methods often lack stable and accurate performance across crops and traits. Here, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm. Compared with the best-performing single base learners, GEG2P improves prediction accuracy by 4.02% on average across maize, wheat, rice, chickpea, and soybean. We use SHAP to quantify the contribution of SNPs to phenotype prediction and find that SNPs with large effects captured by different base learners are functionally complementary. This study provides a robust and accurate genomic prediction method for crop breeding.
Why it matches plant phenotyping methods作物形質の遺伝子型から表現型を予測するアンサンブル計算法を開発し、複数作物で精度比較・検証しており、表現型推定手法が研究の中心である。
abstractHere, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm.
Reproduction assets foundThe paper provides public author code (GitHub GEG2P repository and Docker Hub image), a Zenodo deposit of significant SNP interaction pairs generated in this study, and a Figshare link with the wheat genotypic and phenotypic data used in the analyses. These are paper-specific, publicly available, and actionable.Code · publicScripts used in this study are available at GitHub [ https://github.com/Deep-Breeding/GEG2P ] 89 .Open asset ↗GitHub · Deep-Breeding/GEG2Plines:236-266Dataset · publicThe genotypic and phenotypic data of wheat are available at Figshare [ https://figshare.com/s/287c2c7f1623008487a5 ] 68 .Open asset ↗Figsharelines:236-266Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate identification of rice diseases from field images is critical for crop health monitoring and sustainable agriculture, particularly in low-resource environments. However, most deep learning approaches depend on large-scale labeled datasets and pretrained backbones, limiting their applicability to rare or emerging diseases. In this work, we formulate a domain-specific prototype-based few-shot framework that avoids pretrained visual backbones and treats rice disease recognition as structured matching over a pathogen-aware class graph. The individual components, including wavelet-scattering features, optimal transport, semantic prototype fusion, and transductive refinement, are established techniques; the contribution lies in their coupled use within a disease-taxonomy-guided few-shot matching process. This design combines fixed visual descriptors, root-to-leaf prototype matching, class symptom descriptors, and confidence-gated refinement to support rice disease recognition under limited labeled data. We evaluate the model on two publicly available rice disease datasets-the Philippines Rice Diseases and Roboflow Rice-under 1-shot and 5-shot classification settings. In in-domain experiments, our approach achieves up to 95.8% accuracy and 94.9% macro-F1 on the Philippines dataset, consistently outperforming a diverse set of baselines including CNN-from-scratch, ResNet-18-from-scratch, Matching Networks, MAML, ProtoNet, RelationNet, SimpleShot, FEAT, and a flat optimal-transport variant. In cross-domain evaluation, the model demonstrates strong generalization capability, attaining up to 91.7% accuracy and 90.6% macro-F1 when transferring across datasets. An ablation study further confirms the consistent contribution of hierarchical structure, semantic fusion, and transductive refinement to performance gains. These results demonstrate that the proposed framework delivers highly accurate, robust, and data-efficient disease recognition, making it well-suited for real-world agricultural deployment under limited supervision.
Why it matches plant phenotyping methodsイネの病害を圃場画像から認識する手法を開発・評価しており、植物の病害状態を画像から推定する方法が研究の中心である。
abstractwe formulate a domain-specific prototype-based few-shot framework
Reproduction assets foundThe paper evaluates its few-shot rice disease recognition framework on two publicly available rice disease image datasets, with explicit public URLs in the Data Availability statement and dataset description sections. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe datasets used in this study are publicly available: Philippines Rice Diseases dataset (https://www.kaggle.com/
datasets/shrupyag001/philippines-rice-diseases)Open asset ↗Kaggle · shrupyag001/philippines-rice-diseasespdf-page:19 lines:1-81Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice seedling salt-tolerance evaluation commonly relies on visual scoring or destructive assays, which are subjective, labor-intensive, and difficult to standardize for population-level analysis. This study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings. The framework integrates controlled hydroponic cultivation, RGB imaging, RicePhenoSeg-assisted annotation and trait extraction, ELMERF-based semantic segmentation, and image-derived quantification of salt-induced shoot injury. Using this framework, we constructed the Rice Seedling-Salt RGB Dataset (RSSD), which contains green shoot tissues, yellow shoot tissues, roots, and background from hydroponically grown rice seedlings. Based on RSSD, ELMERF achieved a mean Intersection over Union of 51.4% and a mean Accuracy of 89.5%, outperforming nine representative segmentation models. We further defined shoot yellowing rate (SYR) as an image-derived quantitative trait describing visible salt-induced shoot injury. The framework was applied to 261 re-sequenced rice accessions for population-level phenotyping and genome-wide association analysis. Compared with standard evaluation score and seedling death rate, SYR showed a more continuous phenotypic distribution and detected 36 significant SNPs, including a major signal near the Saltol/OsHKT1; 5 region. Notably, 34 SYR-associated SNPs were not detected by conventional visual scores. Overall, this study provides a targeted hydroponic RGB phenotyping framework for standardized rice seedling salt-stress evaluation and genetic analysis.
Why it matches plant phenotyping methods深層学習によるRGB画像セグメンテーション、形質抽出、データセット構築、性能比較を中核とし、画像由来の塩ストレス傷害形質を定量化する植物フェノタイピング手法である。
abstractThis study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits datasets, source code, and supporting data in a public GitHub repository (ELMERF), which covers the RSSD RGB image dataset, segmentation code, and phenotyping/GWAS analysis assets. RiceVarMap is a cited external SNP database, not a paper-specific asset.Code · publicThe datasets, source code, and other supporting data are openly available on the ELMERF repository (https://github.com/PhenoCodexh/ELMERF).Open asset ↗PhenoCodexh/ELMERFhtml-lines:446-478Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Food productivity, quantity and quality are at stake when plant diseases such as rice diseases undermine the food security. Rice leaf disease treatment necessitates accurate and timely diagnosing. This study describes a deep learning model for categorizing and forecasting rice plant diseases. Using the remora optimization algorithm (ROA) on a rice leaf dataset demonstrates its potential for plant disease classification. The ROA-DM method detects rice leaf diseases using the ROA algorithm, a deep maxout network (DMN), and a deep autoencoder (DAE). ROA is applied to the learning parameters of deep model in order to achieve better convergence and avoiding local minima, which usually happens with conventional gradient-based optimizers. Experiments show that the suggested framework is accurate and precise across illness categories. The confusion matrices display the training and validation accuracy, losses of this model. The performance of our optimal learning method with respect to other methods indicated its potential for identifying leaf diseases. The accuracy of the ROA-DM method is 98.5%.
Why it matches plant phenotyping methodsイネ葉の観察画像から病害状態を分類・検出する深層学習手法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。
abstractThis study describes a deep learning model for categorizing and forecasting rice plant diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset collected from https://www.kaggle.com/datasets/emmarex/plantdisease (PlantVillage dataset) for algorithm testing in plant disease diagnosis 35 . The selected rice leaf disease samples from PlantVillage dataset consisting of 3050 colour leaf images across four classes.Open asset ↗Kaggle · emmarex/plantdiseaselines:85-97Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice leaf diseases pose a major challenge to crop health and agricultural productivity, particularly when timely and accurate diagnosis is required under natural field conditions. The development of automated disease recognition systems depends heavily on the availability of large, well-annotated image datasets. However, many existing rice leaf disease datasets are limited in terms of environmental variability, disease representation, and real-field imaging conditions. To address this gap, this paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors from the experimental fields of the Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh, between July 2023 and July 2024. The dataset contains 4152 images belonging to five classes: Bacterial Leaf Blight, Bacterial Leaf Streak, Sheath Blight, Leaf Blast, and Healthy Leaf. The images were acquired from two rice varieties, BR11 and BRRI dhan34, under natural field conditions across varying illumination environments in order to reflect practical disease recognition scenarios. All images were manually annotated by trained annotators under expert supervision. The dataset is systematically organized and publicly released to support reproducible research in rice disease classification. In addition to dataset presentation, benchmark experiments using Xception, NASNetMobile, and InceptionV3 are provided to demonstrate its applicability for deep learning-based disease recognition. BanglaRiceLeaf is expected to serve as a useful resource for plant disease analysis, comparative model evaluation, and future research in precision agriculture and agricultural computer vision.
Why it matches plant phenotyping methodsイネ葉の病徴・健全状態を画像で分類する公開ベンチマークデータセットであり、データ収集・注釈・ベンチマーク評価が中心です。
abstractthis paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors
Reproduction assets foundThe paper's core asset is the BanglaRiceLeaf rice leaf disease image dataset (4152 field images, five classes), publicly released on Harvard Dataverse with DOI 10.7910/DVN/XAOBYW. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicData Identification Number: https://doi.org/10.7910/DVN/XAOBYW
Direct URL to Data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XAOBYW
Access Instructions: This dataset is publicly available on the Harvard Dataverse repository and can be accessed for academic, research, and instructional purposes.Open asset ↗Harvard Dataverse · doi:10.7910/DVN/XAOBYWhtml-lines:100-131Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
RiceRootTissueMorphology / geometry measurementSegmentationRoot system architecture
Abstract Background Quantification of rice root anatomical traits such as cortical aerenchyma lacunae is key to understanding rice adaptation to diverse water regimes and to support climate-smart breeding. Aerenchyma lacunae contributes to rice internal gas transport and influences methane emissions from flooded systems and can also limit rice water conductivity. It could be an interesting anatomical trait for breeding, however, large-scale anatomical phenotyping remains limited because manual analysis of root cross-sections is labor-intensive, subjective, and difficult to scale across heterogeneous imaging conditions. Existing pipelines often require parameter tuning and do not generalize well across environments. Results We developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae. The model was trained on 1,760 annotated images collected across multiple countries, growth stages, cultivation systems, and experimental contexts, using a collaboratively defined annotation protocol. The final model achieved high segmentation accuracy, with mean intersection over union values exceeding 0.92 for cortical tissues and lacunae. Quantification of the lacuna-to-cortex ratio showed strong agreement with manual annotations, with a coefficient of determination of 0.98 on an independent test set. An independent expert review indicated that model predictions were at least as consistent as manual annotations and reduced large annotation inconsistencies. The pipeline is released as open-source software and includes an interactive online demonstrator, and is accompanied by an online test dataset to support testing and reproducibility. Application across six experimental use cases revealed reproducible differences in aerenchyma lacunae across genotypes, water regimes, environments, and developmental stages. Conclusions This work provides a robust, scalable, and transferable tool for automated root anatomical phenotyping under heterogeneous experimental conditions. Transformer-based segmentation enables consistent and high-throughput quantification of lacunae, facilitating integration of these anatomical traits into breeding, physiological studies, and climate-smart crop improvement programs.
Why it matches plant phenotyping methodsイネ根の通気組織空隙を画像から自動セグメンテーション・定量するTransformerベースの表現型解析パイプラインを開発し、独立データで精度検証、ソフトウェアとテストデータセットを公開しているため、植物フェノタイピング手法が中心である。
abstractWe developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae.
Reproduction assets foundThe paper releases its authors' phenotyping pipeline (preprocessing/training code archived on Zenodo and an interactive Hugging Face Space demonstrator with a test dataset subset) as public assets. The full multi-environment training image dataset is only available upon reasonable request, so it is not a public asset.Code · publicall code used for preprocessing and
training is released under an open-source licence on GitHub, tagged v1.0.2, and
archived with a Zenodo DOI (Atef, 2025).Open asset ↗Zenodopdf-page:46 lines:1-65Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Paddy leaf disease (PLD) detection has grown more difficult, yet early detection might prevent significant losses due to decreased crop yield. However, existing models struggle to accurately classify diseases under difficult circumstances like intricate backgrounds, fluctuating lighting, and overlapping leaves. Additionally, existing models do not incorporate efficient optimization strategies, leading to suboptimal accuracy and poor generalization on unseen data. To address these challenges, a novel deep learning-based YOLO-LEAFNET method for PLD detection utilizing IGT-YOLO, integrating the YOLOv8 disease detection with the Improved Gorilla Troops (IGT) optimization. The input paddy leaf images are pre-processed using Bilateral Contrast Limited Adaptive Histogram Equalization (B-CLAHE) to enhance image quality and improve local contrast while preserving disease boundaries. YOLOv8 model is utilized to detect and classify paddy leaf diseases by accurately localizing affected regions with bounding boxes. Then, the IGT algorithm boosts the disease detection accuracy by optimizing YOLOv8 through effective hyperparameter tuning. The proposed YOLO-LEAFNET method effectiveness was evaluated using recall, F1 score, specificity, accuracy, and precision. B-CLAHE enhanced noise-free images improve contrast and detection accuracy, while the IGT-YOLO model ensures scalable, efficient early diagnosis of paddy leaf diseases with 99.07% accuracy. The YOLO-LEAFNET enhanced the total accuracy by 3.21%, 5.25%, and 1.98% related to CNN, DeepRice, and FasterR-CNN, respectively.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から検出・分類するYOLOベース手法を提案し、前処理・最適化・性能評価を中心に扱っているため、植物フェノタイピング手法として該当する。
abstractTo address these challenges, a novel deep learning-based YOLO-LEAFNET method for PLD detection utilizing IGT-YOLO, integrating the YOLOv8 disease detection with the Improved Gorilla Troops (IGT) optimization.
Reproduction assets foundThe paper's phenotyping input is the public UCI Rice Leaf Diseases dataset (paddy leaf images of bacterial leaf blight, leaf smut, brown spot), also mirrored on Kaggle. No author analysis code, trained model, or supplementary assets are disclosed.Dataset · publicThe dataset is publicly available
at: https://archive.ics.uci.edu/dataset/486/rice+leaf+diseases. The dataset is distributed
under the Creative Commons Attribution 4.0 (CC BY 4.0) license.Open asset ↗rice+leaf+diseasespdf-page:7 lines:1-33Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Global food security is largely based on the accurate and timely diagnosis of crop diseases, where paddy rice is an extremely essential staple of more than half of the world population. The conventional disease identification techniques tend to be laborious, time consuming and demand a great deal of domain knowledge, which becomes a bottleneck in the efficient management of the farms. Although deep learning [and especially Convolutional Neural Networks (CNNs)] have demonstrated a spectacular performance in automated classification of diseases based on leaf images, they tend to overlook important contextual features that are implicitly processed by agronomic experts. The visual defects of a disease might be unclear and this can greatly differ depending on factors like the genetic variety of the plant and the stage of development. We overcome this shortcoming by proposing a new multi-modal deep learning framework, Multi-Modal Factorized Bilinear Pooling (MFBP) model which is capable of a more holistic and precise paddy health measurement. The proposed method is the only one that combines high-level visual information obtained using leaf images and related tabular information, namely the paddy type and number of days. The MFBP model uses Factorized Bilinear Pooling (FBP) rather than the simple feature concatenation which commonly loses the complex relationship between different data types. This systematic method efficiently encodes all the complex interactions between all components of the visual and tabular features vectors in such a way that helps the model to pick up subtle, context-specific patterns. As an example, it will only be possible to educate the model that a specific visual blemish is predictive of a given disease through a specific species at a specific age. We test our model on the Paddy Doctor: Paddy Disease Classification dataset, which is a detailed public dataset comprising of more than 10,000 labeled images and containing relevant metadata, and thus it forms a perfect testing bed to conduct multi-modal research. Through our detailed experiments, we have shown that the proposed MFBP model is much better than a baseline model based on concatenation fusion, which proves that deep, multiplicative interactions can be best modeled in this task. The findings highlight the massive possibilities of multi-modes AI in the development of more robust, more accurate, and more context-aware diagnostic instruments and precision agriculture to enable more sustainable and productive agricultural activities.
Why it matches plant phenotyping methods葉画像とメタデータを統合してイネの健康状態・病害を推定する新規深層学習手法を提案し、ベースライン比較で検証しているため、植物フェノタイピング手法が中心である。
abstractWe overcome this shortcoming by proposing a new multi-modal deep learning framework, Multi-Modal Factorized Bilinear Pooling (MFBP) model which is capable of a more holistic and precise paddy health measurement.
Reproduction assets foundThe paper's phenotyping inputs are the public Kaggle 'Paddy Doctor: Paddy Disease Classification' dataset (10,407 leaf images with tabular metadata for variety and age), explicitly named in the Data Availability statement with a persistent public URL. No author analysis code, trained models, or checkpoints are reportedDataset · publicThe datasets used and/or analysed during the current study are publicly available in the
“Paddy-doctor: paddy disease classification” repository at the following persistent
URL: https://www.kaggle.com/datasets/vbookshelf/paddy-disease-classification.Open asset ↗Kaggle · vbookshelf/paddy-disease-classificationpdf-page:20 lines:1-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice is a fundamental food source for more than half of the global population, making stable yields and quality improvements vital for food security and sustainable agricultural development. Early infections of rice leaf diseases often exhibit subtle symptoms, while conventional control methods based on empirical judgment and routine pesticide application result in both yield losses and environmental pollution. A Multi-scale closed-loop tuning via spatial frequency collaborative sensitivity (MCCA-YOLO) model has been proposed in this paper with a multiscale closed-loop tuning and spatial frequency collaborative attention mechanism for the early detection and classification of rice crop diseases. MCCA-YOLO incorporates a closed-loop tuning compound network architecture that combines a dual-backbone feature extractor with a spatial frequency enhancement module to achieve system self-verification feedback, reducing transmission errors and enhancing the texture features of leaves. The framework implements a cross-scale weighted fusion and a deformable spatial hybrid attention enhanced bidirectional feature pyramid fusion network for dynamic feature adaptation, effectively accommodating the complex morphology of rice leaf lesions. By conducting comprehensive ablation studies and comparative experiments with existing techniques on the rice plant diseases v8 dataset, the proposed approach achieves a mean average precision (mAP) of 92.2%, outperforming well-established methods, while delivering superior precision (0.915) and recall (0.900). Extensive empirical validation of additional v9 and Rice Leaf Spot Disease (RLSD) datasets for rice plant diseases further demonstrates the model's outstanding performance.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類するYOLOベース手法を開発し、アブレーション、比較実験、複数データセットで性能検証しており、植物病害表現型の取得手法が中心である。
abstractA Multi-scale closed-loop tuning via spatial frequency collaborative sensitivity (MCCA-YOLO) model has been proposed in this paper with a multiscale closed-loop tuning and spatial frequency collaborative attention mechanism for the early detection and classification of rice crop diseases.
Reproduction assets foundThe paper's rice leaf disease image datasets (Roboflow v8/v9, Kaggle RLSD) are explicitly declared publicly available, and the authors' MCCA-YOLO analysis code is stated to be open source on GitHub with a public URL.Code · publicOur code is publicly accessible as open source at: https://github.com/sstan12/MCCA-YOLOOpen asset ↗GitHub · sstan12/MCCA-YOLOlines:147-153Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Rice and potatoes are major crops in Bangladesh, frequently affected by major disease outbreaks that challenge food security. Inaccurate disease identification often contributes to yield losses. Recently, machine learning garnered much attention in identifying crop diseases. The present study was conducted to develop a deep learning model-based image‑analysis system that automatically identifies key diseases of Bangladeshi rice and potato, and integrates it into a web app to provide farmers with rapid, accurate diagnoses. The system employs a convolutional neural network (CNN) implemented with TensorFlow’s Sequential API, featuring ReLU-activated hidden layers and a Softmax output layer. A dataset of 4,809 images, comprising both healthy and diseased, was collected and processed through pre-processing, feature extraction, and classification. A web-based application was deployed utilizing the Python Streamlit framework. This application integrates the proposed model to predict 2 rice diseases viz. blast ( Magnaporthe oryzae ), bacterial leaf blight ( Xanthomonas campestris ), and 2 potato diseases viz. Early blight (Alternaria solani) and Late blight ( Phytophthora infestans ) from uploaded images, providing a confidence score for the predictions with approximately 92.84% for all detected diseases. The proposed model achieved a training accuracy of 0.9357, a validation accuracy of 0.8983, and a test accuracy of 0.9333. The developed web application indicates strong diagnostic performance for four major diseases, offering Bangladeshi farmers an accessible tool to make timely management decisions.
Why it matches plant phenotyping methodsイネ・ジャガイモ葉の病徴を画像から分類するCNNと実用Webアプリを開発・評価しており、植物の病害状態推定が中心的な方法論的貢献である。
abstractdevelop a deep learning model-based image‑analysis system that automatically identifies key diseases of Bangladeshi rice and potato
Reproduction assets foundThe paper's rice/potato leaf disease image dataset partially comes from Kaggle, and the data availability statement points to PlantVillage for additional image data; both are public image assets used for the paper's CNN phenotyping/disease-classification analysis. No author analysis code, trained model checkpoints, or专Dataset · publicch, M.Y.H. analyzed the data, A.A.J.,
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M.Y.H. and M.S. wrote this manuscript, M.R.I., F.M.A. and S.O.N. reviewed and edited the
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manuscript. All authors have read and agreed to the published version of the manuscript.
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Data availability statement
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Some of the datasets used in this study was obtained from Kaggle
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(https://www.kaggle.com/datasets). Additional datasets used and/or analyzed during the current
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study are available from the corresponding author upon reasonable request. More image data can
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be found at https://www.plantvillage.org/en/plant_images
459Open asset ↗Kagglepdf-raw-page:24 lines:1-57Dataset · publiche manuscript.
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Data availability statement
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Some of the datasets used in this study was obtained from Kaggle
456
(https://www.kaggle.com/datasets). Additional datasets used and/or analyzed during the current
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study are available from the corresponding author upon reasonable request. More image data can
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be found at https://www.plantvillage.org/en/plant_images
459Open asset ↗PlantVillagepdf-raw-page:24 lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.
Why it matches plant phenotyping methods植物CT画像から3D形状を抽出する、アノテーション不要・学習不要の分割手法を開発し、複数作物データセットで性能評価しており、表現型取得手法が研究の中心である。
abstractwe introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT.
Reproduction assets foundThe paper open-sources the DiffPlantCT implementation code on GitHub and benchmarks on two public plant CT datasets (walnut fruit via figshare; barley spike via Plant Methods), all with explicit availability statements and matching allowed URLs.Code · publicThe datasets and implementation code of the DiffPlantCT framework are open-sourced on GitHub at https://github.com/WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentation .Open asset ↗WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentationlines:220-287Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Monitoring and precisely localizing rice diseases is essential for agricultural productivity and food security. Existing detection methods face challenges such as high computational complexity, semantic information loss, difficulty detecting small targets, and limited robustness. To address these issues, this study proposes ECL-RTDETR, an enhanced RT-DETR-based rice disease detection model. First, a lightweight EfficientViT backbone is employed for feature extraction, incorporating a streamlined multi-head self-attention module to improve inference speed, reduce computational cost, and strengthen local feature extraction. Second, the CARAFE upsampling operator is introduced to better preserve detailed feature information without added computational burden, enhancing fine-grained representation. Finally, standard convolution in the neck network is replaced with LDConv (lightweight dynamic convolution) to enable adaptive feature learning under complex conditions, addressing variations caused by illumination, occlusion, and disease diversity. Experimental results show that ECL-RTDETR improves mAP@0.5 by 0.7%, increases detection speed by 22.2 FPS, and reduces computational cost by 81.8 GFLOPs and parameters by 22.12M compared with the baseline RT-DETR. Overall, ECL-RTDETR delivers superior accuracy, speed, and efficiency, offering a robust solution for intelligent rice disease detection and localization, and advancing smart agriculture and sustainable food security.
Why it matches plant phenotyping methodsイネ病害の検出・局在化を目的とする画像解析モデルを開発し、精度・速度・計算量を実験的に比較検証しており、植物の病害状態を推定する方法が研究の中心である。
titleResearch on an improved RT-DETR-based model for rice disease detection.
Reproduction assets foundThe paper's rice disease image dataset (drone-collected, annotated, augmented) is explicitly stated to be publicly available on figshare. No author analysis code or trained model checkpoint is explicitly deposited; the Ultralytics repository is a generic third-party library, not a paper-specific asset.Dataset · publicData Availability: All relevant data for this study are publicly available from the figshare repository ( https://figshare.com/s/b491aeb44611dea9c481 ).Open asset ↗figsharelines:1-123Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Accurate timing of rice panicle anthesis is critical for quantifying sterility risk under heat and humidity, yet minute-scale field measurement remains challenging because anthesis is transient and spikelets are tiny and difficult to detect. To address this, we present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model. For slender panicle detection and panicle pose normalization, YOLO-SnakePanNet was introduced by using Dynamic Snake Convolution with a lightweight box-rotated head. Ablation experiments show that YOLO-SnakePanNet achieved mAP@50 of 94.4%, improving by 3.6% over the YOLOv11 while reducing computation by 0.7 GFLOPs. For panicle-level anthesis pinpointing, PanicleTimeMAE was proposed by incorporating a pyramid-dilated temporal convolutional network and a confidence-aware smoothing gate into the transformer, reaching Acc@±1 of 0.85 on 5-min sampled sequences (±1 frame = ±5 min), yielding a 40% decrease in MAE over VideoMAEv2. Finally, correlation analysis between variety-level anthesis start time (T start ) and peak time (T peak ) and same-day meteorology showed that higher photosynthetically active radiation (r = -0.543/-0.573 for T start /T peak ) and temperature (r = -0.288/-0.272) advanced anthesis, whereas higher relative humidity (r = 0.397/0.438) and rainfall (r = 0.428/0.502) delayed anthesis. The variance decomposition within fixed-effects model for Tstart ( R2 = 0.651) and Tpeak ( R2 = 0.648) prediction shows that variance mainly attributed to meteorological effects (64%) and variety effects (33.5%). Overall, OPAL-Flow enables variety selection for heat- and humidity-resilient anthesis in rice breeding and supports ecophysiological dissection of anthesis regulation.
Why it matches plant phenotyping methodsイネ穂の開花時刻という植物形質を圃場画像・動画から推定する検出、追跡、超解像、時刻推定パイプラインを開発し、性能評価も行っているため、植物フェノタイピング手法が研究の中心である。
abstractwe present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model.
Reproduction assets foundThe paper's Data availability statement explicitly states that the source code and test samples for OPAL-Flow are publicly available on GitHub at the authors' repository. This is a paper-specific, publicly actionable code asset. The phenotype datasets (panicle detection dataset, start/peak annotation sequences) are notCode · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/OPAL-FLOW . Additional data can be made available upon reasonable request.Open asset ↗gfjiyue/OPAL-FLOWlines:578-590Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The vertical heterogeneity of rice canopy structure limits the accuracy of inverting leaf physicochemical parameters using traditional radiative transfer models, while LiDAR-based 3D reconstruction remains costly for large-scale applications. To address these challenges, this study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies. The Precision Mode builds detailed structural models based on measured morphological parameters, validated via the LESS 3D radiative transfer model. To overcome the limitations of obtaining detailed morphology via UAV remote sensing, the Rapid Mode employs machine learning algorithms-specifically Support Vector Machine (SVM), Random Forest (RF), and XGBoost-to map easily accessible parameters (LAI, Above-ground Biomass, Plant Height, and Transplanting Date) to detailed 3D structural parameters. Results indicate that XGBoost achieves the highest accuracy in the Rapid Mode. Furthermore, simulated spectra under both modes showed high consistency with measured spectra, yielding average RMSE values of 0.0104 (R 2 = 0.9965) for the Precision Mode and 0.0307 (R 2 = 0.9694) for the Rapid Mode. Although the spectral accuracy of the Rapid Mode is slightly lower, its modeling efficiency is significantly enhanced, retaining a strong capability to reproduce spectral response characteristics across growth stages. This approach provides an effective tool for analyzing vertical spectral response mechanisms and offers an efficient data simulation scheme for UAV remote sensing parameter inversion based on 3D radiative transfer models.
Why it matches plant phenotyping methodsイネ群落の3D構造を構築・推定する手法を開発し、放射伝達モデルと実測スペクトルで検証しており、植物形質の取得・再現が研究の中心です。
abstractthis study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies.
Reproduction assets foundThe paper's Data Availability statement says the collected phenotype/structural/spectral data are publicly available on the authors' GitHub repository (allowed URL), while the analysis code is only available from the corresponding author upon request (request_only, no public URL).Dataset · publicThe data collected and used in this study are publicly available at: https://github.com/baijc4095-code/2024data . The code used for analysis can be obtained from the corresponding author upon reasonable request.Open asset ↗baijc4095-code/2024datalines:240-256Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published23 May 2026Informatika: Jurnal Teknik Informatika dan MultimediaCited by 1 · OpenAlex ↗
Early identification of Rice leaf diseases remains a challenge in agricultural practices, as detection is commonly performed through manual visual observation that is time-consuming and prone to misclassification. Diseases such as blast, Bacterial Leaf Blight, tungro, and Brown Spot often exhibit similar visual characteristics, particularly at early stages. To address this problem, an Android-based application was developed to classify Rice leaf diseases using a Convolutional Neural Network (CNN) with a transfer learning approach based on the MobileNetV3 architecture. The model was trained using a labeled Rice leaf image Dataset obtained from Hugging Face, with preprocessing and data augmentation applied to improve generalization performance. The trained model was deployed through Hugging Face Space using an API-based architecture, allowing image classification to be performed without heavy computational requirements on mobile devices. Experimental results demonstrate that the proposed model achieved an accuracy of approximately 90% on the testing Dataset, exceeding the predefined minimum target accuracy of 85%, with precision and recall values above 80% across all disease classes based on confusion matrix evaluation. These results indicate that the MobileNetV3-based transfer learning approach provides reliable classification performance with good computational efficiency, making it suitable for mobile-based Rice leaf disease detection applications.
Why it matches plant phenotyping methodsイネ葉の画像から病害状態を分類するCNN手法を開発・評価し、モバイル実装まで行っているため、植物病害フェノタイピング手法が中心です。
abstractan Android-based application was developed to classify Rice leaf diseases using a Convolutional Neural Network (CNN) with a transfer learning approach based on the MobileNetV3 architecture
Reproduction assets foundThe paper's plant-phenotyping input is a public labeled rice leaf disease image dataset obtained from Hugging Face (girish787/riceLeafDataset), used to train the MobileNetV3 classifier. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · public[12] G. Kumar, “riceLeafDataset.” Apr. 25, 2024. Accessed: Oct. 20, 2025. [Online]. Available:
https://huggingface.co/Datasets/girish787/riceLeafDatasetOpen asset ↗huggingface.co/Datasets/girish787/riceLeafDataset · girish787/riceLeafDatasetpdf-page:10 lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many countries greatly rely on agriculture as a means of livelihood and economic growth. Even the most industrialized countries need food, medicine, clothing, and shelter produced by crops. Rice is one of the most significant and widely grown crops worldwide. Nonetheless, the severely impacted crops in rice production are those of bacteria, fungi, and viruses, which decrease yield and quality. Manual disease detection is hectic, challenging, and, in most cases, inaccurate. Recent advances in deep learning and computer vision have demonstrated significant potential to improve the detection and classification of diseases. This study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases. This method consists of five key stages: image preprocessing, segmentation, augmentation, multi-feature extraction via adaptive fusion, and classification. There are five rice leaf diseases to discuss and recognize: Blight, brown spot, sheath blight, tungro, and leaf blast. The first step is global contrast enhancement, which improves image quality. After that, the segmentation is performed using Otsu's Thresholding to extract the leaf area. Then, the modified VGG16 and modified ResNet50 networks are used in parallel to extract features using a transfer-learning approach. The adaptive fusion technique combines these features to obtain a dominant, proper feature representation. Lastly, the classification is done using an adaptive fusion score technique. Experimental results show excellent performance, with class-wise Precision in the range of 95.5-100%, class-wise recall in the range of 97.4-100%, and overall test accuracy of 98.5%.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から自動検出・分類する深層学習ワークフローが研究の中心であり、葉領域抽出、特徴抽出、分類性能まで評価しているため、植物病害フェノタイピング手法に該当する。
abstractThis study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset employed in this study is accessible online at https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm.Open asset ↗Kaggle · rajeshbhattacharjee/rice-diseases-using-cnn-and-svmhtml-lines:929-951Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Common beanRiceWheatLeafClassificationDisease symptoms / severity
In Bangladesh, crop leaf diseases create a serious risk to food security and production from agriculture. Timely identification of leaf diseases in rice, wheat, and bean crops is considered crucial for the implementation of effective disease detection and classification strategies. To address this challenge, a MobilenetV2-based disease identification and classification system is proposed in this research. Previous studies focus on classifying diseases of a single species, leaving the need to train models separately for each species. This research focuses on forming a single standard model to perform leaf disease classification for multiple crop species including rice, wheat, and beans. The approach makes use of transfer learning with the MobilenetV2 model, which is fine-tuned using a dataset of annotated crop leaf images specific to Bangladesh. Following a comprehensive evaluation, an overall accuracy of 97.87% was achieved in the classification of crop leaf diseases, which surpasses the accuracy of a number of previous studies focusing on leaf disease detection of a single crop. The system demonstrates the capability to rapidly diagnose diseases in real time by enabling the users to prompt intervention to mitigate potential crop losses, ultimately leading to amplified crop yield and food security. Overall, the research highlights the promise of AI-powered solutions in tackling crop leaf disease detection, which in turn encourages greater research and technology adoption to support sustainable farming methods especially in the crop disease classification domain in Bangladesh and throughout the world. Received: 24 May 2025 | Revised: 9 March 2026 | Accepted: 14 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Bangladeshi Crops Disease Dataset at https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset. Author Contribution Statement Md. Mahmudul Hasan: Conceptualization, Methodology, Visualization, Supervision. Md. Omar Faruq: Software, Validation, Writing – original draft. Mahadi Hasan Musa: Formal analysis, Investigation. Mohammad Mamunur Rashid: Resources, Data curation, Writing – review & editing. Khandaker Mohammad Mohi Uddin: Writing – review & editing, Project administration, Supervision.
Why it matches plant phenotyping methods葉画像から作物の病害状態を推定する深層学習手法を開発・評価しており、植物病害フェノタイピングが中心的な技術貢献である。
abstracta MobilenetV2-based disease identification and classification system is proposed in this research.
Reproduction assets foundThe paper's Data Availability Statement openly provides the Bean Disease Dataset on Kaggle, which is one of the two public image datasets used to train the multi-crop leaf disease classification model. The Bangladeshi Crops Disease Dataset URL is not among the allowed URLs, so only the bean dataset is reported. No codeDataset · publict
The authors declare that they have no conflicts of interest to
this work.
Data Availability Statement
The data that support the findings of this study are openly
available in the Bangladeshi Crops Disease Dataset at https://
www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-
dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset.Author Contribution Statement
Md. Mahmudul Hasan: Conceptualization, Methodology,
Visualization, Supervision. Md. Omar Faruq: Software, Valida-
tion, Writing – original draft. Mahadi Hasan Musa: Formal
analysis, Investigation. Mohammad Mamunur Rashid: Resources,
Data curation, Writing – review & editing.Open asset ↗Kaggle · therealoise/bean-disease-datasetpdf-raw-page:11 lines:1-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. Timely disease detection remains challenging due to the scale of cultivation and reliance on manual monitoring. Developing reliable, ongoing computerized systems for plant health management is essential for efficient disease control. A deep learning approach is used as the core method to identify diseases in rice leaves. This methodology employs a range of advanced deep learning architectures to achieve top-tier feature extraction and classification. The publicly available rice leaf disease dataset on Zenodo supports research reproducibility and data transparency. We systematically process a balanced dataset of 1914 image samples using Python with TensorFlow and a GPU to enable high-speed computation for large-scale image processing. This study conducts a systematic comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L and MobileNetV2) trained independently. The base backbone models are then integrated with guided GrabCut segmentation with contour-detection method for interpretable disease localization. In this work, the methods of segmentation by GrabCut and contour detection are introduced to make the results of the study easier to interpret and explain the disease areas, but the final classification outcomes are obtained only on the basis of the underlying deep transfer learning models. As a result, infected leaf areas can be identified more effectively, allowing for better understanding and explainable of the disease.To enhance interpretability, GrabCut segmentation and contour detection are applied as post-hoc visualization techniques to highlight diseased regions corresponding to CNN predictions. These techniques do not influence the classification training process. All five models InceptionV3, DenseNet201,ResNet152V2,EfficientNetV2L and MobileNetV2 demonstrated their effectiveness in detecting rice diseases during training, validation, and testing phases, with models trained over 30 epochs. The training methods and accuracy rates of the models were compared during validation and final testing. InceptionV3 demonstrated the most moderate performance of 98.80% training, 98.44% validation, and 98.43% test accuracy, which means that it has strong generalization and consistent learning behavior. The performance of very high-density networks such as DenseNet201 (98.72% train, 98.43% val, 98.43% test), ResNet152V2 (99.02% train, 99.22% val, 97.39% test), EfficientNetV2L model accuracies (39.01% train, 48.70% val, 44.50% test) also showed competitive results, which validated the effectiveness of deep transfer learning in the classification of rice leaf disease, while MobileNetV2 model accuracies (98.09% train, 98.18% val, 96.87% test) indicate that a lightweight model can still achieve reliable classification performance with lower computational complexity. In general, the comparative analysis defines InceptionV3 as the most stable and efficient model in the framework proposed. These results illustrate InceptionV3 superior generalization ability, supported by explainable methods for improved feature localization, confirming the viability of transfer learning for accurate and practical rice disease detection using GrabCut segmentation and contour detection technique. The complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.
Why it matches plant phenotyping methodsイネ葉の病徴領域を画像から分類・局在化する深層学習ワークフローが研究の中心であり、GrabCut・輪郭検出と複数モデルの比較評価を含むため、植物病害状態の画像ベース表現型計測として採用。
abstractA deep learning approach is used as the core method to identify diseases in rice leaves.
Reproduction assets foundThe paper explicitly states that the complete implementation code and the rice leaf disease image dataset (1914 samples) used in this study are publicly available: code on the authors' GitHub repository and the dataset on Zenodo (DOI 10.5281/zenodo.15817084). Both are paper-specific, public, and actionable.Code · publicThe complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.Open asset ↗ummershakeel03/Rice-Leaf-Diseases-Classificationhtml-lines:1357-1368Dataset · publicThe dataset for this research study is available at: https://doi.org/10.5281/zenodo.15817084.Open asset ↗10.5281/zenodo.15817084html-lines:1357-1368Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Plant disease is a serious threat to agricultural productivity and food security worldwide. Traditional diagnostic methods such as manual observation and laboratory testing are time-consuming, labor-intensive and error prone. The emergence of artificial intelligence (AI) and deep learning (DL) offer scalable solutions for precision agriculture in plant disease detection using advanced computational techniques to process large datasets. Hybrid deep learning architecture integrates Convolutional Neural Networks (CNNs) along with Artificial Neural Networks (ANNs) can leverage both visual and contextual data to improve detection performance. The hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics). The CNN module extracted spatial and textural features from plant images, while the ANN module processed environmental parameters. These outputs were fused into a unified feature vector for disease classification. A total of 15 plant species and their associated diseases were analyzed using 200-270 training samples and 150-190 testing samples for each disease across a total of 1000 images. The model was judged by metrics such as detection accuracy, AUC, sensitivity etc. Data augmentation, pre-trained architectures (e.g., ResNet50) and early stopping techniques were utilized to improvise model performance. The hybrid model saliently achieved detection accuracy consistently above 87% with majority of diseases surpassing 90%. Highperforming cases like Rice Blast (92.5%), Tomato Early Blight (93.8%), and Coffee Rust (93.0%), with AUC values of 0.93 or higher, sensitivity exceeding 94% and specifically above 90%. Diseases of Sugarcane Red Rot and Tea Blister Blight exhibited sensitivities of 92.4% and 92.1% and specificities of 91.1% and 90.5% respectively. Moderate accuracy for Coconut Bud Rot (87.5%) and Mustard Alternaria Blight (87.8%) was due to smaller training sample sizes.
Why it matches plant phenotyping methods植物画像から病害状態を分類するハイブリッドCNN-ANN手法を開発し、精度・AUC・感度などで評価しており、病害フェノタイピング手法が研究の中心である。
abstractThe hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics).
Reproduction assets foundThe paper's plant disease detection model was trained on public plant image datasets: the PlantSeg dataset (Zenodo record 13958858, DOI 10.5281/zenodo.13293891) and the UCI Machine Learning Repository Plants dataset. Both are cited in Materials and Methods as sources of the visual data used for the CNN module. No code,Dataset · publicebao (ZiranKexueBan)/Journal of
Huazhong University of Science and Technology
(Natural Science Edition). 2021;49(8).
37. Ma C, Mu X, Sha D. Multi-Layers Feature Fusion of
Convolutional Neural Network for Scene
Classification of Remote Sensing. IEEE Access.
2019;7.
38. Hämäläinen W.Plants Dataset[Internet]. 2024.
Available from:
https://archive.ics.uci.edu/dataset/180/plants
39. WeiT. PlantSeg: A Large-Scale In-the-wild Dataset
for Plant Disease Segmentation [Internet]. 2018.
Available from: https://zenodo.org/records/13958858Open asset ↗180pdf-raw-page:15 lines:1-48Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Crop phenology is a critical determinant for yield prediction and germplasm evaluation. However, precise phenological monitoring in large-scale rice breeding trials faces significant challenges due to the inherent phenological asynchrony among hundreds of cultivars and the trade-off between spatial resolution and temporal continuity in unmanned aerial vehicle (UAV) remote sensing. To address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement. We introduce a Missing Aware Gated Fusion (MAGF) mechanism to dynamically integrate multi-resolution features on non-aligned timelines, enabling robust modeling under irregular sampling conditions. Validated on a massive dataset covering approximately 500 rice cultivars and over 100,000 images across 2023 and 2024 growing seasons, the proposed method significantly outperformed single-temporal-scale baselines despite multiple growth stages coexisting within the same dates. The integration of multi-spatial-scale fusion with LSTM temporal modeling yielded superior performance considering efficiency, achieving an Overall Accuracy (OA) and F1-score of 0.873, with a Kappa coefficient of 0.84. A hybrid sampling strategy (daily MR image combined with weekly HR image) demonstrates that weekly flight time can be reduced from 28 h to approximately 6 h while maintaining high accuracy. Notably, even when HR acquisition was reduced to a once every 14 days frequency, the fusion performance remained significantly superior to that of daily MR monitoring alone. The model exhibited strong generalization capabilities. When directly applying the model trained on 2024 data to the 2023 dataset, it maintained an OA of 0.774 and an F1-score of 0.738 under a 3-day error tolerance, with recall for the maturity stage consistently exceeding 0.96. This framework offers a flexible, scalable, and cost-effective solution for high-throughput phenotyping in precision breeding.
Why it matches plant phenotyping methodsUAVリモートセンシング画像と深層学習によるイネの生育ステージ（フェノロジー）推定手法を開発・検証し、大規模育種データで性能評価しているため、フェノタイピング手法が中心である。
abstractTo address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement.
Reproduction assets foundThe article explicitly states that the authors' source code and test samples for the rice phenology identification framework are publicly available on GitHub, matching an allowed URL. No public dataset deposit is stated; additional data is only on request.Code · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/Rice-phenology-identification-by-UAV . Additional data can be made available upon reasonable request.Open asset ↗https://github.com/gfjiyue/Rice-phenology-identification-by-UAVlines:601-709Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Disease prevention and water management are important to all the crops, particularly rice and sugarcane production in India. The article proposes a reinforcement learning (RL) based intelligent irrigation management system that is capable of optimising water consumption and crop nutrition in response to the changing agricultural climatic conditions. Decentralised reinforcement learning (RL) is used in a network of irrigation agents that utilise soil and microclimate sensor networks to set the terms of water allocation, water use efficiency (WUE) and crop health. At the same time, deep convolutional networks can be used to differentiate between plant stress/disease and leaf images and take applicable proactive actions. It is a framework that incorporates satellite-derived indices (NDVI, EVI, land surface temperature) with local sensor measurements and image-based health measurements through multimodal deep learning. Far-reaching simulations (including Indian climate and crop calendars) demonstrate that the multi-agent system lowers water consumption and preserves the yields and properly notifies stressed plants. The scores of disease detection with plantvillage-based fine-tuned on rice (120 (3 disease types) and 3829 (5 disease types) and sugarcane (2569 images for all disease types, Convolutional Neural Network (CNN) yield results of >98 % accuracy. Crop mapping (rice/sugarcane) Satellite/LSTM-based crop mapping (with Sentinel-1 / Sentinel-2) achieves more than 97 % accuracy. The suggested structure provides a data-driven, scalable system for precision agriculture to enhance the management of irrigation periods and crop health. Simulation experiments show that the RL-based controller can reduce water consumption while preserving optimal soil moisture levels when compared to rule-based irrigation strategies.
Why it matches plant phenotyping methods画像・衛星・センサーを統合して植物ストレス／病害状態を推定するマルチモーダル基盤が提案され、病害検出性能も評価されているため、植物表現型推定が実質的な構成要素である。
abstractdeep convolutional networks can be used to differentiate between plant stress/disease and leaf images
Reproduction assets foundThe paper reports simulation-based experiments using public leaf-image datasets. The only paper-specific public asset explicitly identified is the Kaggle rice leaf diseases dataset (vbookshelf/rice-leaf-diseases) cited as a data source for the rice disease fine-tuning set. No authors' code, trained models, or data dépDataset · publicConflict of interest: Authors do not have any conflict of interest 2026 Mar 31). Available from: https://www.kaggle.com/datasets/Open asset ↗Kagglepdf-page:16 lines:1-58Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant diseases threaten global agriculture, and deep learning-based disease recognition has become crucial for addressing this challenge. While DenseNet excels in plant disease classification due to its dense connectivity, its large size limits deployment on resource-constrained edge devices. This paper proposes Connection-Aware DenseNet Pruning (CADP), achieving efficient compression through three collaborative modules. First, the EdgePrune module explicitly models inter-channel feature flows via an edge weight network, using dual-channel importance scoring that fuses activation correlation and gradient information to remove redundant connections while preserving critical propagation paths. Second, connection-guided CP decomposition leverages EdgePrune's importance information, adaptively assigning differentiated ranks through the Connection Importance Index (CII) to balance preservation of critical layers with deep compression of secondary layers. Third, dual-stream knowledge distillation integrates throughout post-pruning and post-decomposition fine-tuning, combining output-level soft labels and intermediate spatial attention transfer to recover compression losses. CADP achieves 88% parameter reduction and 89% computational savings on DenseNet-121, maintaining 99.67% and 99.66% accuracy on PlantVillage and RiceLeaf datasets, achieving competitive accuracy with significantly fewer parameters. This provides a promising approach for resource-constrained deployment with potential generalizability and practical value.
Why it matches plant phenotyping methods植物画像から病害状態を推定する分類モデルの軽量化手法を開発し、PlantVillageおよびRiceLeafで性能を評価しているため、病害フェノタイピング手法が中心である。
titleCADP: Connection-Aware DenseNet Pruning for lightweight plant disease classification.
Reproduction assets foundThe paper uses two publicly available plant image datasets (PlantVillage and Rice Leaf Disease) hosted on Mendeley Data, explicitly linked in the Data Availability statement. No author analysis code, models, or checkpoints are shared.Dataset · publicThe datasets used in this study are publicly available. The PlantVillage dataset can be accessed at https://data.mendeley.com/datasets/tywbtsjrjv, and the Rice Leaf Disease dataset is available at https://data.mendeley.com/datasets/fwcj7stb8r/1.Open asset ↗fwcj7stb8rhtml-lines:698-748Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The decline in environmental quality caused by industrial pollution and climate change has weakened the natural resistance of rice plants (Oryza sativa), increasing their susceptibility to various diseases. Conventional disease identification methods that rely on manual observation are often limited by subjectivity and human visual constraints. This study proposes a deep learning–based system for automatic rice leaf disease classification using the You Only Look Once version 8 (YOLOv8) architecture. The model was trained using a publicly available rice leaf image dataset consisting of 6,889 images categorized into eight classes: Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Sheath Blight, Narrow Brown Leaf Spot, Rice Hispa, and Healthy Rice Leaf. The research methodology includes image pre-processing, data augmentation, dataset splitting, and training using the YOLOv8n-cls model for 50 epochs. Experimental results demonstrate high classification performance with an accuracy of 99.5%, precision of 99%, recall of 98%, and an F1-score of 0.99. The trained model was then deployed into a web-based application that allows users to upload rice leaf images and obtain real-time disease classification results. The proposed system provides a practical tool to support early detection of rice plant diseases and assist farmers in improving crop management in modern agriculture.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定するYOLOv8画像解析手法の開発と性能評価が中心であり、植物病害フェノタイピングに該当する。
abstractThis study proposes a deep learning–based system for automatic rice leaf disease classification using the You Only Look Once version 8 (YOLOv8) architecture.
Reproduction assets foundThe paper's rice leaf disease image dataset (6,889 images, eight classes) used for YOLOv8n-cls training is a publicly available Kaggle dataset cited by the authors with an explicit URL. No author code, trained model, or other paper-specific assets are reported.Dataset · publicThe primary dataset was obtained from a
publicly available dataset on Kaggle [16], which provides a
comprehensive collection of rice leaf disease images for
machine learning research.Open asset ↗Kagglepdf-raw-page:3 lines:1-102Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice ( Oryza sativa ) production underpins food security in many rice-consuming nations. As a critical developmental transition that directly determines yield and grain quality, flowering dates and timing are genetically complex and highly sensitive to environmental fluctuations. This complexity requires new methods to quantify diurnal floral characteristics, which are essential to hybrid breeding in cereals. Here, we present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice. After monitoring 172 rice accessions selected from the Chinese Rice Mini-Core Collection using cost-effective time-lapse imaging platforms for 16 days, we acquired over 530,000 accession-level images and established the Open Rice Flowering Training (ORFT) dataset, with over 39,000 panicles and 350,000 anthers annotated. Next, a two-stage customised DL model (i.e. YOLACT-Panicle for panicle segmentation and UNet-Anther for anther identification) was trained using the ORFT set, enabling ultra-high-frequency measures of anther extrusion at the minute level. Based on trait analysis, we further fitted curves to dynamically identify diurnal flowering patterns, including key timepoints such as the initial flowering timepoint ( T Ini. ), quickest flowering timepoint ( T Qck. ), and peak flowering time ( T Peak ), and novel traits such as the duration of rapid flowering phase ( P Rpd. ) and flowering density across key phases. After validating BloomSight-derived traits against manual observations, we classified the japonica and indica accessions into three patterns: Slow, Moderate, and Fast, all of which had distinct flowering windows. These analyses helped us integrate phenotypic variations into a genome-wide association study (GWAS), revealing many significant single nucleotide polymorphisms (SNPs) associated with known (e.g. EMF1 , OsMYB8 , and PME42 ) and several repeatedly identified unknown loci (one of these loci has been recently verified by other groups), demonstrating the value of the BloomSight framework. Taken together, we believe that BloomSight provides an ultra-high-frequency framework for diurnal flowering phenotyping, enabling the measurement of biological meaningful floral traits with minute-level resolution that can enable flowering-related developmental studies and hybrid-breeding applications in rice and more broadly benefit the plant and crop research community.
Why it matches plant phenotyping methodsイネの開花動態を高頻度画像と深層学習で抽出するフェノタイピング基盤を開発し、データセット構築と手動観測による検証も行っているため、方法が研究の中心である。
abstractwe present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice
Reproduction assets foundThe paper's Data and code availability statement explicitly provides public access to the ORFT annotated image dataset (BioStudies S-BSST2157), Python source code for floral trait analysis (GitHub The-Zhou-Lab/BloomSight), and trained DL models (GitHub releases). SRA accessions are molecular sequencing data, not phenotCode · publicPython-based source codes for automating floral trait analysis using the above data are accessible via our GitHub repository ( https://github.com/The-Zhou-Lab/BloomSight ).Open asset ↗The-Zhou-Lab/BloomSightlines:336-349Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
We introduce ``Primitive Indian Paddy Panicle Images,'' a benchmark image dataset of 22 primitive Indian rice panicle varieties (Sethy, Prabira; Pamerelli, Ranjith, 2026; Mendeley Data, V1, doi:10.17632/khfd7pzskd.1) and present an identification approach based on deep residual transfer learning. Using a transfer-learned ResNet-50 with image augmentation and an 80/10/10 train/validation/test split, the model attains 100.0% validation accuracy and 98.74% accuracy on the held-out test set. Per-class one-vs-rest AUCs on validation are 1.000 for all 22 classes; test AUCs range from 0.9924 to 1.000 (mean ≈ 0.999), with separate confusion matrices and ROC curves provided for validation and test partitions. These results demonstrate that deep residual transfer learning can robustly discriminate closely related panicle morphotypes when trained on a carefully curated dataset. We release the dataset to support reproducible research in germplasm identification, varietal purity assessment, and automated phenotyping.
Why it matches plant phenotyping methodsイネ穂画像のベンチマークデータセットと、深層学習による穂形態の自動識別手法が研究の中心であり、再現可能な植物表現型解析基盤として明示されている。
abstractWe introduce ``Primitive Indian Paddy Panicle Images,'' a benchmark image dataset of 22 primitive Indian rice panicle varieties
The aspect of crop monitoring takes into consideration the timely detection of crop stages, leaf disorders, and deficiencies to enhance crop yield and decrease losses in agriculture. However, most of the current methods are limited to either disease detection or nutrient evaluation and do not examine the conditions of crops at various stages of growth, even though several AI -IoT-based solutions have been suggested to be applied to crop health monitoring. In addition, the estimation of the severity of the diseases is neglected, and this restricts decision-making in favor of the farmers. To address these constraints, the paper presents a Parametrized Elliptical Cauchy Fuzzy Inference System (PECFIS) combined with a Deep Glorot Bessel Elliott Softplus Convolutional Neural Network (DGBESCNN), proposed as an AI-based solution for crop monitoring and IoT support. The IoT devices in the form of drones are used to get real-time field images, and they are preprocessed in terms of noise reduction, contrast enhancement by LHM-CLAHE, conversion to HSV color space, and feature discrimination by vegetation indexing, as well as C3MEK-Means. PECFIS is used to determine eight key stages of rice growth and the severity of leaf diseases, whereas DGBESCNN provides proper classification of leaf diseases and nutrient deficiencies at each growth stage. The evaluation of the proposed framework was conducted using publicly available datasets on rice leaf disease and nutrient deficiency. The results of the experiments show that the system achieves high classification performance, with an accuracy of 98.82, a precision of 98.65, a recall of 98.73, an F1-score of 98.59, and low error rates (MSE = 0.0135, RMSE = 0.116). The findings show that the developed AI-IoT system is superior to available approaches and can serve as a dependable, real-time, and scalable solution in precision agriculture and intelligent crop monitoring.
Why it matches plant phenotyping methodsドローン画像からイネの生育段階と葉病害の重症度を推定・分類するAI-IoT手法が研究の中心であり、植物状態の取得・抽出方法を技術的に評価している。
abstractThe IoT devices in the form of drones are used to get real-time field images
Reproduction assets foundThe paper evaluates its PECFIS-DGBESCNN crop monitoring framework on two publicly available Kaggle datasets (Nutrient Deficiency Symptoms in Rice, 1,156 images; Rice Leaf Diseases, 120 images), with explicit dataset links provided by the authors. No author code, models, or other paper-specific assets are shared.Dataset · publicn of the low-cost ground-based IoT and weather sensors and enhanced robustness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. SuOpen asset ↗Kaggle · guy007/nutrientdeficiencysymptomsinricepdf-raw-page:21 lines:1-50Dataset · publicstness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. Sustainability, 15(16), 12149.
https://doi.org/10.3390/su151612149
[2] AlfOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:21 lines:1-50Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Many Artificial Intelligence and Machine Learning technologies have been applied to detect rice diseases. These approaches are either unable to identify the diseases or have a slow recognition speed. Therefore, an improved Faster-RCNN (Faster-RCNN-Pro) model is proposed to overcome these issues. First, SENet attention modules are embedded in the backbone of Faster-RCNN to enhance confidence of objects that are difficult to recognize by enhancing key image information and suppressing background information. Second, structure of the feature extraction network and RPN are improved by using multi-feature scale fusion to increase the utilization of micro-target features. Third, the quantization error introduced in the process of pooling the region of interest is then eliminated by ROI Align. Finally, a balanced L1 loss function is designed to effectively reduce the imbalance between samples with a large gradient that are difficult to learn, and samples with a small gradient that are easy to learn. The experiment results show that the improved model has a better detection accuracy and robustness in recognizing the fine features of rice leaf diseases. Therefore, the application of this model to the intelligent identification of rice leaf disease can significantly improve the accuracy and reduce the misjudgment rate.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から識別する改良Faster-RCNNを開発・評価しており、植物病害表現型の取得手法が研究の中心である。
abstractan improved Faster-RCNN (Faster-RCNN-Pro) model is proposed to overcome these issues.
Reproduction assets foundThe paper's rice leaf disease detection experiments were performed on a public Kaggle image dataset (rice blast, brown spot, hispa, healthy leaves), which the authors explicitly state is publicly available with a URL matching the allowed list. No author analysis code or trained model checkpoints are disclosed.Dataset · publical Internet of Things, aiming to recognize large-scale rice leaf diseases. Moreover, it is beneficial for the modernization of the agricultural industry.
Acknowledgments
The authors would like to thank the anonymous reviewers.
Data Availability
All relevant data for this study are publicly available from the Kaggle repository ( https://www.kaggle.com/minhhuy2810/rice-diseases-image-dataset ).
Funding Statement
This work is supported by the National Natural Science Foundation of China (62402308).
References
1. Mondal S, Ghosh S, Mukherjee A. Application of biochar and vermicompost against the rice root-knot nematode (Meloidogyne graminicola): an eco-friendly approach in nematode management. JOpen asset ↗Kaggle · minhhuy2810/rice-diseases-image-datasetlines:220-237Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Deploying rice disease detectors in the field remains challenging because models that are accurate in the lab are often poorly calibrated and provide limited uncertainty estimates, raising the risk of costly misclassification. This paper proposes a multi-objective Big-Bang Big-Crunch (MO-BBBC) framework that jointly performs disease detection and variety classification while optimizing six deployment-oriented criteria: classification error, calibration quality, uncertainty estimation, model size, inference latency, and energy consumption. The proposed framework presents conditional temperature scaling, an adaptive scheme that mitigates over-calibration and preserves reliability. The framework is implemented in Python on a lightweight two-headed classifier and evaluated on the Paddy Doctor dataset, MO-BBBC base framework achieves 90.6% disease accuracy and 97.9% variety accuracy; improves calibration to [Formula: see text] ([Formula: see text]% better than strong post-hoc baselines); achieves micro-AUC of 0.994/0.999 and micro-AP of 0.961/0.994 (disease/variety); delivers robust OOD detection (AUROC = 0.887/0.886); and supports real-time inference at [Formula: see text] ms and [Formula: see text] ms per 64-sample batch on CPU/GPU with Monte Carlo Dropout uncertainty. The resulting Pareto set enables practitioners to trade accuracy for efficiency and reliability, narrowing the gap between prototype validation and field deployment in precision agriculture.
Why it matches plant phenotyping methods植物病害状態を画像から検出する分類・校正・不確実性推定フレームワークが研究の中心であり、植物の病害表現型を対象とした手法開発と評価に該当する。
abstractThis paper proposes a multi-objective Big-Bang Big-Crunch (MO-BBBC) framework that jointly performs disease detection and variety classification while optimizing six deployment-oriented criteria
Reproduction assets foundThe paper publicly releases its authors' analysis code (MO-BBBC framework, calibration, leakage audits, evaluation scripts) on GitHub, and a Zenodo deposit containing the paper-specific split indices, metadata, and reproducibility notebook. The underlying PaddyDoctor plant image dataset used for all phenotyping/class- Code · publicaddyDoctor images and to reproduce the results reported in the manuscript.
All code used to implement the MO–BBBC framework, multitask classifier, uncertainty-aware curricula, calibration routines, and evaluation scripts (including leakage audits, OOD evaluation, and generation of all tables and figures) is freely available at: https://github.com/manhas82/MO-BBBC-Rice.git .
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethics statement
This study uses only publicly available plant imagery and does not involve human participants, animalOpen asset ↗https://github.com/manhas82/MO-BBBC-Rice.gitlines:361-386Dataset · publicAll data underlying the findings in this study are available without restriction. The minimal dataset underlying the reported analyses (including the exact group-aware train/validation/test split indices, supporting metadata, and a complete reproducibility notebook) is publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18471419 .
The underlying images and labels used in this work come from the public PaddyDoctor image dataset [ 18 ], which can be accessed from the official project page https://paddydoc.github.io/dataset/ and via its IEEE DataPort record https://ieee-dataport.org/documents/paddy-doctor-visual-image-dataset-automated-paddy-disease-classOpen asset ↗Zenodo · 10.5281/zenodo.18471419lines:361-386Dataset · publicvalidation/test split indices, supporting metadata, and a complete reproducibility notebook) is publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18471419 .
The underlying images and labels used in this work come from the public PaddyDoctor image dataset [ 18 ], which can be accessed from the official project page https://paddydoc.github.io/dataset/ and via its IEEE DataPort record https://ieee-dataport.org/documents/paddy-doctor-visual-image-dataset-automated-paddy-disease-classification-and-benchmarking . The Zenodo record contains the files needed to reconstruct our exact experimental partitions from the original PaddyDoctor images and to reproduce the results reportedOpen asset ↗lines:361-386Dataset · publicproducibility notebook) is publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18471419 .
The underlying images and labels used in this work come from the public PaddyDoctor image dataset [ 18 ], which can be accessed from the official project page https://paddydoc.github.io/dataset/ and via its IEEE DataPort record https://ieee-dataport.org/documents/paddy-doctor-visual-image-dataset-automated-paddy-disease-classification-and-benchmarking . The Zenodo record contains the files needed to reconstruct our exact experimental partitions from the original PaddyDoctor images and to reproduce the results reported in the manuscript.
All code used to implement the MO–BBBC framework, multiOpen asset ↗IEEE DataPortlines:411-413Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
1 Summary Zinc (Zn) deficiency limits rice productivity and poses a risk to human health, particularly in populations reliant on rice-based diets. Although rice germplasm exhibits wide variation in Zn-deficiency tolerance, the underlying physiological mechanisms remain poorly resolved. Evidence across the literature for Zn-deficiency–induced secretion of 2′-deoxymugineic acid (DMA) is inconsistent. This study clarifies the role of DMA secretion as a Zn-deficiency stress response. We developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates. Five rice genotypes with contrasting Zn-deficiency tolerance were grown hydroponically and DMA secretion measured. Zn-deficiency increased DMA exudation across all genotypes, with sensitive genotypes also showing higher secretion compared with control, supporting DMA’s role as a general response to Zn stress rather than being restricted to efficient genotypes. Fold-change responses exceeded previous studies, likely due to more severe stress exposure. Our results confirm that DMA secretion is induced under Zn-deficiency in rice as part of the micronutrient stress response. However, the lack of increased Zn uptake indicates that additional tolerance mechanisms are involved. These findings reconcile inconsistencies in the literature and position DMA secretion as an important, but not exclusive, component of Zn-deficiency adaptation in rice.
Why it matches plant phenotyping methodsイネ根滲出液中のDMAを選択的に検出するLC–MS法を開発・検証し、亜鉛欠乏応答という植物生理状態を測定しているため、化学分析が単なる付随測定ではなく中心的な方法貢献である。
abstractWe developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this study (DMA exudation and Zn uptake measurements in rice).Dataset · publicthe experiments, developed the
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methods and analysed the results. The experimental data were collected by C.R. assisted by
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G.L.M., C.T. and D.J.W. Data analysis and writing of paper by all authors.
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Data availability
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The data sets generated and/or analysed during the current study are available on Zenodo,
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CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted March 18, 2026.
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https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Disease detection and categorization in rice leaf play a crucial role in mitigating crop damage and supporting sustainable agriculture. Traditional approaches, which often rely on manual inspection, are limited by labor intensity, variability and error susceptibility. This paper introduces a Hybrid Convolutional Vision Transformer (CVT) model with Spatial Attention (SA) to enhance the detection accuracy and classification reliability in rice leaves. The proposed CVT framework integrates a Convolutional Neural Network (CNN), which is the backbone for initial feature extraction with a Vision Transformer (ViT) in advanced feature representation. Convolutional Neural Network captures the essential textures and shapes, while the Vision Transformer applies attention across image patches, effectively learning the complex spatial dependencies necessary for identifying disease-specific characteristics within diverse field environments. Further, SA module refines the model by assigning greater weight to diseased regions, reducing interference from non-leafbackground areas. Experimental results on rice leafdataset demonstrate that the hybrid CVT with SA model achieves over 98.12% feature extraction accuracy, 98.56% classification accuracy in dataset 1 and 98.26% feature extraction accuracy, 98.67% classification accuracy in dataset 2 across multiple rice leaf categories, outperforming baseline CNN and ViT models. Spatial Attention heat maps highlight the most important locations during decision-making process, making the model more interpretable. This hybrid CVT model offers a scalable solution for rice leaf disease detection and categorization, with potential applications in precise agriculture systems, including drone-based or mobile implementations for field monitoring. The presented model exhibits maximum performancethan the othertraditional methods.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から推定する深層学習モデルの開発と比較評価が論文の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis paper introduces a Hybrid Convolutional Vision Transformer (CVT) model with Spatial Attention (SA) to enhance the detection accuracy and classification reliability in rice leaves.
Reproduction assets foundThe paper uses two public Kaggle rice leaf disease image datasets as its phenotyping inputs, with explicit URLs. The authors' code and generated data are only available on request, so no code/model asset qualifies.Dataset · publicconducted interviews with Department
of Agriculture—particularly those from the Regional Crop Protection Center. Images of several
rice plant diseases are collected using the means available, which included digital cameras and
smart phones. After gathering, all the imagesare pre-processed and included in the dataset.
Dataset 1: https://www.kaggle.com/datasets/nashehannafii/datasetleafblast
Dataset 2: https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases
4.2 Data preprocessingOpen asset ↗kaggle · nashehannafii/datasetleafblastpdf-raw-page:13 lines:1-17Dataset · publicm the Regional Crop Protection Center. Images of several
rice plant diseases are collected using the means available, which included digital cameras and
smart phones. After gathering, all the imagesare pre-processed and included in the dataset.
Dataset 1: https://www.kaggle.com/datasets/nashehannafii/datasetleafblast
Dataset 2: https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases
4.2 Data preprocessingOpen asset ↗kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:13 lines:1-17Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice production is integral to the agricultural sector of India; over 65% of the populations are dependent on rice as their major staple. The cultivation of rice sustains this important agricultural sector; yet, there are many challenges encountered by rice producers, one of which is several types of disease that negatively impact yield and quality. Due to the fact that rice leaf smut, brown spot and bacterial leaf blights are among the most important types of diseases that can significantly reduce the yield and quality of rice, it is important to be diligent when identifying these diseases using accurate and speedy methods on an annual basis for successful and sustainable production of rice crops. As technology advances there continue to be emerging technologies such as Deep Learning (DL) as applied in agriculture to identify diseases and therefore reshape the agricultural paradigm so as to address agricultural disease challenges more readily. This research proposes a previously undemonstrated approach for identifying Rice Leaf Disease using EfficientNetV2; a Diffusion Bounded Attention method for disease detection. The quality of the input imagery has been greatly increased using a Preceding Noise Reduction (PNR) using the Guided Filopic Diffusion (GFD) technique, retaining important characteristics of Rice Leaves (Leaf Texture) which are critical for disease classification within agricultural imaging. To evaluate the performance of our model we utilized the Dice Similarity Coefficient (DSC). This coefficient measures how much the predicted image areas representing disease overlap with the actual affected areas of the image. Therefore, DSC is a reliable way to evaluate model segmentation capability. The Rice Leaf Diseases Dataset we used to identify and classify Rice Leaf Diseases was very comprehensive. Our model achieved an accuracy rate of 98.92% and also attained the best recall, precision and F1 score.
Why it matches plant phenotyping methodsイネ葉の病害症状を画像から検出・分類・セグメンテーションする手法が研究の中心であり、植物の病害状態を直接推定している。
abstractThis research proposes a previously undemonstrated approach for identifying Rice Leaf Disease using EfficientNetV2; a Diffusion Bounded Attention method for disease detection.
Reproduction assets foundThe paper's sole data asset is the public Kaggle Rice Leaf Diseases Dataset used for all experiments; no author code or models are deposited.Dataset · publicl analysis and data collection. N.K has done the initial drafting and statistical analysis. P.R. did the investigation. All the authors of the article have read and approved the final article.
Funding
Open access funding provided by Vellore Institute of Technology.
Data availability
The rice leaf disease data are assessed using https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Upadhyay N Gupta N
Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified ResNeXt approach
Environ. Monit. Assess. 2024 196 7 610
10.1007/s10661-024-12790-0
38862723
Upadhyay, N. & Open asset ↗Kaggle · vbookshelf/rice-leaf-diseaseslines:553-627Code / dataset availability confirmedEurope PMC · checked 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 · Europe PMC · OpenAlex · checked 5 Sept 2026
Quantitative characterization of complete canopy architecture is essential for accurate evaluation of crop photosynthesis and yield potential, thereby supporting crop ideotype design. Although various sensing technologies enable three-dimensional (3D) reconstruction of individual plants and canopies, they often fail to describe canopy architecture accurately because of severe occlusion in dense populations. To address this limitation, we developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model. A complete point cloud generation pipeline was first established to enable automated training data annotation, allowing discrimination between surface points and occluded points within the canopy. The proposed crop population point cloud completion network (CP-PCN) integrates a multi-resolution dynamic graph convolutional encoder, a point pyramid decoder, a dynamic graph convolutional feature extractor, and a generative adversarial network-based loss function to predict occluded canopy points. CP-PCN achieved chamfer distance values of 3.35 to 4.51 cm across four growth stages, outperforming the state-of-the-art transformer-based method PoinTr. Ablation analyses confirmed that each of the four modules contributes to overall model accuracy. In addition, validation experiments showed that the improved architectural completeness achieved by CP-PCN resulted in more accurate yield estimation compared with incomplete and PoinTr-completed point clouds. CP-PCN also demonstrated strong cross-crop generalizability by successfully reconstructing mature rice canopies. Overall, this framework provides a scalable approach for quantitative analysis of complex canopy architectures in field-grown crops.
Why it matches plant phenotyping methodsUAVマルチビュー画像から遮蔽点を補完し、作物群落の3Dキャノピー構造を再構成する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model
Reproduction assets foundThe paper's Data and code availability statement explicitly deposits all source code and test data for the CP-PCN phenotyping pipeline on a public GitHub repository, matching an allowed URL.Code · publicAll source code and test data used in this study are publicly available on GitHub ( https://github.com/Ziyue-Guo/CP-PCN.git ).Open asset ↗https://github.com/Ziyue-Guo/CP-PCN.git · CP-PCNlines:133-158Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Agriculture and global food security are critically dependent on accurate and timely identification of plant diseases and pests. Traditional approaches to disease identification rely heavily on visual inspection and expert knowledge, which frequently lack the accuracy, speed, and scalability needed to address growing agricultural challenges. Early and precise disease detection enables proactive interventions that can prevent widespread crop damage and reduce excessive pesticide use, thereby supporting sustainable agricultural practices. Artificial intelligence, particularly deep learning methods, has emerged as a transformative solution for automated plant disease diagnosis. Convolutional neural networks (CNNs) have demonstrated remarkable capabilities in image classification tasks, evolving from individual architectures to sophisticated ensembles and transferring learning models. However, existing CNN-based research on rice disease identification has typically focused on a limited number of disease classes, restricting their practical applicability in real-world agricultural settings. This study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification. We utilized a dataset comprising seven of the most common rice diseases, significantly expanding the scope beyond previous studies. The model employs transfer learning with pre-trained ImageNet weights and is optimized using the Adam optimizer with carefully tuned hyperparameters. The experimental evaluation on an independent test set demonstrates that our proposed model achieves an overall accuracy of 97.9%, with individual disease classification accuracy ranging from 94% to 99.67%. The model exhibits balanced performance across multiple metrics, including precision (96.2%), recall (97.97%), and F1-score (97%), confirming its robustness and generalizability. These results establish DenseNet121 as a highly effective framework for automated rice disease diagnosis, offering a practical tool for enhancing agricultural productivity and food security.
Why it matches plant phenotyping methodsイネ葉の画像から病害状態を分類する深層学習手法が研究の中心であり、独立テストセットによる性能評価も実施しているため、植物病害フェノタイピング手法として収載する。
abstractThis study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification.
Reproduction assets foundThe paper's rice disease classification experiments use the public Kaggle Paddy Disease Classification dataset (8030 images, 7 disease classes), explicitly cited and linked by the authors in the Data Availability Statement. No author code or trained model checkpoints are disclosed.Dataset · publicThe data presented in this study are available in Kaggle42.Open asset ↗Kagglehtml-lines:487-556Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP 25 of 84.55%, outperforming mainstream algorithms.
Why it matches plant phenotyping methods米の3D形質取得・再構成・分割を中核とする手法開発であり、データセット／ベンチマークも提供しているため、植物フェノタイピング手法文献に該当する。
abstractwe propose a novel method named Multi-Scale NeRF(MSNeRF)
Reproduction assets foundThe paper's authors explicitly state that the source code for MSNeRF and VRKGNet is publicly available on GitHub with testing scripts and test cases to reproduce the main results. The MMR dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publicof Hefei Artificial Intelligence Breeding Accelerator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
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MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial iOpen asset ↗https://github.com/qfwysw/MSNeRF.gitlines:620-663Code · publicator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
•
MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could haveOpen asset ↗https://github.com/qfwysw/VRKGNet.gitlines:620-663Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
RiceRootMorphology / geometry measurementSegmentationRoot system architecture
Root phenotypic traits such as length and number are critical indicators of plant growth and productivity. However, accurate extraction of these traits remains challenging due to the slender morphology, dense overlap, and frequent occlusion within root systems. Traditional digital image processing methods suffer from low throughput and limited robustness, while most deep learning-based approaches rely on semantic segmentation, which fails to distinguish individual roots and therefore limits their applicability in instance-level phenotypic analysis.To address these limitations, we propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping, with rice roots as a representative case. Building upon the MP-Former framework, our model introduces two key components: the Guided-Enhancement Pixel Decoder (GEPD) and the Asymmetric Dual-Query Decoder (ADQD). The GEPD enhances multi-scale feature representations via Hybrid Convolution Aggregator, Semantic-Guided Fusion Module and Frequency-Guided Feature Enhancement Module, effectively capturing fine root structures and low-contrast regions. ADQD employs asymmetric interaction between semantic and instance queries to improve long-range dependency modeling and instance separation in occluded scenarios.Additionally, we present the Rice Root Segmentation Dataset (RRSD), comprising of 343 high-resolution images with instance-level annotations. Experimental results show that DGA-MP-Former achieves state-of-the-art performance on RRSD, with 57.2% AP 0.5:0.95 and 87.4% AP 0.5 . Importantly, the accurate instance segmentation results enable reliable computation of instance-level geometric traits, such as root perimeter and area. To quantitatively assess phenotypic measurement accuracy, Relative Area Error (RAE) and Relative Perimeter Error (RPE) are further introduced, achieving 26.4% and 20.2%, respectively. These results demonstrate that the proposed method effectively bridges instance segmentation accuracy and phenotypic quantification reliability, supporting high-throughput and precise root phenotyping.
Why it matches plant phenotyping methodsイネ根の個体別セグメンテーションモデルを開発し、データセット提供、性能評価、および根の形態形質推定まで行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe Rice Root Segmentation Dataset is open sourced for the research community at ”https://github.com/Run-19/DGA-mpformer”.Open asset ↗Run-19/DGA-mpformerhtml-lines:442-469Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate and timely diagnosis of rice leaf diseases is crucial for smart agriculture leveraging vision sensors. However, existing lightweight convolutional neural networks (CNNs) often struggle in complex field environments, where small lesions, cluttered backgrounds, and varying illumination complicate recognition. This paper presents I-GhostNetV3, an incrementally improved GhostNetV3-based network for RGB rice leaf disease recognition. I-GhostNetV3 introduces two modular enhancements with controlled overhead: (1) Adaptive Parallel Attention (APA), which integrates edge-guided spatial and channel cues and is selectively inserted to enhance lesion-related representations (at the cost of additional computation), and (2) Fusion Coordinate-Channel Attention (FCCA), a near-neutral SE replacement that enables efficient spatial-channel feature fusion to suppress background interference. Experiments on the Rice Leaf Bacterial and Fungal Disease (RLBF) dataset show that I-GhostNetV3 achieves 90.02% Top-1 accuracy with 1.831 million parameters and 248.694 million FLOPs, outperforming MobileNetV2 and EfficientNet-B0 under our experimental setup while remaining compact relative to the original GhostNetV3. In addition, evaluation on PlantVillage-Corn serves as a supplementary transfer sanity check; further validation on independent real-field target domains and on-device profiling will be explored in future work. These results indicate that I-GhostNetV3 is a promising efficient backbone for future edge deployment in precision agriculture.
Why it matches plant phenotyping methods画像からイネ葉の病徴を認識・分類する軽量深層学習手法を開発し、複数データセットで精度と計算量を評価しているため、植物フェノタイピング手法が中心である。
abstractThis paper presents I-GhostNetV3, an incrementally improved GhostNetV3-based network for RGB rice leaf disease recognition.
Reproduction assets foundThe paper's phenotyping inputs are two publicly available plant image datasets explicitly linked by the authors: the RLBF rice leaf disease dataset on Mendeley Data (primary evaluation) and the PlantVillage-Corn dataset on GitHub (cross-domain transfer). No author analysis code or trained model checkpoints are stated.Dataset · publicThe Rice Leaf Bacterial and Fungal Disease Dataset can be accessed at https://data.mendeley.com/datasets/hx6f852hw4/2 (accessed on 20 July 2025)Open asset ↗hx6f852hw4lines:688-704Dataset · publicthe PlantVillage-Corn Dataset is available at https://github.com/gabrieldgf4/PlantVillage-Dataset (accessed on 27 August 2025)Open asset ↗github.com/gabrieldgf4/PlantVillage-Datasetlines:688-704Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Modern agriculture demands precise genomic prediction to accelerate elite crop breeding, yet traditional genomic prediction approaches, such as genomic best linear unbiased prediction (GBLUP) and Bayesian methods, focus primarily on the cumulative effect of individual SNPs, thus neglecting the concerted influence that the surrounding sequence context has on the phenotype. To overcome these limitations, we propose two novel feature embedding modes (SNP-context and whole-genome) based on DNABERT-2, a cross-species genomic foundation model that uses self-attention mechanisms and transfer learning to automatically identify conserved sequence features across diverse evolutionary lineages without prior biological assumptions. The whole-genome feature embedding aggregates genomic information at a global scale by pooling vectors from chunked sequences processed by DNABERT-2, whereas the context feature embedding captures local information by directly encoding variable-length (500–3000 bp) sequences centered on target SNPs. To reduce noise in the high-dimensional feature embeddings, we employed principal component analysis (PCA) and partial least squares (PLS) to project the features into a lower-dimensional space. We generated two kinds of feature embedding for three crop datasets (rice413, rice395, and maize301), investigated the impact of 500–3000 bp flanking SNP contexts on phenotypic prediction, and compared prediction accuracy variations across algorithms at 4–768 feature dimensions among the PCA, PLS, and no dimensionality reduction strategies. The results demonstrate that machine learning (ML) algorithms operating under the SNP-context embedding mode achieve greater accuracy and lower mean absolute errors (MAEs) than traditional SNP features, with performance peaking at optimal context lengths that proved to be trait-dependent (e.g., 1000 bp to 3000 bp), particularly for traits with low-to-moderate heritability (H 2 ∈ (0.2, 0.7]). In contrast, using whole-genome embeddings as input for ML can further improve the prediction accuracy for highly heritable traits (H 2 ∈ (0.7, 1.0]), even outperforming state-of-the-art deep learning models (such as DNNGP and ResGS) that rely on SNP markers. The proposed feature embedding methods, which leverage DNABERT-2 to capture the contextual features of SNPs, effectively overcome the limitations of traditional prediction models. This study demonstrates that the SNP-context mode is superior for traits with low-to-moderate heritability, while the whole-genome embedding mode excels for highly heritable ones. Our work provides plant breeders with a flexible and powerful analytical framework, enabling them to select the most suitable phenotypic prediction method based on the complexity of the target trait, thereby accelerating genetic gain in the breeding of elite crop varieties.
Why it matches plant phenotyping methodsDNABERT-2を用いた遺伝情報から作物形質を予測する新規特徴埋め込み・機械学習手法の開発と比較評価が中心であり、植物形質推定の計算手法に該当する。
titleCrop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their crop phenotype prediction method (SNP-context and whole-genome DNABERT-2 embedding pipeline) is publicly available on GitHub. Other URLs in the text (samtools, bcftools, pysam, pyfaidx, PyCaret) are generic third-party libraries, not paper-ownCode · publicThe Python implementation of our method is publicly available and downloadable from the GitHub repository: https://github.com/oliveSpring/Crop_DNA_Embedding.git.Open asset ↗oliveSpring/Crop_DNA_Embeddinglines:513-602Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI's superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.
Why it matches plant phenotyping methods植物ストレス状態を推定する動的植生指数と強化学習フレームワークを開発し、実圃場・ベンチマークデータで性能検証しているため、フェノタイピング手法が中心である。
abstractThis study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection.
Reproduction assets foundThe paper publicly releases its authors' field-captured mobile RGB rice canopy dataset on Kaggle and its full RL-VI analysis code (RL formulation, preprocessing, VI computation, training, evaluation) on GitHub. Sentinel-2 imagery and benchmark datasets are third-party public sources, not paper-specific deposits.Dataset · publicThe Mobile RGB dataset, consisting of field-captured rice canopy images collected by the authors at Polur, Tamil Nadu, India, is publicly available on Kaggle under a CC BY-NC 4.0 license (DOI: [https://doi.org/10.34740/kaggle/dsv/14105754](https:/doi.org/10.34740/kaggle/dsv/14105754)).Open asset ↗Kaggle · 10.34740/kaggle/dsv/14105754html-lines:616-683Code · publicAll custom code developed for this work including the RL-VI (Reinforcement Learning–based Vegetation Index) formulation algorithm, image preprocessing scripts, vegetation index computation modules, model training pipelines, and evaluation routines is openly accessible in a public GitHub repository. The code is available without restriction for non-commercial research use and fully available at Github Repository (https://github.com/Poornisrm/Vegetation-Index.git).Open asset ↗GitHub · Poornisrm/Vegetation-Indexhtml-lines:684-711Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice leaf diseases pose a significant and escalating threat to global food security. Timely and accurate detection, particularly in the critical early stages characterized by subtle lesions, is paramount for effective disease management. However, existing solutions often struggle with the complexities of real-world field environments (e.g., variable lighting, occlusions, complex backgrounds), computational constraints on edge devices, and limited generalizability across diverse disease types and plant species. To address these challenges, this study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection. Our key innovations include: (1) A Multi-branch Large-kernel Fusion Depthwise (MLFD) module enhancing multi-scale contextual feature extraction critical for identifying subtle early lesions; (2) A Multi-scale Dilated Transformer Attention (MDTA) module integrating spatial and channel attention mechanisms to improve feature representation under complex conditions; (3) A Lightweight Detection Head (Lo-Head) optimized with grouped and depthwise convolutions, drastically reducing model complexity without sacrificing accuracy. Crucially, extensive experiments demonstrate the framework's superior performance. On a dedicated rice leaf disease dataset, it achieves a mean Average Precision mAP@0.5:0.95 of 62.62%, outperforming state-of-the-art lightweight detectors including YOLOv5n (56.73%), YOLOv8n (57.41%), YOLOv10n (56.14%), and the baseline YOLOv11n (60.85%), while maintaining low computational demands (6.3 GFLOPs, 2.66M parameters). Significantly, rigorous generalization experiments validate the model's exceptional transferability. Evaluated on independent datasets encompassing potato and tomato leaf diseases, the proposed framework consistently surpasses comparable models in mAP@0.5:0.95, demonstrating its robust capability to detect diseases across different plant species. This combination of high accuracy, computational efficiency, and remarkable cross-crop generalizability positions our framework as a highly promising tool for practical deployment on resource-limited edge devices (e.g., drones, field sensors) in smart agriculture systems, enabling proactive disease surveillance and precision control strategies across diverse crops.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出する深層学習手法の開発と、独立データセットによる性能・汎化性検証が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractthis study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection.
Reproduction assets foundThe paper's rice leaf disease detection dataset was curated from three publicly available repositories (one Kaggle, two Roboflow), and the cross-species generalization used two additional public Roboflow datasets (tomato and potato leaf diseases). All five URLs are explicitly listed in the article as data sources. No作者Dataset · publicData Sources: The dataset utilized in this study was curated and screened from the following publicly available online repositories:.Open asset ↗html-lines:110-216Dataset · publicThe Tomato Leaf and Potato Leaf disease datasets were acquired from public domain resources. The dataset links are: Tomato Leaf Diseases: https://universe.roboflow.com/dyploma/tomato-leaf-diseases-4xa5iOpen asset ↗dyploma/tomato-leaf-diseases-4xa5ihtml-lines:747-783Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Digital agriculture and smart farming require crop health monitoring methods that balance detection accuracy with computational cost. Rice leaf diseases threaten yield, while field images often contain small multi-scale lesions, variable illumination and cluttered backgrounds. This paper investigates SCD-YOLOv11n, a lightweight detector designed with these constraints in mind. The model replaces the YOLOv11n backbone with a StarNet backbone and integrates a C3k2-Star module to enhance fine-grained, multi-scale feature extraction. A Detail-Strengthened Cross-scale Detection (DSCD) head is further introduced to improve localization of small lesions. On this architecture, we design a DepGraph-based mixed group-normalization pruning rule and apply channel-wise feature distillation to recover performance after pruning. Experiments on a public rice leaf disease dataset show that the compressed model requires 1.9 MB of storage, achieves 97.4% mAP@50 and 76.2% mAP@50:95, and attains a measured speed of 184 FPS under the tested settings. These results provide a quantitative reference for designing lightweight object detectors for rice disease monitoring in digital agriculture scenarios.
Why it matches plant phenotyping methodsイネ葉の病斑を画像から検出・局在化する軽量モデルを開発し、精度・圧縮性能・速度を評価しており、植物病害状態の取得手法が研究の中心である。
abstractThis paper investigates SCD-YOLOv11n, a lightweight detector designed with these constraints in mind.
Reproduction assets foundThe paper's rice leaf disease image dataset (6715 annotated images) is explicitly stated to be publicly available on Roboflow, and an MDPI supplementary file is provided with additional dataset information. No author analysis code or trained model checkpoints are publicly deposited.Dataset · publicThe rice disease detection dataset used in this study is publicly available at: https://universe.roboflow.com/dreamydaisy-cdagn/rice-dyl9n/dataset/4 (accessed on 10 December 2025).Open asset ↗dreamydaisy-cdagn/rice-dyl9n/dataset/4lines:480-547Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice leaf diseases significantly reduce productivity and cause economic losses, highlighting the need for early detection to enable effective management and improve yields. This study proposes Artificial Neural Network (ANN)-based image-processing techniques for timely classification and recognition of rice diseases. Despite the prevailing approach of directly inputting images of rice leaves into ANNs, there is a noticeable absence of thorough comparative analysis between the Feature Analysis Detection Model (FADM) and the Direct Image-Centric Detection Model (DICDM), specifically when it comes to evaluating the effectiveness of Feature Extraction Algorithms (FEAs). Hence, this research presents initial experiments on the Feature Analysis Detection Model, utilizing various image Feature Extraction Algorithms, Dimensionality Reduction Algorithms (DRAs), Feature Selection Algorithms (FSAs), and Extreme Learning Machine (ELM). The experiments are carried out on datasets encompassing 3829 original rice leaf images across six classes (bacterial leaf blight, brown spot, leaf blast, leaf scald, sheath blight rot, and healthy leaf). A Direct Image-Centric Detection Model is established without the utilization of any FEA, and the evaluation of classification performance relies on different metrics. Ultimately, an exhaustive contrast is performed between the achievements of the Feature Analysis Detection Model and the Direct Image-Centric Detection Model in classifying rice leaf diseases. The results reveal that the highest performance is attained using the Feature Analysis Detection Model. We have also applied Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretability of the model's predictions. The adoption of the proposed Feature Analysis Detection Model for detecting rice leaf diseases holds excellent potential for improving crop health, minimizing yield losses, and enhancing the overall productivity and sustainability of rice farming.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する手法を中心に、特徴抽出モデルと直接画像モデルを比較・評価しており、植物病害フェノタイピング手法の開発・検証に該当する。
abstractThis study proposes Artificial Neural Network (ANN)-based image-processing techniques for timely classification and recognition of rice diseases.
Reproduction assets foundThe paper's rice leaf disease classification experiments are built on a publicly available Kaggle image dataset (3829 rice leaf images across six classes), explicitly linked in the Data Availability Statement. Datasets produced during the study are only available upon request from the corresponding author.Dataset · publicThe dataset is publicly available at https://www.kaggle.com/datasets/vbookshelf/riceleafdiseases.Open asset ↗Kaggle · vbookshelf/riceleafdiseaseshtml-lines:883-951Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction The real-time, accurate detection and classification of rice seeds are crucial for improving agricultural productivity, ensuring grain quality, and promoting smart agriculture. Although significant progress has been made using deep learning, particularly convolutional neural networks (CNNs) and attention-based models, earlier methods such as threshold segmentation and single-grain classification faced challenges related to computational efficiency and latency, especially in high-density seed agglutination scenarios. This study addresses these limitations by proposing an integrated intelligent analysis model that combines object detection, real-time tracking, precise classification, and high-accuracy phenotypic measurement. Methods The proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation, which builds upon the YOLOv11 architecture. YOLOv11-LA incorporates several enhancements over YOLOv11, including separable convolutions, CBAM (Convolutional Block Attention Module) attention mechanisms, and module pruning strategies. These modifications not only improve detection accuracy but also significantly reduce the number of parameters by 63.2% and decrease computational complexity by 51.6%. For classification, the model employs a custom-designed, lightweight RiceLCNN classifier. Additionally, the DeepSORT algorithm is employed for real-time multi-object tracking, and sub-pixel edge detection along with dynamic scale calibration mechanisms are applied for precise phenotypic feature measurement. Results Compared to YOLOv11, the YOLOv11-LA model increases the mAP@0.5:0.95 score by 1.9%, showcasing its superior detection performance while maintaining lower computational overhead. The RiceLCNN classifier achieved classification accuracies of 89.78% on private datasets and 96.32% on public benchmark datasets. The system demonstrated high accuracy in measuring phenotypic features such as seed size and roundness, with measurement errors kept within 0.1 millimeters. The DeepSORT algorithm effectively managed multi-object tracking, reducing duplicate identifications and frame loss in real-time. Discussion Experimental validation confirmed that the YOLOv11-LA model outperforms the original YOLOv11 in terms of both detection speed and accuracy, while also maintaining low computational complexity. The integration of the YOLOv11-LA, RiceLCNN, and DeepSORT algorithms, combined with advanced measurement techniques, underscores the model's potential for industrial applications, particularly in enhancing smart agricultural practices.
Why it matches plant phenotyping methodsイネ種子画像からサイズや真円度を抽出するリアルタイム画像解析手法を開発し、精度・速度・測定誤差を検証しており、表現型取得が研究の中心です。
abstractThe proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository (RiceLCNN) containing the study's rice seed datasets and analysis code. The supplementary material link is generic and not confirmed to contain paper-specific assets.Dataset · publicang , Southwest Forestry University, China
Guodong Sun , Beijing Forestry University, China
Xiaofei Fan , Hebei Agricultural University, China
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/5120191452/RiceLCNN .
Author contributions
DZ: Methodology, Software, Writing – original draft. SS: Funding acquisition, Resources, Writing – review & editing. JL: Validation, Writing – review & editing. WX: Data curation, Resources, Writing – review & editing. NX: Formal Analysis, Visualization, Writing – review & editing.
Conflict of interest
ThOpen asset ↗https://github.com/5120191452/RiceLCNN · RiceLCNNlines:619-662Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract High‐throughput and noninvasive phenotyping methods are promising technology for improving efficiency in plant research and breeding. In this study, we evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice ( Oryza sativa ). The DPS was used to estimate plant height, biomass, color, leaf morphology, and tiller angle in four rice varieties (Koshihikari, Nipponbare, PL9, and Tachiaoba). The results show high accuracy and correlation between manually measured and DPS‐derived traits. Notably, the 3D volume analysis can quantify biomass accumulation and growth dynamics and revealed distinct differences among varieties. The strong correlation between the green‐red normalized difference index (a red‐green‐blue‐based index) and soil plant analysis development also demonstrated the viability of the system in monitoring leaf color without using a multispectral instrument. The analysis also captured growth patterns over time, including canopy development and senescence, which are often challenging to quantify through manual measurements alone. Furthermore, the tiller angle estimation derived from DPS provided an alternative method to plant architecture evaluation, demonstrating its potential for use in breeding programs aimed to optimize canopy structure. These findings establish DPS as a reliable and scalable tool for a digital phenotyping platform that enables comprehensive trait analysis with reduced labor and increased precision and the capability to continuously monitor plant growth and biomass accumulation. This study shows the potential of this novel digital tool for automating manual measurements, which can increase efficiency and expedite research and breeding in rice and other crops.
Why it matches plant phenotyping methods3Dモデル再構築に基づくデジタル表現型解析システムを開発・評価し、イネの複数形質を手測定と比較検証しているため、方法が研究の中心です。
abstractwe evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice
Reproduction assets foundThe paper's data availability statement explicitly says the analysis code is openly available on GitHub at the authors' repository Rice_VTGa.O, which contains the digital phenotyping/leaf-tracing analysis code for this study. No phenotype dataset or image deposit is stated.Code · publicGrant Number 39 [2023] and 38 [2024]),
and Microbiome and Metabolome Control Project, University
of Miyazaki, Japan.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Rice_VTGa.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Kenji Aoki https://orcid.org/0000-0001-7003-1994
MasatsuguHashiguchi https://orcid.org/0000-0003-0637-2780
RyoAkashi https://orcid.org/0000-0002-5651-8285
Yuji Kishima https://orcid.org/0000-0002-0942-3371
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154Open asset ↗https://github.com/sandysan42/Rice_VTGa.O · Rice_VTGa.Opdf-raw-page:13 lines:1-84Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
RiceWheatField / plotMesh / voxelNeRF / 3D Gaussian SplattingLiDAR / point cloudPanicle / ear / spikeLeafRootSeed / grain
Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. It utilizes radiance field information to lift 2D masks, segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction. On a rice dataset, IPENS achieves a grain-level segmentation mean Intersection over Union (mIoU) of 63.72%. For phenotypic trait estimation, it achieves a grain voxel volume coefficient of determination R 2 = 0.7697 (Root Mean Square Error, RMSE = 0.0025), leaf surface area R 2 = 0.84 (RMSE = 18.93), and leaf length and width prediction accuracies of R 2 = 0.97 and R 2 = 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset, IPENS further improves segmentation performance to a mIoU of 89.68%, with exceptional phenotypic estimation results: panicle voxel volume R 2 = 0.9956 (RMSE = 0.0055), leaf surface area R 2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reaching R 2 = 0.99 and R 2 = 0.92 (RMSE = 0.23 and 0.15). Without requiring annotated data, IPENS rapidly extracts grain-level point clouds for multiple targets within three minutes using single-round image interactions. These features make IPENS a high-quality, non-invasive phenotypic extraction solution for rice and wheat, offering significant potential to enhance intelligent breeding.
Why it matches plant phenotyping methods植物形質抽出のためのNeRF-SAM2融合手法を開発し、作物データセットで分割性能と形質推定精度を検証しているため、方法開発・検証が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub. The rice/wheat MMR/MMW phenotype datasets (multi-view images, point clouds, annotations) are only available upon reasonable request, so they are not public.Code · publicCode is available at https://github.com/Vincent-Songwentao/IPENS-Code.git .Open asset ↗https://github.com/Vincent-Songwentao/IPENS-Code.gitlines:472-496Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Agriculture is crucial to human survival. The growing of biotic rice plants is very helpful for feeding a lot of people around the world, especially in places where rice is a main food. The detection of rice leaf disease is critical to increasing crop productivity. Methods To improve the accuracy of rice leaf disease prediction, this paper proposes a hybrid Vision Transformer (ViT) with pre-trained ResNet18 models (ViT-ResNet18). In general, the input images apply to the pre-trained ViT and ResNet18 models independently. The output features of these two models are combined and fed into the final Fully Connected (FC) layer, followed by a Softmax layer for final classification. Results The output of rice leaf diseases from the FC layer of the proposed hybrid ViT with ResNet18 model achieved 94.4% accuracy, a precision of 0.948, a recall of 0.944, an F1-Score of 0.942, and an Area Under Curve (AUC) of 0.985. Discussion The proposed hybrid model ViT-ResNet18 shows a 5%, 1%, and 1% improvement in accuracy compared to VGG16 with Neural Network, Inception V3 with Neural Network, and SqueezeNet with Neural Network classifier, respectively.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する深層学習手法の提案・比較評価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。
abstractthis paper proposes a hybrid Vision Transformer (ViT) with pre-trained ResNet18 models (ViT-ResNet18).
Reproduction assets foundThe paper trains a hybrid ViT-ResNet18 model for rice leaf disease classification on public rice leaf disease image datasets. Two public image datasets are cited in the references with explicit URLs: the Kaggle rice diseases image dataset and the Mendeley rice leaf diseases dataset. No authors' analysis code or trainedDataset · publicly represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
( 2024 ). Rice diseases image dataset . Available online at: https://www.kaggle.com/datasets/minhhuy2810/rice-diseases-image-dataset (Accessed November 5, 2024).Open asset ↗kaggle · minhhuy2810/rice-diseases-image-datasetlines:512-537Dataset · public( 2023 ). Rice leaf diseases dataset . Available online at: https://data.mendeley.com/datasets/dwtn3c6w6p/1:~:text=Overview%3A%20The%20Rice%20Life%20Disease,and%20Leaf%20Smut%20(LS) (Accessed November 5, 2024 ).
Abasi A. K.
Makhadmeh S. N.
Alomari O. A.
Tubishat M.
Mohammed H. J.
( 2023 ).
Enhancing rice leaf disease classification: a customized convolutional neural network approach
. Sustainability
15 , 15039 . doi:
10.3390/su152015039
Aggarwal M.
Khullar V.
Goyal N.
Singh A.
Tolba A.
Thompson E. B.
(Open asset ↗mendeley · dwtn3c6w6p/1lines:538-750Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency. Breeding crop varieties with a "smart" canopy, characterized by erect upper-layer leaves and flat lower-layer leaves, can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture. Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency. In this study, we developed a strategy to estimate the leaf optical properties based on leaf anatomical features. We developed a Directional Spectrum Detection Instrument (DSDI) system and associated Bidirectional Reflectance Distribution Function (BRDF) analysis software to precisely describe leaf light distribution. BRDF parameters were quantified with high accuracy ( R2>0.95 ) for adaxial and abaxial surfaces of maize, rice, cotton, and poplar leaves across canopy layers. Leaf phenotypic traits, surface roughness, pigments content, specific leaf weight and thickness were also assessed. Ensemble learning (EL) model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R 2 between 0.83 and 0.99. Compared to existing BRDF measurement systems, the DSDI achieves broader angular coverage (-π/36 to 35π/36) via mechanical rotation design, and the ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits. This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties, which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.
Why it matches plant phenotyping methods葉の光学特性と表現型形質を取得・予測する測定機器、BRDF解析ソフトウェア、機械学習モデルを開発しており、植物フェノタイピング手法が研究の中心である。
abstractthe ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits.
Reproduction assets foundThe paper's BRDF analysis code (adaptive grid search fitting and Roughness Calculator) is publicly available at github.com/PlantSystemsBiology/brdf, and the modified fastTracer ray tracing software used for canopy light simulations is at github.com/PlantSystemsBiology/fastTracerPublic. Phenotype/measurement data are '…Code · publicAn adaptive grid search algorithm was developed in this study, and this algorithm utilized a 2-layered grid (step sizes of 1 × 10 − 2 and 1 × 10 − 4 respectively) structure to incrementally optimize each parameter, providing a more precise approximation of true values. By iteratively narrowing the search range and increasing resolution, this method gradually converges on the optimal solution. The source code of Python for adaptive grid search algorithm was available at https://github.com/PlantSystemsBiology/brdf .Open asset ↗PlantSystemsBiology/brdflines:212-227Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.
Why it matches plant phenotyping methods衛星データとAI/MLを用いて水稲の生育状態（phenology）と収量を圃場レベルで推定する手法を開発・評価しており、植物形質の取得・推定が研究の中心である。
abstractdeveloped several crop-based indicators to assess crop phenology
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.29858924) containing the data supporting the study's rice monitoring findings, which qualifies as a paper-specific public asset.Dataset · publicuddin Shaik: Software, Visualization, Writing –
original draft
Suman Saraswathibatla: Investigation, Project admin-
istration, Supervision
Mukund Patil: Validation, Writing – review & editing
Data Availability Statement
The data that support the findings of this study can be found
at https://figshare.com/s/b611c04368825e6a028b
(https://doi.org/10.6084/m9.figshare.29858924).Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Mandapati, R., Gumma, M. K., Metuku, D. R., Bel-lam, P. K.,
Panjala, P., Maitra, S., Maila, N. Crop yield assessment using
field-based data and crop models at the village level: A case
study on a homogeneous rice area in Telangana, India.
AgOpen asset ↗figshare · 10.6084/m9.figshare.29858924pdf-raw-page:21 lines:1-101Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction The germination rate of rice seed is a critical indicator in agricultural research and production, directly influencing crop yield and quality. Traditional assessment methods based on manual visual inspection are often time-consuming, labor-intensive, and prone to subjectivity. Existing automated approaches, while helpful, typically suffer from limitations such as rigid germination standards, strict imaging requirements, and difficulties in handling the small size, dense arrangement, and variable radicle lengths of rice seeds. Methods To address these challenges, we present SeedRuler, a versatile, web-based application designed to improve the accuracy, efficiency, and usability of rice seed germination analysis. SeedRuler integrates three core components: SeedRuler-IP, a traditional image processing-based module; SeedRuler-YOLO, a deep learning model built on YOLOv5 for high-precision object detection; and SeedRuler-SAM, which leverages the Segment Anything Model (SAM) for fine-grained seed segmentation. A dataset of 1,200 rice seed images was collected and manually annotated to train and evaluate the system. An interactive module enables users to flexibly define germination standards based on specific experimental needs. Results SeedRuler-YOLO achieved a mean average precision (mAP) of 0.955 and a mean absolute error (MAE) of 0.110, demonstrating strong detection accuracy. Both SeedRuler-IP and SeedRuler-SAM support interactive germination standard customization, enhancing adaptability across diverse use cases. In addition, SeedRuler incorporates an automated seed size measurement function developed in our prior work, enabling efficient extraction of seed length and width from each image. The entire analysis pipeline is optimized for speed, delivering germination results in under 30 seconds per image. Conclusions SeedRuler overcomes key limitations of existing methods by combining classical image processing with advanced deep learning models, offering accurate, scalable, and user-friendly germination analysis. Its flexible standard-setting and automated measurement features further enhance usability for both researchers and agricultural practitioners. SeedRuler represents a significant advancement in rice seed phenotyping, supporting more informed decision-making in seed selection, breeding, and crop management.
Why it matches plant phenotyping methodsイネ種子の発芽状態と種子サイズを画像から抽出するウェブ型フェノタイピング手法を開発し、データセットで性能評価しているため、方法が研究の中心である。
abstractwe present SeedRuler, a versatile, web-based application designed to improve the accuracy, efficiency, and usability of rice seed germination analysis.
Reproduction assets foundThe paper's rice seed germination image dataset (1,200 annotated images) is publicly deposited on Kaggle, and the SeedRuler platform (web tool plus offline software package with user manual) is freely available at the authors' lab site.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://www.kaggle.com/jinfengzhao/riceseedgermination .Open asset ↗Kaggle · jinfengzhao/riceseedgerminationlines:744-763Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Accurate and early disease detection in paddy crops is essential for maximizing crop yield which ensures food security. Traditional methods are often labor-intensive, time-consuming, and domain-specific expertise. Feed-forward deep-learning models will perform accurate disease detection through the identification of spatial patterns. However, they cannot predict the diseases at the early stages due to the lack of temporal information. Temporal observations will help perform continuous monitoring and detect minute changes in the crops at the early times. To tackle this problem, we proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively. The SSDHR network uses multi-branch convolution kernels to extract distinct discriminative characteristics rather than conventional leaf-based indicators. It incorporates spatial, and temporal-based attention mechanism Symmetric Fusion Attention (SFA) to improve feature selection and XGBoost (XGB) classifier for better stability. According to experimental findings, the suggested framework achieves a 99.25% accuracy rate in identifying and classifying 13 paddy classes, including normal, blast, hispa, tungro, white stem borer, brown spot, leaf roller, downy mildew, yellow stem borer, bacterial leaf blight, bacterial leaf streak, black stem borer, and bacterial panicle blight.
Why it matches plant phenotyping methodsイネ病害の症状を空間・時間画像データから検出・分類する深層学習フレームワークを提案し、その性能を評価しているため、植物フェノタイピング手法が中心です。
abstractwe proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively.
Reproduction assets foundThe paper's phenotyping inputs are the publicly available Paddy Doctor image dataset (16,225 annotated paddy disease images) hosted on IEEE DataPort, with an explicit dataset link and data availability statement. No author analysis code or trained models are shared.Dataset · publicThe dataset used in our study was obtained from the publicly available repository titled “Paddy Disease and Pest Image Dataset” on IEEE Data Port. The dataset comprises 16,225 high-quality images across 13 classes, including 12 paddy disease and pest categories along with healthy samples.Open asset ↗IEEE Data Porthtml-lines:118-200Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published1 Oct 2025IAES International Journal of Artificial Intelligence (IJ-AI)Cited by 0 · OpenAlex ↗
Rice crop disease detection and its diagnosis methods are vitally important for the agriculture field to be sustainable. Traditional methods suffer from paddy yield, complex issues, and crop diseases, leading to inefficiencies in the agriculture domain. Our research provides space for a novel approach, combining the Laurent series with an intelligent multidimensional object optimization (LIMO) classification framework based on generative adversarial networks (GANs) to recognize various types of crop diseases in agricultural fields. Through our proposed research work, IoT nodes sense the values of the field crop, and gathered information is shared with processing units through base station communication. Multi-objective and cognitive learning routing (MOCLEAR) protocol supports choosing the optimal path for data transmission improvement. Then, for image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images. After receiving segment input images, perform feature extraction and classification using significant attributes. The proposed Laurent series with IMO is newly formulated by integrating the Laurent series with Intelligent IMO algorithms. Through extensive experimentation and analysis, the proposed LIMO-based GAN network provides effective and improved performance metrics with overall accuracy, sensitivity, and specificity values at 91.5%, 92.6%, and 92.41%, respectively.
Why it matches plant phenotyping methodsイネ病害の画像セグメンテーション、特徴抽出、分類を中心とする計算的な病害状態推定手法を開発・評価しており、植物フェノタイピング手法が研究の中核である。
abstractfor image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images.
Reproduction assets foundThe paper's rice crop disease image dataset is publicly available via three author-deposited Figshare datasets (smut diseases, blast disease, bacterial blight) cited as references [11]-[13] and used in Table 1. No code or model availability is stated; other data is only on request.Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R 2 = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.
Why it matches plant phenotyping methodsマルチビュー画像によるイネ幼苗の3D再構成プラットフォームとデータセットを開発し、再構成精度および抽出形質を実測値と検証しており、表現型取得手法が研究の中心である。
abstractdeep learning-based feature extraction and matching methods are employed in this study
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub; the phenotype/image dataset is only available upon request, so it does not qualify as a public asset.Code · publicThe code used in this study is available at https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.git .Open asset ↗https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.gitlines:433-485Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction Agriculture underpins global food security by providing food, raw materials, and livelihoods, contributing 4% to global GDP and up to 25% in rural areas. Rice, a staple for more than half of the world's population, is nutritionally vital but highly vulnerable to diseases such as Hispa, leaf blast, and brown spots, which significantly reduce yield and quality. Achieving Sustainable Development Goal (SDG) 2 requires innovative approaches to mitigate these threats. Artificial intelligence (AI), particularly computer vision and machine learning, offers promising tools for early disease detection. Methods This study developed a convolutional neural network (CNN)-based model for rice leaf disease detection and classification. A publicly available dataset containing 3,355 labeled images across four categories-Brown Spot, Leaf Blast, Hispa, and Healthy leaves-was used to train and evaluate the model. To improve classification accuracy, the CNN was enhanced with spatial and channel attention mechanisms, enabling it to focus on the most discriminative image regions. The system was designed for modular deployment, allowing lightweight, real-time implementation on edge devices. Results The enhanced CNN achieved high accuracy and robust performance metrics across all disease categories. Attention mechanisms significantly improved precision in identifying subtle disease patterns. The lightweight design ensured efficient operation on edge devices, demonstrating feasibility for real-world agricultural applications. Discussion and conclusion The proposed AI-driven system provides reliable and scalable rice leaf disease detection, supporting timely intervention to reduce yield loss. By strengthening rice production and promoting sustainable practices, the model contributes to SDG 2 by advancing global food security. This research highlights AI's transformative role in agriculture, fostering mechanization, ecological stability, and resilience in food systems.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から分類するCNN手法を開発・評価しており、植物の病害状態を直接推定するフェノタイピング手法が研究の中心である。
abstractThis study developed a convolutional neural network (CNN)-based model for rice leaf disease detection and classification.
Reproduction assets foundThe paper's data availability statement points to a public Kaggle dataset of 3,355 labeled rice leaf disease images (Brown Spot, Leaf Blast, Hispa, Healthy) used to train and evaluate the CNN model. No author code or trained model deposit is mentioned.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/minhhuy2810/rice-diseases-image-dataset/data .Open asset ↗Kaggle · minhhuy2810/rice-diseases-image-datasetlines:927-976Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Manual assessment of toxic fungal infection levels in crop seeds is important for developing antifungal-resistant cultivars, yet it has long been recognized as health-risking and inherently subjective. This study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds. The edge computing-based computer vision approach, termed Edge CV, was developed using the Jetson Nano, embedded cameras, and deployed with the proposed Edge CV model to enable intelligent evaluation with constrained computing resources and GPU power. The Edge CV model: First, leveraging semantic segmentation in computer vision tasks to differentiate between A. flavus -infected and uninfected; Second, utilizing post-processing techniques to accurately separate connected peanut seeds while merging segments belonging to the same ones; Third, analyzing and quantifying infection indices, as well as results presentation. Finally, deep transfer learning was employed to validate the model's transferability for other crop seeds. As a result, Edge CV inference showed agreement with manual measurements (R 2 = 0.901, RMSE = 0.07) and superior consistency, with only a 0.01 % fluctuation compared to 4.2 % for human assessments. Moreover, Edge CV demonstrated its transferability to other crop seeds, such as maize (R 2 = 0.968, RMSE = 0.13) and rice (R 2 = 0.949, RMSE = 0.26). These results underscore the potential of Edge CV as a transferable solution for assessing toxic fungal infections. The approach developed also offers valuable insights for enhancing proximal machine vision, improving the distinction of adjacent seeds, and enabling more accurate calculation of the infection index.
Why it matches plant phenotyping methods種子の真菌感染状態を画像から定量化するエッジコンピューティング画像手法を開発し、手動測定との一致および他作物種への移 transfer 性を検証しており、植物状態の取得・抽出が研究の中心である。
abstractThis study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' data and code for the Edge CV peanut A. flavus infection assessment pipeline.Code · publicThe data and code will be made available on this URL: https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessment.Open asset ↗https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessmenthtml-lines:349-374Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditional techniques of identifying rice diseases are time-consuming, labor-intensive, and rely heavily on specialist knowledge. As a result, a rapid, cost-effective, and automated method for detecting rice illnesses is critical for modernizing agricultural techniques and ensuring sustainable food production. This paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos. We extracted deep features from rice leaf images using pre-trained CNN models-MobileNetV2, DarkNet19, and ResNet18. These features are then classified using machine learning classifiers with various kernel functions, which apply a strong 10-fold cross-validation technique to assure model reliability. Using a medium Gaussian kernel of the SVM classifier, the proposed system achieved a classification accuracy of 98.61%, specificity of 98.85%, and sensitivity of 97.25%. The framework is computationally efficient and scalable, allowing for greater dataset testing. The proposed technique provides a dependable and efficient solution for accurate identification of rice leaf diseases, reducing farmers' reliance on manual inspection and supporting timely intervention.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を自動推定する深層学習・機械学習手法が研究の中心であり、交差検証による性能評価も行っているため、植物フェノタイピング手法として含める。
abstractThis paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos.
Reproduction assets foundThe paper's Data Availability statement lists three public leaf-image datasets used as phenotyping inputs: a Kaggle rice leaf diseases dataset, the UCI Machine Learning Repository rice leaf dataset, and the IEEE Dataport Indian Rice Disease Dataset (IRDD). No author analysis code, models, or checkpoints are shared.Dataset · publicnt, scalable, and user-friendly agricultural disease management solutions.
Data Availability
The leaf images utilized in our research were gathered from various reputable sources, including the UCI and Kaggle datasets, along with specific images obtained from the IEEE dataset repository. The links to the dataset are: 1. kaggle. https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases . 2. UCI Machine Learning Repository. https://doi.org/10.24432/C5R013 . 3. IEEE Dataport. https://ieee-dataport.org/documents/indian-rice-disease-dataset-irdd (doi: 10.21227/4rmf-gd63 ).
Funding Statement
The author(s) received no specific funding for this work.
References
1. Gulati A, Kapur D, Bouton MM. ROpen asset ↗Kaggle · vbookshelf/rice-leaf-diseaseslines:292-309Dataset · publicf images utilized in our research were gathered from various reputable sources, including the UCI and Kaggle datasets, along with specific images obtained from the IEEE dataset repository. The links to the dataset are: 1. kaggle. https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases . 2. UCI Machine Learning Repository. https://doi.org/10.24432/C5R013 . 3. IEEE Dataport. https://ieee-dataport.org/documents/indian-rice-disease-dataset-irdd (doi: 10.21227/4rmf-gd63 ).
Funding Statement
The author(s) received no specific funding for this work.
References
1. Gulati A, Kapur D, Bouton MM. Reforming Indian agriculture. Economic & Political Weekly. 2020;55(11):35–42.
2. Haggblade SOpen asset ↗UCI Machine Learning Repository · 10.24432/C5R013lines:292-309Dataset · publicvarious reputable sources, including the UCI and Kaggle datasets, along with specific images obtained from the IEEE dataset repository. The links to the dataset are: 1. kaggle. https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases . 2. UCI Machine Learning Repository. https://doi.org/10.24432/C5R013 . 3. IEEE Dataport. https://ieee-dataport.org/documents/indian-rice-disease-dataset-irdd (doi: 10.21227/4rmf-gd63 ).
Funding Statement
The author(s) received no specific funding for this work.
References
1. Gulati A, Kapur D, Bouton MM. Reforming Indian agriculture. Economic & Political Weekly. 2020;55(11):35–42.
2. Haggblade S, Hazell P, Reardon T. The rural non-farm economy: prospects fOpen asset ↗IEEE Dataport · 10.21227/4rmf-gd63lines:292-309Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeMilletRiceSeed / grainMorphology / geometry measurementSegmentationRoot system architecture
Radicle length is a critical indicator of seed vigor, germination capacity, and seedling growth potential. However, existing measurement methods face challenges in automation, efficiency, and generalizability, often requiring manual intervention or re-annotation for different seed types. To address these limitations, this paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops. The method leverages the Segment Anything Model (SAM) as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids, which are then fed into SAM for precise segmentation of the seed coat and radicle. The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length. Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944, demonstrating excellent segmentation performance. Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error (MAE) was 0.273 mm and the coefficient of determination (R 2 ) was 0.982, confirming the method's high precision for rice. On the Otherseed dataset, the predicted radicle lengths for maize ( Zea mays ), pearl millet ( Pennisetum glaucum ), and rye ( Secale cereale ) are consistent with the observed radicle length distributions, demonstrating strong cross-species performance. These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice, with validated applicability to other crop species.
Why it matches plant phenotyping methodsイネを中心に複数作物の幼根長を画像から自動抽出・推定する手法を開発し、セグメンテーション性能と測定精度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。
abstractthis paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops.
Reproduction assets foundThe authors explicitly state that the LenRuler code and software are publicly available on GitHub, providing the paper's radicle-length phenotyping analysis pipeline (SAM-based segmentation, YOLO detection, Gaussian classification).Code · publicThe code and software are available on GitHub at https://github.com/cccccabbage/LenRuler .Open asset ↗cccccabbage/LenRulerlines:351-417Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This research presents a fully automated two-step method for segmenting rice seedlings and assessing their health by integrating spectral, morphological, and textural features. Driven by the global need for increased food production, the proposed method enhances monitoring and control in agricultural processes. Seedling locations are first identified by the excess green minus excess red index, which enables automated point-prompt inputs for the segment anything model to achieve precise segmentation and masking. Morphological features are extracted from the generated masks, while spectral and textural features are derived from corresponding red-green-blue imagery. Health assessment is conducted through anomaly detection using a one-class support vector machine, which identifies seedlings exhibiting abnormal morphology or spectral signatures suggesting stress. The proposed method is validated by visual inspection and Silhouette score, confirming effective separation of anomalies. For segmentation, the proposed method achieved mean dice scores ranging from 72.6 to 94.7. For plant health assessment, silhouette scores ranged from 0.31 to 0.44 across both datasets and various growth stages. Applied across three consecutive rice growth stages, the framework facilitates temporal monitoring of seedling health. The findings highlight the potential of advanced segmentation and anomaly detection techniques to support timely interventions, such as pruning or replacing unhealthy seedlings, to optimize crop yield.
Why it matches plant phenotyping methodsイネ幼苗の画像セグメンテーションと形態・スペクトル・テクスチャ特徴に基づく健康状態推定を中心とする手法開発・検証研究であり、植物表現型の取得と異常判定が中核です。
abstractThis research presents a fully automated two-step method for segmenting rice seedlings and assessing their health by integrating spectral, morphological, and textural features.
Reproduction assets foundThe paper uses two publicly available rice seedling image datasets as its phenotyping inputs: the Taiwan UAV Rice Seedling Dataset (GitHub) and the Heilongjiang seedling image dataset (Science Data Bank). No author analysis code, models, or checkpoints are reported as publicly available.Dataset · publicThe first dataset used in this study was obtained from Rice Seedling Dataset repository, originally published in “A UAV Open Dataset of Rice Paddies for Deep Learning Practice” by Yang et al., 2021 [ 7 ]. The dataset is publicly available in a GitHub repository and can be accessed at the following link: https://github.com/aipal-nchu/RiceSeedlingDatasetOpen asset ↗aipal-nchu/RiceSeedlingDatasetlines:130-332Dataset · publicThe second dataset was obtained from repository of “Image Dataset of Wheat, Corn, and Rice Seedlings in Heilongjiang Province”, originally published in 2022 by Qin Jia Le and Guo Leifeng [ 46 ]. The dataset is publicly available in the Science Data Bank repository and can be accessed at the following link: https://www.scidb.cn/en/detail?dataSetId=a511f28b23444235b5378953c76c47c6#p4Open asset ↗lines:130-332Code / 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 15 Sept 2026
Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions.
Why it matches plant phenotyping methods衛星画像から圃場レベルのイネ作付面積・収量を推定する分類および回帰手法が研究の中心であり、精度評価も実施しているため、植物形質推定の方法論として適格。
abstractThis research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that all data and code to reproduce the rice area and yield maps are publicly available in the authors' GitHub repository (ozdogan15/srilanka), which directly reproduces this paper's rice mapping and yield estimation analysis.Code · publicbrella Facility for Trade trust fund
(financed by the governments of the Netherlands, Norway, Sweden, Switzerland, and the United
Kingdom) and the World Bank’s Research Support Budget for financial support.
Data Availability Statement: All data and code to reproduce rice and yield maps are publicly
available at this repository: https://github.com/ozdogan15/srilanka#.
Acknowledgments: The authors acknowledge funding from the World Bank Whole of Economy
Program. We also thank the reviewers. The findings, interpretations, and conclusions expressed in
this paper are solely those of the authors and do not necessarily represent the views of the World
Bank, its affiliated organizations, or the ExOpen asset ↗ozdogan15/srilankapdf-layout-page:23 lines:1-59Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
Understanding the genetic basis of quantitative traits related to crop growth, yield, and stress response requires the acquisition of large-scale, high-quality phenotypic datasets. High-throughput phenotyping platforms have become effective tools for meeting this requirement. Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads, their operational flexibility, and their proximity to crops, which allows for higher imaging resolution. In this study, we introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions. The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field phenotyping, with integrated visual and satellite navigation systems enabling autonomous operation. We validated the performance of the robot through a series of experiments involving various crop canopies. By capturing RGB images of rice and wheat, we independently performed wheat ear detection and rice panicle segmentation. For wheat ear detection, we achieve a precision of 0.783, a recall of 0.822, and a mean average precision (mAP) of 0.853 when the YOLOv8m model is used. For rice panicle segmentation, the SegFormer_B0 model yielded a mean intersection over union (mIoU) of 0.949 and an accuracy of 0.987. Additionally, by capturing RGB-D data of maize canopies, we performed 3D reconstructions to calculate plant height, achieving an R 2 of 0.99 compared with manual measurements. Similar experiments with rapeseed yielded an R 2 of 0.97. Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories, with classification accuracies ranging from 0.977 to 0.996. Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.
Why it matches plant phenotyping methods圃場用自律ロボットと複数の画像・分光センシング、形質抽出手法を開発し、作物キャノピーで性能検証しているため、植物フェノタイピング手法が研究の中心である。
abstractwe introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions.
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the data and code supporting this PhenoRob-F phenotyping study; remaining data are available on request.Code · publicPart of the data and code supporting this study are openly available with the following link: https://github.com/balloonhaha/PhenoRob-F. All other reasonable requests for data and research materials will be fulfilled upon contacting the corresponding authors.Open asset ↗balloonhaha/PhenoRob-Fhtml-lines:193-220Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on performance metrics such as classification accuracy, precision and recall, they fail to assess whether the models are considering relevant features for decision-making. The main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models. Eight pre-trained deep learning models - ResNet50, InceptionResNetV2, DenseNet 201, InceptionV3, EfficientNetB0, Xception, VGG16 and AlexNet,were evaluated using a three-stage methodology. First, the models are assessed using traditional classification metrics. Second, Local Interpretable Model-agnostic Explanations (LIME) is employed to visualize and quantitatively evaluate feature selection using metrics such as Intersection over Union (IoU) and the Dice Similarity Coefficient (DSC). Third, a novel overfitting ratio metric is introduced to quantify the reliance of the models on insignificant features. In the experimental analysis, ResNet50 emerged as the most accurate model, achieving 99.13% classification accuracy as well as the most reliable model demonstrating superior feature selection capabilities (IoU: 0.432, overfitting ratio: 0.284). Despite the high classification accuracies, models such as InceptionV3 and EfficientNetB0 showed poor feature selection capabilities with low IoU scores (0.295 and 0.326) and high overfitting ratios (0.544 and 0.458), indicating potential reliability issues in real-world applications. This study introduces a novel quantitative methodology for evaluating deep learning models that goes beyond traditional accuracy metrics, enabling more reliable and trustworthy AI systems for agricultural applications. This methodology is generic and researchers can explore the possibilities of extending it to other domains that require transparent and interpretable AI systems.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類する深層学習モデルについて、性能と説明可能性を評価する三段階の方法論を開発・検証しており、植物病害状態のフェノタイピング手法が中心である。
abstractThe main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models.
Reproduction assets foundThe paper uses the public Kaggle rice leaf disease dataset and explicitly states that the authors' analysis code is publicly available on GitHub and MATLAB File Exchange via short URLs, both of which are in the allowed URL list.Dataset · publicChinna Gopi Simhadri: Formal analysis, investigation, resources, data collection, writing an original draft, Review & editing. All authors have read and agreed to the published version of the manuscript.
Data availability
In this work, the publicly available dataset was used. The data set is available through the link to Kaggle https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset.
Code availability
The code developed and used in this study has been uploaded and is available on GitHub and MATLAB File Exchange. These repositories include all the necessary scripts, tools, and instructions required to replicate the results presented in this work. The repository can be Open asset ↗Kaggle · dedeikhsandwisaputra/rice-leafs-disease-datasetlines:1332-1347Code · publicode availability
The code developed and used in this study has been uploaded and is available on GitHub and MATLAB File Exchange. These repositories include all the necessary scripts, tools, and instructions required to replicate the results presented in this work. The repository can be accessed via the following links: Github: https://shorturl.at/Github_Quant_XAI MATLAB File Exchange link: https://shorturl.at/MATLAB_Quant_XAI
Declarations
Competing interestsOpen asset ↗GitHublines:1332-1347Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して表現型を推定する統計手法であり、複数種・多数形質で性能評価され、Rパッケージも開発されているため、表現型推定法が研究の中心です。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:6 lines:1-56Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Xanthomonas oryzae pv. oryzae ( Xoo ), the causal agent of bacterial blight in rice, is primarily studied in the context of foliar infections. However, infected stubble and irrigation water may serve as reservoirs and be responsible for seedling stage root infections in the field, especially during transplanting. Here, we established a coleoptile crown root-based infection protocol to investigate whether gene-for-gene interactions between Xoo TAL effectors and SWEET sucrose uniporter susceptibility genes occur in the root xylem, and whether the disease can propagate from roots to seedling shoots. Using translational SWEET11a-GUS reporter lines under control of the native SWEET11a promoter, we observed progressive infection as indicated by accumulation of SWEET11a-GUS fusion protein in infected coleoptile crown roots. However, we did not detect progression of GUS accumulation beyond the coleoptile node, nor did we detect blight symptoms on the young leaves. Notably, the xylem, at least during early stages of infection remained functional as shown by Rhodamine B tracer, consistent with transfer of xylem constituents via living cells at the coleoptile node that did not allow bacteria to pass. Furthermore, the root infection protocol is a ∼4x faster compared to standard leaf-clipping assays (roots assay: 11 days from sowing, compared to 39 days for clip infection), enabling more rapid assessment of TAL effector repertoire and plant defense responses with translational SWEET-GUS reporter lines. Our findings expand our understanding of Xoo infection routes and provide a valuable tool for resistance testing and pathogen surveillance.
Why it matches plant phenotyping methodsイネ病害の進展・抵抗性を迅速に評価する根部感染プロトコルを確立し、従来法との速度差とレポーターによる感染進展を示しているため、病害フェノタイピング手法が中心である。
abstractHere, we established a coleoptile crown root-based infection protocol to investigate whether gene-for-gene interactions between Xoo TAL effectors and SWEET sucrose uniporter susceptibility genes occur in the root xylem, and whether the disease can propagate from roots to seedling shoots.
Reproduction assets foundThe preprint explicitly states that raw data underlying the root infection, GUS, and Rhodamine B measurements are publicly deposited at a DOI (10.60534/7s2cg-r2j06), which is an allowed URL. This is a paper-specific, publicly accessible raw data repository. No author analysis code with a public URL is stated (only use-Dataset · publicData availability:
Raw data are available at https://doi.org/10.60534/7s2cg-r2j06Open asset ↗10.60534/7s2cg-r2j06pdf-page:5 lines:1-61Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This manuscript presents a comprehensive, expert-annotated dataset comprising 19,000 rice leaf images, including 2,753 original images and 16,247 augmented images, sourced from the Bangladesh Rice Research Institute (BRRI). The dataset includes seven disease classes: Healthy (603 original images), Rice Blast (696 original images), Scald (421 original images), Leaf-folder Injury (247 original images), Insect Infestation (281 original images), Rice Stripes (266 original images), and Tungro Disease (239 original images). These images, captured under varying environmental conditions using smartphone cameras, accurately reflect real-world conditions. The images have been meticulously annotated by agronomy experts for reliable disease labeling. To enhance dataset diversity, data augmentation methods such as rotation, scaling, brightness adjustment, and horizontal flipping were systematically applied, expanding the dataset by creating additional variants from the original images. The dataset serves as a rich resource for developing machine learning models for the automatic detection of rice diseases. This initiative aims to enable early disease detection, promote sustainable farming practices, and improve food security, particularly in rice-dependent developing countries.
Why it matches plant phenotyping methodsイネ葉画像を用いて病害状態を表現型として扱う、専門家注釈付きデータセットの構築・提供が中心であり、植物病害フェノタイピング手法の基盤となる。
Reproduction assets foundThe paper is a Data in Brief article describing a rice leaf disease image dataset (19,000 images, 7 classes) publicly deposited on Mendeley Data with DOI 10.17632/vwv3nry3wr.1. This is the paper's own phenotyping image dataset and is directly actionable. No separate analysis code or trained model checkpoint is reportedDataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/vwv3nry3wr.1
Direct URL to data: https://data.mendeley.com/datasets/vwv3nry3wr/1Open asset ↗Mendeley Data · 10.17632/vwv3nry3wr.1lines:1-60Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In the context of intelligent agriculture in China, rapid and accurate identification of crop diseases is essential for ensuring food security and improving crop yield. Although lightweight convolutional neural networks (CNNs) are widely adopted for plant disease recognition due to their computational efficiency, they often suffer from limited feature representation and classification accuracy. To address these challenges, we propose LiSA-MobileNetV2, an improved MobileNetV2-based model designed for rice disease classification. First, we restructure the inverted residual module to simplify the network architecture, achieving a test accuracy of 92.32%, representing a 2.41% improvement over the original MovileNetV2 (89.91%). This indicate that a more lightweight network can enhance feature representation in specific disease recognition. Second, integrating the Swish activation function further improves accuracy to 94.04% by enhancing the model's non-linear feature learning. Finally, the addition of a squeeze-and-excitation attention mechanism raises accuracy to 95.68%, representing a 5.77% improvement over the original model. Importantly, the parameter size and FLOPs are reduced by 74.69% and 48.18%, respectively, maintaining strong computational efficiency. These results demonstrate that combining structural simplification, advanced activation, and efficient attention mechanisms significantly improves CNN performance. LiSA-MobileNetV2 provides a high-accuracy, resource-efficient solution for real-time rice disease detection in smart farming systems.
Why it matches plant phenotyping methodsイネ病害の症状を画像から分類する深層学習モデルを開発・評価しており、植物の病害状態の取得・推定が中心的な方法論的貢献である。
abstractwe propose LiSA-MobileNetV2, an improved MobileNetV2-based model designed for rice disease classification.
Reproduction assets foundThe article provides an authors' public GitHub repository containing the Python code implementing the LiSA-MobileNetV2 rice disease classification model. The rice disease image dataset itself is a third-party public dataset (Paddy Doctor) cited from prior work, not a paper-specific deposit, so only the code qualifies.Code · publicThe code implementing the LiSA-MobileNetV2 model is written in Python and is available at https://github.com/ZhijunBioinf/LiSA-MobileNetV2 .Open asset ↗ZhijunBioinf/LiSA-MobileNetV2lines:406-488Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
High-quality disease segmentation plays a crucial role in the precise identification of rice diseases. Although the existing deep learning methods can identify the disease on rice leaves to a certain extent, these methods often face challenges in dealing with multi-scale disease spots and irregularly growing disease spots. In order to solve the challenges of rice leaf disease segmentation, we propose KBNet, a novel multi-modal framework integrating language and visual features for rice disease segmentation, leveraging the complementary strengths of CNN and Transformer architectures. Firstly, we propose the Kalman Filter Enhanced Kolmogorov-Arnold Networks (KF-KAN) module, which combines the modeling ability of KANs for nonlinear features and the dynamic update mechanism of the Kalman filter to achieve accurate extraction and fusion of multi-scale lesion information. Secondly, we introduce the Boundary-Constrained Physical-Information Neural Network (BC-PINN) module, which embeds the physical priors, such as the growth law of the lesion, into the loss function to strengthen the modeling of irregular lesions. At the same time, through the boundary punishment mechanism, the accuracy of edge segmentation is further improved and the overall segmentation effect is optimized. The experimental results show that the KBNet framework demonstrates solid performance in handling complex and diverse rice disease segmentation tasks and provides key technical support for disease identification, prevention, and control in intelligent agriculture. This method has good popularization value and broad application potential in agricultural intelligent monitoring and management.
Why it matches plant phenotyping methodsイネ葉の病斑という植物の病態を画像からセグメンテーションする新規手法を開発しており、病害識別のための表現抽出・境界推定が研究の中心である。
abstractwe propose KBNet, a novel multi-modal framework integrating language and visual features for rice disease segmentation
Reproduction assets foundThe paper's rice disease segmentation images (1550 leaf images of Blast, Bacterial Blight, and Tungro) were sourced from the public PlantVillage dataset on Kaggle, which is publicly available at the allowed URL. However, the paper-specific derived assets — the authors' pixel-level Labelme segmentation masks, text描述, 4:Dataset · publici: 10.1007/s10462-024-10717-2.
26. Li A., Jiao L., Zhu H., Li L., Liu F. Multitask Semantic Boundary Awareness Network for Remote Sensing Image Segmentation. IEEE Trans. Geosci. Remote Sens. 2022;60:1–14. doi: 10.1109/TGRS.2021.3050885.
27. Kaggle, PlantVillage Dataset. 2019. [(accessed on 19 September 2022)]. Available online: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset .
28. Li Z., Li Y., Li Q., Wang P., Guo D., Lu L., Jin D., Zhang Y., Hong Q. LViT: Language Meets Vision Transformer in Medical Image Segmentation. IEEE Trans. Med. Imaging. 2024;43:96–107. doi: 10.1109/TMI.2023.3291719.
29. Liu Z., Wang Y., Vaidya S., Ruehle F., Halverson J., Soljacic M., Hou T.Y., TOpen asset ↗Kaggle · plantvillage-datasetlines:409-431Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Rice, the world's most important food crop, requires an early and accurate identification of the diseases that infect rice panicles and leaves to increase production and reduce losses. Most conventional methods of diagnosing diseases involve the use of manual instruments, which are ineffective, imprecise, and time-consuming. In light of such drawbacks, this article introduces an improved deep learning and transfer learning method for diagnosing and categorizing rice leaf diseases proficiently. First, all input images are preprocessed; the images are resized to a fixed size before applying a sophisticated contrast enhanced adaptive histogram equalization procedure. Diseased regions are then segmented through the developed gravity weighted kernelised density clustering algorithm. In terms of feature extraction, EfficientNetB0 is fine-tuned by subtracting the last fully connected layers, and the classification is conducted with the new fully connected layers. Also, the tent chaotic particle snow ablation optimizer is added into the learning process in order to improve the learning process and shorten the time of convergence. The performance of the proposed framework was tested on two benchmark datasets and presented accuracy results of 98.87% and 97.54%, respectively. Comparisons of the proposed method with six fine-tuned models show the performance advantage and validity of the proposed method.
Why it matches plant phenotyping methodsイネ葉の病変領域を画像からセグメンテーションし、病害を分類する深層学習手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法に該当する。
abstractthis article introduces an improved deep learning and transfer learning method for diagnosing and categorizing rice leaf diseases proficiently.
Reproduction assets foundThe paper uses two public rice leaf disease image datasets (Kaggle and Mendeley) and releases its authors' source code on Zenodo. The Kaggle dataset URL and Zenodo DOI are explicitly given in the Data Availability statement and match allowed URLs; the Mendeley dataset DOI is not among allowed URLs so it is excluded.Code · publicThe source code is available at Zenodo: Samia Nawaz. (2025). Samia-Nawaz/Segmentation-and-classification-of-rice-leaf-disease: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.15373402.Open asset ↗Zenodo · 10.5281/zenodo.15373402html-lines:919-999Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The architecture of rice tillers plays a pivotal role in yield potential, yet conventional phenotyping methods have struggled to capture these intricate three-dimensional (3D) structures with high fidelity. In this study, a 3D model reconstruction method was developed specifically for rice tillers to overcome the challenges posed by their slender, feature-poor morphology in multi-view stereo-based 3D reconstruction. By applying strategically designed colorful reference markers, high-resolution 3D tiller models of 231 rice landraces were reconstructed. Accurate phenotyping was achieved by introducing ScaleCalculator, a software tool that integrated depth images from a depth camera to calibrate the physical sizes of the 3D models. The high efficiency of the 3D model-based phenotyping pipeline was demonstrated by extracting the following seven key agronomic traits: flag leaf length, panicle length, first internode length below the panicle, stem length, flag leaf angle, second leaf angle from the panicle, and third leaf angle. Genome-wide association studies (GWAS) performed with these 3D traits identified numerous candidate genes, nine of which had been previously confirmed in the literature. This work provides a 3D phenomics solution tailored for slender organs and offers novel insights into the genetic regulation of complex morphological traits in rice.
Why it matches plant phenotyping methodsイネ分げつの3D再構成とScaleCalculatorによるスケール校正を開発し、7つの形態形質を抽出するフェノタイピング手法が研究の中心であるため。
abstracta 3D model reconstruction method was developed specifically for rice tillers
Reproduction assets foundThe paper's 3D tiller models for 231 rice landraces are publicly deposited on Zenodo, and the authors' ScaleCalculator phenotyping source code is publicly available on GitHub, both explicitly stated in the Data Availability Statement. SNP genotype data are unpublished and excluded.Code · publicvelopment Co. LTD, and
Jiangsu Collaborative Innovation Center for Modern Crop Production.
Data Availability Statement: The 3D tiller models created in this study are available for research pur-
poses at https://zenodo.org/records/16080993 (accessed on 18 July 2025).The source code of ScaleCal-
culator is available on GitHub at https://github.com/ganlab/OSTRA/tree/master/ScaleCalculator
(accessed on 18 July 2025).
Acknowledgments: We thank Jianmin Wan for their valuable suggestions and Jiaqi Deng for their
technical help.
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this article.
References
1. Food and Agriculture OrganizatOpen asset ↗github · ganlab/OSTRApdf-raw-page:16 lines:1-50Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して植物形質を推定する統計手法として開発・検証され、Rパッケージも提供されているため、形質取得・推定手法が中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice and corn hold significant importance due to their daily consumption worldwide. Naked-eye observations are not accurate. Therefore, we need an autonomous system that can accurately detect and classify diseases in both plants. We trained and validated publicly available datasets in three deep convolutional neural network (DCNN)-based deep learning models using different learning rates and found that the lowest learning rate was the most effective in achieving the highest accuracy. We added a new dense layer to the known DCNN-based deep learning models and achieved improved accuracy. The best results were observed when our invariants of the InceptionV3, ResNet152, and MobileNetV2 deep learning models were used on corn plant leaves (98.09%, 98.51%, and 89.73%, respectively). These models also performed well on rice plant leaves (98.51%, 93.59%, and 98.57%, respectively). Because InceptionV3 performed well for both plants, we implemented it in NVIDIA Jetson Nano as an end device for the detection and classification of diseases from both plant leaves. Received: 4 January 2025 | Revised: 26 May 2025 | Accepted: 13 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset. Author Contribution Statement Zubair Saeed: Conceptualization, Methodology, Software, Validation, Resources, Data curation, Writing – original draft, Project administration. Uzma Nawaz: Validation, Formal analysis, Writing – review & editing. Ali Raza: Conceptualization, Validation, Formal analysis, Writing – review & editing, Visualization. Kamran Javed: Validation, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する深層学習手法を開発・検証し、エッジデバイスにも実装しており、植物状態の取得・推定が研究の中心です。
abstractWe trained and validated publicly available datasets in three deep convolutional neural network (DCNN)-based deep learning models using different learning rates and found that the lowest learning rate was the most effective in achieving the highest accuracy.
Reproduction assets foundThe paper's Data Availability Statement explicitly names two public Kaggle image datasets (rice leaf diseases and corn/maize leaf disease) that were used directly as the phenotyping inputs for this study's disease-classification experiments. No author code, models, or checkpoints are reported as publicly available.Dataset · public.
Ethical Statement
This study does not contain any studies with human or animal
subjects performed by any of the authors.
Conflicts of Interest
The authors declare that they have no conflicts of interest to this
work.
Data Availability Statement
The data that support the findings of this study are openly
available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjit-ghose/corn-or-maize-leaf-disease-dataset.Author Contribution Statement
Zubair Saeed: Conceptualization, Methodology, Software,
Validation, Resources, Data curation, Writing – original draft,
Project administration. Uzma Nawaz: Validation, Formal analysis,
WritinOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:10 lines:1-81Dataset · publicuman or animal
subjects performed by any of the authors.
Conflicts of Interest
The authors declare that they have no conflicts of interest to this
work.
Data Availability Statement
The data that support the findings of this study are openly
available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjit-ghose/corn-or-maize-leaf-disease-dataset.Author Contribution Statement
Zubair Saeed: Conceptualization, Methodology, Software,
Validation, Resources, Data curation, Writing – original draft,
Project administration. Uzma Nawaz: Validation, Formal analysis,
Writing – review & editing. Ali Raza: Conceptualization, Validation,
ForOpen asset ↗Kaggle · smaranjit-ghose/corn-or-maize-leaf-disease-datasetpdf-raw-page:10 lines:1-81Code / dataset availability confirmedCrossref · checked 15 Sept 2026
This study presents a novel and efficient approach to accurately assess post-sowing rice plant density by leveraging unmanned aerial vehicles (UAVs) equipped with high-resolution RGB cameras. In contrast to labor-intensive and spatially limited traditional methods that rely on manual sampling and extrapolation, our proposed methodology uses UAVs to rapidly and comprehensively survey entire paddy fields at optimized altitudes (4, 6, 8, and 10 m). Aerial imagery was autonomously acquired 17 days post-sowing, following a pre-defined flight path. The robust rice plant density estimation process incorporates two key innovations: first, a dynamic system of 12 adaptive segmentation thresholding blocks that effectively detects rice seed presence across diverse and variable background conditions. Second, a tailored three-layer convolutional neural network (CNN) accurately classifies vegetative situations. To maximize the training efficiency and performance, we implemented both a pretrained model and a deep learning model, conducting a rigorous comparative analysis against the state-of-the-art YOLOv10. Notably, under favorable imaging conditions, our findings indicate that a 6-m flight altitude yields optimal results, achieving a high degree of accuracy with rice plant density estimates that closely align with those obtained through traditional ground-based methods. This investigation unequivocally highlights the significant advantages of UAV-based monitoring as an economically viable, spatially comprehensive, and demonstrably accurate tool for precise rice field management, ultimately contributing to enhanced crop yields, improved food security, and the promotion of sustainable agricultural practices.
Why it matches plant phenotyping methodsUAV RGB画像と適応的セグメンテーション、CNNを用いてイネ個体密度を推定する手法を開発・比較・検証しており、植物形質の取得が研究の中心です。
abstractThis study presents a novel and efficient approach to accurately assess post-sowing rice plant density by leveraging unmanned aerial vehicles (UAVs) equipped with high-resolution RGB cameras.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's datasets (UAV RGB imagery/labels used for rice plant density estimation). No separate author analysis code repository is stated.Dataset · publicvaluate the accuracy of the proposed labels, subsequently enhancing the training model's speed, convergence, accuracy, and efficiency.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://zenodo.org/records/10960906 .
Author contributions
TH: Writing – original draft. TN: Data curation, Resources, Validation, Writing – original draft. QN: Data curation, Writing – review & editing. HN: Funding acquisition, Investigation, Methodology, Writing – review & editing. PP: Methodology, Software, Supervision, Writing – review & editing.
Funding
TheOpen asset ↗zenodo · 10960906lines:500-523Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
RiceFlowerObject detectionGrowth / time-series analysisGrowth / development / phenology
Flower opening time (FOT) is a key trait for successful reproduction and reproductive isolation. In crop science, FOT is critical for stress avoidance and efficient breeding practices. This study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology. This approach enabled high-resolution monitoring of flowering dynamics in two cultivars: the japonica cultivar Taichung 65 (T65) and the indica cultivar IR24. The system accurately identified regions containing open flowers, and the estimated FOTs varied within a 3-h range, with a root mean square error of approximately 30 min compared to manual detection. A significant difference in estimated FOTs between IR24 and T65 demonstrated the system's potential for genetic screening applications. FOT of both cultivars exhibited a significant negative correlation with daily mean temperature. Notably, a temperature-sensitive period was identified in the morning, suggesting that temperature influences not only flower opening but also preceding physiological processes such as panicle and spikelet development. This study presents a novel approach to investigating FOT dynamics in rice and provides insights into the interaction between environmental factors and internal regulatory mechanisms governing this critical reproductive trait.
Why it matches plant phenotyping methods低コストタイムラプスカメラと機械学習によるイネの開花時刻という植物形質の自動検出・推定システムを開発し、手動検出との誤差で検証しているため、方法が中心的です。
abstractThis study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology.
Reproduction assets foundThe authors explicitly state that the Python scripts, training dataset (annotated time-lapse rice flower images), and trained YOLOX model used for flower-opening detection are publicly available on their GitHub repository (mwbotan/FLpanicle). This is a paper-specific, public, actionable asset directly reproducing the FCode · publicThe Python scripts, training dataset, and trained model used in this analysis are available on GitHub ( https://github.com/mwbotan/FLpanicle ).Open asset ↗mwbotan/FLpaniclelines:73-84Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Genomic selection (GS) and phenotypic selection (PS) are widely used for accelerating plant breeding. However, the accuracy, robustness, and transferability of these two selection methods are underexplored, especially when addressing complex traits. In this study, we introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion. The GPS framework was rigorously tested using an extensive suite of models, including statistical approaches (GBLUP and BayesB), machine learning models (Lasso, RF, SVM, XGBoost, and LightGBM), a deep learning method (DNNGP), and a recent phenotype-assisted prediction model (MAK). These models were applied to large datasets from four crop species, maize, soybean, rice, and wheat, demonstrating the versatility and robustness of the framework. Our results indicated that: (1) data fusion achieved the highest accuracy compared with the feature fusion and result fusion strategies. The top-performing data fusion model (Lasso_D) improved the selection accuracy by 53.4% compared to the best GS model (LightGBM) and by 18.7% compared to the best PS model (Lasso). (2) Lasso_D exhibited exceptional robustness, achieving high predictive accuracy even with a sample size as small as 200 and demonstrating resilience to single-nucleotide polymorphism (SNP) density variations, underscoring its adaptability to diverse data conditions. Moreover, the model's accuracy improved with the number of auxiliary traits and their correlation strength with target traits, further highlighting its adaptability to complex trait prediction. (3) Lasso_D demonstrated broad transferability, with substantial improvements in predictive accuracy when incorporating multi-environmental data. This enhancement resulted in only a 0.3% reduction in accuracy compared to predictions generated using data from the same environment, affirming the model's reliability in cross-environmental scenarios. This study provides groundbreaking insights, pushing the boundaries of predictive accuracy, robustness, and transferability in trait prediction. These findings represent a significant contribution to plant science, plant breeding, and the broader interdisciplinary fields of statistics and artificial intelligence.
Why it matches plant phenotyping methods植物の形質予測を目的とするGPSデータ融合フレームワークを開発し、複数作物・モデルで精度、頑健性、環境間移 transferability を評価しており、形質推定手法が研究の中心である。
abstractwe introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion.
Reproduction assets foundThe paper's authors publicly released their GPS analysis scripts on GitHub, and the study used public genomic+phenotypic datasets (rice, maize, wheat, SoyNAM) with explicit URLs. DNNGP and MAK repositories are cited third-party tools, not paper-specific assets.Code · publicThe GPS scripts are available in the release package on GitHub ( https://github.com/Jinlab-AiPhenomics/BioGPS ). All public datasets used in this study are listed in the main text ( Table 1 ).Open asset ↗Jinlab-AiPhenomics/BioGPSlines:244-249Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
Rice panicle detection is a key technology for improving rice yield and agricultural management levels. Traditional manual counting methods are labor-intensive and inefficient, making them unsuitable for large-scale farmlands. This paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images. The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling with multi-branch dilated convolutions to preserve fine-grained spatial information; and a Dynamic Bidirectional Feature Pyramid Network (DynamicBiFPN) employing input-adaptive kernels to optimize cross-scale feature fusion. The model was trained and evaluated on the open-access Dense Rice Panicle Detection (DRPD) dataset, which comprises UAV images captured at 7 m, 12 m, and 20 m altitudes. Experimental results demonstrate that our method significantly outperforms existing advanced models, achieving an AP50 of 0.8931 and an F2 score of 0.8377 on the test set. While ensuring model accuracy, the parameters of the proposed model decreased by 42.87% and the GFLOPs by 48.95% compared to Panicle-AI. Grad-CAM visualizations reveal that FRPNet exhibits superior background noise suppression in 20 m altitude images compared to mainstream models. This work establishes an accuracy-efficiency balanced solution for UAV-based field phenotyping.
Why it matches plant phenotyping methodsUAV画像からイネ穂の検出・計数という植物形質を抽出する軽量深層学習手法を開発し、公開データセットで評価しており、表現型取得手法が中心である。
abstractThis paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images.
Reproduction assets foundThe paper states its data (the DRPD rice panicle UAV dataset used for training/evaluation) is publicly available via a GitHub release URL, which matches an allowed URL.Dataset · publicData Availability Statement: Data is available at https://github.com/changcaiyang/Panicle-AI/
releases/tag/v1.0.Open asset ↗Panicle-AI · v1.0pdf-page:22 lines:1-59Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published30 May 2025Journal of Internet Services and Information SecurityCited by 3 · OpenAlex ↗
Rice is one of the highest-produced staples all over the world. The production of rice is frequently threatened by leaf diseases in rice plants, which impacts farmers. This has an impact on economic growth in general. As a result, prompt diagnosis is essential, and appropriate action should be taken to enhance rice yield. The proposed framework in this article introduces a novel deep learning-based method named Convolutional Attentional Bidirectional Gated Recurrent Unit (CAtt_BiGRU) to classify rice leaf diseases. First, pre-processing techniques such as image rescaling and upgraded wiener filtering are applied to the input rice plant leaf images. After pre-processing, slice-based residual U-Net is used to segment the affected regions. The segmented images are then fed to the classification framework. The CAtt_BiGRU model was employed to classify diseases in rice plants. A Convolutional Neural Network (CNN) was employed for feature extraction, and the classification task was carried out by a Bidirectional Gated Recurrent Unit (BiGRU). Our approach surpasses other state-of-the-art methods by achieving an accuracy of 99.64%.
Why it matches plant phenotyping methodsイネ葉の病害領域を画像からセグメンテーションし、病害を分類する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収載する。
abstractThe proposed framework in this article introduces a novel deep learning-based method named Convolutional Attentional Bidirectional Gated Recurrent Unit (CAtt_BiGRU) to classify rice leaf diseases.
Reproduction assets foundThe paper uses a public Kaggle rice leaf disease image dataset as its phenotyping input; no author code or model release is stated.Dataset · publicigure 7 illustrates the classified images. Image 1 is classified as Bacterial Blight disease, image 2 is
classified as leaf smut disease, and image 3 is classified as Brown spot disease. Thus, the input images
are effectively classified using the proposed CAtt_BiGRU model.
3 Results and Discussion
The Rice Leaf Disease Dataset (https://www.kaggle.com/bahribahri/riceleaf) is utilized for analysing
the proposed model in classification task. The utilized dataset is gathered from kaggle repository which
involves four directories. Four categories of rice leaves like healthy, brown spot, bacterial leaf blight,
and leaf smut are present in the data set. Each classes includes 4000 number of files anOpen asset ↗kaggle · bahribahri/riceleafpdf-layout-page:11 lines:1-45Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Various high-throughput screening methods have been developed to explore plant phenotypes, primarily at the organ and whole plant levels. There is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap. This study used double-resonator piezoelectric cytometry biosensors to capture the dynamic changes in mechanical phenotypes of living cells of two rice species, drought-resistant Lvhan No. 1 and drought-sensitive 6527, under PEG6000 drought stress. In rice cells of Lvhan No. 1 and 6527, mechanomics parameters, including cell-generated surface stress (ΔS) and viscoelastic parameters (G', G″, G″/G'), were measured and compared under 5-25% PEG6000. Lvhan No. 1 showed larger viscoelastic but smaller surface stress changes with the same concentration of PEG6000. Moreover, Lvhan No. 1 cells showed better wall-plasma membrane-cytoskeleton continuum structure maintaining ability under drought stress, as proven by transient tension stress (ΔS > 0) and linear G'~ΔS, G″~ΔS relations at higher 15-25% PEG6000, but not for 6527 cells. Additionally, two distinct defense and drought resistance mechanisms were identified through dynamic G″/G' responses: (i) transient hardening followed by softening recovery under weak drought, and (ii) transient softening followed by hardening recovery under strong drought. The abilities of Lvhan No. 1 cells to both recover from transient hardening to softening and to recover from transient softening to hardening are better than those of 6527 cells. Overall, the dynamic mechanomics phenotypic patterns (ΔS, G', G″, G″/G', G'~ΔS, G″~ΔS) verified that Lvhan No. 1 has better drought resistance than that of 6527, which is consistent with the field data.
Why it matches plant phenotyping methods植物細胞の機械的表現型を取得する高スループットなバイオセンサー手法を用い、乾燥ストレス応答を定量化しており、表現型取得法が研究の中心である。
abstractThere is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap.
Reproduction assets foundThe paper's DRPC phenotyping measurements (frequency and motional resistance traces underlying the ΔS, G′, G″ analyses) are provided as downloadable supplementary figures at the MDPI supplementary URL. No standalone public dataset or author analysis code repository is stated; the Data Availability Statement only offersSupplement · publicansient softening under strong drought. The results presented in this work demonstrated the potential to develop a new cellular mechanical phenotype platform to screen for biotic and abiotic stress-resistant crop varieties, as shown in Figure 11 .
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bios15060334/s1 , Figure S1: Changes in frequency and motional resistance of 9 MHz AT and BT cut chips during the adhesions of Lvhan No.1 rice cells followed by the treatments of different concentrations of PEG6000 stresses. (A, B, C, D, E): AT cut, (A1, B1, C1, D1, E1): BT cut, (A, A1): 5% PEG6000, (B, B1): 10%PEG6000, (C, C1) 15%Open asset ↗lines:136-159Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring, essential for crop-growth management. However, the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application. This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS) aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information. Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical imaging. A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was proposed, and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information. Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest (RF) achieved better results in predicting leaf area index (LAI) and above-ground biomass (AGB) for wheat and rice. For wheat, the R 2 and root mean square error (RMSE) values for the LAI and AGB prediction models were 0.81 and 0.85, and 0.682 and 1.127 t/ha, respectively. For rice, the R 2 and RMSE values for the LAI and AGB prediction models were 0.89 and 0.93, and 0.818 and 0.866 t/ha, respectively. Overall, SMICGS provides a reliable foundational tool for real-time, non-destructive monitoring of field crop growth information, offering significant potential for the precise management of agricultural production.
Why it matches plant phenotyping methods作物生育情報を定量化するUAVマルチスペクトルセンサー、補正法、処理システムを開発し、LAIと地上部バイオマス推定を検証しており、植物フェノタイピング手法が中心である。
abstractThis paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS)
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 figures, tables and data mentioned in the article can be downloaded from https://github.com/ikjkj2/Plant-Phenomics .Open asset ↗ikjkj2/Plant-Phenomicslines:395-413Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published10 May 2025JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied SciencesCited by 0 · OpenAlex ↗
Background In this paper, the ensemble average and deep neural networks approach is proposed for detecting rice leaf diseases. The system consists of two different deep neural networks represented by GoogLeNet and MobileNetV2 where trained separately by dataset of rice leaves images. Materials and Methods These images represent various conditions of the leaves, including healthy leaves, and five types of diseases. The dataset is preprocessed by resizing and normalizing to standardize the inputs for the networks. Then it is split into training and testing sets to ensure robust model evaluation. After training of these two networks, the ensemble average module combines the predictions from both networks by averaging them during the testing phase. Results The proposed model was compared with the other two models based on the performance metrics accuracy, precision, recall, and F1-score, and the proposed model highlights the superiority of the proposed approach in detecting rice leaf diseases. The proposed model achieved the highest accuracy at 97.07%, precision at 97.1%, recall at 97.06%, and F1-score at 97.08% which outperformed the other two models across all metrics because the averaging process reduces variance and enhances the accuracy of the final decision of detection the rice leaf diseases Conclusion This ensemble-based approach illustrates superior performance for rice leaf diseases detection, offering a more reliable and precise tool for managing the agricultural diseases.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する深層学習・アンサンブル手法の開発と性能比較が中心であり、植物病害フェノタイピング手法に該当する。
abstractthe ensemble average and deep neural networks approach is proposed for detecting rice leaf diseases
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset is used in this paper represents a rice leaf disease dataset that collected via the internet
and independently. The dataset consists of 6 classes of healthy and rice leaf diseases that grouped in the
train and validation folders.Open asset ↗pdf-page:6 lines:1-34Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-enabled vision transformer (ViT) architecture to identify the diseases in plants. The proposed Inception-enabled ViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently use computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model archives an accuracy rate of 99.17% for the apple leaf dataset, 99.32% for the rice dataset, 96.89% for the ibean dataset, 75.42% for the cassava leaf dataset, and 99.33% for the plantvillage dataset.
Why it matches plant phenotyping methods植物病害の画像から病害状態を推定するコンピュータビジョン手法を開発・比較しており、植物表現型（病害状態）の抽出が中心です。
abstractIn this paper, we have proposed an Inception-enabled vision transformer (ViT) architecture to identify the diseases in plants.
Reproduction assets foundThe paper evaluates an Inception-enabled ViT model on five publicly available plant disease image datasets. The Data Availability section explicitly lists Kaggle URLs for the apple, bean (ibean), rice/wheat-rust, PlantVillage, and cassava datasets. These are public, paper-specific image datasets directly used for the模型Dataset · publicThe datasets generated and/or analysed during the current study
are available in Kaggle repository at:
https://www.kaggle.com/datasets/piantic/plantpathology-apple-datasetOpen asset ↗Kaggle · piantic/plantpathology-apple-datasetpdf-page:22 lines:1-48Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolute error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with validation R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and from 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.
Why it matches plant phenotyping methods低コストマルチスペクトルセンサーによる葉のクロロフィル測定法を評価・比較し、交差検証で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す。
abstractThis study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement.
Reproduction assets foundThe authors publicly release raw sensor data, analysis scripts, firmware, and GUI in the GitHub repository KyleLopin/asm_chloro_test, plus supplementary information including extracted chlorophyll reference measurements (S2) at the MDPI supplement URL.Code · publicRaw data, scripts to generate the data and figures used in the manuscript, programs to run the sensors, and GUI used to collect the data are available at https://github.com/KyleLopin/asm_chloro_test (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:187-200Code · publicThe microcontroller code to operate the sensor and a GUI for data collection are available at https://github.com/KyleLopin/asm_chloro_test/tree/master/source (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:155-167Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s25072198/s1 . Supplementary Information S1: Device Electrical Characterization. Supplementary Information S2: Extracted Chlorophyll Reference Measurements.Open asset ↗lines:176-186Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This research presents DepMulti-Net, a novel rice disease and pest identification model, designed to overcome the challenges of complex background interference, difficult disease feature extraction, and large model parameter volume in rice leaf disease identification. Initially, a comprehensive rice disease dataset comprising 20,000 images was meticulously constructed, covering four common types of rice diseases: bacterial leaf blight, rice blast, brown spot, and tungro disease. To enhance data diversity, various data augmentation techniques were applied. Subsequently, a novel VGG-block module was introduced. By leveraging depth-separable convolution, the model's parameter quantity was significantly reduced. A multi-scale feature fusion module was also designed to effectively enhance the model's ability to extract disease features from complex backgrounds. Moreover, the integration of the feature reuse mechanism and inverse bottleneck structure further improved the model's recognition accuracy for fine-grained disease features. Experimental results show that the DepMulti-Net model has only 13.50M parameters and achieves an average accuracy of 98.56% in identifying the four types of rice diseases. This performance significantly outperforms existing rice leaf disease identification methods. In conclusion, this study offers an efficient and lightweight solution for crop disease identification, which holds great significance for promoting the development of smart agriculture.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から識別する深層学習モデルを開発し、データセット構築と性能評価を行っており、植物病害状態の表現型推定手法が中心である。
abstractThis research presents DepMulti-Net, a novel rice disease and pest identification model
Reproduction assets foundThe paper's Appendix A explicitly states that the self-built rice leaf disease dataset (20,000 field images) and the authors' analysis code for DepMulti-Net are publicly available at the authors' GitHub URL, which matches an allowed URL.Code · publicDataset and code are available at: https://github.com/kuihu-hk.Open asset ↗kuihu-hkpdf-page:19 lines:1-12Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
RiceRootMorphology / geometry measurementSegmentationRoot system architectureStress response / tolerance
Summary The dissection of genetic architecture for rice root system is largely dependent on phenotyping techniques, and high‐throughput root phenotyping poses a great challenge. In this study, we established a cost‐effective root phenotyping platform capable of analysing 1680 root samples within 2 h. To efficiently process a large number of root images, we developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits. Based on this root phenotyping platform and RPT, we screened 18 candidate (quantitative trait loci) QTL regions from 219 rice recombinant inbred lines under drought stress and validated the drought‐resistant functions of gene OsIAA8 identified from these QTL regions. This study confirmed that RPT exhibited a great application potential for processing images with various sources and for mining stress‐resistance genes of rice cultivars. Our developed root phenotyping platform and RPT software significantly improved high‐throughput root phenotyping efficiency, allowing for large‐scale root trait analysis, which will promote the genetic architecture improvement of drought‐resistant cultivars and crop breeding research in the future.
Why it matches plant phenotyping methods根系画像のセグメンテーションと形質定量を行う高スループット基盤およびRPTソフトウェアの開発が研究の中心であり、植物フェノタイピング手法に該当する。
abstractwe developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits.
Reproduction assets foundThe paper explicitly states that the RPT source code is publicly available on GitHub and the root training label images are available on Google Drive, both with explicit availability language and URLs matching allowed_urls entries.Code · publicThe source code for RPT can be downloaded from https://github.com/shijiawei124/RPT.gitOpen asset ↗https://github.com/shijiawei124/RPT.gitlines:210-444Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research. However, the lack of publicly available high-quality image datasets with detailed annotations has severely hindered the development of these models. In this work, we present a comprehensive multicultivar and multiview rice plant image dataset (CVRP) created from 231 landraces and 50 modern cultivars grown under dense planting in paddy fields. The dataset includes images capturing rice plants in their natural environment, as well as indoor images focusing specifically on panicles, allowing for a detailed investigation of cultivar-specific differences. A semiautomatic annotation process using deep learning models was designed for annotations, followed by rigorous manual curation. We demonstrated the utility of the CVRP by evaluating the performance of four state-of-the-art (SOTA) semantic segmentation models. We also conducted 3D plant reconstruction with organ segmentation via images and annotations. The database not only facilitates general-purpose image-based panicle identification and segmentation but also provides valuable resources for challenging tasks such as automatic rice cultivar identification, panicle and grain counting, and 3D plant reconstruction. The database and the model for image annotation are available at https://bic.njau.edu.cn/CVRP.html.
Why it matches plant phenotyping methodsイネ画像データセットとアノテーションモデルを開発・評価し、セグメンテーション、器官再構成、穂・粒数計測などの再利用可能な表現型解析を中心に扱っているため。
abstractwe present a comprehensive multicultivar and multiview rice plant image dataset (CVRP)
Reproduction assets foundThe paper's own CVRP rice image dataset (images + annotations), accompanying code, and trained Mask2Former annotation model are explicitly stated as publicly available on Hugging Face and the authors' NJAU site.Dataset · publicThe CVRP dataset is publicly available on Hugging Face at https://huggingface.co/datasets/CVRPDataset/CVRP for academic use under the specified license.Open asset ↗CVRPDataset/CVRPhtml-lines:236-252Code · publicThe accompanying code and trained models are available at https://huggingface.co/CVRPDataset/Model.Open asset ↗CVRPDataset/Modelhtml-lines:236-252Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Location-based methods for counting rice panicles have often been underestimated, primarily due to their perceived inferior performance when compared to detection-based techniques. However, we argue that the potential of these location-based methods has not been fully realized, largely owing to the limitations of existing model architectures. In response to this challenge, we introduce LKNet, an innovative model developed on the foundation of the location-based framework P2Pnet. To enhance the performance of panicle counting across diverse types and growth stages, we implemented several key strategies. Firstly, we reconstructed the localization loss function as a predictive probability distribution to reduce the influence of manual labeling. Additionally, we dynamically adapted the receptive field to better accommodate different panicle types through the use of large kernel convolutional blocks. We evaluated LKNet on several publicly available counting task datasets and achieved state-of-the-art performance on the Diverse Rice Panicle Detection dataset. Furthermore, we employed a rice panicle dataset collected at an altitude of 7 m, which includes various panicle types and growth stages for model training and evaluation. The results showed that LKNet effectively accommodates variations in panicle morphology, with R 2 values ranging from 0.903 to 0.989. These findings highlight LKNet's potential to enhance precision in panicle counting in rice breeding programs.
Why it matches plant phenotyping methodsイネ穂の画像ベース計数モデルを開発し、複数データセットで評価しており、植物表現型取得・抽出手法が中心である。
abstractwe introduce LKNet, an innovative model developed on the foundation of the location-based framework P2Pnet.
Reproduction assets foundThe paper's authors explicitly state that the LKNet analysis code is publicly available on GitHub, matching an allowed URL. No separate phenotype dataset deposit by the authors is stated (the 7 m rice panicle dataset and public benchmarks like DPRD/MTC/SHTech are described but no authors' dataset URL is given).Code · publicCode is available at https://github.com/L129921/LKnet .Open asset ↗LKnetlines:297-319Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The problem of plant diseases is huge as it affects the crop quality and leads to reduced crop production. Crop-Convolutional neural network (CNN) depiction is that several scholars have used the approaches of machine learning (ML) and deep learning (DL) techniques and have configured their models to specific crops to diagnose plant diseases. In this logic, it is unjustifiable to apply crop-specific models as farmers are resource-poor and possess a low digital literacy level. This study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops. The designed architecture incorporates parallel convolution layers of different dimensions in order to localize the lesions with multiple scales accurately. The experimentation results show that the designed network achieves the accuracy of 88.54% as well as overcomes several benchmark CNN models: VGG19, EfficientNetb6, ResNeXt, DenseNet201, AlexNet, YOLOv5 and MobileNetV3. In addition, the validated model demonstrates its effectiveness as a multi-purpose device by correctly categorizing the healthy and the infected class of individual types of crops, providing 99.81%, 87.11%, and 98.45% accuracy for CRW crops, respectively. Furthermore, considering the best performance values achieved and compactness of the proposed model, it can be employed for on-farm agricultural diseased crops identification finding applications even in resource-limited settings.
Why it matches plant phenotyping methods植物の病変を画像から検出・分類する深層学習モデルの開発とベンチマーク比較が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractThis study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops.
Reproduction assets foundThe paper uses public leaf-image datasets (PlantVillage via TensorFlow, Kaggle rice leaf and wheat leaf datasets) as phenotyping inputs for its Slender-CNN disease classification. No author analysis code, trained model checkpoints, or deposited supplements are stated in the supplied blocks. The Kaggle rice and wheat-dsDataset · public6. Yang Y, Liu Z, Huang M, Zhu Q, Zhao X. Automatic detection of multi-type 28. Rice Leafs. Available: https://www.kaggle.com/datasets/shayanriyaz/RicelOpen asset ↗kagglepdf-page:21 lines:1-49Dataset · publicmodel. J Food Eng. 2023;336: 111213. 29. Wheat Leaf Dataset. Available: https://www.kaggle.com/datasets/olyadOpen asset ↗kagglepdf-page:21 lines:1-49Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and association studies. Unlike existing approaches which correct for population structure, WISER offers a generalized framework that can be applied across diverse experimental setups, species, and omics datasets, such as single nucleotide polymorphisms (SNPs), near-infrared spectroscopy (NIRS), and metabolomics. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby removing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して表現型を推定する計算手法として開発され、複数作物・多数形質で検証されている。Rパッケージも提供され、表現型推定が研究の中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authorities. This study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery. Employing the U-Net architecture with a ResNet-101 backbone, we explore three band combinations-multispectral, multispectral+NDVI, and multispectral+NDRE-to achieve superior segmentation accuracy. Due to the lack of suitable UAV-based datasets for rice disease, we generate our own dataset through disease inoculation techniques in experimental paddy fields. The dataset is increased using data augmentation and patch extraction methods to improve training robustness. Our findings demonstrate that the U-Net model incorporating ResNet-101 backbone trained with multispectral+NDVI data significantly outperforms other band combinations, achieving high accuracy metrics, including mean Intersection over Union (mIoU) of up to 97.20%, mean accuracy of up to 99.42%, mean F1-score of up to 98.56%, mean Precision of 97.97%, and mean Recall of 99.16%. Additionally, this approach efficiently segments healthy rice from other classes, minimizing misclassification and improving disease severity assessment. Therefore, the experiment concludes that the accurate mapping of the disease extent and severity level in the field is reliable to accurately allocating the compensation. The developed methodology has the potential for broader application in diagnosing other rice diseases, such as Blast, Bacterial Panicle Blight, and Sheath Blight, and could significantly enhance agricultural management through accurate damage mapping and yield loss estimation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と深層学習によるイネ病害の症状・重症度推定パイプラインを開発し、精度評価とデータセット構築を行っており、植物表現型取得が中心である。
abstractThis study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery.
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: We have provided the aerial datasets in figshare after getting permission from the landowner. Please see https://doi.org/10.6084/m9.figshare.26955862.v1 .Open asset ↗figshare · 10.6084/m9.figshare.26955862.v1lines:146-158Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
With the rapid advancement of plant phenotyping research, understanding plant genetic information and growth trends has become crucial. Measuring seedling length is a key criterion for assessing seed viability, but traditional ruler-based methods are time-consuming and labor-intensive. To address these limitations, we propose an efficient deep learning approach to enhance plant seedling phenotyping analysis. We improved the DeepLabv3+ model, naming it DFMA, and introduced a novel ASPP structure, PSPA-ASPP. On our self-constructed rice seedling dataset, the model achieved a mean Intersection over Union (mIoU) of 81.72%. On publicly available datasets, including Arabidopsis thaliana, Brachypodium distachyon, and Sinapis alba, detection scores reached 87.69%, 91.07%, and 66.44%, respectively, outperforming existing models. The model generates detailed segmentation masks, capturing structures such as the embryonic shoot, axis, and root, while a seedling length measurement algorithm provides precise parameters for component development. This approach offers a comprehensive, automated solution, improving phenotyping analysis efficiency and addressing the challenges of traditional methods.
Why it matches plant phenotyping methods幼苗画像から器官を分割し、幼苗長を自動測定する深層学習ベースのフェノタイピング手法を開発・検証しており、方法が中心的です。
abstractwe propose an efficient deep learning approach to enhance plant seedling phenotyping analysis.
Reproduction assets foundThe paper uses a public Kaggle plant segmentation dataset (Arabidopsis thaliana, Brachypodium distachyon, Sinapis alba) for model validation, which is a paper-specific, publicly actionable asset. The authors' homemade rice seedling dataset and any code/models are not publicly deposited; the data availability statement仅Dataset · publicthis way, a homemade labeled dataset with the file suffix “.json” was obtained. Processed by the program, 115 sets of images were finally obtained. The sample image is shown in
Figure 1B
.
The public dataset was created using the Plant Segmentation Dataset, which was made public on the Kaggle platform by Orsolya Dobos et al. ( https://www.kaggle.com/tivadardanka/plant-segmentation ) in 2019. This dataset contains images of three seedlings, including Arabidopsis thaliana , Brachypodium distachyon , and Sinapis alba . The authors manually placed seedlings of these three plants on the surface of 1% agar plates and collected images using an EPSON PERFECTION V30 scanner. Images were saved in “.tiOpen asset ↗Kaggle · tivadardanka/plant-segmentationlines:45-67Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeRiceClassificationGrowth / development / phenologyStress response / tolerance
Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether the expression levels of a subset of highly variable genes (HVGs) can be used to accurately identify the phenotypes of plants. Using maize ( Zea mays L.) as an example, we built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples. We showed that the ML models achieved excellent prediction accuracy using only the HVGs to identify different phenotypes, including tissue types, developmental stages, cultivars and stress conditions. By ML models, several important functional genes were found to be associated with different phenotypes. We performed a similar analysis in rice ( Orzya sativa L.) and found that the ML models could be generalized across species. However, the models trained from maize did not perform well in rice, probably because of the expression divergence of the conserved HVGs between the two species. Overall, our results provide an ML framework for phenotype prediction using gene expression profiles, which may contribute to precision management of crops in agricultural practices.
Why it matches plant phenotyping methods遺伝子発現データから植物の組織、発育段階、品種、ストレス状態を予測する機械学習フレームワークを開発しており、表現型推定手法が研究の中心である。
abstractwe built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples.
Reproduction assets foundThe paper's maize/rice gene expression datasets are publicly deposited on FigShare, and the authors' analysis source code is available on GitHub with a Zenodo DOI archive. The underlying expression profiles were originally downloaded from the PlantExp database (maize taxonId=4577, rice taxonId=39947).Code · publicSource code is available at https://github.com/Zefeng2018/plant-phenotype-prediction-by-gene-expression and https://doi.org/10.5281/zenodo.14358186 .Open asset ↗GitHub · Zefeng2018/plant-phenotype-prediction-by-gene-expressionlines:100-176Code · publicSource code is available at https://github.com/Zefeng2018/plant-phenotype-prediction-by-gene-expression and https://doi.org/10.5281/zenodo.14358186 .Open asset ↗Zenodo · 10.5281/zenodo.14358186lines:100-176Dataset · publicthe maize gene expression data were downloaded from https://biotec.njau.edu.cn/plantExp/info.php?taxonId=4577Open asset ↗PlantExp · taxonId=4577lines:29-36Dataset · publicthe rice gene expression data were downloaded from https://biotec.njau.edu.cn/plantExp/info.php?taxonId=39947Open asset ↗PlantExp · taxonId=39947lines:29-36Code / 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 .
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Competing interests
Te authors declare no competing interests.
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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 .
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Competing interests
Te authors declare no competing interests.
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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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models in GS lies in their low robustness and poor interpretability. To address these challenges, we developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks. This framework combines convolutional neural networks with multiple self-attention mechanisms to improve accuracy. The ability of Cropformer to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops: maize, rice, wheat, foxtail millet, and tomato. Evaluation results show that Cropformer outperforms other GS methods in both precision and robustness, achieving up to a 7.5% improvement in prediction accuracy compared to the runner-up model. Additionally, Cropformer enhances the analysis and mining of genes associated with traits. We identified numerous single nucleotide polymorphisms (SNPs) with potential effects on maize phenotypic traits and revealed key genetic variations underlying these differences. Cropformer represents a significant advancement in predictive performance and gene identification, providing a powerful general tool for improving genomic design in crop breeding. Cropformer is freely accessible at https://cgris.net/cropformer.
Why it matches plant phenotyping methods作物の表現型形質を予測する深層学習フレームワークを開発・評価しており、計算的な形質推定が研究の中心である。
abstractwe developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks.
Reproduction assets foundThe paper's authors publicly released the Cropformer analysis code on GitHub, and the paper's phenotypic/genotypic analysis datasets (wheat, foxtail millet, tomato, rice, maize) are publicly available at author-cited URLs, directly reproducing this paper's genomic-prediction measurements and analysis.Code · publicThe Cropformer software, including documentation and tutorials, is available on GitHub ( https://github.com/jiekesen/Cropformer ).Open asset ↗jiekesen/Cropformerlines:131-156Dataset · publicThe wheat dataset was derived from 2403 Iranian bread wheat ( Triticum aestivum ) landrace accessions in the CIMMYT wheat gene bank ( https://hdl.handle.net/11529/10548918 ).Open asset ↗lines:90-98Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
Dissecting the drought resistance (DR) mechanism and designing drought-resistant rice varieties are promising strategies to address the challenge of climate change. Here, we selected a typical drought-avoidant (DA) variety IRAT109 and drought-tolerant (DT) variety Hanhui15 as the parents to develop a stable recombinant inbred line (RIL) population (F 8 , 1,262 lines). The de novo assembled genomes of both parents were released. Through re-sequencing of the RIL population, a set of 1,189,216 reliable SNPs were obtained and used for constructing a dense genetic map. Using both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period and identified 32,586 drought-responsive quantitative trait loci (QTLs) including 2,097 unique QTLs. The QTLs related to panicle i-traits occurred on the middle of chromosome 8 over 600 times, while the QTLs related to leaf i-traits on the 5’ end of chromosome 3 over 800 times, indicating potential effect of these QTLs on plant phenotypes. We chose three candidate genes ( OsMADS50, OsGhd8, OsSAUR11 ) related to leaf, panicle, and root traits respectively and verified their functions in resisting drought. Gene OsMADS50 was found to negatively regulate DR by modulating leaf dehydration, grain size, and root downward growth. Furthermore, a total of 18 and 21 composite QTLs significantly related to grain weight and plant biomass were screened from 597 lines in RIL population under drought conditions in field experiments, and composite QTL region was highly overlapped (76.9%) with known DR gene region. Based on three candidate DR genes, we proposed the haplotype design suitable for different environments and breeding objectives. This study provides a valuable reference for multi-modal and time-series phenomic analyses, deciphers the genetic mechanism of DA and DT rice varieties, and offers a molecular navigation map for breeding DR variety.
Why it matches plant phenotyping methods地下・地上フェノミックプラットフォームとマルチモーダルカメラで全生育期間の画像形質を大量取得しており、フェノタイピング手法の適用と技術的ワークフローが研究の中核です。
abstractUsing both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period
Reproduction assets foundThe paper's phenome data (aboveground and belowground rice images/i-traits) and the authors' data-handling code and deep-learning model are explicitly deposited at public URLs listed in the Data Availability Statement. Genome data (riceome.hzau.edu.cn) is molecular omics and excluded.Code · publicAll the phenome data and core data-handling code have been deposited online.Open asset ↗lines:140-175Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Detecting rice leaf diseases is essential for agricultural stability and crop health. However, the diversity of these diseases, their uneven distribution, and complex field environments create challenges for precise, multi-scale detection. While YOLO object detection algorithms show strong performance in automated detection, further optimization is needed. This paper presents G-YOLO, a novel architecture that combines a Lightweight and Efficient Detection Head (LEDH) with Multi-scale Spatial Pyramid Pooling Fast (MSPPF). The LEDH enhances detection speed by simplifying the network structure while maintaining accuracy, reducing computational demands. The MSPPF improves the model’s ability to capture intricate details of rice leaf diseases at various scales by fusing multi-level feature maps. On the RiceDisease dataset, G-YOLO surpasses YOLOv8n with 4.4% higher mAP@0.5, 3.9% higher mAP@0.75, and a 13.1% increase in FPS, making it well-suited for resource-constrained devices due to its efficient design.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から検出するYOLOベース手法を開発・評価しており、植物病害表現型の取得・推定が中心的な貢献である。
abstractThis paper presents G-YOLO, a novel architecture that combines a Lightweight and Efficient Detection Head (LEDH) with Multi-scale Spatial Pyramid Pooling Fast (MSPPF).
Reproduction assets foundThe paper's rice leaf disease detection experiments were run on a publicly available Kaggle dataset (RiceDisease, 850 images of Bacterial Leaf Blight, Blast, and Brown Spot), which is a paper-specific public phenotype image asset. No author analysis code, trained model checkpoints, or other paper-specific assets are公开;Dataset · publicIn this study, we utilized the publicly available RiceDisease dataset from Kaggle [ 23 ], which contains 850 images of rice leaf diseases.Open asset ↗lines:73-123Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Rice tiller angle is a key agronomic trait that regulates plant architecture and plays a critical role in determining rice yield. Given that tiller angle is regulated by multiple genes, it is important to identify quantitative trait loci (QTL) associated with tiller angle. Recently, with the advancement of imaging technology for plant phenotyping, it has become possible to quickly and accurately measure agronomic traits of breeding populations. In this study, we extracted tiller angle and various image-based parameters from Red-Green-Blue (RGB) images of a recombinant inbred line (RIL) population derived from a cross between Milyang23 (Indica) and Giho (Japonica). Correlations among the obtained data were analyzed, and through dynamic QTL mapping, five major QTLs (qTA1, qTA1-1, qTA2, qTA2-1, and qTA9) related to tiller angle were detected on chromosomes 1, 2, and 9. Among them, 26 candidate genes related to auxin signaling and plant growth, including the TAC1 (Tiller Angle Control 1) gene, were identified in qTA9 (RM257-STS09048). These results demonstrate the potential of image-based phenotyping to overcome the limitations of traditional manual measurements in crop structure research. Furthermore, the identification of key QTLs and candidate genes related to tiller angle provides valuable genetic insights for the development of high-yielding varieties through crop morphology control.
Why it matches plant phenotyping methodsRGB画像からイネの分げつ角度と画像パラメータを抽出する画像ベース表現型解析を、育種集団で実質的に適用・評価しており、表現型取得法が中心です。
abstractRecently, with the advancement of imaging technology for plant phenotyping, it has become possible to quickly and accurately measure agronomic traits of breeding populations.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13233288/s1 , Figure S1: Quantitative trait loci (QTL) analysis associated with tiller angle in rice using RIL population; Figure S2: QTL distribution for tiller angle across different development stages.Open asset ↗lines:89-100Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Timeseries data captured by unoccupied aircraft systems (UASs) are increasingly used for agricultural applications requiring accurate prediction of plant phenotypes from remotely sensed imagery. However, prediction models often fail to generalize well from one year to the next or to new environments. Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). Manifold learning on raw imagery was better suited for the prediction of phenological traits due to the preservation of local structure in image embeddings at some time points. Together, these results highlight the competitiveness of classical ML approaches for UAS image analysis alongside computationally expensive deep learning models. Along with a new benchmark dataset for rice, our results help extend the toolkit for UAS image analysis, contributing to improved phenotype prediction in plant breeding and precision agriculture applications.
Why it matches plant phenotyping methodsUASマルチスペクトル時系列画像から収量・生育期形質を予測する機械学習手法を比較・評価し、米のベンチマークデータセットも提供しており、表現型取得・推定手法が研究の中心である。
abstractprediction models often fail to generalize well from one year to the next or to new environments.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw and processed UAS imagery, extracted features, and agronomic data on Dryad, and the authors' analysis code on GitHub. Both are paper-specific, public, and actionable.Dataset · publicts complied with the
current laws of the United States, the country in which they
were performed.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
Emily S. Bellis is a full time employee of Avalo, Inc., a crop
improvement company.
DATA AVA I L A B I L I T Y S TAT E M E N T
Raw and processed UAS images are available on Dryad
(https://doi.org/10.5061/dryad.v41ns1s4z) along with
extracted features and agronomic data for the 2021 and 2022
field seasons. Code to reproduce the analyses are available at
https://github.com/FareedFarag/TPPJ-Modeling-Code.O RC I D
FaredFarag https://orcid.org/0000-0002-4659-6781
Trevis D. Huggins https://orcid.org/0000-0002-1937-6687
JeremyD. Edwards https://orcidOpen asset ↗Dryad · 10.5061/dryad.v41ns1s4zpdf-raw-page:16 lines:1-86Code · public61/dryad.v41ns1s4z) along with
approaches for rice trait prediction using UAS imagery as extracted features and agronomic data for the 2021 and 2022
the primary data source. While showcasing the potential of field seasons. Code to reproduce the analyses are available at
various modeling approaches, it also emphasizes the trade- https://github.com/FareedFarag/TPPJ-Modeling-Code.
offs between performance and interpretability for applications
in precision agriculture and plant breeding. Looking for- ORCID
ward, extending the study over multiple years, extending to Fared Farag https://orcid.org/0000-0002-4659-6781
hyperspectral sensors, and exploring additional remotely Trevis D. Huggins https:/Open asset ↗GitHub · FareedFarag/TPPJ-Modeling-Codepdf-layout-page:16 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Transcriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait. Results Using the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R 2 = 0.59 when using all genes; R 2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as "gibberellic acid sensitivity" showed higher predictive ability than using all genes for root dry weight. Conclusions Our results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.
Why it matches plant phenotyping methods遺伝子発現データから植物の複合形質を予測する統計的手法を開発・評価しており、形質予測モデルの性能比較が研究の中心である。
abstractTranscriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation.
Reproduction assets foundThe authors state that all analysis code for the transcriptome-based prediction study is publicly available on Figshare, which is a paper-specific, publicly actionable code asset. Phenotype data are only in a prior study's supplementary file and transcriptome data in GEO (GSE162313), which are cited prior deposits, notCode · publicw sequence data were deposited in the DNA Data Bank of Japan Sequence Read Archive in a previous study [27]. Transcriptome data are available from the Gene Expression Omnibus ( GSE162313 ) and phenotype data are available in the supplementary file of a previous study [28]. All codes for the data analysis are shown in Figshare ( https://doi.org/10.6084/m9.figshare.26067532.v1 ).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Abbreviations
WRCOpen asset ↗Figshare · 10.6084/m9.figshare.26067532.v1lines:370-396Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
RiceThermalLeafStress / disease detectionStress response / tolerancePlant / canopy temperature
Diatraea saccharalis (Fabricius) is one of the main pests of rice crops and its early detection, that is, before the plants show damage, is essential to avoid yield losses and define effective and rational control. This work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels. Between 2019 and 2020, two experiments were conducted in a protected environment with the cultivars IR 40 and BR IRGA 409, which presented, in a previous study, different resistance reactions. Rice plants grown in pots were manually infested with first-instar larvae of D. saccharalis, from 0 to 10 caterpillars/plant, with the plants kept in cages covered with voile fabric throughout the test. With the adjustment of regression models, it was noticed that the leaf surface temperature is related to the level of infestation and could be used to detect which IR 40 is susceptible.
Why it matches plant phenotyping methods赤外線サーモグラフィーでイネ葉面温度から害虫感染レベルと感受性を推定する方法が研究の中心であり、植物状態の取得・推定に該当する。
abstractThis work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels.
Reproduction assets foundThe preprint's Data Availability Statement points to the authors' experimental dataset (leaf temperature, infestation, and resistance trait measurements) deposited in Harvard Dataverse under DOI 10.7910/DVN/Q1DRVV. This is a paper-specific, publicly accessible phenotype dataset. No author analysis code or trained modelDataset · publicn (AIC) and the root-mean-squared-error (RMSE) were used to choose and evaluate the
goodness-of-fit of models. The analyses were performed with the R software (www.r-project.org).
Data Availability Statement: The experimental data that support the results and findings of this study are
openly available in Harvard DataverseV1 at https://doi.org/10.7910/DVN/Q1DRVV.
References
1. Bortoli, S. A. D., Dória, H. O. S., Albergaria, N. M. M. S., & Botti, M. V. (2005). Biological aspects and damage
of Diatraea saccharalis (Lepidoptera: Pyralidae) in sorghum, under different doses of nitrogen and
potassium. Ciência e Agrotecnologia, 29(2), 267-273. https://doi.org/10.1590/S1413-70542005000200001Open asset ↗Harvard Dataverse · 10.7910/DVN/Q1DRVVpdf-layout-page:7 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Rice is regarded as the preferred crop for saline-alkali soil improvement by researchers. At present, the identification method for saline-alkali tolerance of rice varieties requires researcher to conduct tedious field investigations based on growth indicators. Therefore, there is an urgent need for an effective technical means to quickly and accurately identify saline-alkali tolerance of rice varieties. Study used 20 japonica rice varieties with three types of saline-alkali tolerance as test materials, by analyzing the identification mechanism of salt-alkali tolerance in Raman spectrum of japonica rice varieties, seven characteristic spectral peaks closely related to salt-alkali tolerance were identified. Various algorithms in Python are used for data standardization, baseline elimination, extraction of characteristic spectral peaks, detection of peaks characteristic information and data noise reduction. Three identification models were established to confirm the highest accuracy of CapsNets identification model, which could provide technical support and reference for breeding saline-alkali resistant japonica rice varieties.
Why it matches plant phenotyping methodsラマン分光とスペクトル処理・AIモデルによりイネ品種の塩類アルカリ耐性を推定する技術を開発・比較しており、表現型状態の取得・判定が研究の中心である。
abstractthere is an urgent need for an effective technical means to quickly and accurately identify saline-alkali tolerance of rice varieties
Reproduction assets foundThe paper's Data Availability statement explicitly states that the data and code (Raman spectral phenotyping data and Python analysis/identification models for saline-alkali tolerant japonica rice) are openly available in the authors' public GitHub repository.Code · publicData Availability：The data and code presented in this study are openly available at:
https://github.com/mabo8210/Mechanism-of-Saline-alkali-Tolerance.Open asset ↗mabo8210/Mechanism-of-Saline-alkali-Tolerancepdf-page:21 lines:1-46Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rapid and accurate diagnosis of rice diseases can prevent large-scale outbreaks and reduce pesticide overuse, thereby ensuring rice yield and quality. Existing research typically focuses on a limited number of rice diseases, which makes these studies less applicable to the diverse range of diseases currently affecting rice. Consequently, these studies fail to meet the detection needs of agricultural workers. Additionally, the lack of discussion regarding advanced detection algorithms in current research makes it difficult to determine the optimal application solution. To address these limitations, this study constructs a multi-class rice disease dataset comprising eleven rice diseases and one healthy leaf class. The resulting model is more widely applicable to a variety of diseases. Additionally, we evaluated advanced detection networks and found that DenseNet emerged as the best-performing model with an accuracy of 95.7%, precision of 95.3%, recall of 94.8%, F1 score of 95.0%, and a parameter count of only 6.97 M. Considering the current interest in transfer learning, this study introduced pre-trained weights from the large-scale, multi-class ImageNet dataset into the experiments. Among the tested models, RegNet achieved the best comprehensive performance, with an accuracy of 96.8%, precision of 96.2%, recall of 95.9%, F1 score of 96.0%, and a parameter count of only 3.91 M. Based on the transfer learning-based RegNet model, we developed a rice disease identification app that provides a simple and efficient diagnosis of rice diseases.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する深層学習手法を開発・比較し、データセットと診断アプリまで構築しており、植物病害表現型の取得・抽出が中心である。
abstractthis study constructs a multi-class rice disease dataset comprising eleven rice diseases and one healthy leaf class.
Reproduction assets foundThe paper's rice disease image dataset was partly acquired from a public Kaggle dataset (trumanrase/rice-leaf-diseases), which is a paper-specific, publicly available plant image asset used directly for the disease classification experiments. No author analysis code, trained model checkpoints, or supplementary depositsDataset · publicease dataset in-
cludes 11 categories of rice diseases and 1 category of healthy leaves, totaling 11,281 images.
The categories in the dataset are illustrated in Figure 1, and the number of images per cate-
gory is detailed in Table 1. The dataset is divided into training, validation, and testing sets,
with a ratio of 60:20:20 (https://www.kaggle.com/datasets/trumanrase/rice-leaf-diseases,
accessed on 22 July 2024). The search engine method involves automatically downloading
images by inputting keywords into Google using a Python script. The downloaded images
are then filtered and cleaned to ensure data accuracy. The images for the disease categories
bacterial leaf streak, Hispa, and rice shOpen asset ↗Kaggle · trumanrase/rice-leaf-diseasespdf-raw-page:3 lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Rice leaf diseases have an important impact on modern farming, threatening crop health and yield. Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification. However, the diversity of rice growing environments and the complexity of leaf diseases pose challenges. To address these issues, this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer. First, it features the sparse global-update perceptron for real-time parameter updating, enhancing model stability and accuracy in learning irregular leaf features. Second, the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module (SRM) and channel reconstruction module (CRM), focusing on salient feature extraction and reducing background interference. Additionally, the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm, gradually reducing the stochastic search amplitude to minimize loss. This enhances the model's adaptability and robustness, particularly against fuzzy edge features. The experimental results show that AISOA-SSformer achieves an 83.1% MIoU, an 80.3% Dice coefficient, and a 76.5% recall on a homemade dataset, with a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code.
Why it matches plant phenotyping methodsイネ葉の病斑部を画像からセグメンテーションする手法を開発・評価しており、植物病害状態の取得が研究の中心である。
abstractAccurate semantic segmentation techniques are crucial for segmenting diseased leaf parts
Reproduction assets foundThe authors explicitly state that the rice leaf disease segmentation dataset (2,005 annotated images of Tungro and brown spot) and the analysis code for AISOA-SSformer are publicly available on GitHub.Dataset · publicwith a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code .
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2024 Mar 24; Accepted 2024 Jun 21; Collection date 2024.
Introduction
Rice is one of the most important food crops in the world [ 1 – 3 ], but its production Open asset ↗ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Codelines:1-28Code · publicThe datasets and code used in this study have been posted on the website https://github.com/ZhouGuoXiong/ Rice-Leaf-Disease-Segmentation-Dataset-Code .Open asset ↗lines:470-494Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0's feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.
Why it matches plant phenotyping methods植物病害を画像等の観察から識別する深層学習・SVM手法の構築と性能比較が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当します。
abstractThis study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn.
Reproduction assets foundThe paper's crop disease image dataset is publicly available on Kaggle (Bangladeshi Crops Disease Dataset), explicitly cited as the open-source image source used for the study's phenotyping/classification experiments. The authors' additionally collected 1334 field images and their analysis code/trained model are not公开;Dataset · publicung-EffNet: Lung Cancer Classification Using EfficientNet from CT-Scan Images
Eng. Appl. Artif. Intell. 2023 126 106902
10.1016/j.engappai.2023.106902
23.
Huang Z.
Su L.
Wu J.
Chen Y.
Rock Image Classification Based on EfficientNet and Triplet Attention Mechanism
Appl. Sci. 2023 13 3180
10.3390/app13053180
24. Available online: https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset (accessed on 11 June 2023)
25.
Atila U.
Uçar M.
Akyol K.
Uçar E.
Plant leaf disease classification using EfficientNet deep learning model
Ecol. Inform. 2021 61 101182
10.1016/j.ecoinf.2020.101182
26.
Padol P.B.
Yadav A.A.
SVM classifier-based grape leaf disease detection
Proceedings of the 2Open asset ↗Kaggle · bangladeshi-crops-disease-datasetlines:139-314Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
SUMMARY There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing the number and size of these organelles in the in leaf is a potential route to elevate leaf-level photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice and thus various attempts are being made to increase chloroplast content in this cell type. In this study we developed and applied a deep learning tool named Chloro-Count to demonstrate that loss of OsHAP3H function in rice increases chloroplast occupancy in bundle sheath cells by 50%. Although limited to a single season, when grown in the field Oshap3H mutants exhibited increased numbers of tillers and panicles as compared to controls or gain of function mutants. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-function OsHAP3H mutants and facilitated a comparison between 2D and 3D quantification methods. In wild-type rice, as the dimensions of bundle sheath cells increase, the volume of individual chloroplasts also increases. However, the larger the chloroplasts the fewer there are per bundle sheath cell. This observation revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed in Oshap3H mutants. The use of Chloro-Count also revealed that 2D quantification, upon which most previous studies have relied, is compromised by the positioning of chloroplasts within the cell. Chloro-Count is therefore a valuable tool for accurate and high-throughput quantification of chloroplasts that has enabled the robust characterization of OsHAP3H effects on chloroplast biogenesis in rice. Whereas previous studies have increased chloroplast occupancy in bundle sheath cells by increasing the size of individual chloroplasts, loss of OsHAP3H function leads to an increase in chloroplast numbers.
Why it matches plant phenotyping methodsChloro-Countという深層学習ツールを開発し、葉肉細胞内の葉緑体数・占有率を高精度かつハイスループットに定量する手法が研究の中心であるため。
abstractwe developed and applied a deep learning tool named Chloro-Count
Reproduction assets foundThe paper's Chloro-Count deep learning tool (Mask R-CNN segmentation of chloroplasts and bundle sheath cells) is the authors' own analysis code, explicitly stated to be publicly available on GitHub. No public image/phenotype dataset deposit is stated; training images and Table S1 raw data are not linked to a public URLCode · publicn validated, they are mapped to
566 individual organelles/cells for volumetric analysis. An overview of the system for detecting and
567 measuring volumes of chloroplasts is presented in Figure 3A. The process for detecting and
568 measuring bundle sheaths follows an analogous workflow. The Chloro-Count code is available on
569 https://github.com/pedropgusmao/chloro-count.
570
571 Data collection and pre-processing
572 A total of 327 slices from 39 different cells were used during the training of both image segmentation
573 networks. Images from 29 cells were used for training, five for validation and five for testing. A total of
574 3,790 segments of chloroplasts were used for training, 287Open asset ↗pedropgusmao/chloro-countpdf-layout-page:16 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
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124
The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
125
Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
126
details of models’ weights, hyperparameters, training scripts and datasets can be found at
127
https://github.com/William-Yao0993/FD_detection.128
129
Model evaluation
130
131
Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
132
calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
133
F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published11 Jun 2024The Plant journal : for cell and molecular biologyCited by 9 · OpenAlex ↗
Stem is important for assimilating transport and plant strength; however, less is known about the genetic basis of its structural characteristics. In this study, a high-throughput method, "LabelmeP rice" was developed to generate 14 traits related to stem regions and vascular bundles, which allows the establishment of a stem cross-section phenotype dataset containing anatomical information of 1738 images from hand-cut transections of stems collected from 387 rice germplasm accessions grown over two successive seasons. Then, the phenotypic diversity of the rice accessions was evaluated. Genome-wide association studies identified 94, 83, and 66 significant single nucleotide polymorphisms (SNPs) for the assayed traits in 2 years and their best linear unbiased estimates, respectively. These SNPs can be integrated into 29 quantitative trait loci (QTL), and 11 of them were common in 2 years, while correlated traits shared 19. In addition, 173 candidate genes were identified, and six located at significant SNPs were repeatedly detected and annotated with a potential function in stem development. By using three introgression lines (chromosome segment substitution lines), four of the 29 QTLs were validated. LOC_Os01g70200, located on the QTL uq1.4, is detected for the area of small vascular bundles (SVB) and the rate of large vascular bundles number to SVB number. Besides, the CRISPR/Cas9 editing approach has elucidated the function of the candidate gene LOC_Os06g46340 in stem development. In conclusion, the results present a time- and cost-effective method that provides convenience for extracting rice stem anatomical traits and the candidate genes/QTL, which would help improve rice.
Why it matches plant phenotyping methodsイネ茎断面画像から解剖学的形質を抽出する高スループット手法を開発し、形質データセット構築と検証に用いており、表現型取得法が研究の中心である。
abstracta high-throughput method, "LabelmeP rice" was developed to generate 14 traits related to stem regions and vascular bundles
Reproduction assets foundThe paper's phenotyping/analysis Python code (LabelmeP rice pipeline) is explicitly provided as authors' supporting information Data S1, publicly available with the online article. Other referenced URLs (SNP-Seek, Q-TARO, RiceRNA, rmbreeding) are external databases, not paper-specific assets.Code · publicThe Python code was provided
as Data S1.Open asset ↗pdf-raw-page:10 lines:1-91Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Currently, foliar diseases of chili have significantly impacted both yield and quality. Despite effective advancements in deep learning techniques for the classification of chili leaf diseases, most existing classification models still face challenges in terms of accuracy and practical application in disease identification. Therefore, in this study, an optimized and enhanced convolutional neural network model named MCCM (MCSAM-ConvNeXt-MSFFM) is proposed by introducing ConvNeXt. The model incorporates a Multi-Scale Feature Fusion Module (MSFFM) aimed at better capturing disease features of various sizes and positions within the images. Moreover, adjustments are made to the positioning, activation functions, and normalization operations of the MSFFM module to further optimize the overall model. Additionally, a proposed Mixed Channel Spatial Attention Mechanism (MCSAM) strengthens the correlation between non-local channels and spatial features, enhancing the model's extraction of fundamental characteristics of chili leaf diseases. During the training process, pre-trained weights are obtained from the Plant Village dataset using transfer learning to accelerate the model's convergence. Regarding model evaluation, the MCCM model is compared with existing CNN models (Vgg16, ResNet34, GoogLeNet, MobileNetV2, ShuffleNet, EfficientNetV2, ConvNeXt), and Swin-Transformer. The results demonstrate that the MCCM model achieves average improvements of 3.38%, 2.62%, 2.48%, and 2.53% in accuracy, precision, recall, and F1 score, respectively. Particularly noteworthy is that compared to the original ConvNeXt model, the MCCM model exhibits significant enhancements across all performance metrics. Furthermore, classification experiments conducted on rice and maize disease datasets showcase the MCCM model's strong generalization performance. Finally, in terms of application, a chili leaf disease classification website is successfully developed using the Flask framework. This website accurately identifies uploaded chili leaf disease images, demonstrating the practical utility of the model.
Why it matches plant phenotyping methodsトウガラシ葉の病徴を画像から分類する深層学習手法を開発・比較評価しており、植物の病害状態を推定するフェノタイピング手法が中心である。
abstractan optimized and enhanced convolutional neural network model named MCCM (MCSAM-ConvNeXt-MSFFM) is proposed
Reproduction assets foundThe paper's phenotyping inputs are public Kaggle image datasets: the chili leaf disease dataset used as the primary experimental data, plus rice and maize leaf disease datasets used for generalization tests. No author analysis code, trained model checkpoints, or dataset deposits are stated; the Flask website is a live-Dataset · publicn of experiments. Section 4 analyzes all research findings, proposes possible improvements, and suggests future research directions. Finally, Section 5 summarizes the main contributions of this study.
2
Materials and methods
2.1
Datasets
The chili leaf disease dataset is sourced from publicly available datasets on the internet( https://www.kaggle.com/datasets/dhenyd/chili-plant-disease/ ). The initial dataset consists of 500 images, encompassing five categories: healthy chili leaves, leaf curl disease, leaf spot disease, whitefly, and yellowing disease. Samples of each category are illustrated in
Figure 1
.
Figure 1
Chili samples. (A) Healthy chili leaves. (B) Leaf curl disease. (C) Leaf spoOpen asset ↗Kaggle · dhenyd/chili-plant-diseaselines:59-78Dataset · publicthe corresponding accuracy rate of each cross-validation.
3.10.2
Rice and maize disease test results
To validate the generalization ability of the MCCM model on different crop diseases, this study conducted performance tests using publicly available datasets, including rice leaf disease dataset and maize leaf disease dataset ( https://www.kaggle.com/datasets/nirmalsankalana/rice-leaf-disease-image and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset ). The rice leaf disease dataset covers four types: rice blast, bacterial leaf blight, brown spot, and rice tungro disease, while the maize leaf disease dataset includes healthy states and three types of diseases:Open asset ↗Kaggle · nirmalsankalana/rice-leaf-disease-imagelines:456-477Dataset · publicaize disease test results
To validate the generalization ability of the MCCM model on different crop diseases, this study conducted performance tests using publicly available datasets, including rice leaf disease dataset and maize leaf disease dataset ( https://www.kaggle.com/datasets/nirmalsankalana/rice-leaf-disease-image and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset ). The rice leaf disease dataset covers four types: rice blast, bacterial leaf blight, brown spot, and rice tungro disease, while the maize leaf disease dataset includes healthy states and three types of diseases: gray leaf spot, rust, and leaf blight. To ensure dataset diversity, data aOpen asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:456-477Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
RiceMesh / voxelX-ray / CTRootObject detectionSegmentationRoot system architecture
Abstract Background X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice ( Oryza sativa ) root segments in CT volumes. Results VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data. Conclusions A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.
Why it matches plant phenotyping methods植物根のCT画像から根をセグメンテーションし、根成長の定量化に用いる手法の開発・技術評価が中心であるため、植物フェノタイピング方法論に該当する。
abstractX-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil.
Reproduction assets foundThe paper's own training/prediction scripts for the 3D semantic segmentation model (SStrainer3D) are publicly available on GitHub with explicit availability language. The CT volume datasets are only available upon request. RSAvis3D and RSAtrace3D are cited prior-work tools, not paper-specific assets.Code · publicThe scripts for the training and prediction of the 3D semantic segmentation are available at the GitHub repository ( https://github.com/st707311g/SStrainer3D/ , branch 1.0).Open asset ↗st707311g/SStrainer3Dlines:156-186Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.
Why it matches plant phenotyping methodsUAV画像から形態・生理関連形質を抽出・解析する統合ソフトウェア基盤の開発と性能実証が中心であり、植物フェノタイピング手法として明確に適格です。
abstractwe developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis.
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 IHUP software developed in this study is available from https://drive.google.com/uc?export=download&id=1aZalN0yqli9l2pqyQAPK0IiACs7UhrHF . For further usage details, please contact the corresponding author.Open asset ↗lines:510-674Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.
Why it matches plant phenotyping methods作物の遺伝型・表現型データから収量を予測する移植学習フレームワークを開発し、複数作物と既存手法で精度比較しているため、植物表現型推定手法が中心である。
abstractWe therefore developed TrG2P, a transfer-learning-based framework.
Reproduction assets foundThe paper's authors publicly released the Python implementation of TrG2P (the transfer-learning G2P analysis tool used to produce the paper's yield-prediction results) on GitHub, with an explicit availability statement, plus a web-based tool. The phenotype/genotype datasets themselves are from previously published, citCode · publicThe Python implementation of TrG2P along with the demo files is available at https://github.com/lijinlong1991/TrG2P . The web-based tool is available at http://trg2p.ebreed.cn:81 .Open asset ↗https://github.com/lijinlong1991/TrG2Plines:224-294Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The technique of detecting and tracking an area's physical properties from a distance by measuring its reflected and emitted radiation is known as remote sensing. It gathered data accurately in near real-time. For this purpose, multispectral cameras mounted on UAVs that capture images with different bands can be used to generate vegetation indexes (NDVI, NDRE), which are useful in precision agriculture. In this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening) in the north-western province of Sri Lanka. The selected sample rice variety is BG300. The images were taken over five days, starting from August 14 to October 5, 2023. The UAV flight took place at 30 m from the canopy level with the multispectral camera titled at an angle of 900. The SPAD Chlorophyll Meter was used to collect ground truth data, which is proportional to the nitrogen level of the leaf. There were 50 randomly selected readings throughout the paddy field. Relevant climate data for five days was provided by the Rice Research and Development Institute, Bathalagoda, which belongs to the paddy field. The purpose of this data creation was to aid researchers who are generally interested in disease diagnosis. Moreover, this dataset allows for studying the effect of using different tilt angles on the 3D reconstruction of the paddy fields and the generation of orthomosaics.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とSPADによる地上真値を含む、植物キャノピーの状態推定に再利用可能なデータセットであり、画像取得・オルソモザイク生成・3D再構成が中心的な方法的貢献です。
abstractIn this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening)
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the authors' own UAV multispectral images, SPAD ground-truth readings, GPS shapefile, and climate data for paddy nitrogen phenotyping. This is a paper-specific, publicly available dataset with an explicit direct URL and DOI.Dataset · publicructions, the flight path was configured to fly on its own (DJI). The dataset includes a shapefile containing the GPS positions of the BG300 rice clusters. The same dates were used to gather SPAD meter values.
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/h8s5mn52j6.1
Direct URL to data: https://data.mendeley.com/datasets/h8s5mn52j6/1
Data source location
Institution: Rice Research and Development Institute
City/Town/Region: Batalagoda, Ibbagamuwa, Kurunegala
Country: Sri Lanaka
Latitude and longitude (and GPS coordinates) for collected samples/data: 7.53240 N, 80.43400E
1.
Value of the Data
•
Data is useful for researchers interested in UAV (unmannedOpen asset ↗Mendeley Data · 10.17632/h8s5mn52j6.1lines:1-80Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval. To address the challenge of matching diverse image categories with complex text descriptions during the retrieval process, we initially employed ViT and BERT to extract fine-grained image and text feature sequences enriched with contextual information. Subsequently, two-way mixed self-attention (TMS) was introduced to enhance both image and text feature sequences, with the aim of uncovering important semantic information in both modalities. Then, we developed false-negative elimination-hard negative mining (FNE-HNM) strategy to facilitate in-depth exploration of semantic connections between different modalities. This strategy aids in selecting challenging negative samples for elimination to constrain the model within the triplet loss function. Finally, we introduced warm-up bat algorithm (WBA) for learning rate optimization, which improves the model's convergence speed and accuracy. Experimental results demonstrated that FHTW-Net outperforms state-of-the-art models. In image-to-text retrieval, it achieved R@1, R@5, and R@10 accuracies of 83.5%, 92%, and 94%, respectively, while in text-to-image retrieval, it achieved accuracies of 82.5%, 98%, and 98.5%, respectively. FHTW-Net offers advanced technical support and algorithmic guidance for cross-modal retrieval of rice leaf diseases.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から扱うクロスモーダル検索手法と専用データセットが研究の中心であり、単なる病害測定ではないため含める。
abstractWe introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval.
Reproduction assets foundThe paper's Data Availability statement points to a public GitHub repository (ZhouGuoXiong/FHTW-Net) hosting the partial datasets used in this rice leaf disease image-text retrieval study; the full bespoke CRLDRD dataset (6,332 image-text pairs) requires contacting the corresponding author, so only the partial public资产Dataset · publicervision and funding acquisition. Y.W.: Supervision and resources. L.L.: Writing—review and editing. Yahui Hu: Visualization and resources.
Competing interests: The authors declare that they have no competing interests.
Data Availability
The partial datasets utilized and examined in this research have been posted on the website https://github.com/ZhouGuoXiong/FHTW-Net . Furthermore, for access to all bespoke datasets used in this study (comprising a total of 6,332 image–text pairs), please contact the corresponding author.
References
1.
Rai
A , Maharjan
MR , Harris Fry
HA , Chhetri
PK , Wasti
PC , Saville
NM
.
Consumption of rice, acceptability and sensory qualities of fortified rice amongstOpen asset ↗ZhouGuoXiong/FHTW-Netlines:497-611Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The Burkholderia glumae bacterium causes bacterial grain rot in rice, posing significant threats to the crop's yield, particularly thriving during the rice flowering and grain filling stages. This disease is especially evident in rice grains before harvest, presenting challenges in the detection and classification of rice panicles. Firstly, diseased grains may mix with healthy ones, complicating their separation. Secondly, the size of grains on a panicle varies from small to large, which can be problematic when detected using object detection methods. Thirdly, disease classification can be conducted by evaluating the extent of infection on rice panicles to assess its impact on yield. Finally, the challenges in detection, classification, and preprocessing for disease identification and management necessitate the adoption of diverse approaches in machine learning and deep learning to develop optimal methods and support smart agriculture.
Why it matches plant phenotyping methodsイネ穂・粒の病徴を画像で検出・分類するデータセットであり、植物の病害状態を推定するフェノタイピング用途が中心です。
titleGrain rot dataset caused by Burkholderia Glumae Bacteria.
Reproduction assets foundThis Data in Brief article describes its own publicly deposited rice grain rot image dataset (1528 annotated images, YOLO format) on Zenodo, with explicit direct URL and DOI, qualifying as a paper-specific public phenotyping image dataset.Dataset · publicrice fields in the Mekong Delta region using a mobile phone.
Data source location
Provinces in the Mekong Delta
Latitude: 10.063363, Longitude: 105.594339
Data accessibility
Repository name: Bacterial Grain Rot Dataset Caused by Burkholderia Glumae Bacteria
Data identification number: 10.5281/zenodo.10805462
Direct URL to data: https://zenodo.org/records/10805462
Guidance on retrieving this dataset: Individuals may obtain the dataset by downloading it from the provided link and then unzipping the files for use.
Related research article
Quach, Luyl-Da, et al. “Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small ObjectsOpen asset ↗zenodo · 10.5281/zenodo.10805462lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Introduction: ) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive. Method: Here we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector. Results: Applying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date. Discussion: Our optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.
Why it matches plant phenotyping methodsUAV画像と深層学習によるイネ穂検出・計数を中核とし、出穂関連形質を高スループットに定量するフェノタイピング手法およびワークフローを評価・適用している。
abstractaerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits
Reproduction assets foundThe article states that all relevant code for the UAV phenotyping and panicle detection pipeline is publicly available in the authors' GitHub repository r1cheu/phenocv. Other URLs (Ultralytics, COCO, WinQTLCart) are generic third-party tools, not paper-specific assets.Code · publicAll relevant code can be accessed at https://github.com/r1cheu/phenocv .Open asset ↗r1cheu/phenocvlines:317-328Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
RadishRiceX-ray / CTRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系の発達を対象に、分布型光ファイバーセンサー、信号処理、根の可視化モデルを開発し、X線CTとの比較検証まで行う、植物フェノタイピング手法が中心の研究です。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe authors publicly provide MATLAB code for virtual root reconstruction and the sample datasets used in the study via their GitHub repository Fiber-RADGET, with explicit availability statements in the Methods and Data availability sections.Code · publicThe custom code for the virtual root reconstruction in MATLAB (MathWorks, Massachusetts, USA) is available at https://github.com/mtei1/Fiber-RADGET.git .Open asset ↗mtei1/Fiber-RADGETlines:126-223Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published1 Feb 2024Public Research Journal of Engineering, Data Technology and Computer ScienceCited by 19 · OpenAlex ↗
Rice is the most important crop in global food security and socioeconomic stability. A part of the world's population makes rice a food requirement but the problem is found that all rice varieties suffer from several diseases and pests. Therefore, it is necessary to ensure the quality of healthy and proper rice growth by detecting diseases present in rice plants and treatment of affected plants. In this study, the Convolutional Neural Network (CNN) algorithm was applied in classifying diseases on the leaves of rice plants by experimenting with several parameters and architecture to get the best accuracy. This study was conducted image classification of rice plant disease using CNN architecture ResNet-50V2 with data using preprocessing Augmentation. The test was conducted with three optimizers such as SGD, Adam, and RMSprop by combining various parameters, namely epoch, batch size, learning rate, and SGD and RMSprop optimizers. Division of image data with 70:30 ratio of training data and test data; 80:20; 90:10. From these results, it was found that Adam was the best optimizer in the 80:20 data division in this study with an accuracy level of 0.9992, followed by the SGD optimizer with an accuracy level of 0.9983, while the RMSProp optimizer was ranked third with an accuracy level of 0.9978.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類するCNN手法が研究の中心であり、植物の病害表現型を直接推定しているため。
abstractthe Convolutional Neural Network (CNN) algorithm was applied in classifying diseases on the leaves of rice plants
Reproduction assets foundThe paper's rice leaf disease image classification uses a publicly available Kaggle dataset of rice leaf images (5932 images across Bacterial blight, Blast, Brown spot, Tungro), which is the paper-specific phenotyping image input. No author analysis code or trained model checkpoints are reported.Dataset · publicowing of leaves, wavy leaves, and
disturbed panicle formation [15].
The research stages for the classification of rice plant disease images in this study are shown in
Flowchart or Figure 1.
2.2 Data Collection
The data collected is image data of leaf disease in rice plants. This secondary data was obtained from
the Kaggle site (https://www.kaggle.com/datasets/shareef0612/riceleaf-dataset). The data used amounted to
5932 images consisting of 1584 Bacterial blight images, 1440 Blast images, 1600 Brown spot images and
1308 Tungro images. Each class will share images for training and testing to train and test models. The data
will be divided into a training subset, a validation subset, and a tesOpen asset ↗Kaggle · shareef0612/riceleaf-datasetpdf-raw-page:2 lines:1-54Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world’s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L’Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.
Why it matches plant phenotyping methodsドローン・衛星リモートセンシングとNDVI等を用いて除草剤効果を評価し、その手法を有効な評価ツールとして検証しているため、植物状態の取得・評価方法が中心である。
abstractTherefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia.
Reproduction assets foundThe article states 'Data are contained within the article' and provides no author code, model, or dataset deposit. The only paper-specific public asset is the MDPI supplementary file, which contains Figure S1 showing the control subplots affected by Echinochloa spp. (field imagery related to the phenotyping experiment,Supplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24030804/s1 . Figure S1. Control subplots affected by Echinochloa spp. (Own elaboration).
Click here for additional data file.
Author ContributionsOpen asset ↗lines:90-101Code / 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
RiceLaboratory / benchtopRootGrowth / time-series analysisRoot system architecture
The study of plant root growth in real time has been difficult to achieve in an automated, high-throughput, and systematic fashion. Dynamic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their environments. We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months. The system uses low-cost, widely available materials. As a proof of concept, we employed GROOT to collect images of root growth of Oryza sativa, Hudsonia montana, and multiple species of orchids including Platanthera integrilabia over six months. Beyond imaging plant roots, our system is highly customizable and can be used to collect time- lapse image data of different container sizes and configurations regardless of what is being imaged, making it applicable to many fields that require longitudinal time-lapse recording.
Why it matches plant phenotyping methods植物根の長期・高スループット画像取得システムを開発し、根成長という植物形質の時系列計測に実証適用しているため、フェノタイピング手法が中心である。
abstractWe designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months.
Reproduction assets foundThe paper's authors explicitly state that all code files (robot control, QR code generation, image sorting/preprocessing) are publicly available on their GitHub organization, with URLs given in the text. No standalone phenotype dataset deposit is stated, but the authors' analysis/control code qualifies as a paper-sphenCode · publico pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All code files are available at https://github.com/the-rhizodynamics-robot .
Data Availability
All code files are available at https://github.com/the-rhizodynamics-robot .
Introduction
All organisms must perform effective environmental exploration for survival. Plants are sessile and interact with the location where they grow; therefore, they can only navigate and explore their (typically) below-groOpen asset ↗the-rhizodynamics-robotlines:71-78Code · publicprocessing and analysis of the resulting imaging data. We have included code and documentation for an image preprocessing and time-lapse movie creation system we have developed. This system uses printed QR code labels to identify the individual growth vessels from the raw images ( S3 File : QR Code Generation, code available at https://github.com/orgs/the-rhizodynamics-robot/repositories ), which are then subsequently sorted into individual subdirectories, after which (optionally) stabilized time-lapse videos are created using the open source video editing software FFMPEG [ 24 ] [ S4 File : Image Sorting, code available at https://github.com/the-rhizodynamics-robot ]. This system has been teOpen asset ↗the-rhizodynamics-robotlines:95-102Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels. The database contains more than 350K single-kernel images with experts' annotations. The grain kernels used in the study consist of four types of cereal grains including wheat, maize, sorghum and rice, and were collected from over 20 regions in 5 countries. The surface information of each kernel is captured by our custom-built device equipped with high-resolution optic sensor units, and corresponding sampling information and annotations include collection location and time, morphology, physical size, weight, and Damage & Unsound grain categories provided by senior inspectors. In addition, we employed a commonly used deep learning model to provide classification results as a benchmark. We believe that our GrainSet will facilitate future research in fields such as assisting inspectors in grain quality inspections, providing guidance for grain storage and trade, and contributing to applications of smart agriculture.
Why it matches plant phenotyping methods穀粒画像と形態・サイズ・重量・損傷状態の注釈を大規模に整備した再利用可能なデータセットで、カスタム撮像装置とベンチマーク分類も含むため、植物器官の表現型取得・解析が中心です。
abstractWe present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels.
Reproduction assets foundThe paper's GrainSet database (annotated single-kernel grain images with DU-grain, weight, size, and mask annotations) is publicly deposited on Figshare under CC BY 4.0, split into four species sub-datasets plus tiny/raw previews, and the authors' validation code and trained models are released on GitHub.Code · publicThe validation code and models are released in the Github repository https://github.com/GrainSpace/GrainSet.Open asset ↗GitHub · GrainSpace/GrainSetpdf-page:10 lines:1-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice is considered as one of the most sought out food crops globally. Salinity resilience plays an essential part in rice cultivation. But, uncontrolled saline level specifically at the seedling stage may unfavorable influence productivity of rice extensively. If not properly monitored, salinity stress brings about acute impairment during the seedling stage of crop growth bringing about an overall loss of 50%. Hence, it is mandatory to design an image classification and grading learning method for salinity stress analysis principally at the seedling stage and then benchmark its performance to circumvent depletion in rice yield. However, the classification using traditional method is both said to be laborious in terms of time and most of the time results in error owing to incorrect classification. To identify and classify salinity stress in rice seedlings using field images, the study reports the need of deep learning built model over traditional method of assessing rice crop's susceptibility to salt stress during the seedling stage. In this work, a method called Homomorphic Fourier Integral-based image classification and Dual Rectified Linear Convolutional (HFI-DRLC) grading learning for salinity stress level analysis in rice crops at seedling stage is proposed. Initially, an image enhancement framework that balances both field image features like as contrast, illumination and key properties that are important for identification of salinity stress level in rice crops at seedling stage. This is performed using Homomorphic Fourier Integral Filter-based Preprocessing model. Next, Dual Rectified Linear Convolutional Neural prediction-based salinity tolerance in rice at seedling stage is designed to ensure both accuracy and precision. To investigate the effects of different improvement methods on the accuracy and precision of salinity tolerance in rice at seedling stage, comparison experiments between HFI-DRLC and traditional methods and comparison experiments with rice seedling dataset and rice seedling samplings from ICAR (Central Coastal Agricultural Research Institute, Old Goa, India) are executed. The method proposed in this study was shown to be more effective than traditional methods in terms of precision, recall, accuracy and error rate, providing a better method for salinity tolerance in rice at seedling stage.
Why it matches plant phenotyping methodsイネ幼苗の塩ストレス状態を圃場画像から分類・等級化する画像処理および深層学習手法を開発し、従来法やデータセットで性能比較しており、表現型取得・推定が研究の中心である。
abstractdesign an image classification and grading learning method for salinity stress analysis principally at the seedling stage
Reproduction assets foundThe paper evaluates its HFI-DRLC salinity-grading method on a public rice seedling image dataset hosted on figshare, explicitly cited with URL, plus a non-public real-time ICAR sample set. The figshare dataset is a public plant-image asset directly used for the paper's salinity phenotyping analysis. No author analysis,Dataset · publicaluation matrices like, precision, recall, accuracy,
mean absolute error (MAE) and prediction time have been evaluated in this experiment to
explain the proposed method using Python high-level general-purpose programming language.
All of the evaluation criteria related to this study are briefly discussed below using the dataset
https://figshare.com/articles/dataset/rice_seedlings_and_weeds/7488830and the real time dataset
obtained from ICAR (Central Coastal Agricultural Research Institute, Old Goa, India).
5. Discussion
In this work, the precision, recall, accuracy and F1-score performance metrics are utilized
in measuring the performance of the salinity stress level in rice crops at seedlinOpen asset ↗figshare · 7488830pdf-raw-page:19 lines:1-35Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Rice ( Oryza sativa ) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM 2 ) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM 2 trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM 2 trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM 2 trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM 2 trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.
Why it matches plant phenotyping methodsイネ穂数という植物形質をドローン画像から定量化するAIプラットフォーム、データセット、検出モデルを開発・検証しており、表現型取得手法が研究の中心である。
abstractwe present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery.
Reproduction assets foundThe paper's Data Availability statement provides a public GitHub releases page containing the authors' source code and the paper-specific DRPD dataset (5,372 annotated rice panicle subimages), plus a public cloud platform URL for panicle detection. These directly reproduce the paper's phenotyping measurements and are,Code · publicRelease page and source code can be found via https://github.com/changcaiyang/Panicle-AI/releases/; the DRPD dataset: 5,372 RGB subimages with annotate 259,498 panicles collected from 229 rice varieties can also be downloaded for the GitHub repository.Open asset ↗https://github.com/changcaiyang/Panicle-AI/releases/lines:230-241Dataset · publicthe DRPD dataset: 5,372 RGB subimages with annotate 259,498 panicles collected from 229 rice varieties can also be downloaded for the GitHub repositoryOpen asset ↗DRPDlines:230-241Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.
Why it matches plant phenotyping methodsイネ籾の長さ・幅・充実度を画像処理で測定する方法を開発し、精度とバックライト条件の影響を検証しており、植物表現型取得が中心です。
abstractA new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques.
Reproduction assets foundThe paper's phenotype reference measurements (grain counts, filled/unfilled counts, and grain sizes for the validation experiments) are reported in Appendices A–C, which are included in the article's supplementary material, publicly available at the Frontiers supplementary-material URL. No author analysis code or imageSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1213486/full#supplementary-material
Click here for additional data file.
References
Al-Tam F., Adam H., Anjos A. D., Lorieux M., Larmande P., Ghesquière A., et al. (2013). P-TRAP: a panicle trait phenotyping tool. BMC Plant Biol.
13 (1), 1–14. doi: 10.1186/1471-2229-13-122Open asset ↗lines:182-208Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice ( Oryza sativa ). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.
Why it matches plant phenotyping methods自動ハイパースペクトル画像からイネの生理形質を推定するモデルを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractHSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository containing the code for the physiological trait prediction models (RReliefF-PLSR, SVM classification of rice hyperspectral data), and a Purdue PURR repository deposit containing the study's datasets (HSI-derived and ground-reference phenCode · publicThe code for each physiological trait prediction model can be accessed through GitHub ( https://github.com/To-Chia/rice_imaging_ms ).Open asset ↗https://github.com/To-Chia/rice_imaging_mslines:384-394Dataset · 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://purr.purdue.edu/publications/4079/ .Open asset ↗https://purr.purdue.edu/publications/4079/lines:442-452Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R 2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.
Why it matches plant phenotyping methodsイネ穂上粒の形態形質を画像・深層学習で抽出する手法を開発し、手動測定との比較で検証しており、表現型取得が研究の中心である。
abstractThis study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology
Reproduction assets foundThe paper's data availability statement explicitly points to a public GitHub repository (BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection) hosting the study's datasets, which per the statement contain the paper's rice panicle images and phenotyping resources. No separate trained-model checkpoint or analysis URLDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection .Open asset ↗BME-PhenoTeam/Method-for-on-panicle-rice-grain-detectionlines:521-553Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop's growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.
Why it matches plant phenotyping methodsイネ葉の病害・栄養欠乏という植物状態を対象に、感染領域のセグメンテーションと分類を組み合わせた画像解析手法を開発・評価しており、表現型取得が中心である。
abstractthis article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification.
Reproduction assets foundThe paper's rice leaf disease and nutrient deficiency image datasets are publicly available on Kaggle, explicitly linked in the data availability statement. No author analysis code or trained models are publicly deposited (available only on request).Dataset · publicarma M; writing – review & editing: Kumar CJ, Talukdar J, Dhiman G, Singh TP, Sharma A.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The three different types of diseased rice leaf images that were utilized in this experiment are accessible at the following links provided as follows: https://www.kaggle.com/datasets/vbookshelf/rice-leafdiseases , https://www.kaggle.com/guy007/nutrientdeficiencysymptomsinrice . The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Contributor Information
Mayuri Sharma, Email: mayurisarmah71@gmail.com.
Chandan Jyoti Kumar, Email: chanOpen asset ↗Kaggle · vbookshelf/rice-leafdiseaseslines:561-586Dataset · publicn G, Singh TP, Sharma A.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The three different types of diseased rice leaf images that were utilized in this experiment are accessible at the following links provided as follows: https://www.kaggle.com/datasets/vbookshelf/rice-leafdiseases , https://www.kaggle.com/guy007/nutrientdeficiencysymptomsinrice . The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Contributor Information
Mayuri Sharma, Email: mayurisarmah71@gmail.com.
Chandan Jyoti Kumar, Email: chandan14944@gmail.com.
Jyotismita Talukdar, Email: jyoti4@tezu.ernOpen asset ↗Kaggle · guy007/nutrientdeficiencysymptomsinricelines:561-586Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice is the most important cereal food crop in the world, and half of the world's population uses rice as a staple food for its energy source. The yield production qualities and quantities are affected by biotic and abiotic factors namely viruses, soil fertility, bacteria, pests, and temperature. Rice plant disease is the most crucial factor behind communal, economic, and agricultural losses in the agricultural field. Farmers detect and identify diseases through the naked eye, which takes more time and resources, leading to crop loss and unhealthy farming. To overcome these issues, this paper presents a novel rice plant disease detection approach named the crossover boosted artificial hummingbird algorithm based AX-RetinaNet (CAHA-AXRNet) approach. This current research paper mainly concentrates on the effectiveness of rice plant disease detection and classification. The hyperparameters of the AX-RetinaNet model are optimized through the CAHA optimization model. In this paper, three types of disease detection datasets namely rice plant dataset, rice leaf dataset, and rice disease dataset are included to classify rice plants as healthy or unhealthy. The most essential performance metrics are precision, F1-score, accuracy, specificity, and recall, employed to validate the effectiveness of disease detection. The proposed CAHA-AXRNet approach demonstrates its effectiveness compared to other existing rice plant disease detection methods and achieved an accuracy rate of 98.1%.
Why it matches plant phenotyping methodsイネ葉・植物の病害状態を画像データから検出・分類する手法を開発し、複数データセットと性能指標で評価しており、植物フェノタイピング手法が研究の中心である。
abstractthis paper presents a novel rice plant disease detection approach named the crossover boosted artificial hummingbird algorithm based AX-RetinaNet (CAHA-AXRNet) approach.
Reproduction assets foundThe paper uses three public rice disease image datasets (Kaggle rice leaf, GitHub rice disease, Kaggle rice plant) as its phenotyping inputs; no author code or model release is stated (data availability is on-request only).Dataset · publicom the Indira
Gandhi Agricultural University, Raipur, Chhattisgarh,
India. The images were captured during the daytime
through Gionee, Canon Powershot SX530HS digital
camera and LYF mobile set. This image background
can reduce the computational cost as well as back-
ground complexity and this rice disease data are cho-
sen from https://github.com/aldrin233/RiceDiseases-DataSet. In the rice plant dataset, there are 5932
images are used to find the unhealthy and healthy
plants from the agricultural field. The most danger-
ous disease in the rice plant is rice blast fungal gen-
erated from the seed of the plant which affects the
entire plant of the field. This data was collected by
https://www.Open asset ↗github.com/aldrin233/RiceDiseases-DataSetpdf-raw-page:13 lines:1-100Dataset · public/RiceDiseases-DataSet. In the rice plant dataset, there are 5932
images are used to find the unhealthy and healthy
plants from the agricultural field. The most danger-
ous disease in the rice plant is rice blast fungal gen-
erated from the seed of the plant which affects the
entire plant of the field. This data was collected by
https://www.kaggle.com/datasets/rajkumar898/rice-plant-dataset. The sample images for each dataset are
delineated in Table 3.
Evaluation measures
The evaluation measures namely precision (RPprecision),
F1-score (RPF1−score), accuracy (RPaccuracy), recall
(RPrecall), specificity (RPspecificity), AUC/ROC and loss
are analyzed by using true positive values (RPTP), false
Open asset ↗kaggle.com/datasets/rajkumar898/rice-plant-datasetpdf-raw-page:13 lines:1-100Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Early diagnosis of rice disease is important because it poses a considerable threat to agricultural productivity as well as the global food security of the world. It is challenging to obtain more reliable outcomes based on the percentage of RGB value using image processing outcomes for rice disease detections and classifications in the agricultural field. Machine learning, especially with a Convolutional Neural Network (CNN), is a great tool to overcome this problem. But the utilization of deep learning techniques often necessitates high-performance computing devices, costly GPUs and extensive machine infrastructure. As a result, this significantly raises the overall expenses for users. Therefore, the demand for smaller CNN models becomes particularly pronounced, especially in embedded systems, robotics and mobile applications. These domains require real-time performance and minimal computational overhead, making smaller CNN models highly desirable due to their lower computational cost. This paper introduces a novel CNN architecture which is comparatively small in size and promising in performance to predict rice leaf disease with moderate accuracy and lower time complexity. The CNN network is trained with processed images. The image processing is performed using segmentation and k-means clustering to remove background and green parts of affected images. This technique proposes to detect rice disease of rice brown spot, rice bacterial blight and leaf smut with reliable outcomes in disease classifications. The model is trained using an augmented dataset of 2700 images (60% data) and validated with 1200 images of disease-affected samples to identify rice disease in agricultural fields. The model is tested with 630 images (14% data); testing accuracy is 97.9%. The model is exported into a mobile application to introduce the real-life application of the outcome of this work. The model accuracy is compared to others work associated with this type of problem. It is found that the performance of the model and the application are satisfactory compared to other works related to this work. The over-all accuracy is notable, showing the reliability and dependability of this model to classify rice leaf diseases.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から分類するCNN、背景除去のセグメンテーション、k-means処理、検証、モバイル実装が中心であり、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 paper introduces a novel CNN architecture which is comparatively small in size and promising in performance to predict rice leaf disease with moderate accuracy and lower time complexity.
Reproduction assets foundThe paper's rice leaf disease image inputs are publicly available: the UCI 'Rice Leaf Diseas' dataset and two Mendeley Data datasets ('Dhan-Shomadhan' and 'Rice Leaf Disease Image Samples') are explicitly named in the Data Availability Statement with public URLs and stated public availability. No author analysis code,训Dataset · publicIn this work, we performed experiments using three datasets named
“Rice Leaf Diseas” from UCI Machine Learning Repository (https://doi.org/10.24432/C5R013)Open asset ↗UCI Machine Learning Repository · 10.24432/C5R013pdf-page:15 lines:1-58Dataset · public“Rice Leaf Disease Image
Samples” from Mendeley Data (https://data.mendeley.com/datasets/fwcj7stb8r/1). All the datasets
are publicly available.Open asset ↗Mendeley Data · fwcj7stb8r/1pdf-page:15 lines:1-58Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Rice ( Oryza sativa L.) is one of the most important cereals, which provides 20% of the world's food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha -1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel -1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting.
Why it matches plant phenotyping methodsRGB画像と深層学習によりイネ収量を推定する手法を開発・検証しており、植物表現型の取得・抽出が研究の中心である。
abstractHere, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images.
Reproduction assets foundThe paper explicitly states that the code to run the developed rice-yield CNN model is publicly available on the authors' GitHub repository. The image/yield database itself is not stated to be publicly deposited, so only the code qualifies as a paper-specific public asset.Code · publicThe code to run the developed CNN model is available at https://github.com/r1wtn/rice_yield_CNN .Open asset ↗r1wtn/rice_yield_CNNlines:57-71Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Crop yield is determined by potential harvest organ size, source organ photosynthesis and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain-filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high-yielding rice cultivars, each yielding approximately 21 t ha−1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain-filling rate could lead to a potential yield increase of 30–40% in an elite rice cultivar. Notably, the instantaneous grain-filling rates around 15- and 38-day post-flowering significantly influence grain yield; and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict the maximum dried brown rice yield (Y, t ha−1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha−1): Y = 0.74 × Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
Why it matches plant phenotyping methods全植物の炭素・窒素動態と収量形成を推定する速度論モデルを開発し、耳の呼吸速度による粒充填速度の定量化手法も導入しているため、表現型取得・推定が中心的です。
abstractHere, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics.
Reproduction assets foundThe paper's authors publicly released the WACNI model source code (the computational framework used for all simulations and analysis) on GitHub, with explicit availability language in the MODEL AND DATA AVAILABILITY section. Supplementary Data 2 contains literature-extracted experimental data but no separate public URLCode · publicip
help improve model parameterization.
cr
MODEL AND DATA AVAILABILITY
us
an
Experimental data extracted from literature, used in model-data comparison, are tabulated in
Supplementary Data 2.
M
The source code used for this study, along with the operational commands and user guide, is
freely available for non-commercial use at https://github.com/rootchang/WACNI-rice.git.
e d
pt
ce
Ac
28Open asset ↗rootchang/WACNI-ricepdf-layout-page:28 lines:1-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Rice disease can significantly reduce yields, so monitoring and identifying the diseases during the growing season is crucial. Some current studies are based on images with simple backgrounds, while realistic scene settings are full of background noise, making this task challenging. Traditional artificial prevention and control methods not only have heavy workload, low efficiency, but are also haphazard, unable to achieve real-time monitoring, which seriously limits the development of modern agriculture. Therefore, using target detection algorithm to identify rice diseases is an important research direction in the agricultural field. Methods In this article a total of 7,220 pictures of rice diseases taken in Jinzhai County, Lu'an City, Anhui Province were chosen as the research object, including rice leaf blast, bacterial blight and flax leaf spot. We propose a rice disease identification method based on the improved YOLOV5s, which reduces the computation of the backbone network, reduces the weight file of the model to 3.2MB, which is about 1/4 of the original model, and accelerates the prediction speed by three times. Results Compared with other mainstream methods, our method achieves better performance with low computational cost. It solves the problem of slow recognition speed due to the large weight file and calculation amount of model when the model is deployed in mobile terminal.
Why it matches plant phenotyping methodsイネ葉の病害を画像から識別する改良YOLOv5s手法を開発し、精度・計算量・速度を評価しており、植物病害状態の画像ベース表現型計測が中心である。
abstractWe propose a rice disease identification method based on the improved YOLOV5s
Reproduction assets foundThe authors publicly deposited the rice disease image dataset (7,220 images of rice leaf blast, bacterial blight, and flax leaf spot) on figshare, with an explicit data availability statement and DOI. Code is only available via supplemental files without an allowed public URL, so it is not listed as a separate asset.Dataset · publicThe data is available at figshare: (2022): dataset. figshare. Figure. https://doi.org/10.6084/m9.figshare.21514515.v1 .Open asset ↗figshare · 10.6084/m9.figshare.21514515.v1lines:383-525Code / 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 confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
As one of the most widely grown crops in the world, rice is not only a staple food but also a source of calorie intake for more than half of the world's population, occupying an important position in China's agricultural production. Thus, determining the inner potential connections between the genetic mechanisms and phenotypes of rice using dynamic analyses with high-throughput, nondestructive, and accurate methods based on high-throughput crop phenotyping facilities associated with rice genetics and breeding research is of vital importance. In this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice. Up to 84.8% of the phenotypic variance of the rice yield could be explained by these i-traits. A total of 285 putative quantitative trait loci (QTLs) were detected for the i-traits, and principal components analysis was applied on the basis of the i-traits in the temporal and organ dimensions, in combination with a genome-wide association study that also isolated QTLs. Moreover, the differences among the different population structures and breeding regions of rice with regard to its phenotypic traits demonstrated good environmental adaptability, and the crop growth and development model also showed high inosculation in terms of the breeding-region latitude. In summary, the strategy developed here for the acquisition and analysis of image-based rice phenomes can provide a new approach and a different thinking direction for the extraction and analysis of crop phenotypes across the whole growth period and can thus be useful for future genetic improvements in rice.
Why it matches plant phenotyping methodsイネの全生育期間にわたる画像ベース形質の取得・解析戦略を開発しており、フェノタイピング手法が研究の中心である。
abstractIn this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice.
Reproduction assets foundThe paper's Data Availability statement points to a public website hosting the related data and code (i-trait phenotype dataset and analysis code) for this rice image-based phenotyping study. The URL matches an allowed entry. Confidence is moderate because the statement uses future tense ('will be made available') and,Dataset · publicted in the experimental design and data analysis. W.Y. and W.H. supervised the project and designed the research. Competing interests: The authors declare that they have no competing interests.
Data Availability
The related data and code supporting the conclusion for this article will be made available on the following website: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .
Supplementary Materials
Supplementary 1
Movie S1. Dynamic graph of the processing method.
Note S1. Trait analysis technical documentation.
Table S1. Information on Oryza sativa .
Table S2. Statistical summary of the 6 developed models for estimating the panicle dry weight.
Table S3. Summary of the 5-fold crossOpen asset ↗plantphenomics.hzau.edu.cnlines:256-286Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network's ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network's feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method's strong performance and feasibility for rice disease classification in real-life scenarios.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する画像処理・ニューラルネットワーク手法の開発と10分割交差検証が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images.
Reproduction assets foundThe paper's rice disease images come from Kaggle/Mendeley sources whose URLs are not in the allowed list, and no author analysis code or trained model is publicly deposited ('All data are presented in the article'). The only qualifying paper-specific public asset is the PlantVillage dataset, which the authors used to验证Dataset · publicthe results of the tests are presented in Table 12 . Our proposed model demonstrated strong performance on three publicly available datasets, suggesting that it possesses a high degree of generalizability to other datasets.
Table 11
Detailed information about the dataset.
Dataset
Category
Total
Available
PlantVillage
38
55,400
https://www.kaggle.com/datasets/hiyash99/plantvillage (accessed on 24 March 2023)
Stanford cars
196
16,185
https://www.kaggle.com/datasets/jutrera/stanford-car-dataset-by-classes-folder (accessed on 24 March 2023)
ImageNetDogs
120
20,580
http://vision.stanford.edu/aditya86/ImageNetDogs/ (accessed on 24 March 2023)
Table 12
Public dataset test results.
Dataset
Accuracy Open asset ↗Kaggle · hiyash99/plantvillagelines:417-573Dataset · publicthree publicly available datasets, suggesting that it possesses a high degree of generalizability to other datasets.
Table 11
Detailed information about the dataset.
Dataset
Category
Total
Available
PlantVillage
38
55,400
https://www.kaggle.com/datasets/hiyash99/plantvillage (accessed on 24 March 2023)
Stanford cars
196
16,185
https://www.kaggle.com/datasets/jutrera/stanford-car-dataset-by-classes-folder (accessed on 24 March 2023)
ImageNetDogs
120
20,580
http://vision.stanford.edu/aditya86/ImageNetDogs/ (accessed on 24 March 2023)
Table 12
Public dataset test results.
Dataset
Accuracy (%)
PlantVillage
96.25
Stanford cars
94.67
ImageNetDogs
97.24
4. DiscussionOpen asset ↗Kaggle · jutrera/stanford-car-dataset-by-classes-folderlines:417-573Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20-40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel self-attention network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the convolutional neural network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing that of current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector.
Why it matches plant phenotyping methodsイネ葉の病徴という植物状態を画像から分類する新規深層学習手法を開発・交差検証しており、植物病害表現型の取得・推定が中心である。
abstractwe present a novel self-attention network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification.
Reproduction assets foundThe paper's phenotyping input is a public rice disease leaf image dataset (Kaggle, HuyDo 2019) used to train and evaluate the SANET model; the dataset is explicitly cited with a public URL. No author analysis code or trained model release is mentioned.Dataset · public35. HuyDo. Rice Diseases Image Dataset: An Image Dataset for Rice and Its Diseases. 2019. Available online:
https://www.kaggle.com/datasets/minhhuy2810/rice-diseases-image-dataset (accessed on 19 May 2023).Open asset ↗Kaggle · minhhuy2810/rice-diseases-image-datasetpdf-page:14 lines:1-53Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.
Why it matches plant phenotyping methodsハイパースペクトル画像からイネ種子の温度処理状態を分類する深層学習手法を開発・検証し、校正・前処理・分類用GUIも提供しており、種子表現型の取得・抽出方法が中心である。
abstractA framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes for the DL framework for hyperspectral seed image calibration, preprocessing, segmentation, and classification are available at: https://gitfront.io/r/vido6/vC64GLsxCDZx/classificationRice/ , accessed on 23 March 2023.Open asset ↗classificationRicelines:95-200Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Rice lodging seriously affects rice quality and production. Traditional manual methods of detecting rice lodging are labour-intensive and can result in delayed action, leading to production loss. With the development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) provide imminent assistance for crop stress monitoring. In this paper, we proposed a novel lightweight detection system with UAVs for rice lodging. We leverage UAVs to acquire the distribution of rice growth, and then our proposed global attention network (GloAN) utilizes the acquisition to detect the lodging areas efficiently and accurately. Our methods aim to accelerate the processing of diagnosis and reduce production loss caused by lodging. The experimental results show that our GloAN can lead to a significant increase in accuracy with negligible computational costs. We further tested the generalization ability of our GloAN and the results show that the GloAN generalizes well in peers' models (Xception, VGG, ResNet, and MobileNetV2) with knowledge distillation and obtains the optimal mean intersection over union (mIoU) of 92.85%. The experimental results show the flexibility of GloAN in rice lodging detection.
Why it matches plant phenotyping methodsUAV画像からイネの倒伏領域（植物状態）を抽出する軽量な検出システムとGloANを提案・評価しており、表現型取得・推定手法が中心である。
abstractwe proposed a novel lightweight detection system with UAVs for rice lodging.
Reproduction assets foundThe authors explicitly state that the rice lodging dataset (UAV images with annotations) and the source code for the GloAN analysis are open sourced and publicly available on GitHub.Dataset · publicThe dataset and source code used in this study have been open sourced and are publicly available at https://github.com/Stephenkgb/GloAN-and-rice-lodging-dataset .Open asset ↗Stephenkgb/GloAN-and-rice-lodging-datasetlines:214-228Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Farmers and agricultural experts can take action on many areas of paddy crop handling and management practices with the use of actionable information from the in-field diagnosis of paddy blast disease. To successfully diagnose the blast disease affecting fifteen different paddy crop varieties, three transfer learning multi-layer convolutional neural network (CNN) models, such as, CapsNet, EfficientNet-B7, and ResNet-50 are presented in this paper. The field images of blast disease are captured and classified based on disease severity levels, such as low, medium, high, and severe. The study employing the CapsNet model with dataset consisting a total of 20,000 labeled images demonstrate the significant results with the testing efficiency of 90.79% and validation efficiency of 93.29%. The ResNet-50 and EfficientNet-B7 models have yielded the average testing efficiencies of 85.10% and 88.72%, respectively. On the held out blast disease affected paddy field image dataset, the CapsNet model outperformed the EfficientNet-B7 and ResNet-50 CNN models related to both classification efficiency and computational efficiency.
Why it matches plant phenotyping methods圃場画像からイネいもち病の重症度を推定し、複数CNNモデルを比較・検証することが研究の中心であるため、植物病害フェノタイピング手法に該当する。
abstractThe field images of blast disease are captured and classified based on disease severity levels, such as low, medium, high, and severe.
Reproduction assets foundThe paper's paddy blast field-image dataset (3,987 captured images augmented to 20,000 labeled images across four severity levels) is explicitly declared publicly available on Kaggle by the authors in the Data availability statement. No code or trained model deposit is stated.Dataset · publicDataset associated with this article are available at https://www.kaggle.com/datasets/girishkleit/paddy-Open asset ↗Kagglepdf-page:13 lines:1-40Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The rapidly changing climate affects an extensive spectrum of human-centered environments. The food industry is one of the affected industries due to rapid climate change. Rice is a staple food and an important cultural key point for Japanese people. As Japan is a country in which natural disasters continuously occur, using aged seeds for cultivation has become a regular practice. It is a well-known truth that seed quality and age highly impact germination rate and successful cultivation. However, a considerable research gap exists in the identification of seeds according to age. Hence, this study aims to implement a machine-learning model to identify Japanese rice seeds according to their age. Since agewise datasets are unavailable in the literature, this research implements a novel rice seed dataset with six rice varieties and three age variations. The rice seed dataset was created using a combination of RGB images. Image features were extracted using six feature descriptors. The proposed algorithm used in this study is called Cascaded-ANFIS. A novel structure for this algorithm is proposed in this work, combining several gradient-boosting algorithms such as XGBoost, CatBoost, and LightGBM. The classification was conducted in two steps. First, the seed variety was identified. Then, the age was predicted. As a result, seven classification models were implemented. The performance of the proposed algorithm was evaluated against 13 state-of-the-art algorithms. Overall, the proposed algorithm has a higher accuracy, precision, recall, and F1-score than the others. For the classification of variety, the proposed algorithm scored 0.7697, 0.7949, 0.7707, and 0.7862, respectively. The results of this study confirm that the proposed algorithm can be employed in the successful age classification of seeds.
Why it matches plant phenotyping methodsRGB画像からコメ種子の品種・年齢を抽出する機械学習手法を開発し、新規データセットを構築して複数手法と性能比較しているため、種子状態の画像ベース表現型解析が中心である。
abstractthis study aims to implement a machine-learning model to identify Japanese rice seeds according to their age
Reproduction assets foundThe paper's authors constructed a novel rice seed image dataset (six varieties, three harvest ages) and explicitly state it is publicly available on Kaggle under the author's account, matching an allowed URL. No code or model release is stated.Dataset · publicgle data repository accessed on 15 January 2023 (https://www.kaggle.com/datasets/namalrathnayake1Open asset ↗Kagglepdf-page:16 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an UAV (unmanned aerial vehicle) to collect the RGB images of the paddy field. Then, we proposed a new rice plant counting, locating, and sizing method (RiceNet), which consists of one feature extractor frontend and 3 feature decoder modules, namely, density map estimator, plant location detector, and plant size estimator. In RiceNet, rice plant attention mechanism and positive-negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps. To verify the validity of our method, we propose a new UAV-based rice counting dataset, which contains 355 images and 257,793 manual labeled points. Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2, respectively. Moreover, we validated the performance of our method with two other popular crop datasets. On these three datasets, our method significantly outperforms state-of-the-art methods. Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.
Why it matches plant phenotyping methodsUAV画像からイネ個体の位置・サイズ・個体数を推定する手法RiceNetを開発し、データセット構築と性能検証まで行っており、植物表現型取得が研究の中心です。
abstractwe proposed a new rice plant counting, locating, and sizing method (RiceNet)
Reproduction assets foundThe paper explicitly states that all RiceNet source code is publicly available at the authors' GitHub repository. The URC dataset (355 UAV images, 257,793 labeled points) is described but no explicit public deposit URL is given, so it is not listed as an actionable asset.Code · publicAll the source code of RiceNet is available at https://github.com/xdbai-source/Rice-Plant-Counting .Open asset ↗xdbai-source/Rice-Plant-Countinglines:51-58Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceSeed / grainCountingSegmentationGrowth / development / phenology
The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.
Why it matches plant phenotyping methodsイネ種子の発芽率という植物形質を画像処理で自動抽出・定量する手法を開発し、誤差と頑健性を評価しており、表現型取得法が中心である。
abstractIn this paper, a new method is proposed for counting the number of grains and germinated grains.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' analysis code and the 90 rice grain images used for germination-rate phenotyping in a public GitHub repository, matching an allowed URL. The web application URL is a live service, not a deposited asset, and is excluded.Code · publicges of Luyou 911 and the germination detection results.
(DOCX)
Click here for additional data file.
Acknowledgments
We are deeply grateful to the editor and reviewers for their assistance with reviews and guidance of the paper.
Data Availability
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .
Funding Statement
This study was supported by the National Natural Science Foundation of China (Grants No. 61373004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Wu W, Zhou L, Chen J, Qiu Z, He Y. GainTKW: a measurement sysOpen asset ↗DoctorXiong123456/CodeOfPaperlines:233-285Dataset · publicmages of II you 534 and the germination detection results.
(DOCX)
Click here for additional data file.
S3 Table
30 images of Luyou 911 and the germination detection results.
(DOCX)
Click here for additional data file.
Data Availability Statement
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .Open asset ↗DoctorXiong123456/CodeOfPaperlines:286-308Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Virtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and development patterns. Virtual plant visualization technology is a core part of virtual plant research. The major limitation of the existing plant growth visualization models is that the produced virtual plants are not realistic and cannot clearly reflect plant color, morphology and texture information. Results This study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information. CropPainter was first tested for virtual rice panicle generation as an example of virtual crop generation at the organ level. Subsequently, CropPainter was extended for visualizing crop plants (at the plant level), including rice, maize and cotton plants. The tests showed that the virtual crops produced by CropPainter are very realistic and highly consistent with the input phenotypic traits. The codes, datasets and CropPainter visualization software are available online. Conclusion In conclusion, our method provides a completely novel idea for crop visualization and may serve as a tool for virtual crops, which can assist in plant growth and development research.
Why it matches plant phenotyping methods与えられた表現型情報から作物画像を生成するGANベースの手法とソフトウェアを開発しており、植物表現型の可視化・再現が研究の中心である。
abstractThis study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information.
Reproduction assets foundThe paper explicitly states that supplementary files including datasets, trained models, software, and source code are publicly available at the authors' HZAU plant phenotyping download site and a GitHub repository. These directly reproduce the paper's phenotyping datasets and CropPainter analysis.Code · publicSupplementary files for this article, which include datasets, trained models, software as well as the source codes used in this study, are available on website: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download and https://github.com/zhwang-hzau/CropPainter-master .Open asset ↗zhwang-hzau/CropPainter-masterlines:157-211Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Advancements in hyperspectral imaging (HSI) and establishment of dedicated plant phenotyping facilities have enabled researchers to gather large quantities of plant spectral images with the aim of inferring target phenotypes non-destructively. However, large volumes of data that result from HSI and corequisite specialized methods for analysis may prevent plant scientists from taking full advantage of these systems. Here, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system. Under contrasting nitrogen conditions, HSI data are used to classify treatment groups with ≥ 83% accuracy by utilizing support vector machines. Out of the 14 physiological traits collected, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could also be predicted from the hyperspectral imaging data with normalized root mean square error of predictions smaller than 14% (R 2 of 0.88 for N and 0.75 for C:N). This study demonstrates the potential of using an automated HSI system to analyze genotypic variation for physiological traits in a diverse panel of rice; to help lower barriers of application of hyperspectral imaging in the greater plant science research community, analysis scripts used in this study are carefully documented and made publicly available. HIGHLIGHT Data from an automated hyperspectral imaging system are used to classify nitrogen treatment and predict leaf-level nitrogen content and carbon to nitrogen ratio during vegetative growth in rice.
Why it matches plant phenotyping methods自動ハイパースペクトル画像を用いてイネの生理形質を非破壊推定し、予測精度を評価しているため、フェノタイピング手法が中心的です。
abstractHere, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system.
Reproduction assets foundThe paper's collected/analyzed datasets (hyperspectral imaging and physiological trait data) are publicly deposited in the Purdue University Research Repository, and the authors' analysis code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicand/or edits.
657 CONFLICT OF INTEREST
658 The authors declare no conflict of interest.
659 FUNDING
660 This work was partially funded by a grant from USDA NIFA to DRW (#2022-67013-36205).
661 DATA AVAILABILITY
662 The datasets collected and analyzed for this study can be found in the Purdue University Research
663 Repository [https://purr.purdue.edu/publications/4079/1].
664
665 REFERENCES
666 Al Makdessi, N., Ecarnot, M., Roumet, P., and Rabatel, G. (2019). A spectral correction method for
667 multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application
668 to nitrogen content assessment in wheat. Precision Agric 20, 237–259. doi: 10.1007/s11119-
669 018-Open asset ↗pdf-layout-page:30 lines:1-64Code · public249
250 Data analysis
251 Data were formatted and analyzed in R 4.1.1 (R Core Team, 2021) with packages dplyr
252 (Wickham et al., 2021) and reshape2 (Wickham, 2007). Plots were made with package ggplot2
253 (Wickham, 2016) or in base R environment. The code for each physiological trait model can be
254 accessed through GitHub (https://github.com/To-Chia/rice_imaging_ms).
255 Physiological trait collection: From the physiological trait measurements, we derived specific
256 leaf area (SLA, cm2g-1), CN ratio (C:N), specific leaf area with respect to carbon (SLA_C (cm2
257 mg-1 (C)) and specific leaf nitrogen (SLN, mg (N) cm-2). The summary statistics are in Table S3.
258 Histograms and normal Open asset ↗GitHub · To-Chia/rice_imaging_mspdf-layout-page:12 lines:1-64Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.
Why it matches plant phenotyping methodsUAV画像から作物のキャノピー高を抽出し、時系列モデルで生育・形質を予測する枠組みが研究の中心であり、実質的な植物フェノタイピング手法の開発・適用に該当する。
abstractHere we provide a novel framework for trait prediction using CH data in rice.
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 ) in 2019, 2020, and 2021 from the aspect of genetics and examined how to use the CH data for the prediction of traits usually measured by hand ( Figure 1 ).Open asset ↗lines:342-376Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceLaboratory / benchtopX-ray / CTRootImage / point-cloud registrationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Background Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. Results We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. Conclusions We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.
Why it matches plant phenotyping methods根系形態を時系列X線CT画像から抽出・追跡する半自動ワークフローを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps.
Reproduction assets foundThe paper's authors publicly released RSAtrace4D, the software implementing the backward-prediction workflow for time-series X-ray CT root system architecture analysis, on GitHub, and state that the datasets used are available via their GitHub account and project homepage.Code · publicThe implementation of this workflow, which is specified for rice, was named RSAtrace4D and is available at the GitHub repository ( https://github.com/st707311g/RSAtrace4D ).Open asset ↗st707311g/RSAtrace4Dlines:116-124Dataset · publicThe datasets used in this study are available at the GitHub repository ( https://github.com/st707311g/ ) and the project homepage ( https://rootomics.dna.affrc.go.jp/en/ ).Open asset ↗st707311glines:136-191Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
More than the half of the global population consume rice as their primary energy source. Therefore, this work focused on the development of a prediction model to minimize agricultural loss in the paddy field. Initially, rice plant diseases, along with their images, were captured. Then, a big data framework was used to encounter a large dataset. In this work, at first, feature extraction process is applied on the data and after that feature selection is also applied to obtain the reduced data with important features which is used as the input to the classification model. For the rice disease datasets, features based on color, shape, position, and texture are extracted from the infected rice plant images and a rough set theory-based feature selection method is used for the feature selection job. For the classification task, ensemble classification methods have been implemented in a map reduce framework for the development of the efficient disease prediction model. The results on the collected disease data show the efficiency of the proposed model.
Why it matches plant phenotyping methods感染イネ画像から色・形状・位置・テクスチャ特徴を抽出し、病害分類モデルを開発しており、植物の病害状態を観測画像から推定する方法が中心である。
titleDevelopment of a Rice Plant Disease Classification Model in Big Data Environment.
Reproduction assets foundThe paper's rice disease classification experiments rely on a public Kaggle rice leaf disease image dataset, cited as the data source (reference 30). No author code, trained models, or paper-specific data deposits are stated; the Data Availability Statement only says data are included in the article.Dataset · public30. Available online: https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases (accessed on 15 June 2022).Open asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-page:14 lines:1-52Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
Background and aims The surface area of mesophyll cells (Smes) and chloroplasts (Sc) facing the intercellular airspace (IAS) are important parameters for estimating photosynthetic activity from leaf anatomy. Although Smes and Sc are estimated based on the shape assumption of mesophyll cells (MCs), it is questionable if the assumption is correct for rice MCs with concave-convex surfaces. Therefore, in this study, we establish a reconstruction method for the 3-D representation of the IAS in rice leaf tissue to calculate the actual Smes and Sc with 3-D images and to determine the correct shape assumption for the estimation of Smes and Sc based on 2-D section images. Methods We used serial section light microscopy to reconstruct 3-D representations of the IAS, MCs and chloroplasts in rice leaf tissue. Actual Smes and Sc values obtained from the 3-D representation were compared with those estimated from the 2-D images to find the correct shape-specific assumption (oblate or prolate spheroid) in different orientations (longitudinal and transverse sections) using the same leaf sample. Key results The 3-D representation method revealed that volumes of the IAS and MCs accounted for 30 and 70 % of rice leaf tissue excluding epidermis, respectively, and the volume of chloroplasts accounted for 44 % of MCs. The shape-specific assumption on the sectioning orientation affected the estimation of Smes and Sc using 2-D section images with discrepancies of 10-38 %. Conclusions The 3-D representation of rice leaf tissue was successfully reconstructed using serial section light microscopy and suggested that estimation of Smes and Sc of the rice leaf is more accurate using longitudinal sections with MCs assumed as oblate spheroids than using transverse sections with MCs as prolate spheroids.
Why it matches plant phenotyping methodsイネ葉組織の3D再構成法を開発し、2D画像による葉肉細胞・葉緑体の表面積推定と比較検証しており、植物形質の取得・推定法が中心である。
abstractwe establish a reconstruction method for the 3-D representation of the IAS in rice leaf tissue to calculate the actual Smes and Sc with 3-D images
Reproduction assets foundThe paper's supplementary materials include 3-D reconstruction videos of the IAS and chloroplast regions facing the IAS (Videos S1 and S2) and a table of shape assumptions used for curvature correction factors (Table S1), all directly reproducing this paper's phenotyping measurements. These are hosted online via the OyDataset · publicSupplementary data are available online at https://academic.oup.com/aob and consist of the following:Open asset ↗lines:139-146Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.
Why it matches plant phenotyping methodsイネ冠部画像から窒素栄養状態をCNNで自動判別する手法を構築・評価しており、植物状態の取得・推定が研究の中心である。
abstractTo realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods.
Reproduction assets foundThe paper's rice canopy image dataset used for CNN nitrogen-nutrition classification is publicly deposited in BioStudies (S-BIAD538), explicitly stated in the Data Availability section. No author analysis code or trained model checkpoints are mentioned.Dataset · publicData Availability: Data files are available from the Biostudies database: https://www.ebi.ac.uk/biostudies/studies/S-BIAD538 .Open asset ↗Biostudies · S-BIAD538lines:177-189Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The accurate segmentation of significant rice diseases and assessment of the degree of disease damage are the keys to their early diagnosis and intelligent monitoring and are the core of accurate pest control and information management. Deep learning applied to rice disease detection and segmentation can significantly improve the accuracy of disease detection and identification but requires a large number of training samples to determine the optimal parameters of the model. This study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment. First, a dataset for rice significant disease segmentation was selected and collated based on 3 open-source datasets, containing 450 sample images belonging to 3 categories of rice leaf bacterial blight, blast and brown spot. Then, to increase the diversity of samples, a data augmentation method, rice leaf disease copy paste (RLDCP), was proposed that expanded the collected disease samples with the concept of copy and paste. The new RSegformer model was then trained by replacing the new backbone network with the lightweight semantic segmentation network Segformer, combining the attention mechanism and changing the upsampling operator, so that the model could better balance local and global information, speed up the training process and reduce the degree of overfitting of the network. The results show that RLDCP could effectively improve the accuracy and generalisation performance of the semantic segmentation model compared with traditional data augmentation methods and could improve the MIoU of the semantic segmentation model by about 5% with a dataset only twice the size. RSegformer can achieve an 85.38% MIoU at a model size of 14.36 M. The method proposed in this paper can quickly, easily and accurately identify disease occurrence areas, their species and the degree of disease damage, providing a reference for timely and effective rice disease control.
Why it matches plant phenotyping methodsイネ葉の病斑領域を画像から分割し、病害の種類と被害程度を推定する手法の開発・評価が中心であり、植物病害状態の表現型取得に該当する。
abstractThis study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment.
Reproduction assets foundThe paper's curated rice disease image dataset with semantic segmentation masks is publicly deposited on Kaggle by the authors, and one of the three source image datasets (Dataset 3) is also a public Kaggle dataset used directly in this study. No author analysis code or trained model checkpoint is explicitly deposited;Dataset · publicThe three varieties of diseased rice leaf images and masks used in this study are available at https://www.kaggle.com/datasets/slygirl/rice-leaf-disease-with-segmentation-labels (accessed on 3 November 2022) and can be shared on request.Open asset ↗Kaggle · slygirl/rice-leaf-disease-with-segmentation-labelslines:314-332Dataset · public26. Leaf Rice Disease | Kaggle. [(accessed on 7 August 2022)]. Available online: https://www.kaggle.com/datasets/tedisetiady/leaf-rice-disease-indonesia .Open asset ↗Kaggle · tedisetiady/leaf-rice-disease-indonesialines:333-333Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published19 Sept 2022International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and ManagementCited by 105 · OpenAlex ↗
The demand for agricultural products increased exponentially as the global population grew. The rapid development of computer vision-based artificial intelligence and deep learning-related technologies has impacted a wide range of industries, including disease detection and classification. This paper introduces a novel neural network-based hybrid model (GCL). GCL is a dataset-augmentation fusion of long-short term memory (LSTM) and convolutional neural network (CNN) with generative adversarial network (GAN). GAN is used for the augmentation of the dataset, CNN extracts the features and LSTM classifies the various paddy diseases. The GCL model is being investigated to improve the classification model's accuracy and reliability. The dataset was compiled using secondary resources such as Mendeley, Kaggle, UCI, and GitHub, having images of bacterial blight, leaf smut, and rice blast. The experimental setup for proving the efficacy of the GCL model demonstrates that the GCL is suitable for disease classification and works with 97% testing accuracy. GCL can further be used for the classification of more diseases of paddy.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を分類する画像解析モデルを開発・評価しており、植物病害表現型の取得・分類手法が中心である。
abstractThis paper introduces a novel neural network-based hybrid model (GCL).
Reproduction assets foundThe paper's plant-phenotyping inputs are rice disease leaf images compiled from public repositories (Kaggle, UCI, GitHub; Mendeley also cited but its URL is not in the allowed list). Three of these public image datasets are directly used as the paper's raw phenotyping data and are actionable via allowed URLs. No authorDataset · publicial-blight . Accessed 02 Mar 2022
27. Blast (leaf and collar)-IRRI Rice Knowledge Bank. http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/blast-leaf-collar . Accessed 02 Mar 2022
28.
Sethy PK
Rice leaf disease image samples
Mendeley Data 2020
10.17632/FWCJ7STB8R.1
29. Leaf Rice Disease|Kaggle. https://www.kaggle.com/tedisetiady/leaf-rice-disease-indonesia . Accessed 02 Mar 2022
30. UCI machine learning repository: rice leaf diseases data set. https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases . Accessed 02 Mar 2022
31. GitHub-aldrin233/RiceDiseases-DataSet: Data Set for Rice Diseases with labels. https://github.com/aldrin233/RiceDiseases-DataSet . AOpen asset ↗Kaggle · leaf-rice-disease-indonesialines:474-517Dataset · publicgement/diseases/item/blast-leaf-collar . Accessed 02 Mar 2022
28.
Sethy PK
Rice leaf disease image samples
Mendeley Data 2020
10.17632/FWCJ7STB8R.1
29. Leaf Rice Disease|Kaggle. https://www.kaggle.com/tedisetiady/leaf-rice-disease-indonesia . Accessed 02 Mar 2022
30. UCI machine learning repository: rice leaf diseases data set. https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases . Accessed 02 Mar 2022
31. GitHub-aldrin233/RiceDiseases-DataSet: Data Set for Rice Diseases with labels. https://github.com/aldrin233/RiceDiseases-DataSet . Accessed 02 Mar 2022Open asset ↗UCI machine learning repository · Rice+Leaf+Diseaseslines:474-517Dataset · publicease|Kaggle. https://www.kaggle.com/tedisetiady/leaf-rice-disease-indonesia . Accessed 02 Mar 2022
30. UCI machine learning repository: rice leaf diseases data set. https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases . Accessed 02 Mar 2022
31. GitHub-aldrin233/RiceDiseases-DataSet: Data Set for Rice Diseases with labels. https://github.com/aldrin233/RiceDiseases-DataSet . Accessed 02 Mar 2022Open asset ↗GitHub-aldrin233/RiceDiseases-DataSet · RiceDiseases-DataSetlines:474-517Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Rice is considered one the most important plants globally because it is a source of food for over half the world's population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20-40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.
Why it matches plant phenotyping methodsイネ葉の画像から病害状態を分類・検出する深層学習手法の開発と性能評価が研究の中心であり、植物病害フェノタイピングに該当する。
abstractwe propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease.
Reproduction assets foundThe paper's rice leaf disease image dataset is explicitly stated to be acquired from a public Kaggle repository (riceleafsv3), which is the image dataset used for the paper's CNN-based disease classification experiments. No author analysis code, models, or checkpoints are disclosed.Dataset · publicThe data used in this research are acquired from publicly available at: https://www.kaggle.com/datasets/adefiqri12/riceleafsv3 (accessed on 24 May 2022).Open asset ↗kaggle.com/datasets/adefiqri12/riceleafsv3lines:349-369Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
To evaluate the application potential of high-density SNPs in rice distinctness, uniformity, and stability (DUS) testing, we screened 37,929 SNP loci distributed on 12 rice chromosomes based on whole-genome resequencing of 122 rice accessions. These SNP loci were used to analyze the DUS testing of rice varieties based on the correlation between the molecular and phenotypic distances of varieties according to UPOV option 2. The results showed that statistical algorithms and the number of phenotypic traits and SNP loci all affected the correlation between the molecular and phenotypic distances of rice varieties. Relative to the other nine algorithms, the Jaccard similarity algorithm had the highest correlation of 0.6587. Both the number of SNPs and the number of phenotypes had a ceiling effect on the correlation between the molecular and phenotypic distances of varieties, and the ceiling effect of the number of SNP loci was more obvious. To overcome the correlation bottleneck, we used the genome-wide prediction method to predict 30 phenotypic traits and found that the prediction accuracy of some traits, such as the basal sheath anthocyanin color, glume length, and intensity of the green color of the leaf blade, was very low. In combination with group comparison analysis, we found that the key to overcoming the ceiling effect of correlation was to improve the resolution of traits with low predictive values. In addition, we also performed distinctness testing on rice varieties by using the molecular distance and phenotypic distance, and we found that there were large differences between the two methods, indicating that UPOV option 2 alone cannot replace the traditional phenotypic DUS testing. However, genotype and phenotype analysis together can increase the efficiency of DUS testing.
Why it matches plant phenotyping methodsイネのDUS試験における分子距離・表現型距離、統計アルゴリズム、ゲノムワイド予測を比較評価しており、表現型評価手法の技術的検証が研究の中心です。
abstractTo evaluate the application potential of high-density SNPs in rice distinctness, uniformity, and stability (DUS) testing
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 · publicPhenotypic data were recorded for 30 morphological traits ( Table 2 ; Supplementary Table S2 ).Open asset ↗Supplementary Table S2lines:702-772Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Submergence during germination impedes aerobic metabolisms and limits the growth of most higher plants. However, some wetland plants including rice can germinate under submerged conditions. It has long been hypothesized that the first elongating shoot tissue, the coleoptile, acts as a snorkel to acquire atmospheric oxygen (O 2 ) to initiate the first leaf elongation and seminal root emergence. Here, we obtained direct evidence for this hypothesis by visualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system. In parallel with the O 2 imaging, we tracked the anatomical development of shoot and root tissues in real-time using an automated flatbed scanner. Three hours after the coleoptile tip reached the water surface, O 2 levels around the embryo transiently increased. At this time, the activity of alcohol dehydrogenase (ADH), an enzyme critical for anaerobic metabolism, was significantly reduced, and the coleorhiza covering the seminal roots in the embryo was broken. Approximately 10 h after the transient burst in O 2 , seminal roots emerged. A transient O 2 burst around the embryo was shown to be essential for seminal root emergence during submerged rice germination. The parallel application of a planar O 2 optode system and automated scanning system can be a powerful tool for examining how environmental conditions affect germination in rice and other plants.
Why it matches plant phenotyping methods水中イネ発芽時の酸素動態と器官発達を、平面O2オプトードおよび自動スキャナーで時空間的に可視化・追跡する手法が研究の中心であり、植物の生理状態・形態発達を測定する実質的なフェノタイピング手法である。
abstractvisualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system.
Reproduction assets foundThe paper's phenotyping measurements (time-lapse growth images and planar optode O2 imaging of submerged rice germination) are publicly available as Supplementary Videos 1–3 hosted at the Frontiers supplementary material URL. No author analysis code or trained models are deposited; ImageJ and UWSC are generic third‑dayDataset · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.946776/full#supplementary-material
Supplementary Video 1
Time-lapse images showing the germination process of submerged rice with normoxic or anoxic atmospheres.
Click here for additional data file.
Supplementary Video 2
Time-lapse images of the growth process following a shift from an anoxic to a normoxic atmosphOpen asset ↗lines:107-237Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
Why it matches plant phenotyping methodsUAV画像からイネの形態・生育形質を自動抽出するオープンソース基盤の開発と信頼性検証が中心であり、遺伝解析への応用も含むため採録。
abstractwe present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials.
Reproduction assets foundThe paper's Data Availability statement explicitly provides the AirMeasurer source code, GUI software, and testing 2D/3D aerial images (orthomosaics and point clouds) at a public GitHub release URL, directly supporting this paper's phenotyping analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/The‐Zhou‐Lab/UAV/releases/tag/V2.0.2 .Open asset ↗The‐Zhou‐Lab/UAV · V2.0.2lines:589-611Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed 'TPanicle-RCNN,' and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.
Why it matches plant phenotyping methodsイネの収量関連形質を高スループットに取得する統合計測・画像解析システムを開発し、検出精度と測定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。
abstracta novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy.
Reproduction assets foundThe article explicitly states that all threshed panicle training and testing data (1,072 threshed panicle images with PASCAL VOC annotations, augmented to 3,432 training and 856 testing images) are publicly available via a Baidu Netdisk link with an extraction code for non-commercial research. This is a paper-specific,Dataset · publicAll the training and testing data are available at https://pan.baidu.com/s/1-XawHGseIc5bboVOP48Fkw?pwd=153w with the extraction code ‘153w’ for non-commercial research purposes.Open asset ↗pan.baidu.com · 1-XawHGseIc5bboVOP48Fkw?pwd=153wlines:330-340Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The ratio of Na+ and K+ is an important determinant of the magnitude of Na+ toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na+ and K+ levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na+ exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m-1. Spectral data was used to create models for Na+ and K+ prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R2. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models.
Why it matches plant phenotyping methods塩ストレス下のイネに対するHSI画像処理パイプラインとPLSR予測モデルを開発・評価し、Na+・K+という植物生理状態の非破壊フェノタイピングへの適用性と予測性能を検証しているため、方法が中心的である。
abstractAnalysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis.
Reproduction assets foundThe authors deposited the paper's hyperspectral image dataset (rice plants under salinity stress, used for Na+/K+ prediction modeling) in the Dryad Digital Repository, with an explicit availability statement and public DOI.Dataset · publicData Availability: The hyperspectral image dataset used in this study is available through the DRYAD Digital Repository: https://doi.org/10.5061/dryad.2jm63xsrm .Open asset ↗Dryad Digital Repository · 10.5061/dryad.2jm63xsrmlines:140-151Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background: Paddy is one of the crops with the largest production worldwide, after corn and wheat. In Indonesia, paddy crops play a role as one of the main boosters of national economic growth based on their contribution to Indonesia's gross domestic product (GDP). Therefore, it is imperative to do research aimed at predicting the yield of paddy crops. Methods: : This research exploits the technology of remote sensing and machine learning methods (i.e. Gradient Boosting Regressor) to predict the yield of lowland paddy crops. Remote sensing with a Landsat 8 satellite was used to obtain the input data in the form of the vegetation index (i.e. NDVI) value, surface temperature, and total pixels of the observed area. Afterward, the input data was arranged into training data by combining paddy yield data and the paddy harvest period. Results: : The obtained training data was modelled to predict the yield of paddy crops using a Gradient Boosting Regressor. The results obtained from experiments conducted in Bandung, Indonesia, showed the scenario with the best parameter combination is an estimator of 2000, a learning rate of 0.001, minimum samples split of 2, and a maximum depth of 4, which has RMSE of 9766.72. Conclusions: : This research succeeded in designing a computational model to predict the yield of lowland paddy crops by involving remote sensing and Gradient Boosting Regressor.
Why it matches plant phenotyping methods衛星リモートセンシングと機械学習を用いて水稲収量を推定する手法が研究の中心であり、収量という植物・作物形質を直接推定している。
abstractThis research exploits the technology of remote sensing and machine learning methods (i.e. Gradient Boosting Regressor) to predict the yield of lowland paddy crops.
Reproduction assets foundThe paper publicly deposits its underlying phenotyping data (NDVI vegetation index data, Indonesian topographical map, and final combined dataset) on OSF under CC0, and its analysis source code on GitHub with a Zenodo archive. Both are paper-specific, public, and actionable.Code · publiclished by BPS-Statistics Indonesia of Bandung:
• Ciparay City: Ciparay, 2013; Ciparay, 2014; Ciparay, 2015.
• Cikancung City:: Cikancung, 2013; Cikancung, 2014; Cikancung, 2015.
• Paseh City: Paseh, 2013; Paseh, 2014; Paseh, 2015.
• Majalaya City: Majalaya, 2013; Majalaya, 2014.
Software availability
Source code available from: https://github.com/lala-s-riza/Remote-sensing-and-machine-learning-for-yield-prediction-of-lowland-paddy-crops.git
Archived source code at time of publication: https://doi.org/10.5281/zenodo.6459715 (Riza et al., 2022b).
License: GNU General Public License (GPL-2.0)
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F1000Research 2022, 11:682 Last updated: 28 JUL 2025Open asset ↗GitHubpdf-raw-page:14 lines:1-43Code · publicCikancung, 2015.
• Paseh City: Paseh, 2013; Paseh, 2014; Paseh, 2015.
• Majalaya City: Majalaya, 2013; Majalaya, 2014.
Software availability
Source code available from: https://github.com/lala-s-riza/Remote-sensing-and-machine-learning-for-yield-prediction-of-lowland-paddy-crops.git
Archived source code at time of publication: https://doi.org/10.5281/zenodo.6459715 (Riza et al., 2022b).
License: GNU General Public License (GPL-2.0)
Page 14 of 19
F1000Research 2022, 11:682 Last updated: 28 JUL 2025Open asset ↗Zenodo · 10.5281/zenodo.6459715pdf-raw-page:14 lines:1-43Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceRaman / spectroscopyClassificationGrowth / development / phenology
Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.
Why it matches plant phenotyping methodsラマン分光とCapsNetを組み合わせ、イネの生育期間という植物形質を推定・分類する手法を開発し、学習・テスト精度で評価しているため、表現型取得・抽出法が中心です。
abstractwe carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets).
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's data (Raman spectral data of 245 japonica rice grain samples) and code (spectral preprocessing and CapsNets classification) are openly available in the authors' public GitHub repository, which is listed in the allowed URLs.Code · publicData Availability Statement: The data and code presented in this study are openly available at:
https://github.com/zxxsnh/Rice_GD_classification_CapsNets (accessed on 14 June 2022).Open asset ↗zxxsnh/Rice_GD_classification_CapsNetspdf-page:12 lines:1-60Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages.
Why it matches plant phenotyping methodsUAV画像からイネ幼苗の生育段階という植物状態を自動推定する機械学習手法を開発・比較し、性能評価しているため、表現型取得が研究の中心である。
abstractThe application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations
Reproduction assets foundThe paper's data availability statement explicitly deposits the UAV rice seedling image datasets, trained models, and analysis code in public locations: a Google Drive folder (datasets and models) and a GitHub repository (code), both matching allowed URLs.Dataset · publicThe datasets, models and code used in this paper are available at the following locations: Datasets and models: https://drive.google.com/drive/folders/1AY-ro3HID9noOpen asset ↗lines:786-795Code · publicCode: https://github.com/imagevision-lab/rice_seedling_growth_stages_detectionOpen asset ↗imagevision-lab/rice_seedling_growth_stages_detectionlines:786-795Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
BACKGROUND: Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. RESULTS: In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. CONCLUSIONS: This study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
Why it matches plant phenotyping methodsイネ葉のハイパースペクトル画像と深層学習により病斑割合・病害重症度を推定し、抵抗性品種選抜へ応用する手法が研究の中心である。
abstractinformative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
Reproduction assets foundThe paper's authors publicly released their LPnet deep learning analysis code on GitHub, explicitly stated in both the Software tools and Availability sections. The hyperspectral phenotype data itself is only available on request, so it is listed separately as request_only.Code · publicRelevant algorithm code is available on the GitHub address ( https://github.com/jinnuozhang/LPnet ).Open asset ↗jinnuozhang/LPnetlines:121-133Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からイネの収量を推定する深層学習ワークフローを比較・評価しており、植物形質の取得・推定手法が研究の中心である。
abstractHere we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas.
Reproduction assets foundThe paper's authors explicitly state that the Python and R code used to process the UAV imagery data, train and evaluate the CNN models, and recreate Figures 2–5 is publicly available on GitHub. No separate public phenotype dataset or trained model checkpoint deposit is stated in the supplied blocks; other URLs are forCode · publicPython and R code used to process data, train and evaluate models, and recreate Figures 2 – 5 , is available at https://github.com/em-bellis/XASU_rice .Open asset ↗em-bellis/XASU_ricelines:521-535Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
BACKGROUND: Rice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed. RESULTS: We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. CONCLUSIONS: We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.
Why it matches plant phenotyping methodsCNNとGrad-CAMにより米粒画像から白未熟粒（chalkiness）を検出・定量する高スループット表現型計測手法の開発であり、方法が研究の中心です。
abstractlack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness
Reproduction assets foundThe authors explicitly state that the datasets (rice grain images) generated and analyzed in this study, as well as the trained models, are publicly available on GitHub at the authors' repository. This is a paper-specific, public, actionable asset directly reproducing the paper's phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during the current study are available on GitHub, https://github.com/cwang16/Phenotyping-of-Chalkiness-in-Rice .Open asset ↗cwang16/Phenotyping-of-Chalkiness-in-Ricelines:212-226Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
Why it matches plant phenotyping methods植物表現型データのデジタル記録・取得を目的とするオープンソースアプリの開発であり、表現型測定ワークフローが中心的です。
abstractTherefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses.
Reproduction assets foundThe paper describes PhenoApp, an open-source Android phenotyping app. Authors provide the app's source code (Gitea, archived on Zenodo) and underlying example input/output phenotype data files on Zenodo under CC0. The SHAPE II project website is a project page, not a paper-specific data deposit, and is excluded.Code · publice ‘in’ folder of the app main directory and no additional source data is required).
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Output_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants fOpen asset ↗gitea.julius-kuehn.de · JKI/pheno-applines:333-433Code · publicput_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants from the German Federal Ministry of Education and Research to FS (SelWineQ, FKZ 031B0889Open asset ↗Zenodo · 10.5281/zenodo.5525779lines:333-433Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.
Why it matches plant phenotyping methods画像からイネの栄養欠乏状態を推定するアンサンブル深層学習法を開発・評価しており、植物状態の取得・分類が研究の中心である。
abstractthis study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system.
Reproduction assets foundThe paper's rice deficiency classification experiments are built on two publicly available image datasets: the Kaggle rice NPK deficiency dataset and the Mendeley rice nitrogen deficiency dataset. Both are paper-specific phenotyping image inputs with public URLs given in the references. No author analysis code or modelDataset · public37. Sethy, P.K. Nitrogen Deficiency of Rice Crop, Mendeley Data, V1. 2020. Available online: https://data.mendeley.com/datasets/Open asset ↗Mendeley Datapdf-page:15 lines:1-54Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceLaboratory / benchtopLiDAR / point cloudPanicle / ear / spikeSegmentation
The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth. The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement. At present, crop spike-shaped point cloud segmentation has problems such as fewer samples, uneven distribution of point clouds, occlusion of stem and spike, disorderly arrangement of point clouds, and lack of targeted network models. The traditional clustering method can realize the segmentation of the plant organ point cloud with relatively independent spatial location, but the accuracy is not acceptable. This paper first builds a desktop-level point cloud scanning apparatus based on a structured-light projection module to facilitate the point cloud acquisition process. Then, the rice ear point cloud was collected, and the rice ear point cloud data set was made. In addition, data argumentation is used to improve sample utilization efficiency and training accuracy. Finally, a 3D point cloud convolutional neural network model called Panicle-3D was designed to achieve better segmentation accuracy. Specifically, the design of Panicle-3D is aimed at the multiscale characteristics of plant organs, combined with the structure of PointConv and long and short jumps, which accelerates the convergence speed of the network and reduces the loss of features in the process of point cloud downsampling. After comparison experiments, the segmentation accuracy of Panicle-3D reaches 93.4%, which is higher than PointNet. Panicle-3D is suitable for other similar crop point cloud segmentation tasks.
Why it matches plant phenotyping methodsイネ穂の3D点群を取得・データセット化し、器官分割による表現型パラメータ測定のための装置と専用ニューラルネットワークを開発・評価しており、表現型取得・抽出手法が中心である。
abstractThe automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth.
Reproduction assets foundThe authors explicitly state that the Python code of the point cloud dataset and the Panicle-3D model is publicly available on GitHub, providing a paper-specific, actionable asset for the rice panicle point cloud segmentation analysis.Code · publicThe Python code of the point cloud dataset and model is available on Github at https://github.com/gitDux/Panicle-3D .Open asset ↗gitDux/Panicle-3Dlines:86-247Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
The three-dimensional (3D) arrangement of cells in tissues provides an anatomical basis for analyzing physiological and biochemical aspects of plant and animal cellular development and function. In this study, we established a protocol for tissue clearing and 3D imaging in rice. Our protocol is based on three improvements: clearing with iTOMEI (clearing solution suitable for plants), developing microscopic conditions in which the Z step is optimized for 3D reconstruction, and optimizing cell-wall staining. Our protocol successfully 3D imaged rice shoot apical meristems, florets, and root apical meristems at cellular resolution throughout whole tissues. Using fluorescent reporters of auxin signaling in rice root tips, we also revealed the 3D distribution of auxin signaling events that are activated in the columella, quiescent center, and multiple rows of cells in the stele of the root apical meristem. Examination of cells with higher levels of auxin signaling revealed that only the central row of cells was connected to the quiescent center. Our method provides opportunities to observe the 3D arrangement of cells in rice tissues.
Why it matches plant phenotyping methodsイネ組織を対象に、組織透明化・最適化した3D顕微鏡撮像・細胞壁染色による細胞配置の取得法を開発しており、植物表現型の画像取得が中心的な技術貢献である。
abstractIn this study, we established a protocol for tissue clearing and 3D imaging in rice.
Reproduction assets foundThe paper's 3D imaging datasets (supplementary videos S1–S6 of rice SAMs, florets, anthers, and root tips, plus figure data) are publicly available via the MDPI supplementary materials link. No separate analysis code repository is mentioned.Dataset · publiccquisition, which took approximately 2 h for 150 μm in depth, the images were processed using LASX software (Leica Microsystems, Tokyo, Japan).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/ijms23010040/s1 .
Click here for additional data file.
Author Contributions
M.S. and H.T. designed the research; M.S., H.A., Y.S. and S.M. performed the research; M.S. and H.T. analyzed the data; M.S. and H.T. wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supporOpen asset ↗lines:70-202Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.
Why it matches plant phenotyping methods画像セグメンテーションと深層学習によりイネ籾の充実・不稔を識別し、種子登熟率という植物形質を自動・高スループット推定する手法が研究の中心である。
abstractThis method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR.
Reproduction assets foundThe paper's rice panicle image dataset used for seed setting rate phenotyping is publicly deposited on Kaggle per the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited; supplementary material contains only figures/tables, not datasets or code.Dataset · publicice researchers to obtain RSSR information more efficiently and accurately, which will be a reliable method for further estimating rice yield.
Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.kaggle.com/soberguo/riceseedsettingrate .
Author Contributions
YG: formal analysis, investigation, methodology, visualization, and writing—original draft. SL: supervision and validation. YL, ZH, and ZZ: project administration and resources. DX: writing—review and editing and funding acquisition. QC: writing—review and editing, funding acquisition, and resoOpen asset ↗Kaggle · soberguo/riceseedsettingratelines:555-571Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.
Why it matches plant phenotyping methods種子のハイパースペクトル画像取得、セグメンテーション、スペクトル解析を一体化したプラットフォームとソフトウェアを開発しており、植物形質取得法が中心である。
abstractwe have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds.
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 software and data for testing is accessible in Github: https://github.com/tgaochn/HyperSeed (accessed on 3 December 2021).Open asset ↗tgaochn/HyperSeedlines:168-188Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC ( r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination ( R 2 ) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるイネの窒素状態・高NUE表現型の非破壊かつ高スループットな推定手法を開発・検証しており、表現型取得法が研究の中心である。
abstractfew easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD)
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2 ), two main aspects were considered: (1) Many varieties from the 3,000 rice genome project were germplasm collections and not used in field practices because of their lower-yielding, varied GD, and/or weak agricultural traits.Open asset ↗lines:380-387Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field. In this study, we show the ability of time-course monitoring of canopy height (CH) to identify quantitative trait loci (QTLs) and to characterise their pleiotropic effect on various traits. We generated a digital surface model from low-altitude UAV-captured colour digital images and investigated CH data of rice multi-parental advanced generation inter-cross (MAGIC) lines from tillering and heading to maturation. Genome-wide association studies (GWASs) using the CH data and haplotype information of the MAGIC lines revealed 11 QTLs for CH. Each QTL showed haplotype effects on different features of CH such as stage-specificity and constancy. Haplotype analysis revealed relationships at the QTL level between CH and, vegetation fraction and leaf colour [derived from UAV red–green–blue (RGB) data], and CH and yield-related traits. Noticeably, haplotypes with canopy lowering effects at qCH1-4, qCH2, and qCH10-2 increased the ratio of panicle weight to leaf and stem weight, suggesting biomass allocation to grain yield or others through growth regulation of CH. Allele mining using gene information with eight founders of the MAGIC lines revealed the possibility that qCH1-4 contains multiple alleles of semi-dwarf 1 (sd1), the IR-8 allele of which significantly contributed to the “green revolution” in rice. This use of remote-sensing-derived phenotyping data into genetics using the MAGIC lines gives insight into how rice plants grow, develop, and produce grains in phenology and provides information on effective haplotypes for breeding with ideal plant architecture and grain yield.
Why it matches plant phenotyping methodsUAV画像からデジタル表面モデルと時系列のイネ群落高を抽出し、遺伝解析に利用する高スループット表現型計測が研究の中心であるため。
abstractHigh-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field.
Reproduction assets foundThe paper's supplementary material explicitly contains the paper-specific phenotyping datasets (Supplementary Data 1: canopy height data; Supplementary Data 2: haplotype data; Supplementary Data 3-4: haplotype counts and time-course effects) used for the haplotype-based GWAS, and is publicly available at the Frontiers'Supplement · publicknowledgments
We thank Emi Abe, Aono Yuko, Terumi Satou, Megumi Suzuki, Yukari Shimazu, Tomomi Koguchi, Miho Shoji, and Mitsue Ito for the field support, and Matthew Shenton for scientific discussion and English editing of the manuscript.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2021.715184/full#supplementary-material
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References
Chen Y., Sidhu H. S., Kaviani M., McElroy M. S., Pozniak C. J., Navabi A. (2019). Application of image-based phenotyping tools to identify QTL for in-field winter survival of winter wheat ( TritiOpen asset ↗lines:290-333Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Bacterial leaf blight (BLB) is caused by Xanthomonas oryzae pv. oryzae and is a major cause of rice yield reductions around the world. When diseased, plants produce a variety of metabolites to resist pathogens. In this study, the various defense metabolites were quantified using high-performance liquid chromatography (HPLC) after Xoo inoculation in a 120 Cheongcheong/Nagdong double haploid (CNDH) population. Quantitative trait locus (QTL) mapping was conducted using the concentration of the plant defense metabolites. HPLC analyzes the concentration of substances according to the severity of disease symptoms. Searching for BLB resistance candidate genes by applying this analysis method is very effective when mapping related genes. These resistance genes can be mapped directly to the causative pathogens. A total of 17 metabolites were detected by means of HPLC analysis after Xoo inoculation in the 120 CNDH population. QTL mapping of the metabolite concentrations resulted in the detection of the BLB resistance candidate gene, OsWRKYq6, in RM3343 of chromosome 6. OsWRKYq6 has a very high homology sequence with WRKY transcription factor 39, and when inoculated with Xoo, the relative expression level of the resistant population was higher than that of the susceptible population. Resistance genes have previously been detected using only phenotypic change data. In this study, resistance candidate genes were detected using the concentration of metabolites produced in plants after inoculation with pathogens. This newly developed analysis method can be used to effectively detect and identify genes directly involved in disease resistance for future studies.
Why it matches plant phenotyping methodsHPLCによる防御代謝物濃度の定量を、イネの病害抵抗性状態の表現型取得・QTL検出法として明示的に開発・適用しており、単なるルーチン測定ではない。
abstractIn this study, the various defense metabolites were quantified using high-performance liquid chromatography (HPLC) after Xoo inoculation in a 120 Cheongcheong/Nagdong double haploid (CNDH) population.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following are available online at https://www.mdpi.com/article/10.3390/plants10102145/s1 , Figure S1: PCA (Principal Component Analysis) statistical analysis between the peak area in the HPLC analysis results and the infection length data of the corresponding leaf samples. Table S1: The plant traits and peak no. of 120 CNDH (Cheongcheong/Nagdong double haploid) populations by HPLC (high-performance liquid chromatography). Table S2: Details of QTL mapping using HPLC analysis results of 120 CNDH population after Xanthomonas oryzae pv. oryzae inoculation.Open asset ↗MDPIlines:71-91Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 9 Sept 2026
Abstract Background Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. Results In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5–10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R 2 > 0.99 and a mean absolute percentage error Conclusions Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
Why it matches plant phenotyping methodsヒヨコマメ個体の3D形態を取得する低コスト撮像システムとオープンソース解析パイプラインを開発し、草丈・表面積を基準値で検証しており、フェノタイピング手法が中心である。
abstractIn this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants.
Reproduction assets foundThe authors deposited the paper's 3D point clouds and meshed chickpea models in an open-access Zenodo repository (DOI 10.5281/zenodo.4018242). Processing scripts are only included as article additional files, and the source images are available only on request from the corresponding author.Dataset · publicThe dataset supporting the conclusions of this article (3D point clouds and meshed models) are available in an open-access Zenodo repository, https://doi.org/10.5281/zenodo.4018242 .Open asset ↗Zenodo · 10.5281/zenodo.4018242lines:149-193Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Phosphorus is one of the second most important nutrients for plant growth and development, and its importance has been realised from its role in various chains of reactions leading to better crop dynamics accompanied by optimum yield. However, the injudicious use of phosphorus (P) and non-renewability across the globe severely limit the agricultural production of crops, such as rice. The development of P-efficient cultivar can be achieved by screening genotypes either by destructive or non-destructive approaches. Exploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study. Eighteen genotypes were selected for hydroponic study from the soil-based screening of 68 genotypes to identify the traits through non-destructive (geometric traits by imaging) and destructive (morphology and physiology) techniques. Geometric traits such as minimum enclosing circle, convex hull, and calliper length show promising responses, in addition to morphological and physiological traits. In 28-day-old seedlings, leaves positioned from third to fifth played a crucial role in P mobilisation to different plant parts and maintained plant architecture under P deficient conditions. Besides, a reduction in leaf angle adjustment due to a decline in leaf biomass was observed. Concomitantly, these geometric traits facilitate the evaluation of low P-tolerant rice cultivars at an earlier stage, accompanying several stress indices. Out of which, Mean Productivity Index, Mean Relative Performance, and Relative Efficiency index utilising image-based traits displayed better responses in identifying tolerant genotypes under low P conditions. This study signifies the importance of image-based phenotyping techniques to identify potential donors and improve P use efficiency in modern rice breeding programs.
Why it matches plant phenotyping methods低リン耐性イネの選抜において、画像から幾何学的形質を抽出するイメージベース表現型解析を中心的に適用・評価しており、単なる生物学的実験のルーチン測定ではない。
abstractExploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study.
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 ) were selected based on the level of tolerance from all quarters of principal component analysis (PCA) to evaluate further under hydroponics and identify the traits through destructive (morphology and physiology) and non-destructive (geometric traits by imaging) techniques in a low phosphorus regime.Open asset ↗lines:313-319Code / dataset availability confirmedOpenAlex · checked 8 Sept 2026
Accurate panicle identification is a key step in rice-field phenotyping. Deep learning methods based on high-spatial-resolution images provide a high-throughput and accurate solution of panicle segmentation. Panicle segmentation tasks require costly annotations to train an accurate and robust deep learning model. However, few public datasets are available for rice-panicle phenotyping. We present a semi-supervised deep learning model training process, which greatly assists the annotation and refinement of training datasets. The model learns the panicle features with limited annotations and localizes more positive samples in the datasets, without further interaction. After the dataset refinement, the number of annotations increased by 40.6%. In addition, we trained and tested modern deep learning models to show how the dataset is beneficial to both detection and segmentation tasks. Results of our comparison experiments can inspire others in dataset preparation and model selection.
Why it matches plant phenotyping methodsイネ穂のセグメンテーション用公開データセットを構築し、半教師あり学習による注釈精緻化とモデル比較を行っており、植物フェノタイピング手法・データセットが研究の中心である。
titlePaddy Rice Imagery Dataset for Panicle Segmentation
Reproduction assets foundThe paper's pixel-level annotated paddy rice panicle imagery dataset (400 4K UAV images, 50,730 annotations) is explicitly stated to be publicly available on Zenodo via DOI 10.5281/zenodo.4430186, a paper-specific public asset directly reproducing the phenotyping measurements.Dataset · publicWe provided a pixel-level labeled rice panicle dataset containing 400 images with
50730 pixel-level annotations. The dataset is publicly available at http://doi.org/
10.5281/zenodo.4430186 for developing rice-panicle detection models.Open asset ↗zenodo · 10.5281/zenodo.4430186pdf-page:9 lines:1-59Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current approaches to quantifying the extent of root colonisation and the abundance of hyphal structures in mutant roots rely on staining and human scoring involving simple, yet repetitive tasks prone to variations between experimenters. We developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks. AMFinder delivered high-confidence predictions on image datasets of roots of multiple plant hosts (Nicotiana benthamiana, Medicago truncatula, Lotus japonicus, Oryza sativa) and captured the altered colonisation in ram1-1, str and smax1 mutants. A streamlined protocol for sample preparation and imaging allowed us to quantify mycobionts from the genera Rhizophagus, Claroideoglomus, Rhizoglomus and Funneliformis via flatbed scanning or digital microscopy including dynamic increases in colonisation in whole root systems over time. AMFinder adapts to a wide array of experimental conditions. It enables accurate, reproducible analyses of plant root systems and will support better documentation of AM fungal colonisation analyses. AMFinder can be accessed here: https://github.com/SchornacklabSLCU/amfinder.git
Why it matches plant phenotyping methods植物根の菌根菌感染状態を画像から自動識別・定量する手法とソフトウェアを開発しており、表現型取得・抽出が研究の中心です。
abstractWe developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks.
Reproduction assets foundThe paper's AMFinder analysis software (amf/amfbrowser) and pre-trained CNN models are publicly available on the authors' GitHub repository under the MIT license. The training image datasets are not public and must be requested from the authors.Code · publicmanuscript. All authors
have read and approved the manuscript.
Data Availability
AMFinder is released under the terms of the open-source MIT license
(https://opensource.org/licenses/MIT) allowing unrestricted usage. Source code, pre-trained models
and detailed installation instructions are available on AMFinder GitHub webpage
(https://github.com/SchornacklabSLCU/amfinder.git). Training datasets are available upon request.
References
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J,
Devin M, et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
Systems. arXiv.
Bally J, Jung H, Mortimer C, Naim F, Philips JG, Hellens R, BombarelyOpen asset ↗SchornacklabSLCU/amfinderpdf-raw-page:21 lines:1-72Code / dataset availability confirmedCrossref · checked 14 Sept 2026
RiceRootMorphology / geometry measurementSegmentationRoot system architecture
Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a successful low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the Frangi filter algorithm is applied to outline the roots. However, the original Frangi only enhances the tube-like primary roots but erases the lateral roots during filtering. In this paper, an improved Frangi filtering algorithm (IFFA), designed for plant roots, is proposed. Firstly, an automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition. Secondly, multilevel image thresholding, connected components labeling, and width correction are used to optimize the output binary image. Thirdly, to enhance the local structure, the Gaussian filtering operator in the original Frangi is redesigned with a truncated Gaussian kernel, resulting in more discernible lateral roots. Compared to the original Frangi filter and commercially available software, IFFA is faster and more accurate, achieving a pixel accuracy of 97.48%. IFFA is an effective morphological filtering approach to enhance the roots of rice for segmentation and further biological research. It is convincing that IFFA is suitable for different 2-D plant root image processing and morphological analysis.
Why it matches plant phenotyping methodsイネ根の画像取得・分割・形態解析のための自動フェノタイピングシステムと改良画像フィルタを開発し、既存手法・商用ソフトと精度比較しているため、植物フェノタイピング手法が中心である。
abstractan automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition
Reproduction assets foundThe paper's Data Availability statement points to a public GitHub repository containing the IFFA algorithm code, original rice root images, and processed images used in this study, making the paper-specific phenotyping assets publicly actionable.Code · publicty using two different Image analyses systems,” Plant and Soil,
vol. 260, no. 1/2, pp. 111–120, 2004.
All additional files, containing the algorithm, original im-
[5] T. C. Kaspar and R. P. Ewing, “ROOTEDGE: software for
ages, and processed images, are provided in the repository measuring root length from desktop scanner images,”
https://github.com/gitDux/IFFA. Agronomy Journal, vol. 89, no. 6, pp. 932–940, 1997.
[6] A. F. Frangi, W. J. Niessen, K. L. Vincken, and
Conflicts of Interest M. A. Viergever, “Multiscale vessel enhancement filtering,”
Medical Image Computing and Computer-Assisted Interven-
The authors declare that there are no conflicts of interest tion-MICCAI’98, pp. 130–137Open asset ↗gitDux/IFFApdf-layout-page:13 lines:1-47Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.
Why it matches plant phenotyping methodsイネの地上部バイオマスという植物形質を、航空マルチスペクトル画像から抽出・推定する特徴抽出手法を開発し、比較検証しているため、フェノタイピング手法が中心です。
abstractThis work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery.
Reproduction assets foundThe paper's multispectral UAV imagery and ground-truth biomass measurements are deposited on OSF (via a view-only link, which is nonetheless publicly reachable with the URL), and the crop-monitoring protocol is on protocols.io. No author analysis code repository is stated.Dataset · publicDatasets supporting the experimental results presented in Figure 4 , Figure 5 and Figure 6 are available at the Open Science Framework: https://osf.io/cde6h/?view_only=1c4e5e03b9a34d3b96736ad8ab1b2774 folder Raw Data—MDPI Sensors.Open asset ↗Open Science Frameworklines:197-212Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物のFVCという形態・生育形質を推定するモデルを開発し、複数作物・圃場実験で精度と頑健性を検証しており、表現型取得手法が研究の中心である。
abstractThis study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' PROSAIL-GP model code and all datasets (UAV-derived canopy reflectance/FVC measurements) in a public GitHub repository, plus detailed protocols on protocols.io. The PROSAIL model itself is a generic prior tool and is excluded.Code · publicle.
Conflict of interest
The authors declare no conflict of interest.
Data availability
Data supporting this work,such as details and source code of the PROSAIL
model used in this study,are openly available at http://teledetection.ipgp.jussieu.fr/prosail/.The code of the PROSAIL-GP model and all of the
datasets are available at https://github.com/WanLiangZJU/Crop-FVC-retrieval. The detailed protocols can be found at protocols.io (https://
dx.doi.org/10.17504/protocols.io.btmynk7w).
References
Aballa A, Cen H, Wan L, Mehmood K, He Y. 2020. Nutrient status diag-
nosis of infield oilseed rape via deep learning-enabled dynamic model. IEEE
Transactions on Industrial Informatics 17, 4379–4389.
BacOpen asset ↗WanLiangZJU/Crop-FVC-retrievalpdf-raw-page:15 lines:1-89Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Digital rice phenotyping requires rapid assessment of protein content of rice to kernels to support high-throughput crop phenotyping experiments. A fast and non-destructive approach can allow rapid decision making to breed and select relevant rice varieties. Hence, this study compares the predictive potential of near-infrared (NIR) spectroscopy for three physical forms of rice i.e., rice kernel (with glume), whole grain brown rice and powdered rice. The aim is to identify the best physical form to be adapted in future use for high-throughput protein content prediction in rice samples. The models were optimized by selecting key wavelengths most correlated to the protein content in rice. For variable selection, a total of 8 recently developed chemometric variable selection techniques were used. As a baseline comparison to variable selection techniques, partial-least square (PLS) regression analysis was used. The results showed that for all forms of rice samples, variable selection improved the predictive performance compared to the PLS regression modelling. The best accuracies were obtained for the brown rice samples with a prediction error of 0.349%. Further, this was achieved with only 12 wavelengths compared to the 304 wavelengths available in the original data set. Based on the results, this study indicates that there is no need to grind the rice samples into powder for using NIR spectroscopy. Hence, NIR spectroscopy can directly be used on brown rice samples and can support the rapid assessment of protein content in rice to support digital phenotyping.
Why it matches plant phenotyping methodsNIR分光と波長選択・回帰モデルを用いて、玄米のタンパク質含量を非破壊かつ高スループットに推定する方法を比較・最適化しており、植物表現型取得が研究の中心である。
abstractDigital rice phenotyping requires rapid assessment of protein content of rice to kernels to support high-throughput crop phenotyping experiments.
Reproduction assets foundThe paper's NIR spectra and reference protein content dataset (201 rice samples in three physical forms) is explicitly stated to be publicly accessible on Mendeley Data. No author analysis code or trained models are reported as available.Dataset · publicThe original data set used in this study is/are accessible at: https://
data.mendeley.com/datasets/zvgy65m2rc/1.data.mendeley.com · zvgy65m2rc/1pdf-raw-page:3 lines:1-78Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から自動推定する深層学習手法を開発・評価しており、植物表現型（病害状態）の取得が研究の中心である。
abstracta faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases
Reproduction assets foundThe paper's Data Availability statement names a public Kaggle rice disease image dataset used in training (allowed URL present) and authors' code in supplemental files plus a Figshare dataset whose URLs are not in the allowed list, so only the Kaggle dataset is reported as an actionable public asset.Dataset · publicData is available at Kaggle:
Do, Huy Minh (2019): Rice Diseases Image Dataset. Kaggle. Dataset. https://www.kaggle.com/minhhuy2810/rice-diseases-image-dataset .Open asset ↗Kagglelines:307-319Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data.
Why it matches plant phenotyping methods米収量という植物・作物群の形質を対象に、Sentinel-2データ用の3D CNNを開発し、データベース構築と既存手法との性能比較・検証を行っており、収量推定手法が中心である。
abstractwe build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicData Availability Statement: The codes related to this work will be released for reproducible research
at https://github.com/rufernan/RicePAL (accessed on 3 April 2021).Open asset ↗rufernan/RicePALpdf-page:23 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types, each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice (Oryza sativa) and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis (Arabidopsis thaliana). In addition, field-emission scanning electron microscopy observation revealed the presence of various cell types in haustoria. Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides cellular and structural insights into haustoria of S. hermonthica and P. japonicum and lays the foundation for understanding haustorium function.
Why it matches plant phenotyping methods植物器官の3次元画像再構成を中心に、ハウストリアの内部構造と細胞配置を可視化しており、形態状態の取得・抽出が研究の主要部分である。
titleThree-dimensional reconstructions of haustoria in two parasitic plant species in the Orobanchaceae
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 · publicAfter automated alignment adjustment, section alignment was manually checked and misaligned sections were re-registered by changing the registration parameters. The tools are available at https://github.com/yk-szk/ssrvtools .Open asset ↗yk-szk/ssrvtoolslines:86-98Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Plants have evolved several adaptive mechanisms to cope with water-limited conditions. While most of them are through constitutive traits, certain "acquired tolerance" traits also provide significant improvement in drought adaptation. Most abiotic stresses, especially drought, show a gradual progression of stress and hence provide an opportunity to upregulate specific protective mechanisms collectively referred to as "acquired tolerance" traits. Here, we demonstrate a significant genetic variability in acquired tolerance traits among rice germplasm accessions after standardizing a novel gradual stress progress protocol. Two contrasting genotypes, BPT 5204 (drought susceptible) and AC 39000 (tolerant), were used to standardize methodology for capturing acquired tolerance traits at seedling phase. Seedlings exposed to gradual progression of stress showed higher recovery with low free radical accumulation in both the genotypes compared to rapid stress. Further, the gradual stress progression protocol was used to examine the role of acquired tolerance at flowering phase using a set of 17 diverse rice genotypes. Significant diversity in free radical production and scavenging was observed among these genotypes. Association of these parameters with yield attributes showed that genotypes that managed free radical levels in cells were able to maintain high spikelet fertility and hence yield under stress. This study, besides emphasizing the importance of acquired tolerance, explains a high throughput phenotyping approach that significantly overcomes methodological constraints in assessing genetic variability in this important drought adaptive mechanism.
Why it matches plant phenotyping methodsイネの乾燥適応形質を評価するための段階的ストレス付与プロトコルを標準化し、高スループット表現型解析として方法論的制約を克服する手法を提示しているため、表現型取得法が中心的です。
abstractafter standardizing a novel gradual stress progress protocol
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2 ) during the Kharif season of 2019 to confirm the trait diversity, particularly for acquired tolerance traits.Open asset ↗lines:351-362Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Accurate measurement of seed size parameters is essential for both breeding efforts aimed at enhancing yields and basic research focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color with capability to process a large number of images in a time-efficient manner. In this context, our application takes ∼2 s for analyzing an image, i.e., significantly less compared to the other tools. As this software is open-source, it can be modified by users to serve more specific needs. The adaptability of SeedExtractor was demonstrated by analyzing scanned seeds from multiple crops. We further validated the utility of this application by analyzing mature-rice seeds from 231 accessions in Rice Diversity Panel 1. The derived seed-size traits, such as seed length, width, were used for genome-wide association analysis. We identified known loci for regulating seed length ( GS3 ) and width ( qSW5/GW5 ) in rice, which demonstrates the accuracy of this application to extract seed phenotypes and accelerate trait discovery. In summary, we present a publicly available application that can be used to determine key yield-related traits in crops.
Why it matches plant phenotyping methods種子画像から形状・色などの表現型を抽出するオープンソースGUIを開発し、複数作物およびイネ231アクセッションで有用性を検証しており、表現型取得手法が中心である。
abstractwe have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color
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 11
Seed color intensities for three channels in RGB color space for the RDP1.Open asset ↗lines:438-529Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits. When manual and automatic measurements were compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm, and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R 2 values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and tillering stages, and the R 2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.
Why it matches plant phenotyping methods深層学習統合micro-CT画像解析パイプラインを開発し、イネ茎の形態・倒伏抵抗性関連形質を非破壊かつ高スループットに抽出・検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits.
Reproduction assets foundThe paper's micro-CT rice culm phenotyping pipeline source code is explicitly stated to be publicly available on the authors' GitHub repository and their Crop Phenomics Group website; phenotypic data are in Supplemental Data 1 (not directly linked here).Code · publicThe source code and user guidelines are available at http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action and https://github.com/diwu861125/diwu123456 .Open asset ↗diwu861125/diwu123456lines:329-354Code · publicthe main source code is provided in Supplemental Video 1 , Supplemental Note 2 , our Crop Phenomics Group website ( http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action ), and a GitHub website ( https://github.com/diwu861125/diwu123456 ).Open asset ↗lines:145-217Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Crop improvement for Nitrogen Use Efficiency (NUE) requires a well-defined phenotype and genotype, especially for different N-forms. As N-supply enhances growth, we comprehensively evaluated 25 commonly measured phenotypic parameters for N response using 4 N treatments in six indica rice genotypes. For this, 32 replicate potted plants were grown in the green-house on nutrient-depleted sand. They were fertilized to saturation with media containing either nitrate or urea as the sole N source at normal (15 mM N) or low level (1.5 mM N). The variation in N-response among genotypes differed by N form/dose and increased developmentally from vegetative to reproductive parameters. This indicates survival adaptation by reinforcing variation in every generation. Principal component analysis segregated vegetative parameters from reproduction and germination. Analysis of variance revealed that relative to low level, normal N facilitated germination, flowering and vegetative growth but limited yield and NUE. Network analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype. It constitutes germination and flowering, shoot/root length and biomass parameters, six of which were common to nitrate and urea. Field-validation confirmed the NUE differences between two genotypes chosen phenotypically. The correspondence between multiple approaches in shortlisting parameters for NUE makes it a novel and robust phenotyping methodology of relevance to other plants, nutrients or other complex traits. Thirty-Four N-responsive genes associated with the phenotype have also been identified for genotypic characterization of NUE.
Why it matches plant phenotyping methodsNUEの複合形質を定義・選抜するため、複数形質の評価、特徴選択、統計解析、フィールド検証を統合したフェノタイピング手法が中心的に開発・検証されている。
abstractNetwork analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype.
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 3
Mean values of the measured 25 phenotypic parameters in nitrate/urea sources and normal (15 mM) or low (1.5 mM) doses.Open asset ↗lines:557-644Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Grain size and weight are important yield components in rice ( Oryza sativa L.). There is still uncertainty about the genetic control of these traits under drought stress, the most pressing emerging issue in many rice cultivation areas. To address this lack of knowledge, we investigated the genetic architecture of seed size, shape, and weight using the rice Global Multi-parent Advanced Generation Intercross (MAGIC) population, grown under well-watered and vegetative drought conditions. We measured variation in seed size and shape with a new high-throughput phenotyping method based on a desktop scanner and the open-source package Plant Computer Vision (PlantCV). Besides being affordable, rapid, and accurate, our method captured the phenotypic divergence between drought and well-watered samples, expressed as 12 different traits that include traditional size metrics and new grain shape measures. Overall, under water deficit, the MAGIC lines produced smaller and shorter seeds. We identified ten MAGIC lines with traits that make them good candidates for the release of rice cultivars with high yield potential under vegetative drought stress. We ran a marker-trait association analysis for the measured seed-related traits. Most of the identified marker-trait associations showed strong genotype-by-environment interactions (GxE), with most allele effects being conditionally neutral. These results suggest dynamic genetic control of seed size, shape, and weight under vegetative drought stress in rice, highlighting the importance of understanding the contribution of GxE interactions on trait variation to develop resilient and high-yielding rice varieties. Our study confirms that combining low-cost and high-throughput phenotyping strategies with a diverse genetic material suited for multi-environmental trial provides solutions for adapting rice cultivation to current and future environmental adversities.
Why it matches plant phenotyping methodsイネ種子の形状・サイズ・重量を、デスクトップスキャナーとPlantCVによる新規かつ高スループットな表現型取得法で測定しており、方法開発と実質的な適用が研究の中心です。
abstractWe measured variation in seed size and shape with a new high-throughput phenotyping method based on a desktop scanner and the open-source package Plant Computer Vision (PlantCV).
Reproduction assets foundThe paper deposits two paper-specific public assets: the authors' PlantCV image-analysis code (Zenodo 4156942) and the raw rice seed scan images used for phenotyping (Zenodo 4158169). Other URLs (PlantCV docs, 3K rice genome registry, R project) are generic resources or cited prior work, not paper-specific assets.Code · publicr standards
179 (white and grey cards) for image exposure normalization, and a ruler as a size standard.
180
181 We processed the RGB (Red Green Blue) images generated with the scanner using a personal
182 laptop with Intel® Core™ i7 8650u CPU @1.90Ghz and 16 GB RAM. The PlantCV code used for
183 this manuscript is available at https://doi.org/10.5281/zenodo.4156942, and more details on
184 the PlantCV functions used in our pipeline can be found in the online user manual of PlantCV
185 (https://plantcv.readthedocs.io/en/latest/). Briefly, for each RGB image, the pipeline first
186 standardizes image exposure using the white standard color. Then, it separates the seeds from
187 the backgrouOpen asset ↗zenodo · 10.5281/zenodo.4156942pdf-raw-page:9 lines:1-32Dataset · publicas described at
196 https://plantcv.readthedocs.io/en/stable/pipeline_parallel/. All trait estimates per seed and per
197 sample are saved in JSON text files, which are then merged and converted to a final CSV table
198 file using the accessory tool “plantcv-utils.py” implemented in PlantCV. All seed images are
199 available at https://doi.org/10.5281/zenodo.4158169.200 We also measured the grain weight of 50 seeds per sample using an analytical scale
201 (Adventurer® Analytical, Ohaus, USA). We then converted the weight of grains to 1000-seed
202 weight for easy comparisons with previous studies.
203 Statistical analyses of phenotypic data
204 We performed all statistical analyses of theOpen asset ↗zenodo · 10.5281/zenodo.4158169pdf-raw-page:10 lines:1-31Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for 21 d using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants exhibited an earlier repression in growth compared with smaller, slow-growing plants, indicating a trade-off between early vigor and tolerance to prolonged water deficits. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.
Why it matches plant phenotyping methods自動画像計測による日次シュートバイオマス推定データを用い、動的な植物表現型応答をモデル化する新しいゲノム対応成長モデルを提案しており、表現型解析ワークフローが中心的です。
abstractWe propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability.
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 · publicAll data and codes used in this study can be accessed at https://github.com/malachycampbell/RiceCGM/tree/master .Open asset ↗malachycampbell/RiceCGMlines:109-215Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Rice density is closely related to yield estimation, growth diagnosis, cultivated area statistics, and management and damage evaluation. Currently, rice density estimation heavily relies on manual sampling and counting, which is inefficient and inaccurate. With the prevalence of digital imagery, computer vision (CV) technology emerges as a promising alternative to automate this task. However, challenges of an in-field environment, such as illumination, scale, and appearance variations, render gaps for deploying CV methods. To fill these gaps towards accurate rice density estimation, we propose a deep learning-based approach called the Scale-Fusion Counting Classification Network (SFC 2 Net) that integrates several state-of-the-art computer vision ideas. In particular, SFC 2 Net addresses appearance and illumination changes by employing a multicolumn pretrained network and multilayer feature fusion to enhance feature representation. To ameliorate sample imbalance engendered by scale, SFC 2 Net follows a recent blockwise classification idea. We validate SFC 2 Net on a new rice plant counting (RPC) dataset collected from two field sites in China from 2010 to 2013. Experimental results show that SFC 2 Net achieves highly accurate counting performance on the RPC dataset with a mean absolute error (MAE) of 25.51, a root mean square error (MSE) of 38.06, a relative MAE of 3.82%, and a R 2 of 0.98, which exhibits a relative improvement of 48.2% w.r.t. MAE over the conventional counting approach CSRNet. Further, SFC 2 Net provides high-throughput processing capability, with 16.7 frames per second on 1024 × 1024 images. Our results suggest that manual rice counting can be safely replaced by SFC 2 Net at early growth stages. Code and models are available online at https://git.io/sfc2net.
Why it matches plant phenotyping methodsイネ個体数という植物形態・密度形質を画像から自動推定する深層学習手法を開発し、新規データセットで精度検証しているため、方法が中心である。
abstractwe propose a deep learning-based approach called the Scale-Fusion Counting Classification Network (SFC 2 Net)
Reproduction assets foundThe paper's rice plant counting (RPC) dataset (382 field images with 211,971 dot annotations) and the authors' SFC2Net code and trained models are explicitly stated to be publicly available at the authors' URL https://git.io/sfc2net, which matches an allowed URL.Dataset · publicThe RPC dataset has been made available at https://git.io/sfc2net .Open asset ↗lines:403-419Code · publicCode and models are available online at https://git.io/sfc2net .Open asset ↗lines:1-28Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning-based convolutional neural networks (CNN) have greatly improved image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning-based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures such as VGG16 and InceptionV3 have been adopted and fine tuned for detecting and recognising rice diseases and pests. Experimental results show the effectiveness of these models with real datasets. (ii) Since large scale architectures are not suitable for mobile devices, a two-stage small CNN architecture has been proposed, and compared with the state-of-the-art memory efficient CNN architectures such as MobileNet, NasNet Mobile and SqueezeNet. Experimental results show that the proposed architecture can achieve the desired accuracy of 93.3% with a significantly reduced model size (e.g., 99% smaller than VGG16).
Why it matches plant phenotyping methodsイネ画像から病害・害虫を認識するCNN手法の開発と比較が論文の中心であり、植物の病害状態を画像から推定するため、植物フェノタイピング手法に該当する。
abstractdeep learning-based approaches have been developed in this paper for detecting diseases and pests from rice plant images.
Reproduction assets foundThe paper publicly releases its own rice disease/pest image dataset (1426 images, 9 classes) via a Google Drive link stated in the conclusion; no code deposit is mentioned.Dataset · publicA dataset of rice diseases and pests consisting of 1426 images has been collected in real life scenario which cover eight classes of rice disease and pest. This dataset is expected to facilitate further research on rice diseases and pests. The dataset is available in the following link:Open asset ↗lines:163-247Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 14 Sept 2026
Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis . Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides structural insights into how organs interact between hosts and parasitic plants. One-sentence summary Three-dimensional image reconstruction was used to visualize the spatial organization of cell types in the haustoria of parasitic plants with special reference to their interaction with host roots.
Why it matches plant phenotyping methods寄生植物のハウストリア内部構造を3次元画像再構成で可視化することが研究の中心であり、植物器官の空間形態を抽出・比較している。
abstractHere, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species
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 tools are available at https://github.com/yk-szk/ssrvtools.Open asset ↗yk-szk/ssrvtoolspdf-page:12 lines:1-46Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R 2 values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding.
Why it matches plant phenotyping methodsX線CTを用いてイネ穀粒の22種類の3D形質を抽出する画像解析パイプラインを開発し、手動測定との一致性で検証しているため、植物フェノタイピング手法が研究の中心です。
abstractIn this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits.
Reproduction assets foundThe paper's MATLAB 3D image analysis pipeline for extracting rice grain traits from X-ray CT is explicitly stated to be publicly available, with an authors' GitHub repository URL matching an allowed URL. The phenotypic data (Supplementary File 1) is only available as a PMC supplement with no allowed URL, so it is not aCode · publicthe source codes of all the scripts are available online in Supplementary File 3 or at the following link: http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action , or https://github.com/cancanzc/ricePanicle_grainTraits_ProcessingOpen asset ↗cancanzc/ricePanicle_grainTraits_Processinglines:109-117Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
Why it matches plant phenotyping methods合成データとインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発し、実画像で性能検証しているため、方法が研究の中心である。
abstractan instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars
Reproduction assets foundThe authors publicly release both the synthetic and real-world seed image datasets and the analysis code (Mask R-CNN deployment and multivariate analysis notebooks) via their GitHub repository, explicitly stated in Data availability and Code availability sections.Dataset · publicSynthetically generated and real-world datasets can be obtained from the following GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code · publicCode to reproduce the deployment of the trained Mask R-CNN and multivariate analysis is formatted as IPython notebooks and can also be obtained from the GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The micro-morphology of leaf epidermises is valuable for the study of leaf development and function, as well as the classification of plant species. There have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis. Here, four specimen preparation methods were used to investigate the leaf epidermis morphology of Arabidopsis , radish, cucumber, wheat, rice, and maize, under an inverted basic light microscope (LM), a laser scanning confocal microscope (LSCM), or a scanning electron microscope (SEM). Optical microscope specimens were obtained using either the direct isolation method or the chloral hydrate-based clearing method. SEM images were obtained using a standard stage for conventional dehydrated samples or a Coolstage for fresh tissue. Different parts of epidermis peels were well focused under the LM. Investigation of samples cleared by chloral hydrate is convenient and autofluorescence of cell walls can be detected in rice. The resolution of images of conventional SEM leaf samples was generally higher than the Coolstage images at the same magnification, whereas local collapse and shrinkage were observed in leaves with high water content when using the conventional method. However, stomatal apparatuses of Arabidopsis , cucumber, radish, and maize deformed and showed poor appearance when using the Coolstage. Moreover, we usually used glutaraldehyde as an SEM fixative when using t-butanol for freeze-drying, though methanol is considered a better fixative in recent studies. In addition, fresh samples were not stable on the Coolstage. Thus, we compared four different t-butanol freeze-drying methods and two Coolstage methods. The dimension and morphology of tissues were compared using the six different methods. The results indicate that methanol fixative obviously reduced shrinkage of SEM samples compared with glutaraldehyde and formaldehyde alcohol acetic acid (FAA) fixatives. The use of methanol and a graded series of steps improved the preservation of samples. Preparing samples with optimal cutting temperature compound and observing at -30°C helped to increase the stability of Coolstage samples. In summary, our results provide an overview of the shortcomings and merits of four different methods, and might provide some information about choosing an optimal method for visualizing epidermal morphology.
Why it matches plant phenotyping methods葉表皮形態の可視化について、複数の試料調製法・顕微鏡法を比較し、組織形態の保存性や画像品質を評価しており、植物形質取得法が研究の中心である。
abstractThere have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis.
Reproduction assets foundThe paper reports LM/LSCM/SEM imaging of leaf epidermises and shrinkage/stability measurements. No author analysis code, trained models, or external repository deposit is mentioned. The only paper-specific public asset is the article's Supplementary Material, which the authors state contains all data generated or analySupplement · publicgy Project of Henan Province (182102110234).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.00133/full#supplementary-material
Click here for additional data file.
Abbreviations
SEM, scanning electron microscope; LSCM, laser scanning confocal microscope; LM, light microscope; DIC, differential interference contrast; CPD, critical point drying; OCT, optimum cutting temperature.
References
Bailes E. J. GlovOpen asset ↗lines:293-368Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background In this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data. Results Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F 1 score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. Conclusions Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.
Why it matches plant phenotyping methodsイネ穂の画像セグメンテーション手法を開発し、UAV画像で性能評価しているため、植物形質取得法が研究の中心である。
abstractan unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV)
Reproduction assets foundThe paper's UAV rice panicle image dataset and the authors' MATLAB analysis code are both explicitly stated as publicly available with direct URLs in the Availability of data and materials section.Dataset · publicThe dataset analyzed during the current study are available at https://wuj.hosted.uark.edu/research/datasets/panicle/UBLRPSUI.zip .Open asset ↗lines:221-277Code · publicAlso, the MATLAB code and related materials can be downloaded from https://github.com/i2pt/UBLRPSUI .Open asset ↗i2pt/UBLRPSUIlines:221-277Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceRootMorphology / geometry measurement2D/3D reconstructionRoot system architectureWater status / transpiration
Background and aims Upland rice is often grown where water and phosphorus (P) are limited and these two factors interact on P bioavailability. To better understand this interaction, mechanistic models representing small-scale nutrient gradients and water dynamics in the rhizosphere of full-grown root systems are needed. Methods Rice was grown in large columns using a P-deficient soil at three different P supplies in the topsoil (deficient, suboptimal, non-limiting) in combination with two water regimes (field capacity versus drying periods). Root architectural parameters and P uptake were determined. Using a multiscale model of water and nutrient uptake, in-silico experiments were conducted by mimicking similar P and water treatments. First, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data, such as nodal root number, lateral types, interbranch distance, root diameters, and root biomass allocation along depth. Secondly, the multiscale model was informed with these 3D root architectures and the actual transpiration rates. Finally, water and P uptake were simulated. Key results The plant P uptake increased over threefold by increasing P and water supply, and drying periods reduced P uptake at high but not at low P supply. Root architecture was significantly affected by the treatments. Without calibration, simulation results adequately predicted P uptake, including the different effects of drying periods on P uptake at different P levels. However, P uptake was underestimated under P deficiency, a process likely related to an underestimated affinity of P uptake transporters in the roots. Both types of laterals (i.e. S- and L-type) are shown to be highly important for both water and P uptake, and the relative contribution of each type depend on both soil P availability and water dynamics. Key drivers in P uptake are growing root tips and the distribution of laterals. Conclusions This model-data integration demonstrates how multiple co-occurring single root phene responses to environmental stressors contribute to the development of a more efficient root system. Further model improvements such as the use of Michaelis constants from buffered systems and the inclusion of mycorrhizal infections and exudates are proposed.
Why it matches plant phenotyping methods3D根系アーキテクチャを観測データで再構成・較正し、根形態と吸水・リン吸収を統合モデルで推定する手法適用が研究の中心である。
abstractFirst, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data
Reproduction assets foundThe paper explicitly states that the multiscale soil-root model code used for the water and phosphorus uptake simulations is publicly shared on GitHub at the Plant-Root-Soil-Interactions-Modelling/dumux-rosi repository (pub/Mai2019 branch), which is the authors' computational analysis code for this study. No public rawCode · publictrient transport models, the
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implementation of the dynamic root growth in the flow and transport model, the root growth model, the
21
mathematical equations, and the multiscale coupling method are presented in Supplementary Information
22
(Text S1) and can be found in Mai et al. (2018). The model code is shared on GitHub
23
(https://github.com/Plant-Root-Soil-Interactions-Modelling/dumux-rosi/tree/pub/Mai2019).24
25
Virtual experiment setup
26
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted January 27, 2020.
;
https://doi.org/10.1101/2020.01.27.921247
doi:
biOpen asset ↗Plant-Root-Soil-Interactions-Modelling/dumux-rosi · pub/Mai2019pdf-raw-page:10 lines:1-58Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Background Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the P anicle I maging Plat form (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.
Why it matches plant phenotyping methodsイネ穂の非破壊3D画像再構成とデジタル形質抽出を行うPI-Platを開発・比較評価しており、植物フェノタイピング手法が研究の中心である。
abstractThese images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity.
Reproduction assets foundThe paper publicly shares (1) a partial raw image dataset on a UNL Box repository and (2) the authors' PI-Plat Panicle-3D-Reconstruction workflow scripts at wrchr.org. Full raw images and the manual phenotyping dataset are only available on request, so those portions would be request_only, but the two public assets areDataset · publicRaw image data is large and hence only part of them is shared for user testing on a UNL Box repository ( https://unl.box.com/s/g0bof1mpfp33hn66b2qabrk9kiwmhbzv ).Open asset ↗unl.box.comlines:117-127Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination ( R 2 ) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.
Why it matches plant phenotyping methodsVis/NIR分光とPLS解析により、イネ葉のクロロフィル含量を定量推定し、交差検証指標で性能評価しており、表現型取得・推定法が中心である。
abstractVisible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels.
Reproduction assets foundThe authors deposit the paper's vis/NIR reflectance spectral data (used for rice variety identification and chlorophyll/SPAD analysis) in the Dryad Digital Repository, with an explicit public URL.Dataset · publicas pattern recognition methods will be adopted to improve the prediction accuracy.
Supplementary Material
Reviewer comments
Acknowledgements
The authors gratefully acknowledge the strong assistance by Zhao Chunli and the support of Prof. Sailing He.
Data accessibility
Our data are available within the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.p8pq7fq [ 46 ].
Authors' contributions
H.Z. participated in the design of the study and drafted the manuscript; J.H., Q.Z. and S.S. contributed to conception and design, and helped draft the manuscript; Z.D., Y.L., G.Z., W.F., S.Z., T.P. and H.Z. carried out the rice spectral measurement work; Z.D. and H.Z. carried out the statisticalOpen asset ↗Dryad Digital Repository · 10.5061/dryad.p8pq7fqlines:424-472Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two state-of-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial.
Why it matches plant phenotyping methods遺伝子型・環境情報からイネ・コムギ等の表現型を予測し、複数の機械学習・統計手法を比較評価する計算的表現型推定研究であり、方法比較が中心です。
abstractWe investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat).
Reproduction assets foundThe paper's data availability statement explicitly lists public URLs for the yeast, wheat, and rice phenotype datasets analyzed in the study, plus a public GitHub repository with the authors' custom R analysis code.Code · publicCustom R code used to analyse the datasets can be found at: https://github.com/stas-g/grinberg-et-al-evaluation-of-ML-codeOpen asset ↗grinberg-et-al-evaluation-of-ML-codelines:194-224Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
To explore the relationship between the attributes of the rice panicle and its weight parameters, 6 different rice cultivars from Sihong City, Jiangsu Province, China were selected for sampling in 2017. Then, their weight parameters were measured. The images of rice panicles were scanned to obtain grain area. The significant correlation between the grain area and the panicle weight was found on the base of the analysis for the data obtained [1]. Now the weight and area data were present here for exploring the rapid yield estimation models and crop phenotype research.
Why it matches plant phenotyping methodsイネ穂の画像スキャンから粒面積を取得し、収量推定や作物表現型研究に再利用するデータを提示しており、画像ベース形質データセットとして方法論的価値がある。
abstractThe images of rice panicles were scanned to obtain grain area.
Reproduction assets foundThis Data in Brief article presents the paper's own rice panicle phenotyping measurements (grain area, panicle/grain weight parameters for 6 cultivars, 1200 panicles) and states that the raw data files (.xlsx) were uploaded as supplementary material, publicly accessible online at the article DOI. No separate author-depDataset · publica-
tional Natural Science Fund, China (31701321).
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relation-
ships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104667.
Reference
[1] S. Zhao, H. Zheng, M. Chi, X. Chai, Y. Liu, Rapid yield prediction in paddy fields based on 2D image modelling of rice panicles,
Comput. Electron. Agric. 162 (2019) 759e766, https://doi.org/10.1016/j.compag.2019.05.020.Open asset ↗pdf-layout-page:6 lines:1-23Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 9 Sept 2026
Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.
Why it matches plant phenotyping methods高スループット表現型由来のイネの投影シュート面積軌跡を、ランダム回帰モデルで予測・検証し、表現型取得頻度の削減も評価しているため、計算的な表現型解析が中心である。
abstractHere, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios.
Reproduction assets foundThe paper's Data Availability statement deposits its paper-specific phenotypic (PSA) data and rice accession genotypic data as Supplementary Files S1 and S2 on Figshare, with a public DOI link. This directly reproduces the paper's plant-phenotyping measurements and is publicly actionable. The ricediversity.org site is Dataset · publicPhenotypic data used herein are available in Supplementary File S1 at Figshare. Genotypic data regarding the rice accessions can be downloaded from the rice diversity panel website ( http://www.ricediversity.org/ ) and also available in Supplementary File S2 at Figshare. Supplemental material available at FigShare: https://doi.org/10.25387/g3.9383543 .Open asset ↗Figshare · 10.25387/g3.9383543lines:62-74Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged and non-lodged rice image samples were established by augmenting for building a UNet model. The research results showed that the dice coefficients in RGB (Red, Green and Blue) image and multispectral image test set were 0.9442 and 0.9284, respectively. The rice lodging recognition effect using the RGB images without feature extraction is better than that of multispectral images. The findings of this study are useful for rice lodging investigations by different optical sensors, which can provide an important method for large-area, high-efficiency, and low-cost rice lodging monitoring research.
Why it matches plant phenotyping methodsUAV画像とUNetによってイネの倒伏状態を抽出・評価する手法を開発し、RGBおよびマルチスペクトル画像で性能検証しているため、植物表現型取得が中心である。
abstracta new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed.
Reproduction assets foundThe paper's UNet training/analysis code for rice lodging segmentation is explicitly stated to be publicly available at the authors' GitHub repository. No public dataset or image deposit is mentioned; the UAV imagery and annotations are not stated as shared.Code · publicThe UNet model training algorithm was implemented with Python 3.6 in Spyder software, and code can be found at the URL “ https://github.com/zhxsking/unet_on_jsj ”.Open asset ↗zhxsking/unet_on_jsjlines:38-45Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice blast recognition method based on CNN. A dataset of 2906 positive samples and 2902 negative samples is established for training and testing the CNN model. In addition, we conduct comparative experiments for qualitative and quantitatively analysis in our evaluation of the effectiveness of the proposed method. The evaluation results show that the high-level features extracted by CNN are more discriminative and effective than traditional hand-crafted features including local binary patterns histograms (LBPH) and Haar-WT (Wavelet Transform). Moreover, quantitative evaluation results indicate that CNN with Softmax and CNN with support vector machine (SVM) have similar performances, with higher accuracy, larger area under curve (AUC), and better receiver operating characteristic (ROC) curves than both LBPH plus an SVM as the classifier and Haar-WT plus an SVM as the classifier. Therefore, our CNN model is a top performing method for rice blast disease recognition and can be potentially employed in practical applications.
Why it matches plant phenotyping methodsイネ葉の画像から病害状態を認識するCNN手法の開発・比較評価が研究の中心であり、植物病害の表現型推定に該当する。
abstractwe propose a novel rice blast recognition method based on CNN.
Reproduction assets foundThe authors publicly released the rice blast disease image dataset (5808 expert-labeled 128×128 patches) used to train and test their CNN model, with an explicit availability statement and URL.Dataset · publicThe dataset is available
from the, http://www.51agritech.com/zdataset.data.zip.Open asset ↗pdf-page:2 lines:1-57Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Manual phenotyping of rice tillers is time consuming and labor intensive, and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive methods of phenotyping rice tiller traits at a high spatial resolution and high throughput for large-scale assessment of rice accessions are urgently needed. In this study, we developed a high-throughput micro-CT-RGB imaging system to non-destructively extract 739 traits from 234 rice accessions at nine time points. We could explain 30% of the grain yield variance from two tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by genome-wide association study, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at time point 9, which contained a major gene, TAC1. Significant variants associated with tiller angle were enriched in the 3'-untranslated region of TAC1. Three haplotypes for the gene were found, and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci containing associations with both vigor-related traits identified by high-throughput micro-CT-RGB imaging and yield. The superior alleles would be beneficial for breeding for high yield and dense planting.
Why it matches plant phenotyping methods高スループットなmicro-CT-RGB画像システムを開発し、イネの形態形質を多数・経時的に非破壊抽出することが研究の中心であるため、GWAS応用を含む植物フェノタイピング手法研究として含める。
abstractautomated, non-destructive methods of phenotyping rice tiller traits at a high spatial resolution and high throughput for large-scale assessment of rice accessions are urgently needed.
Reproduction assets foundThe paper deposits its rice tiller phenotyping datasets, images, and analysis source code at Dryad (doi:10.5061/dryad.gm18v5f), and makes the raw phenotypic data and CT/RGB images publicly downloadable via the HZAU plant phenomics database. Both are paper-specific, public, and actionable.Dataset · publicData collected from 234 rice accessions, including genotype ID, cultivar name, and all phenotypic traits.
Dataset S1. Rice accession information and phenotypic traits (RGB, CT, and manual traits) used in this work.
Dataset S2. GWAS results.Open asset ↗lines:207-246Dataset · publicAll the phenotypic data and images can be viewed and downloaded via the link http://plantphenomics.hzau.edu.cn/checkiflogin_en.action by following these steps: (i) select ‘rice’; (ii) select ‘2015-tiller’ in the year section; (iii) select one of the accession IDs in the ID section and then press ‘search images’; (iv) nine CT images and nine side-view color images can be viewed and downloaded; (v) a similar process can be used to view and download phenotypic traits by pressing ‘search data’. The detailed procedure for the database is shown in Fig. S10 available at Dryad.Open asset ↗lines:207-246Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models' key parameters. The results showed that the combination of the red edge and NIR bands (766 nm and 830 nm) as well as the combination of SWIR bands (1114 nm and 1190 nm) were optimal for producing the narrowband NDVI. The models built on the first derivative spectra yielded more accurate results than the corresponding models built on the raw spectra. Properly selected model parameters resulted in comparable accuracy and robustness with the empirical optimal parameter and significantly reduced the model complexity. The machine learning methods were more accurate and robust than the VI methods and partial least squares regression. When validating the calibrated models against the standalone validation dataset, the VI method yielded a validation RMSE value of 1.17 for NDVI(766,830) and 1.01 for NDVI(1114,1190), while the best models for the partial least squares, support vector machine and artificial neural network methods yielded validation RMSE values of 0.84, 0.82, 0.67 and 0.84, respectively. The RF models built on the first derivative spectra with mtry = 10 showed the highest potential for estimating the LAI of paddy rice.
Why it matches plant phenotyping methods水稲の葉面積指数（LAI）という植物形質を、ハイパースペクトルデータと機械学習で推定する手法を開発・比較・検証しており、形質取得手法が研究の中心です。
abstractThe performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study.
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 · publicS1 File. The dataset used in this study.Open asset ↗lines:291-301Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Digital image processing is widely used in the non-destructive diagnosis of plant nutrition. Previous plant nitrogen diagnostic studies have mostly focused on characteristics of the rice canopy or leaves at some specific points in time, with the long sampling intervals unable to provide detailed and specific "dynamic features." According to plant growth mechanisms, the dynamic changing rate in leaf shape and color differ between different nitrogen supplements. Therefore, the objective of this study was to diagnose nitrogen stress levels by analyzing the dynamic characteristics of rice leaves. Scanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB. Newly developed shape characteristics such as etiolation area (EA) and etiolation degree (ED), in addition to shape (area, perimeter) and color characteristics (green, normalized red index, etc.), were used to quantify the process of leaf change. These characteristics allowed sensitive indices to be established for further model validation. Our results indicate that the changing rates in dynamic characteristics, in particular the shape characteristics of the first incomplete leaf (FIL) and the characteristics of the 3rd leaf (leaf color and etiolation indices), expressed obvious distinctions among different nitrogen treatments. Consequently, we achieved acceptable diagnostic accuracy (training accuracy 77.3%, validation accuracy 64.4%) by using the FIL at six days after leaf emergence, and the new shape characteristics developed in this article (ED and EA) also showed good performance in nitrogen diagnosis. Based on the aforementioned results, dynamic analysis is valuable not only in further studies but also in practice.
Why it matches plant phenotyping methodsイネ葉画像を連続取得・画像処理し、新規形状指標を開発して窒素ストレス診断モデルを検証しており、植物表現型の取得・抽出手法が研究の中心である。
abstractScanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB.
Reproduction assets foundThe paper's Data Availability statement deposits all underlying data (rice leaf image-derived phenotype measurements used for nitrogen diagnosis) on figshare with a public DOI link.Dataset · publicAll data underlying the findings are fully available without restriction from figshare: http://dx.doi.org/10.6084/m9.figshare.5965846 .Open asset ↗figshare · 10.6084/m9.figshare.5965846lines:918-936Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future.
Why it matches plant phenotyping methods画像からイネの乾燥応答や葉巻きを定量化する手法を開発・検証し、圃場と制御条件で頑健性を評価しているため、植物表現型手法の中心的研究である。
abstractwe demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions.
Reproduction assets foundThe paper's supplementary material, hosted at the Frontiers supplementary-material URL, contains paper-specific phenotyping assets: Supplementary Videos 1 and 2 are the RGB image series of rice accessions PeiC122 and Maweinian used for the drought-response feature extraction, and Supplementary Presentations 2–4 containDataset · publicSupplementary Video 1
RGB image series of accession PeiC122 at daily intervals.Open asset ↗lines:113-151Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Traditional phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, dynamic phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We used these traits to predict the grain yield in the early growth stage, and 30% of the grain yield variance was explained by 2 tiller traits in the early growth stage. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and tiller angles of rice accessions containing haplotype H3 were much smaller. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.\n\nHighlightCombining high-throughput micro-CT-RGB phenotyping facility and genome-wide association study to dissect the genetic architecture of rice tiller development by using the indica subpopulation.
Why it matches plant phenotyping methods高スループットのマイクロCT-RGB画像システムを開発し、イネの形態形質を多数・時系列で抽出することが研究の中心であるため。GWASはその応用にあたる。
abstractwe developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points.
Reproduction assets foundThe paper's own micro-CT-RGB phenotyping outputs (CT images, side-view RGB images, and extracted phenotypic traits for 234 rice accessions at 9 time points) are explicitly stated to be publicly viewable and downloadable from the authors' Huazhong Agricultural University plant phenotyping database. RiceVarMap is an exteDataset · public276
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Phenotyping database extracted by HCR at 9 time points
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During the tillering stage, 234 rice plants were automatically measured by HCR at 9
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different development time points (once every 3 d, starting from 41 ~ 67 d after
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sowing). All the phenotypic data and images can be viewed and downloaded via the
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link http://plantphenomics.hzau.edu.cn/checkiflogin_en.action and then following
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these steps: (1) select ‘rice’; (2) select ‘2015-tiller’ in the year section; (3) select one
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of the accession IDs in the ID section and then press ‘search images’; (4) 9 CT images
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and 9 side-view color images can be viewed and downloaded; (5) a similar process
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can be used to viOpen asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:10 lines:1-76Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Background Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field's complex background, rice panicle segmentation in the field is a very large challenge. Results In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online. Conclusions In conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.
Why it matches plant phenotyping methodsイネ穂の画像ベース表現型解析に用いるセグメンテーション手法を開発・比較検証し、ソフトウェアとデータセットも提供しているため、方法が中心的である。
abstractrice panicle segmentation is the first and key step for image-based panicle phenotyping
Reproduction assets foundThe paper's 48 testing rice panicle images and the Panicle-SEG segmentation software (CPU/GPU installers) are explicitly stated to be publicly available online at the authors' Huazhong Agricultural University plant phenomics site, with access credentials provided in the article.Dataset · publicThe whole testing images and the Panicle-SEG installation file in CPU/GPU mode are available online at: http://plantphenomics.hzau.edu.cn/checkiflogin_en.action (username: UserPP; password: 20170108pp)Open asset ↗plantphenomics.hzau.edu.cnlines:45-48Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 10 Sept 2026
Maize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds - primarily recently off patent lines - were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.
Why it matches plant phenotyping methods高速画像・ハイパースペクトル等を用いた植物表現型データセットの構築と、画像測定値を手動測定と比較する技術的検証が中心であるため、収載する。
abstractA total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released.
Reproduction assets foundThe paper releases ~485 GB of maize phenotyping image data (RGB, hyperspectral, fluorescence, thermal) publicly at plantvision.unl.edu/dataset, and the authors' validation/analysis source code is posted on GitHub (https://github.com/shanwai1234/Maize Phenotype Map). Both are paper-specific, public, and actionable.Dataset · publicA subset of the RGB images within
this dataset were previously analyzed in18 , and were made available for download from http://plantvision.unl.edu/dataset under
the terms of the Toronto Agreement.Open asset ↗pdf-page:6 lines:1-51Code · publicSource codes for all validation analysis are posted online (https://github.com/shanwai1234/Maize Phenotype Map).Open asset ↗shanwai1234/Maizepdf-page:6 lines:1-51Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties-four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64)-was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate ( P max ) throughout canopy depth, hypothesizing that light is the sole determinant of productivity in these conditions. First, we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal P max responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However, measured P max , especially in lower layers, was consistently higher than the optimal P max indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency.
Why it matches plant phenotyping methodsステレオカメラによる3D植物再構成を用いてイネの群落構造形質を取得し、光環境・光合成との関係を解析しており、表現型取得ワークフローが研究の中心である。
abstractRay tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S2
Physiological characteristics of the 15 parental MAGIC lines + IR64 used in the initial screening . All measurements, apart from harvest dry weight and seed dry weight, were taken 55–60 days after transplanting (DAT), corresponding to the vegetative growth stage.Open asset ↗lines:538-570Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
To ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. Here we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor-intensive measures of flowering time, height, biomass, grain yield, and harvest index. Genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.
Why it matches plant phenotyping methods圃場ハイスループット表現型解析を大規模イネ集団に適用し、従来測定との精度・有効性を比較してQTL検出に利用しており、表現型取得法が研究の中心である。
abstractHere we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits.
Reproduction assets foundThe paper's QTL/phenotyping analysis code is publicly hosted on Bitbucket, and the authors state that the code and original phenotyping data files are deposited in Dryad under a DOI. Both are paper-specific, public, and actionable.Code · publicR code is currently hosted at bitbucket.org ( https://bitbucket.org/paultanger/rilpopr ) and code as well as original data files will be deposited into http://dx.doi.org/10.5061/dryad.53bj8 and www.datadryad.org for publication and availability.Open asset ↗bitbucket.org/paultanger/rilpoprlines:62-105Dataset · publicR code is currently hosted at bitbucket.org ( https://bitbucket.org/paultanger/rilpopr ) and code as well as original data files will be deposited into http://dx.doi.org/10.5061/dryad.53bj8 and www.datadryad.org for publication and availability.Open asset ↗Dryad · 10.5061/dryad.53bj8lines:62-105Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 11 Sept 2026
High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels, indica and aus, containing a total of 553 genotypes, are phenotyped in waterlogged conditions. Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment (control and salt) and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salinity, in particular into the effects of salinity on plant growth and transpiration.
Why it matches plant phenotyping methods植物の成長・蒸散をハイスループットに定量する解析手法を開発し、イネ集団への適用と遺伝子座同定まで行っており、表現型取得・抽出が研究の中心である。
abstractHere we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array.
Reproduction assets foundThe paper's phenotyping data (raw data underlying trait calculation, trait values) and the authors' analysis code (trait production code and GWAS interaction-model code) are publicly deposited in Dryad under doi:10.5061/dryad.3118j, with explicit availability language in the Data availability section.Code · publicthe codes used in producing the trait values and the code for the interaction model used for GWAS analyses are all available in Dryad ( http://datadryad.org/, doi:10.5061/dryad.3118j ).Open asset ↗Dryad · doi:10.5061/dryad.3118jlines:88-149Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Number of spikelets per panicle (NSP) is a key trait to increase yield potential in rice (O. sativa). The architecture of the rice inflorescence which is mainly determined by the length and number of primary (PBL and PBN) and secondary (SBL and SBN) branches can influence NSP. Although several genes controlling panicle architecture and NSP in rice have been identified, there is little evidence of (i) the genetic control of panicle architecture and NSP in different environments and (ii) the presence of stable genetic associations with panicle architecture across environments. This study combines image phenotyping of 225 accessions belonging to a genetic diversity array of indica rice grown under irrigated field condition in two different environments and Genome Wide Association Studies (GWAS) based on the genotyping of the diversity panel, providing 83,374 SNPs. Accessions sown under direct seeding in one environement had reduced Panicle Length (PL), NSP, PBN, PBL, SBN and SBL compared to those established under transplanting in the second environment. Across environments, NSP was significantly and positively correlated with PBN, SBN and PBL. However, the length of branches (PBL and SBL) was not significantly correlated with variables related to number of branches (PBN and SBN ), suggesting independent genetic control.Twenty- three GWAS sites were detected with P-values ≤ 1.0E-04 and 27 GWAS sites with p ≤ 5.9E-04. We found 17 GWAS sites related to NSP, 10 for PBN and 11 for SBN, 7 for PBL and 11 for SBL. This study revealed new regions related to NSP, but only three associations were related to both branching number (PBN and SBN) and NSP. Two GWAS sites associated with SBL and SBN were stable across contrasting environments and were not related to genes previously reported. The new regions reported in this study can help improving NSP in rice for both direct seeded and transplanted conditions. The integrated approach of high-throughput phenotyping, multi-environment field trials and GWAS has the potential to dissect complex traits, such as NSP, into less complex traits and to match single nucleotide polymorphisms with relevant function under different environments, offering a potential use for molecular breeding.
Why it matches plant phenotyping methods画像フェノタイピングを用いてイネの穂・分枝形態を大規模かつ複数環境で評価し、GWASと統合しているため、表現型取得法の応用が研究の中心です。
abstractThis study combines image phenotyping of 225 accessions belonging to a genetic diversity array of indica rice grown under irrigated field condition in two different environments
Reproduction assets foundThe paper's supplementary material (including Table S1 accession list and Table S3 phenotypic measurements from the panicle image analysis of 1,350 panicles across two field trials) is publicly available online at the Frontiers article URL. No author analysis code or trained models are explicitly deposited; P-TRAP is aSupplement · publicmental setup and Image Analysis using PTRAP.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.01384
References
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AL-Tam F. Adam H. dos Anjos A. Lorieux M. Larmande P. Ghesquière A. et al . ( 2013 ). P-TRAP: a panicle trait phenotyping tool . BMC Plant Biol. 13 : 122 . 10.1186/1471-2229-13-122
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Ando T. Yamamoto T. Shimizu T. Ma X. F. ShomuOpen asset ↗lines:725-766Code / dataset availability confirmedOpenAlex · Europe PMC · checked 11 Sept 2026
BACKGROUND: In this study we carried out a genome-wide association analysis for plant and grain morphology and root architecture in a unique panel of temperate rice accessions adapted to European pedo-climatic conditions. This is the first study to assess the association of selected phenotypic traits to specific genomic regions in the narrow genetic pool of temperate japonica. A set of 391 rice accessions were GBS-genotyped yielding-after data editing-57000 polymorphic and informative SNPS, among which 54% were in genic regions. RESULTS: In total, 42 significant genotype-phenotype associations were detected: 21 for plant morphology traits, 11 for grain quality traits, 10 for root architecture traits. The FDR of detected associations ranged from 3 · 10-7 to 0.92 (median: 0.25). In most cases, the significant detected associations co-localised with QTLs and candidate genes controlling the phenotypic variation of single or multiple traits. The most significant associations were those for flag leaf width on chromosome 4 (FDR = 3 · 10-7) and for plant height on chromosome 6 (FDR = 0.011). CONCLUSIONS: We demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice. We identified strong associations that may be used for selection in temperate irrigated rice breeding: e.g. associations for flag leaf width, plant height, root volume and length, grain length, grain width and their ratio. Our findings pave the way to successfully exploit the narrow genetic pool of European temperate rice and to pinpoint the most relevant genetic components contributing to the adaptability and high yield of this germplasm. The generated data could be of direct use in genomic-assisted breeding strategies.
Why it matches plant phenotyping methods高スループット表現型解析プラットフォームの開発・適用が明示され、植物形態・根系・穀粒形質の測定とGWASを結び付けているため、表現型取得基盤が研究の主要部分と判断する。
abstractWe demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice.
Reproduction assets foundThe authors state that all relevant data (phenotypic and genotypic data underlying the GWAS) are publicly available in a Zenodo repository, which qualifies as a paper-specific public data asset.Dataset · publicData Availability All relevant data are publicly available in a Zenodo repository at the following URL: https://zenodo.org/record/50803#.VytVnrp97CI .Open asset ↗Zenodo · record/50803lines:48-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets.
Why it matches plant phenotyping methods植物フェノタイピング画像を大規模処理し、画像からデジタル形質を抽出するオープンソース基盤が研究の中心であるため。
abstractHere, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms.
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 entire data set of can be accessed through the iPlant Collaborative upon publication. ( http://mirrors.iplantcollaborative.org/browse/iplant/home/shared/walia _rice_salt)Open asset ↗iPlant Collaborative · shared/walialines:34-44