Plant phenotyping methods.

WheatScoper: A lightweight organ-based framework for multi-view wheat phenotyping using time-series RGB images ↗

Accurate and dynamic monitoring of wheat phenotypes is essential for breeding decision-making and crop management. However, RGB image-based phenotyping still suffers from expensive pixel-level annotation, unstable organ-level segmentation across growth stages, and limited multi-trait extraction under complex field conditions. To address these issues, a high-throughput phenotyping framework (WheatScoper) was proposed, enabling organ-level segmentation and plot-level multi-trait extraction. To reduce annotation cost, a structure-aware geometry-assisted annotation (SAGA) algorithm was developed, yielding an approximately 7.6-fold improvement in annotation efficiency over fully manual annotation. To enable efficient organ-level segmentation, a lightweight semantic segmentation network (WheatScopeNet) was developed by integrating parallel hybrid spatial modeling with cross-scale feature fusion. On the held-out test set from the same site and growing season, WheatScopeNet achieved an mIoU of 0.869 and an mDice of 0.930. Leveraging the segmentation results, an automated system was established to extract 41 multi-view image-derived traits (I-traits) across four core phenotypic dimensions. The extracted I-traits supported the estimation of eight manually measured agronomic traits, with R 2 values ranging from 0.477 to 0.697. Notably, the correlations between stay-green-related-traits and yield varied distinctly with viewing-position. Only upper side-view indicators remained significantly correlated with yield, with Side-up final GPAR showing the strongest association, whereas top-view GPAR-derived indicators showed weak associations. Finally, a web-based platform integrating cascaded inference, segmentation visualization, and automatic I-trait extraction was developed. The platform provides an end-to-end solution for field wheat phenotyping and supports breeding decision-making and crop management.

Why it matches plant phenotyping methodsRGB画像から小器官を分割し、多数の植物形質を自動抽出・推定する手法とプラットフォームが研究の中心であるため、植物フェノタイピング手法として明確に採用。

abstracta high-throughput phenotyping framework (WheatScoper) was proposed, enabling organ-level segmentation and plot-level multi-trait extraction.

Video-based fruit detection and tracking: effects of scanning conditions on fruit load estimation ↗

Automated fruit counting and yield estimation systems are necessary for efficient orchard management. This study presents a computer vision system based on video multi-object-tracking for fruit load estimation in apple orchards and provides a comprehensive analysis of the system performance under diverse scanning conditions. The system integrates fruit detection, tracking, localization within orchard, and fruit load map generation. Experiments were carried out in an experimental apple orchard containing 420 apple trees. Data was collected with two different RGB-D sensors (Azure Kinect DK and ZED 2) at three different scanning distances (125 cm, 175 cm, and 225 cm) on two different dates prior to the harvest. Comparing the two evaluated sensors, Azure Kinect provided more consistent performance across different dates. Results also show that the longer scanning distance improves accuracy due to seeing the full tree view gives better fruit counts than close partial views. Between the two dates, best results were achieved near harvest due to fruit color at this stage, achieving a Mean Absolute Percentage Error (MAPE) of 6.91 % and a determination coefficient (R 2 ) of 0.733 (using ZED2 sensor at 225 cm distance). Finally, a test comparing scanning from one or both sides of the tree row showed that bilateral scanning improved fruit load estimation at the stretch level by incorporating information from both sides of the canopy. The results of this work demonstrate the effectiveness of the video fruit tracking systems as a useful tool for automating fruit load estimation.

Why it matches plant phenotyping methods動画ベースの果実検出・追跡手法を開発・評価し、リンゴ樹の果実負荷量を推定することが研究の中心であるため、植物フェノタイピング手法として含める。

abstractThis study presents a computer vision system based on video multi-object-tracking for fruit load estimation in apple orchards and provides a comprehensive analysis of the system performance under diverse scanning conditions.

Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks. ↗

Key message Yield-Graph enables accurate maize yield prediction from incomplete multi-stage phenotypic and environmental data by modeling higher-order environment-trait interactions, with robust applicability across growth stages, regions, and crop species. Accurate yield prediction before maize harvest is crucial for advancing agricultural management and ensuring food security. Unlike conventional approaches that rely on traits from a single growth stage, this study models multiple traits across different developmental stages, all targeting final yield, thereby uncovering their stage-specific contributions and demonstrating the feasibility of early yield prediction. We introduce Yield-Graph, an innovative framework that evaluates phenotypic data at distinct developmental stages for yield prediction. The method employs a bipartite graph structure to impute missing trait values at each stage and leverages a hypergraph attention mechanism to capture high-order sample relationships. Comprehensive benchmark experiments demonstrate that Yield-Graph matches the top-tier predictive accuracy of exhaustively optimized tree models. Moreover, the framework exhibits strong robustness across growth stages, high adaptability to regional variations, and effective generalization across datasets. These findings highlight the potential of graph-enhanced multi-stage modeling for early-stage yield prediction, offering a scalable solution for precision agriculture and intelligent crop management.

Why it matches plant phenotyping methods作物収量という植物形質を、複数時期の表現型データから推定するグラフニューラルネットワーク手法を開発し、ベンチマーク評価しているため、計算型フェノタイピング手法が中心である。

abstractWe introduce Yield-Graph, an innovative framework that evaluates phenotypic data at distinct developmental stages for yield prediction.

Improving cotton biomass estimation by assimilating SAR data into a modified crop growth model with simple calibration ↗

Aboveground biomass density (AGBD) is a key indicator in agricultural systems, directly reflecting crop carbon storage potential and yield levels. The World Food Studies (WOFOST) model is widely used for crop growth simulation due to its process-based interpretability. However, its application is limited by complex calibration demands and struggles with spatial heterogeneity. To address these limitations, this paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model, which requires simple calibration. WOFOST is run in potential production mode, which assumes ideal conditions to reduce input data requirements. Before assimilation, phenology-related temperature sums are aligned with phenology and meteorological data to match local growth stages. Two quantities are then estimated and updated in the model through data assimilation by minimizing the difference between SAR-derived and simulated AGBD. These quantities are the proposed yield reduction factor, which represents the proportional decrease in potential CO 2 assimilation under stresses, and the initial total dry weight at sowing. Validation experiments were conducted using multi-year cotton datasets from two farms in Georgia, USA, differing in whether irrigation was applied. Compared to WOFOST simulations and evaluated against in situ AGBD measurements, the assimilation results improve agreement and reduce error (approximately 43% RMSE reduction at the rainfed site and 15% at the irrigated site). It also delivers spatial maps of biomass, together with model-derived yield and harvest-index diagnostics, and remains operational under frequent cloud cover where optical observations are sparse. This SAR-based assimilation strategy reduces calibration demands, providing a novel and practical pathway to extend WOFOST applications to diverse agricultural scenarios.

Why it matches plant phenotyping methodsSARデータを作物成長モデルに同化して綿の地上部バイオマスを推定する手法を開発し、複数年・複数圃場データで検証しているため、植物形質取得が中心である。

abstractthis paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model

Finlay-Wilkinson random regression for yield and yield stability prediction in cereals. ↗

Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.

Why it matches plant phenotyping methodsFWRRを用いて穀類の収量安定性を推定・予測する統計的手法を検討し、環境数やデータ構成による予測性能を評価しているため、収量形質の計算的フェノタイピング手法が中心です。

abstractMethods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability.

AI-enabled Low-Cost 3D Maize Ear Morphometry Platform at Breeding Scale ↗

Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.

Why it matches plant phenotyping methodsトウモロコシ雌穂の3D形態形質を抽出する低コスト画像計測パイプラインを開発し、手動測定および体積測定で技術検証しているため、方法が研究の中心である。

abstractWe developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear

Explainable TabPFN-Based Machine Learning for Single-Plant Yield Estimation and Trait Prioritization in Faba Bean (Vicia faba L.) ↗

Faba bean yield reflects complex relationships among genotype, environment, and agronomic traits. This study evaluated an explainable Tabular Prior-data Fitted Network (TabPFN) framework for estimating plot-mean single-plant yield and prioritizing traits using 398 plot-level observations, 13 measured agronomic predictors, and six derived features. On the reference 80/20 split, TabPFN achieved the best values for all four test metrics (R2 = 0.8746, RMSE = 1.9132 g plant−1, MAE = 1.0819 g plant−1, and MAPE = 8.16%). The Friedman test detected differences among the six models (χ2(5) = 16.75, p = 0.005); Nemenyi comparisons distinguished TabPFN from HistGradientBoosting and SVR, whereas the Holm-corrected Wilcoxon analysis confirmed only the TabPFN–SVR difference. Across 10 repeated 80/20 splits, TabPFN obtained the highest mean test R2 (0.8614 ± 0.0691), ranked first in eight splits, and produced a higher R2 than every tuned baseline in at least eight splits. SHAP, permutation importance, and LOCO analyses emphasized pod-, seed-, and biomass-related predictors. Repeated-split ablation showed that derived features improved TabPFN consistently, whereas removing selected target-proximal yield variables reduced performance for every model. The framework is therefore a harvest-time trait-estimation and trait-prioritization tool rather than an early-season forecasting system. Notably, TabPFN achieved this performance without the 100-trial Optuna search used for each baseline; only n_estimators was screened over four prespecified values.

Why it matches plant phenotyping methods単一個体収量を推定し、形質優先順位付けを行う機械学習フレームワークを評価・比較しており、植物形質抽出手法が研究の中心である。

abstractThis study evaluated an explainable Tabular Prior-data Fitted Network (TabPFN) framework for estimating plot-mean single-plant yield and prioritizing traits

Structural failure in cereal stems under climate extremes: insights from barley head loss. ↗

Structural failure of cereal stems during late-season climate extremes is a critical determinant of yield stability. In barley, breakage of the stem below the spike, known as head loss, leads to major yield losses, particularly in hot and dry regions where the crop is widely grown. Despite a predicted increase in head loss risk due to global warming, current understanding of the genetic, physiological, anatomical, and environmental factors that control head loss remains limited. Overcoming these knowledge gaps is essential to providing a systems-level strategy for barley breeders to develop climate-ready cultivars that are resilient to stem breakage and suitable for industry adoption. Here, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting, and genetic modification strategies informed by studies on hormonal regulation and cell wall composition. Coupled with genotypic data, these efforts will enable the development of a genomic selection platform to facilitate future breeding programs. The framework and tools discussed here are broadly applicable to improving stem resilience in other cereal crops.

Why it matches plant phenotyping methods茎の強度・柔軟性や穂数を対象とする表現型計測手法をレビューし、機械試験とドローンによる高スループット計測を育種基盤として論じているため、表現型手法が中心的です。

abstractHere, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting

Phenomic Prediction I: Plot-Level Prediction of Lodging Severity in Sorghum Breeding Trials Using UAV-Based Photogrammetric Height Data ↗

Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned aerial vehicle (UAV)-derived metrics provide a potential high-throughput alternative; however, it remains unclear whether photogrammetric heights derived from UAV imagery can estimate plot-level lodging severity in large sorghum breeding trials. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV-derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest root mean square error (RMSE=16-18%), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.

Why it matches plant phenotyping methodsUAV画像と写真測量点群からソルガム区画の倒伏程度を推定する取得・解析フレームワークを開発し、実データで精度評価しており、植物表現型測定法が研究の中心である。

abstractThis study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots.

Explainable Machine Learning Prediction of Soybean Lodging Grade and Key Trait Analysis Under High-Density Drip Irrigation Cultivation ↗

To establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging under high-density drip irrigation cultivation, 356 spring soybean germplasm accessions were used as experimental materials in this study. Morphological and mechanical traits including plant height (PH), stem pulling force (SPF), internode number (IN) and petiole length (PL) were measured over two consecutive years of field phenotyping. Two composite evaluation indices, plant height/stem pulling force ratio (PH/SPF) and plant height/internode number ratio (PH/IN), were further constructed. Four machine learning algorithms were adopted to develop multi-classification models for soybean lodging grade prediction. SHAP analysis combined with three global sensitivity approaches (perturbation analysis, Sobol’ method and Morris screening) was applied to decipher the regulatory patterns of key traits. The results showed that lodging grade significantly affected soybean grain yield and explained 25–28% of the phenotypic yield variation; yield reduction tended to plateau under severe lodging. Compared with single indicators such as SPF and PL, the two derived composite indices could stably distinguish soybean accessions with different lodging grades and exhibited stronger discriminatory power. Model comparison revealed that the XGBoost model achieved optimal prediction accuracy and generalization stability for lodging grade, with a weighted F1-score of 95.34% on the test set, significantly outperforming the conventional linear model. Interpretability analysis demonstrated that the PH/IN, PH, and PH/SPF acted as the primary positive traits promoting lodging, while SPF was the sole protective trait. Driving factors of lodging presented obvious gradient heterogeneity: mild lodging was dominated by the imbalance of plant architecture ratio, whereas severe lodging was governed by the cumulative effects of PH and IN. Strong interactions existed among all measured traits. The interpretable machine learning framework established in this study can provide theoretical support and technical references for lodging-resistant germplasm screening and targeted plant architecture regulation for densely planted soybean under drip irrigation systems.

Why it matches plant phenotyping methods大豆の倒伏状態を形態・力学形質から機械学習で推定し、モデル性能比較と解釈性解析を行う枠組みが研究の中心であり、単なる生物学的実験の routine 測定ではない。

abstractTo establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging

FitoView: A Decision Support Application Integrating Weather Forecasts and CNNs for Plant Disease Classification and Severity Assessment - Case Study on Cercospora Leaf Spot in Chili Pepper ↗

Abstract Plant diseases represent major constraints on agricultural productivity, often resulting in significant yield losses. This study presents FitoView, a cloud-based mobile decision support system that integrates deep learning with real-time weather forecasting for sustainable plant disease management. Demonstrated through a case study on Cercospora leaf spot in chili pepper, the system employs custom YOLOv8 models for automated disease detection, classification, and pixel-level severity quantification, combined with meteorological data from the OpenMeteo API. The core innovation lies in an integrated decision matrix that considers three dimensions: AI-assessed disease severity, 48-hour climatic risk forecasts, and optimal spraying conditions, generating four contextualized management scenarios with tailored re-evaluation periods (3-10 days). OpenMeteo API validation across four cities in Sergipe demonstrated very strong correlations, with Pearson coefficients (r) of 0.90-0.97 for temperature, 0.81-0.95 for humidity, and 0.92-0.95 for solar radiation, corresponding to R² values of 0.65-0.94. The YOLOv8 object detection model achieved perfect precision (100%) and macro-averaged recall of 89% across all disease classes, with Cercospora leaf spot detection reaching perfect metrics (100% precision, recall, and F1-score). Field validation in Lagarto, Sergipe, confirmed the system’s practical use: it accurately detected Cercospora leaf spot, estimated severity, and, combined with climatic risk, generated recommendations for alternative treatment and short-term re-evaluation. The Progressive Web Application architecture, deployed on a Cloud Platform, ensures accessibility without installation requirements, while the modular design enables scalability to additional crops and diseases, representing a significant advancement toward democratizing AI-powered precision agriculture tools for smallholder farmers in Brazil.

Why it matches plant phenotyping methods植物病害の検出・分類と病斑のピクセルレベル重症度推定をYOLOv8で実装・検証したシステムであり、植物状態の取得・定量化が中心的な技術貢献です。

abstractThe core innovation lies in an integrated decision matrix that considers three dimensions: AI-assessed disease severity, 48-hour climatic risk forecasts, and optimal spraying conditions

Application of Remote Sensing Technologies in Crop Health Monitoring and Disease Surveillance ↗

The rapid detection and continuous monitoring of crop health and disease outbreaks are critical components of modern precision agriculture, essential for maintaining global food security. Traditional field-based scouting methods, while accurate, are often labor-intensive, time-consuming, and limited by spatial coverage, making them inadequate for large-scale agricultural operations. Remote sensing (RS) technologies—spanning satellite imagery, drone-based aerial platforms, and proximal sensors—offer a powerful, non-destructive, and scalable alternative for capturing high-resolution spectral and temporal data. This paper provides a comprehensive evaluation of current remote sensing applications in crop health monitoring and disease surveillance. We analyze how vegetation indices derived from multispectral and hyperspectral data, such as NDVI and red-edge parameters, serve as sensitive indicators of physiological stress and pathogen infection, often manifesting before visible symptoms appear. Furthermore, we explore the integration of machine learning and artificial intelligence algorithms in automating disease identification and severity mapping. By synthesizing recent advancements in sensor technology and data analytics, this paper demonstrates that remote sensing is indispensable for proactive, site-specific management. The findings emphasize that a multi-scale RS approach—integrating broad-scale satellite monitoring with high-resolution drone sorties—enables farmers to optimize input efficiency, minimize yield losses, and enhance the overall resilience of agro-ecosystems against biotic and abiotic stressors.

Why it matches plant phenotyping methods作物の健康・病害を対象に、リモートセンシング、センサー、植生指数、機械学習による状態・重症度推定を包括的に評価するレビューであり、フェノタイピング手法が中心です。

abstractThis paper provides a comprehensive evaluation of current remote sensing applications in crop health monitoring and disease surveillance.

Towards Intelligent Disease Phenotyping in Peach: A Deep Feature Extraction Framework for Leaf Disease Detection Under Real Field Conditions ↗

Abstract In modern agriculture, it is essential to identify the early symptoms of plant diseases and to accurately maintain the productivity of the crop and reduce economic losses. Foliar diseases are a special concern in peach because they can cause yield as well as quality if not timely detected. Artificial intelligence, machine learning and deep learning are some of the advanced technologies that are gaining great importance in today's agriculture, especially with the image analysis applications. In this study, a deep learning method for automated detection and classification of three peach leaf diseases and healthy class was presented based on image data. The dataset were taken at different phenological and disease stages under temperate conditions in Kashmir with four classes Healthy, Leaf Curl, Shot hole and Rust. Three convolutional neural networks (CNNs) architectures were applied, VGG-16, ResNet 50 and Xception were trained using transfer learning and Inception-V4 was trained from scratch for a comparative study of the learning strategies. Data augmentation techniques were applied to improve generalization. Results show that all models were able to learn disease specific features well. The result of Inception-V4 was found to be highest with 97.50%, followed by ResNet-50 with 94.49%, VGG-16 with 92.04% and Xception with 75.95%. The results of transfer learning-based architectures were also good and competitive but the best results obtained from the Inception-V4 architecture reveal its capability in modelling complex visual patterns. The results highlight the potential of deep learning techniques for early detection of diseases in peach, supporting precision agriculture and better disease management.

Why it matches plant phenotyping methodsモモ葉の画像から病害症状を自動検出・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しているため。

abstracta deep learning method for automated detection and classification of three peach leaf diseases and healthy class was presented based on image data.

YOLO-Based Deep Learning for Citrus Fruit Detection, Counting, and Yield Estimation in Complex Orchard Environments: A Systematic Review ↗

Abstract A systematic review based on 174 Scopus-records of studies using YOLO-type one-stage detectors for detecting citrus fruits, their count, and yield estimation followed PRISMA guidelines 2020. The title/abstract-screening process, done in duplicate (κ=0.920) yielded 90 included study-records, followed by two further post-hoc exclusions. Each study in the 80 reporting on fruit-level detection showed an average precision of 89.6%, recall of 85.9%, and mAP@0.5 of 91.0%. However, coverage for any individual metric rarely exceeded half of the studies, and only 13% were able to report the more stringent mAP@0.5:0.95. Both YOLOv8 and YOLOv5 were each utilized as the backbone architecture by approximately 22.2% of the studies. From 2025, YOLOv11 has also been emerging. Half of all studies modified architectural components including attention modules, lightweight architectures, and variants of IoU loss functions. Original contributions are generally concentrated in downstream tracking, sensor fusion, and yield modeling rather than the detector itself. A custom-made seven-domain risk of bias tool was developed and utilized by two reviewers who arbitrated discrepancies (91.5%). Results showed that all but one of the reviewed studies had a high level of risk due to almost universal lack of statistical validation and limited dataset diversity; a sensitivity analysis excluding the most risky studies left the performance profiles nearly identical. We conclude that the field has converged around a common technical toolkit but continues to lack standardized benchmarks, multispectral data, and rigorous field-deployment validation.

Why it matches plant phenotyping methods柑橘果実の検出・計数・収量推定に用いる画像解析手法を体系的にレビューし、性能評価、リスク・オブ・バイアス、標準化やベンチマーク不足を検討しており、植物フェノタイピング手法が中心である。

abstractA systematic review based on 174 Scopus-records of studies using YOLO-type one-stage detectors for detecting citrus fruits, their count, and yield estimation followed PRISMA guidelines 2020.

Phenology-adaptive machine learning for early mapping of field-scale corn crop yield using fusion of Sentinel-2 satellite spectral imagery, and weather-based accumulated heat units ↗

Abstract Purpose Timely, accurate, and field-scale crop yield mapping is essential for precision crop management, yet most existing studies rely on late- or full-season data, limiting in-season decision-making. This study aims to develop a stage-aware earliest possible corn yield mapping framework that balances early data availability, predictive accuracy, and spatial fidelity by integrating Sentinel-2 imagery, vegetation indices (VIs), and accumulated growing degree days (AGDD). Methods Corn yield data were collected from a commercial farm over three growing seasons (2018–2020). The final modeling dataset included 51,794 Sentinel-2 10 m aggregated yield samples across three seasons: 2018 ( n = 16400), 2019 ( n = 18534), and 2020 ( n = 16860). Sentinel-2 raw spectral bands and derived VIs were organized for V4, V6, R1, R5, and R6 growth stages based on AGDD- and DAP-defined corn growth-stage windows, also confirmed visually on-ground, allowing observations from different planting and harvest dates across the three growing seasons to be aligned by crop developmental stage rather than calendar date. A stage-wise Pearson correlation and frequency-based selection identified informative and non-redundant VI subsets across crop development. Four machine learning models: Random Forest (RF), XGBoost (XGB), k-Nearest Neighbors (kNN), and a Neural Network (NNET; multi-layer perceptron) were trained using 13 input configurations, including individual growth stages and multi-stage combinations capturing phenological progression. Models were tuned via randomized search, trained on 2018–2019 data, and independently validated on the 2020 season. Yield predictions were mapped directly at 10 m Sentinel-2-pixel resolution without spatial interpolation to preserve fine-scale variability. Results Model performance was strongly influenced by phenological stage selection. Among single-stage inputs, R1 was the earliest stage where reliable yield mapping could be availed (RF: R² = 0.56, RMSE = 29.50%). While combining V6 with R1 stage inputs substantially improved predictive performance (RF: R² = 0.70, RMSE = 24.13%) for the yield mapping at the R1 stage, where V6-stage signals provided complementary yield-related information. The full-season combination (V4 + V6 + R1 + R5 + R6) produced the highest accuracy (RF: R² = 0.72, RMSE = 23.28%) but would be less suitable for early in-season decision-making. Early- or late-stage-only inputs (V4, R5, R6) showed weaker and less stable cross-year performance. Among algorithms, RF consistently generalized best to the independent 2020 dataset and is recommended for operational use. XGB showed strong training performance but reduced cross-year stability, kNN yielded moderate accuracy, and NNET achieved accuracy comparable to RF while closely reproducing observed spatial patterns such as center-pivot effects and edge gradients. Direct 10 m mapping preserved yield heterogeneity and avoided smoothing artifacts common in interpolation-based approaches. Conclusion Stage-aware feature selection and phenology-informed input combinations are critical for balancing yield prediction timeliness and accuracy. For operational in-season yield mapping, the RF model using the V6 + R1 stage combination provides a practical and reliable solution, enabling early, accurate, and spatially detailed yield estimates with robust cross-year performance. This study presents a deployable framework for integrating satellite time series and weather data into conventional (non-sequential) machine learning models to support proactive, within-season decision-making in precision agriculture.

Why it matches plant phenotyping methods衛星画像・気象データと機械学習を統合し、作物の収量という明示的な植物形質を圃場内10 m解像度で推定する手法を開発・独立年で検証しており、フェノタイピング手法が中心である。

abstractThis study aims to develop a stage-aware earliest possible corn yield mapping framework that balances early data availability, predictive accuracy, and spatial fidelity by integrating Sentinel-2 imagery, vegetation indices (VIs), and accumulated growing degree days (AGDD).

Genomic prediction vs. gene-based crop models: a case study on rice trait prediction. ↗

Conventional breeding for ideotypes in target environments remains challenging due to genotype-by-environment interactions and the genetic complexity of key agronomic traits. Traditional multi-environment field trials are costly and time-consuming, limiting rapid genetic gain. These challenges highlight the need for digital tools to support rice breeding. However, two major approaches, genomic prediction (GP) and gene-based crop models (GBCMs), have distinct advantages. In this study, a dataset derived from a natural rice population comprising 210 genotypes, genotyped with about 650,000 markers, rice dry matter, and yield across three environments, was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework. The effectiveness of these models in predicting rice traits and assisting in breeding selection was subsequently evaluated. Prediction results indicated that biomass and yield could be effectively predicted by all models, with Normalized Root Mean Square Error (NRMSE) values ranging from 10.60% to 18.59% and 9.93% to 18.19%, respectively. In terms of predictive accuracy, parameter-based crop models achieved the highest predictive accuracy, although it was confined to theoretical simulations. This was followed by the GBCM and CNN, whereas the GBLUP exhibited the lowest performance. Furthermore, GGE biplot analysis revealed the predictions of the GBCM aligned more closely with field observations than those of the CNN, emphasizing the potential of GBCM as a practical surrogate for digital breeding. These results provide valuable insights into modeling genotype-by-environment interactions and support the development of data-informed breeding strategies for future rice improvement.

Why it matches plant phenotyping methodsイネの乾物量・収量という植物形質を予測する複数の計算モデルを開発・比較評価しており、形質推定手法が研究の中心である。

abstractIn this study, a dataset derived from a natural rice population comprising 210 genotypes, genotyped with about 650,000 markers, rice dry matter, and yield across three environments, was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework.

A Hex-View Perspective on Plant Disease Detection Using Remote Sensing ↗

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease-monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, the measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): plant–pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): the diverse disease-detection tasks and their corresponding research objectives. (3) Sensor (S): the sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): the environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot, and regional scales. (5) Algorithm (A): the classical and state-of-the-art data-analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): the data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease-detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production.

Why it matches plant phenotyping methods植物病害を対象としたリモートセンシングによる病徴・病害状態の検出方法を、センサー、条件、アルゴリズム、データセットの観点から体系化する方法論レビューであり、植物フェノタイピング手法が中心です。

abstractThis review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework.

Optimizing cell segmentation and downstream processing for plant probe-based spatial transcriptomics ↗

Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.

Why it matches plant phenotyping methods植物組織の細胞セグメンテーションとトランスクリプト割当てを改善する実用ワークフローを開発し、複数種・組織でベンチマークしている。植物形態そのものの測定ではないが、細胞レベルの空間状態を抽出する解析手法が中心である。

abstractHere, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics.

A Novel Drought-Resistance Index Balancing Foxtail Millet Yield and Quality and Its Prediction Based on UAV Multimodal Data ↗

Drought stress severely limits foxtail millet yield and quality, yet current drought-resistance indices are exclusively yield-oriented and ignore grain-filling quality. Our two-year (2024–2025) experiments with 24–48 varieties revealed that yield and blighted grain rate (BGR) are partially decoupled (e.g., Zhangzagu 18: yield 2307 kg/ha, BGR 0.444; Zhonggu 19: yield 1622 kg/ha, BGR 0.280). We therefore constructed the Yield–Quality Synergy Index (YQSI = DYI − BGR), which penalizes varieties with poor grain filling. The YQSI tied for first place with DYI in comprehensive screening performance and achieved the highest inter-annual stability (Spearman ρ = 0.823, Jaccard = 0.438, composite score = 1.261). Sensitivity analysis confirmed robustness of the equal-weight formula across a 4-fold range of quality-penalty weights. Six strongly drought-resistant germplasms with balanced yield and quality were identified. Using UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening. Feature-importance analysis highlighted NDVI, WDRVI, and red-edge texture as key predictors. This study provides a quality-constrained drought-resistance evaluation framework and demonstrates the potential of UAV-based high-throughput phenotyping for foxtail millet breeding.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データから干ばつ耐性指標を予測する高スループット表現型解析手法が研究の中心であり、モデル性能も評価している。

abstractUsing UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening.

From Field Photosynthesis to Genetic Architecture: Insights from the First Dedicated Photosynthesis Hackathon ↗

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

Why it matches plant phenotyping methods圃場光合成データから時間分解特徴量や遺伝的シグナルを抽出する計算手法を中心に扱っており、植物生理形質の実質的なフェノタイピング手法応用に該当する。

abstractmechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield.

Machine learning-optimized spectral indices for high-throughput phenotyping of chlorophyll and yield of wheat breeding lines under salinity stress conditions ↗

High-throughput phenotyping is key in modern breeding for rapidly and cost-effectively evaluating salt-adaptive traits. However, few studies have combined spectral reflectance indices (SRIs) with deep learning to assess field-grown wheat under salt stress. In this study, we developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY) in 32 recombinant inbred lines (RILs) and four cultivars under 150 mM NaCl field conditions. ANOVA revealed that genotype contributed the largest proportion of the treatment sum of squares across all traits (60–80%), followed by the genotype × year interaction (8–15%), whereas year contributed the smallest proportion (1–5%). Heatmap clustering of these traits clearly distinguished salt-tolerant from salt-sensitive genotypes. The study findings highlight using four key traits as screening criteria for salt tolerance in wheat. Our optimized 2D/3D spectral indices showed moderate to strong predictive power (R 2 = 0.25–0.75), outperforming earlier indices. Multi-season data improved accuracy by 15–25%, with best predictions for Chl a and TChl (R 2 = 0.34–0.75) versus Chl b and GY (R 2 = 0.25–0.64). Top models included ANN-3D-SRIs-8 for Chl a (R 2 = 0.735/0.644), ANN-3D-SRIs-3 for Chl b (R 2 = 0.611/0.549), ANN-2D-3D-SRIs-3 for TChl (R 2 = 0.713/0.619), and ANN-2D-SRIs-2 for GY (R 2 = 0.648/0.553). This framework combines optimized indices and machine learning for scalable, high-throughput phenotyping to advance precision breeding of salt-tolerant wheat.

Why it matches plant phenotyping methodsスペクトル指数とニューラルネットワークを開発・評価し、コムギのクロロフィルと収量を高スループット推定する方法が研究の中心である。

abstractwe developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY)

Rice evapotranspiration estimation and irrigation optimization based on coupling UAV multispectral and thermal infrared imagery with the FAO-56 model ↗

Abstract China's rice production and environmental sustainability are largely dependent on the cold black soil region in Northeast China, where precise water and nitrogen management is challenged by water scarcity and high carbon emissions. To overcome the limitations of conventional empirical management and improve the accuracy of evapotranspiration (ET) estimation in controlled-irrigation paddy fields, this study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model. To parameterize and validate this methodology, field data comprising four lower limit thresholds for controlled irrigation and four nitrogen fertilizer application rates were acquired from the Rice Research Site of Farm 856, Heilongjiang Province, China. This integrated approach was used to systematically evaluate rice growth, water consumption, resource use efficiency, and greenhouse gas emissions under different water-nitrogen treatments. Based on these evaluations, an irrigation optimization scheme was developed using daily crop evapotranspiration (ETc). The results indicated that water, nitrogen, and their interaction significantly affected rice yield, irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFPN), and global warming potential (GWP). Treatments W3N2 (80%+155 kg/ha N) and W3N3 (80%+200 kg/ha N) achieved the highest yields, 11,883.51 and 11,436.82 kg/ha, respectively, whereas W2N1 (70%+110 kg/ha N) exhibited the best comprehensive performance, with a TCQ value of 0.65. Among the tested vegetation indices, the normalized difference vegetation index (NDVI) showed the strongest correlation with the basal crop coefficient, with an R²of 0.85. The NDVI -crop water stress index ( CWSI ) coupled model achieved the highest ET c estimation accuracy (R 2 = 0.89, RMSE = 0.39 mm/day), reducing the RMSE by 10.3% compared to the traditional, Multi-objective optimization revealed obvious trade-offs among high yield, water saving, high nitrogen efficiency, and low emissions. Scenario S5 was identified as the optimal solution, with an irrigation amount of 669.94 mm, a nitrogen rate of 117.48 kg/ha, a yield of 11,473.43 kg/ha, and the highest coordination degree of 0.86. These results demonstrate that coupling UAV multispectral and thermal infrared imagery with the FAO-56 model can effectively improve ETc estimation and provide reliable data support for water-nitrogen multi-objective optimization in cold-region rice production.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像とFAO-56を結合し、イネの蒸発散量を推定する手法を開発・検証しており、ETc推定精度も定量評価しているため、単なる灌漑試験ではなく植物状態の計測手法が中心です。

abstractthis study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model.

CNN Models used in Agriculture- A Comprehensive Study of Nutrients and Micronutrients in Papaya Crop ↗

India’s economy is heavily reliant on agriculture, with a diverse array of crops grown on vast tracts of land. Fruit cultivation, especially papaya, has become more popular in recent years because of its high nutritional and financial value. To increase yield, maximize resource use and lessen reliance on chemical pesticides, modern techniques like protected cultivation and hydroponics are being used more and more. Fruit crops grown in controlled or semi-controlled environments are still susceptible to nutrient imbalances despite these developments, which can have a substantial impact on plant health, fruit quality and overall productivity. Papaya leaf nutrient deficiencies frequently show up in the early stages of growth and can result in poor fruit development and decreased yield if they are not detected in time. To support early diagnosis and better crop management, the current study focuses on creating an effective method for identifying nutrient deficiencies in papaya leaves using a deep learning (DL) framework based on transfer learning (TL). In this nutrient and micronutrient deficiency study and field work observation during year 2024 to 2026 with different climate and weather conditions done in order to tackle a new but related classification task, in the context of plant health assessment, several well-established architectures including InceptionV3, VGG19, DenseNet and Xception have been widely explored for leaf image analysis. Studies commonly utilize publicly available datasets, such as papaya leaf image repositories hosted on platforms like IEEE DataPort, to fine-tune these models for efficient feature extraction and accurate identification of nutrient and micronutrient deficiency patterns. This body of work demonstrates the growing role of transferring convolutional neural network (CNN) models in advancing automated crop monitoring and decision support systems.

Why it matches plant phenotyping methodsパパイヤ葉画像から栄養・微量栄養素欠乏という植物状態を深層学習で識別する手法の開発が研究の中心であり、植物フェノタイピングに該当する。

abstractthe current study focuses on creating an effective method for identifying nutrient deficiencies in papaya leaves using a deep learning (DL) framework based on transfer learning (TL).

Decoding genetic basis of nitrogen use efficiency in maize using AI-generated deep phenotypes. ↗

Improving nitrogen use efficiency (NUE) is essential for sustainable agriculture, yet conventionally measured plant characteristics have limited value as NUE proxies. Here we show that artificial intelligence (AI) can uncover previously unrecognized phenotypic variation associated with NUE, revealing genetic variation that is largely missed by conventional phenotypes. We trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy. The learned features were defined as deep phenotypes. Compared with conventional phenotypes, deep phenotypes showed greater phenotypic variation and higher heritability, enabling the identification of 523 significant loci compared with 21 for conventional phenotypes. We next investigated candidate genes underlying these loci and used these findings to interpret the learned features. Lower CNN layers primarily reflected visual patterns overlapping with conventional phenotypes, whereas deeper layers encoded additional features associated with N-responsive genetic variation. To validate candidate genes identified by the AI framework, we functionally characterized Liguleless2 (LG2), a basic-leucine zipper (bZIP) transcription factor, and demonstrated that lg2 mutants exhibit enhanced root architecture and increased N uptake efficiency. Field trials of 200 hybrids across diverse N environments further supported the AI findings, with each beneficial allele increasing ear weight by an average of 18 g per plot under low-N conditions. These results show how integrating AI and biology can uncover biologically relevant variation underlying complex traits such as NUE and enhance the interpretability of AI models.

Why it matches plant phenotyping methodsCNNで植物画像からN応答に関連する「deep phenotypes」を抽出する手法が研究の中心であり、従来形質との比較や遺伝的妥当性検証も行っている。

abstractWe trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy.

Quinoa genotypes under deficit irrigation: integrating phenotyping and remote sensing for water use efficiency in arid Peru. ↗

Within the context of climate change, quinoa ( Chenopodium quinoa Willd.) is a climate-resilient crop with high nutritional value. The effects of deficit irrigation on quinoa growth and physiological performance under arid conditions remain insufficiently understood. This study evaluated ten quinoa genotypes (two commercial varieties and eight accessions) under two irrigation regimes to identify traits and spectral indices associated with water-stress tolerance. We combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively. Deficit irrigation reduced plant height (18%), specific leaf area (8%), yield (43%), harvest index (26%), relative water content (7%), and dry matter accumulation (36%), while relative chlorophyll content (SPAD, Soil Plant Analysis Development) and stomatal density increased by 16% and 13%, respectively; accession ACC_23 exhibited the highest water-use efficiency (5.9 g kg -1 ). A univariate analysis of 35 vegetation indices across 13 dates showed that: Health Index(HIV), Normalized Green-Red Difference Index (NGRD), Red-Green Ratio (RG) and Plant Senescence Reflectance Index (PSRI), were the most sensitive, detecting significant differences between irrigation treatments in up to 32 of the 130 possible genotype-by-date comparisons. Integrating remote sensing into crop phenotyping represented a significant methodological improvement by enhancing phenotyping efficiency, improving detection of deficit irrigation effects, and facilitating identification of tolerant quinoa genotypes for arid production systems.

Why it matches plant phenotyping methodsリモートセンシングと多時点の植 phenotyping を統合し、35の植生指数の感度比較によって水ストレス関連形質を抽出する方法適用が、研究の主要な技術的要素として明示されています。

abstractWe combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively.

RADIX: a deep learning framework that maps root barriers across species and reveals genetic and environmental contributions ↗

Root anatomical barriers, including the suberized and lignified walls of the endodermis and exodermis, and cortical aerenchyma, regulate water and nutrient transport, gas exchange, and rhizosphere interaction. Their adaptive function places them as an important target for breeding environmentally resilient plant species. Quantifying these structures at high resolution is a manual bottleneck that limits experimental scale. We present RADIX (Root Anatomy Deep- learning Image segmentation across species and platforms), a framework that adapts a large self-supervised vision-transformer foundation encoder (DINOv3), pre-trained on billions of natural images, to root anatomy by fine-tuning its encoder with a dense-prediction-transformer decoder. Transferring these general-purpose vision encoders to a specialized biological domain with a high-quality annotated dataset is what allows RADIX to generalize across species and imaging platforms. We train and evaluate it on the first expert-annotated benchmark of root anatomical structures at scale, comprising 1,695 high-quality fluorescence images spanning 17 monocot and dicot species, six anatomical structures, and three imaging platforms. RADIX segments all six structures at inter-annotator-level accuracy and generalizes to unseen species, genotypes, growth conditions, and an imaging platform from an independent laboratory. A single unified model surpasses monocot- and dicot-specialist models without sacrificing in-group accuracy. Predicted masks yield aerenchyma and suberin/lignin measurements matching expert annotation at ∼1.2 s per image with a single GPU, reducing weeks of manual analysis to minutes. Applying RADIX across genotypes, microbial treatments, and growth systems, we show that these cell type features form a coordinated, multidimensional, and context-dependent system shaped by genetic and environmental factors.

Why it matches plant phenotyping methods根の解剖学的構造を画像から自動抽出・定量する深層学習フレームワークを開発し、注釈付きベンチマーク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。

abstractQuantifying these structures at high resolution is a manual bottleneck that limits experimental scale.

Automated Video Tracking to Phenotype Plant Resistance to Aphid-Transmitted Yellow Dwarf Viruses in Grass Seed Crops ↗

Grass seed crops are susceptible to yellow dwarf viruses transmitted by aphids. The Willamette Valley in Oregon, United States, is the leading producer of cool-season grass seed crops globally, and industry reports have attributed seed yield loss and shortened stand longevity to aphid-transmitted yellow dwarf viruses. Genetic resources are needed for effective and sustainable management of this pest, specifically the Rhopalosiphum padi–PAV pathosystem, in grass seed production to reduce foliar insecticide applications and maintain optimum seed yield potential. High-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs. An automated video tracking procedure was optimized to evaluate host plant resistance in cool-season grass seed crops to R. padi–PAV with live plants and viruliferous and nonviruliferous aphid populations. Feeding behavior recorded with automated video tracking was strongly correlated with “ground-truthed” observations by human observers. Partial resistance (antixenosis and antibiosis) and tolerance traits were detected in select perennial ryegrass and tall fescue cultivars evaluated with traditional phenotyping methods in a greenhouse setting and with high-throughput phenotyping using automated video tracking in the laboratory. Across grass cultivars, nonviruliferous aphids had greater fitness and preference for noninfected grass plants compared with viruliferous aphids. Automated video tracking can be used as a high-throughput phenotyping method for continued evaluation of host plant resistance in grasses grown for seed production, as well as to identify resistant genotypes in other grass crops susceptible to aphid–yellow dwarf virus virus–vector systems.

Why it matches plant phenotyping methods自動動画追跡を用いてアブラムシ媒介ウイルスに対する植物抵抗性を高スループットに評価する手法を最適化・検証し、従来観察との相関および抵抗性形質の検出を示しており、表現型取得法が研究の中心である。

abstractHigh-throughput phenotyping methods are needed to screen grass seed cultivars to identify resistant traits for traditional breeding programs.

Structured Multi-Kernel Heteroscedastic Gaussian Process for Crop Straw-to-Grain Ratio Prediction and Uncertainty Quantification ↗

Crop straw-to-grain ratio (SGR) estimation underpins regional straw resource assessment, yet national inventories rely on fixed coefficients that ignore structured variation across variety, environment, and phenotype. We introduce a Structured Multi-Kernel Heteroscedastic Gaussian Process (GP) framework that models SGR variation through three additive kernels heuristically motivated by the genotype–environment–phenotype (G+E+P) framework—capturing variety-associated variation, spatially structured variation, and environmental and management covariates—and employs an input-dependent noise model for prediction-specific uncertainty quantification. To prevent information leakage, target encoding and feature scaling are recomputed within each cross-validation fold. Evaluated via internal leave-one-out cross-validation on 80 rice samples (42 varieties, six Chinese provinces), the model achieves R2=0.541 with a prediction interval coverage probability of 0.95. Ablation identifies variety-associated variation as the largest contributor among the modeled factors (ΔR2=−0.024) and the multi-kernel design, by incorporating variety-specific information, substantially improves upon a covariate-only RBF GP (ΔR2=0.103). On point-prediction accuracy, Gradient Boosting achieves R2=0.58, slightly ahead of the Heteroscedastic GP (R2=0.54), underscoring that the primary advantage of the GP lies in its input-dependent uncertainty quantification. However, leave-one-county-out validation yields R2≈0 (with σ escalating to 24.4), confirming that the model does not yet generalize to unsampled counties; all reported performance is therefore internal to the nine sampled counties. The framework couples an agronomically motivated additive kernel structure with input-dependent uncertainty quantification, offering a path toward uncertainty-aware prediction from small field datasets.

Why it matches plant phenotyping methods作物のわら・穀粒比という植物関連形質を対象に、不確実性定量化を備えた予測手法を開発・検証しており、単なるルーチン測定ではなく計算的な形質推定が中心である。

abstractWe introduce a Structured Multi-Kernel Heteroscedastic Gaussian Process (GP) framework that models SGR variation

Bridging Magnetic Field Agriculture and UAV-Based Precision Monitoring: An Integrated Dual-Stream Evidence Synthesis and Conceptual Framework for Field-Scale Validation ↗

Magnetic field (MF) technologies have been explored in agriculture since the 1930s, with research activity increasing markedly since 2016. However, they have not achieved mainstream adoption, partly because no MF-specific validated methodology exists for evaluating their effects under realistic field conditions. Unmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, thereby enabling, for the first time, the systematic evaluation and validation of MF treatment responses under open-field conditions. To realise this potential, however, a common evidential basis must first be established by identifying crop physiological variables that are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream evidence synthesis of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency (NUE)/nitrogen assimilation, above-ground biomass (AGB), leaf area index (LAI), and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R2 = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The review revealed a complete absence of integration between the two research domains within the reviewed corpus, despite their strong biological and methodological compatibility. The proposed framework is conceptual and remains to be experimentally validated; it provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems.

Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングによる作物生理形質の推定をレビューし、地上真値・植生指数・機械学習を統合した検証フレームワークを提案しており、植物表現型の取得・推定方法が中心です。

abstractUnmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale

Ozone suppresses rice photosynthesis and yield in China's middle-lower yangtze plain: satellite evidence from SIF and panel regression. ↗

Ground-level ozone (O 3 ) adversely affects rice physiology and is associated with yield reductions. This study developed a high-resolution assessment framework integrating multi-source satellite remote sensing with econometric methods to quantify the impacts of O 3 on rice production in China's primary rice-growing region-the Middle and Lower Reaches of the Yangtze River (MLYR)-from 2019 to 2023. We fused Sentinel-5P TROPOMI total ozone column (TOC) data, a harmonized multi-satellite solar-induced chlorophyll fluorescence (SIF) product (LHSIF), high-precision rice distribution maps, and ERA5 meteorological reanalysis data. In addition to SIF, we examined multiple vegetation indicators (chlorophyll content, leaf area index, and vegetation indices) to capture broad physiological responses. A bidirectional fixed-effects panel model was employed to control for spatiotemporal confounders, revealing a significant inhibitory effect of O 3 on photosynthesis (β = -1.334 × 10 -5 , p 3 concentrations would increase regional SIF by 36.36%, while a commensurate 10% reduction in annual exposure could elevate rice yields by approximately 8.4%. This spaceborne remote sensing approach provides a robust and transferable methodology for the precise regional monitoring of ozone stress and for informing targeted mitigation strategies to safeguard crop productivity.

Why it matches plant phenotyping methods衛星リモートセンシングによるSIF等の植物生理指標を用いてイネのオゾンストレスを地域スケールで推定する評価フレームワークが研究の中心であり、単なる生物学的実験の routine 測定ではない。

abstractThis study developed a high-resolution assessment framework integrating multi-source satellite remote sensing with econometric methods to quantify the impacts of O 3 on rice production

HagPF: Hierarchical-annotation-guided phenotypic framework for stem instance segmentation and length measurement in plant point clouds ↗

As a critical structural component that connects almost all other types of plant organs, the stem system not only supports the weight of the total plant, but also serves as a vital channel for nutriment transportation. Accurate phenotypic measurement of stem instances is of practical significance for assessing crop growth dynamics and predicting yield. To address current 3D phenotyping challenges of crops such as the difficulty in separating stem segments from the stem system and the low accuracy in stem length measurement, we propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds. Specifically, a hierarchical leaf-stem organ instance annotation strategy is devised to effectively train a PSegNet network for leaf and stem instance segmentation. The segmentation is then followed by a shape-adaptive measurement algorithm to automatically measure the length of stem segments that are morphologically diverse in space. On a 3D dataset comprising four crop species, the proposed framework achieved an Intersection over Union (IoU) of 95.45% for organ semantic segmentation and a Mean Weighted Coverage (mWCov) of 87.87% for instance segmentation (both stem and leaf). Regarding to the stem length measurement, the method obtained an average Root Mean Square Error (RMSE) of 1.044 cm and a relative error of 11.907%, outperforming 7 mainstream methods. The relevant dataset and source code can be found at: https://github.com/Jinx00/stem-length-measurement .

Why it matches plant phenotyping methods植物点群から茎のインスタンスを分割し、茎長を自動測定する3D表現型解析フレームワークの開発・比較検証が中心である。

abstractwe propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds.

An integrated YOLOv11-based framework for wheat spike phenotyping segmentation and grain yield estimation ↗

Spike count and spatial arrangement are among the strongest determinants of grain yield in wheat, yet reliable spike-level phenotyping under field conditions remains challenging. Field-acquired images are often affected by occlusion, heterogeneous illumination, and dense canopy overlap. In addition to these visual complexities, the effort required to generate large-scale instance-level annotations makes it difficult to build the extensive labeled datasets typically needed for robust segmentation models. This paper addresses both problems. The core methodological contribution is a semi-automated iterative annotation workflow: a YOLOv11x-seg model trained on a small manually annotated set is applied to unlabeled field images, and its predictions, after post-processing to remove duplicated, fragmented, and merged detections, are incorporated back into training. To prevent augmentation from distorting the training distribution, we introduce a distribution-aware augmentation strategy guided by Fréchet Inception Distance (FID), which retains only those augmented samples that remain within an acceptable distance from the original data distribution. Together, these components allowed us to build an effective training set from 3,899 high-resolution RGB images (4000 × 3000 pixels) of durum wheat collected at the CREA Research Centre for Cereal and Industrial Crops, with substantially reduced manual annotation effort. On the independent test set(242 images), the final YOLOv11x-seg model (M5 model) achieved a mask-level precision of 86.73%, recall of 83.02%, F1-score of 84.83%, mAP@50 of 89.42%, and mAP@50:95 of 60.51%. Spike masks were used to derive image-based traits including spike count, spike density, canopy coverage, spike area, spatial distribution, and vegetation indices. Their relationships with measured grain yield were explored through statistical analysis and machine-learning-based yield estimation.Both statistical and machine-learning analyses demonstrated that image-derived spike traits provided meaningful information for grain yield estimation, with canopy coverage showing the strongest positive association with yield. Using repeated nested cross-validation with out-of-fold (OOF) predictions, XGBoost achieved the highest yield estimation performance ( R OOF 2 = 0.312 , RMSE = 106.61 g/plot), supporting the potential of near-image phenotyping for late-stage yield estimation in wheat. These results show that semi-automated iterative annotation can enable practical wheat spike segmentation and image-based phenotyping under realistic open-field conditions. While grain yield estimation should be interpreted within the context of the experimental setting, the proposed framework highlights the value of image-derived spike traits for late-stage phenotyping and yield assessment in breeding experiments rather than for early-season yield forecasting.

Why it matches plant phenotyping methods半自動アノテーション、YOLOv11x-segによる小麦穂の画像セグメンテーション、分布認識型データ拡張、形質抽出と検証が中心的な方法論的貢献である。

abstractThe core methodological contribution is a semi-automated iterative annotation workflow

Smartphone-based high-fidelity 3D semantic segmentation of finger millet panicles using 3D Gaussian splatting for automated phenotyping and yield estimation ↗

Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.

Why it matches plant phenotyping methods3D画像再構成・セグメンテーション・形質抽出を統合した植物フェノタイピング手法の開発が中心であり、収量関連形質の定量と予測まで技術的に評価している。

abstractthis study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images.

Development of web-based YCPM-UAV interface for early yield prediction of canola crop using UAV multi-sensor data ↗

• Development of yellow color index (YCI) for yield estimation at flowering stage • Development of web-based interface (YCPM-UAV) for canola yield prediction using UAVs • Global application capability for UAVs datasets to predict canola yield using YCPM-UAV • Multi-sensor and multi-spectrum data fusion to find most suited indices for canola • Multiple and stepwise regression analysis for selection of most influencing VIs Canola ( Brassica napus L.) is a globally significant oilseed crop, yet accurate yield estimation remains challenging due to the complex and unique nature of the crop, especially at the flowering stage. Traditional field-based yield estimation methods are labor-intensive, time-consuming, and destructive, necessitating innovative approaches for early and non-destructive yield prediction. The main objective of the study is to develop a novel web-based platform, YCPM-UAV (Yellow Color Prediction Model using Unmanned Aerial Vehicles), for early and accurate canola yield estimation using high-resolution multi-sensor datasets acquired through low-altitude UAVs (LA-UAVs). To achieve this objective, a comprehensive two-year field study (2022-2024) was conducted across ten farmers’ fields in different geographical locations. Multisensor data (RGB, multispectral, and thermal) were acquired using UAVs at seven growth stages. Several vegetation indices (VIs), yellow color-based indices, and a thermal index were calculated. Linear, multiple, and stepwise regression analyses were performed to evaluate relationships of remote sensing indices with ground-truth yield data collected from 1200 sampling points. Multiple and stepwise regression analyses indicated that the newly developed Yellow Color Index (YCI) exhibited the strongest correlation with actual canola yield at the flowering stage across both years (Year 1: R 2 = 0.84, RMSE = 39.30 g m⁻²; Year 2: R² = 0.88, RMSE = 31.57 g m⁻²). Based on proposed predictive modeling, the YCPM-UAV web interface was developed, featuring automated data processing and spatial analysis with a testing accuracy of 88%. The YCPM-UAV platform provides farmers, researchers, and policymakers with a timely, user-friendly, and actionable decision-support tool for canola yield estimation at the field scale, contributing to improved crop management and food security. Future studies should incorporate additional canola varieties, irrigated and non-irrigated fields, and deep learning algorithms to further improve model robustness.

Why it matches plant phenotyping methodsUAVマルチセンサー画像からカノーラ収量を推定する指標・回帰モデル・Webプラットフォームを開発し、複数年データで検証しており、植物形質取得・推定が中心である。

titleDevelopment of web-based YCPM-UAV interface for early yield prediction of canola crop using UAV multi-sensor data

Genomic selection and genome-wide association studies using UAV-derived plant height and aboveground biomass of maize ↗

Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize ( Zea mays L . ). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years ( R 2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy ( R 2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.

Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから草丈と地上部バイオマスを推定する高スループット表現型測定モデルを開発・検証しており、フェノタイピング手法が研究の中心です。

abstractwe employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB

A double-sigmoid approach for high-throughput phenotyping of winter wheat growth dynamics ↗

Multi-temporal data from unoccupied aerial systems (UAS) offer insights into growth parameters for winter wheat breeding decisions. Weekly UAS data were collected during the 2019 and 2020 growing seasons from dryland and irrigated nurseries at Bushland, Texas, within the Texas A&M Uniform Variety Trials. Canopy cover (CC) was extracted from orthomosaic images and modeled using a double-sigmoid function with a second-order derivative that captured genotypic variation in canopy growth and senescence with high coefficients of determination (R² > 0.99) and low root mean square error (RMSE) values ranging from 1.73 to 4.06. Analysis of variance (ANOVA) revealed highly significant genotypic effects (p<0.001) for yield, heading, and Green Leaf Area Duration (LAD) in all environments except 2020 dryland, where no significant differences among genotypes were detected. Extracted parameters showed positive correlations with agronomic traits, particularly under rainfed and stress-prone conditions. The end decrease stage (EDS) was correlated with grain yield (r=0.56 in 2019 dryland, and r=0.53 in 2020 irrigated, p<0.001), and the start decrease stage (SDS) was highly correlated with yield (r=0.53 in 2020 irrigated, p<0.001). The maximum decrease rate date (MDRD) was positively correlated with yield in 2019 dryland (r=0.55, p<0.001), while LAD had a correlation of r=0.56 in 2019 dryland and r=0.58 in 2020 irrigated (p<0.001). These findings demonstrate that double-sigmoid model provides a powerful, non-invasive framework for quantifying canopy development, senescence timing, and stress responses. By distinguishing genetics from environmental influences on canopy dynamics, this approach enhances selection accuracy and accelerates the development of stress-resilient winter wheat cultivars.

Why it matches plant phenotyping methodsUAS画像からキャノピー被覆を抽出し、二重シグモイドモデルで生育・老化動態を定量化する手法が研究の中心であり、精度評価と遺伝型・農業形質との検証も行っている。

titleA double-sigmoid approach for high-throughput phenotyping of winter wheat growth dynamics

Estimation of cotton plant moisture content using UAV multimodal data and machine learning ↗

Accurate monitoring of cotton plant moisture content (PMC) is crucial for guiding irrigation practices. To address the limited capacity of single-source remote sensing data to characterize the water status of cotton plants, as well as the lack of quantitative reference values for suitable PMC levels at different growth stages, this study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data. Furthermore, the suitable reference levels of PMC at different growth stages were investigated according to the response relationship between PMC and yield at each growth stage. Five soil moisture gradients were established, and at each growth stage, fresh and dry weights of cotton shoots were measured to calculate the PMC. A UAV platform equipped with multiple sensors was used to collect visible-light (RGB), multispectral (MS), and thermal infrared (TIR) images of the cotton canopy. Three feature selection methods were employed to identify moisture-sensitive parameters: Pearson correlation analysis, principal component analysis (PCA) for dimensionality reduction, and recursive feature elimination (RFE). Using the selected parameters, four machine learning algorithms, AdaBoost, random forest (RF), CatBoost, and k-nearest neighbors (KNN), were applied to construct and validate PMC estimation models. The suitable PMC levels at different growth stages were identified based on the response relationship between measured PMC and yield under different water gradients. The results showed that the RFE feature selection method identified eight water-sensitive parameters, and the CatBoost model integrating multimodal data performed best, with R² and RMSE reaching 0.807 and 0.033%, respectively, on the test set, providing a reliable method for high-resolution spatial mapping of field-scale PMC. On this basis, the response of yield to PMC was analyzed, revealing that when PMC was maintained at 83.8%, 85.9%, 79.3%, 78.0%, and 67.7% at the bud, initial flowering, peak flowering, peak boll-setting, and boll opening stages, respectively, the theoretical maximum yield of 6579–6667 kg/hm² could be achieved. This study realized high-precision remote sensing monitoring of PMC and further explored the appropriate moisture content thresholds for different growth stages, providing a quantitative reference for precision water regulation in cotton fields.

Why it matches plant phenotyping methodsUAVのマルチモーダル画像と機械学習により、綿植物の水分含量を推定・検証する手法が研究の中心であり、植物状態の高解像度マッピングにも応用している。

abstractthis study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data

An integrative high-throughput phenotyping framework for assessing canopy growth dynamics and light interception in potato ↗

Quantifying the canopy growth dynamics and light interception capacity under different management practices laid the physiological foundation for potato yield formation. However, the traditional manual measurement methods are labour-intensive, time-consuming, and incapable of capturing time-series dynamics. To address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model. Furthermore, how Nitrogen(N)-Potassium(K) interaction affects the temporal canopy growth dynamics, light interception, and tuber yield was determined. The results indicated that: (1) Among the 11 secondary indices extracted from the canopy growth dynamic curves, the interaction of N and K had the greatest effect on the maximum canopy duration and the total canopy growth curve integral. The direct path coefficients of N and K inputs on these two parameters were 0.847 and 0.805, and 0.234 and 0.148, respectively. (2) There was a strong linear relationship between the integral area under the curve (S∫) and the total plant dry weight, with R² at 0.90 in 2023-2024. A simplified net photosynthetically active radiation utilisation assessment framework that achieved high accuracy with minimal parameter was built. (3) Prolonging the maximum canopy continuous coverage time is the main way to improve potato yield. The overall effect of N input on yield was significantly higher than that of K fertiliser, with a total effect value of 1.428. Optimising the N-K interaction improves nutrient precision and light interception. The integration of UAV remote sensing and the crop physiological-ecological model enables the tracking of potato canopy dynamics, which is helpful for optimising management practices to improve potato yield.

Why it matches plant phenotyping methodsUAV RGB画像と生理モデルを統合した高スループット手法を開発し、ジャガイモのキャノピー成長動態と光 interception を時系列で推定することが中心である。

abstractTo address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model.

Diurnal cross-temporal features from UAV multispectral and thermal imagery enhance foxtail millet yield prediction accuracy under different irrigation regimes ↗

Accurate prediction of foxtail millet yield is essential for effective field management and high-throughput breeding. Despite advances in UAV-based yield prediction for major crops, existing studies predominantly rely on single-temporal features (SFs) extracted at noon, overlooking significant diurnal dynamic signals that characterize crop responses to water stress. To address this research gap, we propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy under different irrigation regimes. During the flowering and grain-filling stages, UAV images were acquired across eight time slots (T1–T8) within a single day to capture the complete diurnal trajectory of canopy physiological responses. SFs were extracted at each time slot, and CFs were derived through summation, averaging, and range operations across multiple slots. A systematic four-step workflow was developed to determine the optimal UAV flight frequency and timing by balancing prediction accuracy with operational costs. Three ensemble learning algorithms (Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost)) were evaluated using multiple feature sets incorporating SFs, CFs, and their integration. Results demonstrated that CFs more comprehensively captured dynamic crop responses to water stress than SFs. Canopy features from afternoon combinations generally exhibited stronger yield correlations than morning combinations. The [T5, T8] combination was identified as optimal, providing a practical balance between prediction accuracy and operational cost. Model comparison revealed that RF exhibited greater robustness across different water treatments, whereas AdaBoost achieved higher accuracy on the test set. Feature importance analysis confirmed the dominance of CFs, with ∑VSWI ranking first across both models and growth stages. This study provides a systematic framework for utilizing diurnal dynamic signals in crop yield prediction, offering new methodological insights for precision agriculture and high-throughput phenotyping of foxtail millet and other dryland crops.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像から作物特徴量を抽出し、収量という植物形質を推定する手法と、撮影頻度・時刻を最適化するワークフローが研究の中心であるため。

abstractwe propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy

Linking plant water status dynamics to yield and fruit cracking in citrus orchards using UAV multi-sensor data and machine learning ↗

Citrus fruit cracking causes substantial yield and economic losses, yet its relationship with plant water status (PWS) and irrigation management remains insufficiently characterized. Unlike previous UAV-based irrigation studies that focused on water-stress detection or yield estimation, this study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale. UAV-based multispectral, thermal, and LiDAR data, combined with field physiological measurements and machine-learning models, were evaluated in an irrigation management experiment in an ‘Ori’ mandarin orchard (Israel) across three contrasting growing seasons (2023–2025). Several irrigation treatments with different irrigation timings and water inputs were applied during the growing season to evaluate their effects on temporal PWS dynamics and fruit cracking. Trunk growth (TG), stem water potential (SWP), stomatal conductance (SC), and plant area index (PAI) were measured throughout the two seasons and estimated using Random Forest models (R 2 > 0.783). These indicators were subsequently used to predict yield and fruit cracking with high accuracy (yield: R² = 0.896; cracking: R² = 0.845). Cracking was lowest in 2023 (∼3%), with ∼25% lower irrigation, suggesting reduced irrigation may reduce cracking risk. Higher cracking in 2024 (∼14%, vs ∼8% in 2025) coincided with intense heat events. Mid-season SWP and SC were strongly associated with yield formation and cracking patterns. These findings demonstrate that monitoring temporal PWS dynamics can support precision irrigation management by identifying high-risk zones and enabling irrigation strategies that stabilize PWS, reduce the incidence of cracking, and improve yield under variable climatic conditions.

Why it matches plant phenotyping methodsUAVマルチセンサーと機械学習により、樹体水分状態などの植物形質を推定し、収量・果実裂果を予測する技術的枠組みが研究の中心である。

abstractthis study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale.

Identification of Unsound Soybean Seeds Based on Hyperspectral Imaging and a Dual‐Channel Residual‐Squeeze‐and‐Excitation Network With Gramian Angular Field Fusion ↗

The precise identification of unsound soybean seeds is a critical step in deep soybean processing and seed selection. The accuracy of this identification directly influences the quality of subsequent processed products, as well as the germination rate and yield of soybean crops. This study proposes a nondestructive identification method for unsound soybean seeds based on hyperspectral imaging (HSI), Gramian Angular Field (GAF), and a Dual-Channel Residual-Squeeze-and-Excitation Network with GAF Fusion (DC-RSEN-GF). According to common damage types, soybeans were categorized into six classes: sound seeds, thermal-damaged seeds, insect-damaged seeds, broken seeds, spotted seeds, and moldy seeds. Spectral data from these six soybean categories were acquired using a hyperspectral camera and transformed into two-dimensional GAF images. The DC-RSEN-GF network integrates one-dimensional spectral data with two-dimensional GAF images. After preprocessing with Savitzky-Golay (SG) smoothing, high-precision classification was achieved through residual blocks, an attention mechanism (using SENet), and feature fusion. Compared to five benchmark models-Extremely Randomized Trees (ERT), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), VGG19, and ResNet18-the DC-RSEN-GF model achieved superior performance, with accuracy, precision, specificity, and F1-scores of 96.36%, 96.43%, 97.92%, and 96.36%, respectively. The accuracy, precision, and F1-scores are all superior to traditional machine learning and existing deep learning models, demonstrating better classification capabilities. In addition, t-distributed Stochastic Neighbor Embedding (t-SNE) was employed for visual analysis of soybean spectra, further validating the reliability of the DC-RSEN-GF model. The proposed detection method, based on HSI and DC-RSEN-GF, enables accurate and nondestructive identification of unsound soybean seeds and holds significant potential for practical application.

Why it matches plant phenotyping methodsハイパースペクトル画像と深層学習を用いて、種子の損傷・病変状態を非破壊的に分類する取得・解析手法が研究の中心であり、植物状態の表現型測定に該当する。

abstractThis study proposes a nondestructive identification method for unsound soybean seeds based on hyperspectral imaging (HSI), Gramian Angular Field (GAF), and a Dual-Channel Residual-Squeeze-and-Excitation Network with GAF Fusion (DC-RSEN-GF).

Field-scale potato yield prediction from sentinel-2 time series using lightweight deep learning models ↗

Accurate and timely crop yield prediction and forecasting are important for improving agricultural productivity and supporting informed management decisions. In this study, we developed and evaluated a framework for estimating in-season potato yield at the field scale using Sentinel-2 satellite time series. A Grouped TreeSHAP Stability Selection (GTSS) was first applied to identify a compact, phenology-aware subset of spectral bands and vegetation indices, thereby reducing redundancy and mitigating overfitting in small-data settings. Two deep learning architectures tailored for limited training data were then introduced: LiteTemporalConv, a lightweight temporal convolutional network, and MS-ConvBiGRU-Attn, a hybrid encoder combining multi-scale convolutions, bidirectional GRUs, and an attention mechanism. Both models were benchmarked against widely used machine learning methods, including Random Forest, Support Vector Machine, Extreme Gradient Boost, Partial Least Squares, as well as standard deep learning baselines (CNN and GRU). Results showed that the proposed models outperformed both machine learning and conventional deep learning baselines, with LiteTemporalConv achieving the highest accuracy under 10-fold cross-validation (R² = 0.84; RMSE = 3.18 t ha⁻¹; rRMSE = 6.36%) and MS-ConvBiGRU-Attn yielding similarly strong performance (R² = 0.82; RMSE = 3.53 t ha⁻¹; rRMSE = 7.03%). By comparison, the best baseline, XGB, achieved an R² of 0.79 with an rRMSE of 9.8%. The two best-performing models were further evaluated on an independent spatial dataset to assess their generalization beyond the training region. In an additional experiment, both deep learning models trained on mid-season observations showed predictive stability for late-season yield estimation. Overall, the results highlight the importance of targeted feature selection and lightweight encoders for yield modeling in data-scarce conditions.

Why it matches plant phenotyping methodsSentinel-2時系列からジャガイモ収量を推定する特徴選択・深層学習手法を開発し、複数モデルとのベンチマークと独立データでの検証を行っており、植物形質取得が中心である。

abstractwe developed and evaluated a framework for estimating in-season potato yield at the field scale using Sentinel-2 satellite time series

Assimilation of UAS remote sensing and deep learning-derived crop parameters into DSSAT model for grain yield prediction ↗

In-season fine-scale (i.e., within-field experiment plot scale) crop grain yield (GY) prediction is critical for optimizing inputs, minimizing environmental impacts, and supporting sustainable food production. Traditional approaches, such as field surveys, are often costly and inefficient over large areas. As an alternative, remote sensing combined with crop simulation models (CSMs) has been increasingly applied for in-season GY prediction. This study investigates the potential of integrating Uncrewed Aircraft Systems (UAS)-based remote sensing data, deep learning, and CSMs to predict maize and soybean GY using a data assimilation approach. UAS multispectral imagery was collected, along with field-measured maize above-ground biomass (AGB) and soybean leaf area index (LAI) during the 2022 and 2023 growing seasons at experimental fields in Brookings, South Dakota. Maize AGB was measured at two growth stages, while soybean LAI was collected across four stages. One-dimensional convolutional neural networks (1D-CNNs) were used to estimate maize AGB and soybean LAI from canopy spectral, textural, and structural features derived from UAS imagery. These UAS and deep learning–derived crop traits were assimilated into DSSAT-Maize and DSSAT-Soybean models to optimize parameters, and the optimized models were subsequently used to predict GY. For maize, the DSSAT-Maize model achieved an R² of 0.62, an RMSE of 717.8 kg ha⁻¹, and an rRMSE of 6.7% for GY prediction. For soybean, the DSSAT-Soybean model achieved an R² of 0.81, an RMSE of 207.3 kg ha⁻¹, and an rRMSE of 4.9%. Overall, these results highlight the potential of combining high-resolution UAS data and deep learning–derived crop traits within a CSM framework through data assimilation, enabling fine-scale, in-season yield predictions and supporting precise agricultural management.

Why it matches plant phenotyping methodsUAS画像と深層学習により、作物のAGBおよびLAIという植物形質を推定する取得・解析手法が研究の中心であり、作物モデルへの同化と性能評価も行っている。

abstractOne-dimensional convolutional neural networks (1D-CNNs) were used to estimate maize AGB and soybean LAI from canopy spectral, textural, and structural features derived from UAS imagery.

Field-scale rice yield prediction using UAV imagery and machine learning in a developing country context ↗

• DL model trained on MS data achieved the highest accuracy with an R 2 of 75.29% • Linear Regression Coefficient-Based feature selection with SVM and PCA with Linear Regression significantly improved model performance. • NIR and red-edge bands in the MS dataset consistently outperformed the RGB dataset • More represented rice variety (Sona) achieved a strong R 2 of 80.21% on MS data Accurate crop yield prediction is critical for agricultural planning, food security assessment, and farm-level decision-making. In Nepal, however, rice yield estimation is still predominantly based on traditional approaches, where local agricultural extension offices collect field-level observations that are subsequently aggregated at district, provincial, and national scales, often limiting spatial detail and timeliness. This study aims to develop a field-scale rice yield estimation framework by integrating Unmanned Aerial Vehicle (UAV)-derived remote sensing data with machine learning (ML) and deep learning (DL) techniques. High-resolution multispectral (MS) and RGB UAV imagery were used to evaluate the influence of Vegetation Indices (VIs), including HUE and VNDVI from RGB data and RGBVI and Simple Ratio (SR) from MS data, along with plant characteristics and farm management practices (e.g., application of Zyme and Zinc Potash) on rice yield. The predictive performance of Support Vector Machines (SVM), Linear Regression (LR), Decision Trees (DT), Random Forests (RF), and deep neural network models were systematically assessed. Data preprocessing included feature selection based on importance ranking, Yeo–Johnson power transformation, and Principal Component Analysis (PCA) to improve model stability and performance. Among conventional ML models, LR combined with PCA achieved a coefficient of determination (R²) of 69.09% using MS data, while SVM yielded the best performance using RGB data (R² = 68.27%). Overall, deep neural networks outperformed other models, achieving R² values of 75.29% and 64.60% for MS and RGB data, respectively. Model performance varied notably across rice varieties; the Sona variety (n = 127) achieved the highest coefficient of determination (R² = 80.21% for MS and 76.34% for RGB), whereas varieties with fewer samples exhibited lower predictive performance. Results further indicate that ranking features by importance, rather than eliminating them, enhances predictive accuracy, particularly when using LR-derived feature importance, which proved critical for improving the performance of both LR and SVM models.

Why it matches plant phenotyping methodsUAV画像からイネ収量を推定する手法・フレームワークの開発と、複数の機械学習モデルの系統的評価が研究の中心であり、単なる収量のルーチン測定ではない。

abstractThis study aims to develop a field-scale rice yield estimation framework by integrating Unmanned Aerial Vehicle (UAV)-derived remote sensing data with machine learning (ML) and deep learning (DL) techniques.

A comprehensive comparison of multispectral and hyperspectral imagery for plot-level crop yield prediction of kidney beans, snap beans, and potatoes ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsマルチスペクトル・ハイパースペクトル画像を比較し、作物の圃場区画レベル収量を推定する方法論が題名上の中心であるため、植物表現型計測の方法比較・検証として含める。

titleA comprehensive comparison of multispectral and hyperspectral imagery for plot-level crop yield prediction of kidney beans, snap beans, and potatoes

Sentinel-2 for crop yield estimation: A systematic review ↗

Accurate and timely crop yield estimation is fundamental for global food security, agricultural policy, and farm management. The Copernicus Sentinel-2 constellation has catalyzed a paradigm shift in Earth observation for agriculture, enabling field and sub-field scale monitoring. This review synthesizes recent advances in crop yield estimation that leverage Sentinel-2 data. A dominant theme is the transition from regional-scale to high-resolution field-level assessments, driven by three approaches: (i) empirical models using vegetation indices coupled with machine and deep learning (e.g., Random Forest, Convolutional Neural Networks); (ii) integration of process-based crop growth models (e.g., WOFOST, SAFY) through data assimilation of Sentinel-2 derived biophysical variables such as Leaf Area Index; and (iii) data fusion of Sentinel-2 with Sentinel-1 Synthetic Aperture Radar to overcome cloud cover. The synthesis shows that Sentinel-2-based frameworks can explain a large fraction of within-field yield variability, while performance remains constrained by limited ground-truth data, cloud gaps, and model transferability. Looking ahead, knowledge-guided models, self-supervised foundation-model pre-training, lightweight edge workflows, improved ground observations, and multi-sensor fusion are key pathways toward robust, operational decision-support tools for precision agriculture.

Why it matches plant phenotyping methods圃場・圃場内スケールの作物収量という植物形質を対象に、Sentinel-2等による推定手法、モデル統合、データ融合、性能制約を体系的にレビューしており、方法論が中心である。

abstractThis review synthesizes recent advances in crop yield estimation that leverage Sentinel-2 data.

Machine learning-based framework for plant disease identification and nutrient deficiency severity assessment using leaf images ↗

Proper diagnosis of plant disease and nutrient deficiency is crucial in enhancing crop productivity, reducing yield losses and early agricultural interventions. This paper introduces the machine learning approach for automated identification of plant diseases and their severity by analyzing the images of plant leaves. The proposed framework involves image preprocessing, feature extraction, classification and severity estimation, which would enable the accurate identification of various types of disease and estimation of their severity levels while detecting nutrient deficiencies. An extensive image dataset of healthy, diseased and nutrient deficient leaves was used to train and test the models. As illustrated by the experimental results, the proposed framework outperforms the existing machine learning and deep learning methods for plant disease identification and nutrient deficiency detection with the classification accuracy of 98.76% and 98.14% respectively. In addition, the severity assessment module estimates well to enable accurate pesticide and nutrient application, which minimizes chemical use. The proposed framework provides a scalable, efficient, and precise approach for smart crop health monitoring and precision agriculture applications.

Why it matches plant phenotyping methods葉画像から植物病害・栄養欠乏の同定と重症度推定を行う機械学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。

abstractThis paper introduces the machine learning approach for automated identification of plant diseases and their severity by analyzing the images of plant leaves.

An Artificial Intelligence-Driven UAV and Ground Sensor Fusion Framework for Crop Growth Assessment in Smart Agriculture ↗

With the rapid development of artificial intelligence, UAV remote sensing, and agricultural Internet of Things technologies, crop growth monitoring is evolving from manual inspection and single-source analysis toward intelligent decision-making based on multisource perception. However, existing methods still suffer from limited robustness under environmental variations, insufficient integration between UAV imagery and sparse ground sensor observations, and weak capability for transforming predictions into practical agricultural management recommendations. This study proposes a UAV–ground sensor collaborative lightweight framework for crop growth assessment and agricultural decision support. The proposed framework integrates UAV RGB and multispectral imagery with ground sensor observations through a region-level aerial–ground alignment mechanism and a sensor-guided attention fusion module, enabling environmental conditions to enhance visual feature interpretation. Furthermore, a fact-constrained decision module is developed to generate management recommendations based on crop status, environmental risks, and field information. Experimental results demonstrate that the proposed method achieves superior performance in crop growth classification and yield-trend prediction, reaching Accuracy, Precision, Recall, and F1-score values of 92.47%, 91.86%, 91.39%, and 91.62%, respectively, with an RMSE of 0.381 and an R2 of 0.902. The lightweight framework requires only 6.18M parameters and 0.91G FLOPs, achieving 39.56 ms inference latency and 25.28 FPS on edge devices. The proposed framework also improves decision reliability, achieving an expert agreement rate of 89.34% and a risk identification accuracy of 90.18%. Economic analysis indicates that the proposed framework reduces labor cost, water consumption, and fertilizer input by 49.7%, 26.7%, and 23.0%, respectively, while increasing net benefit by 46.1% compared with conventional field management practices. These results demonstrate that the proposed method provides an accurate, interpretable, and deployable AI-driven solution for intelligent crop management in smallholder and medium-sized farming systems.

Why it matches plant phenotyping methodsUAV画像と地上センサーを融合し、作物生育状態を評価する取得・推定フレームワーク自体を開発しており、植物状態の推定方法が中心的です。

abstractThis study proposes a UAV–ground sensor collaborative lightweight framework for crop growth assessment and agricultural decision support.

Genomic Selection Integrated with High-Throughput Phenotyping and Speed Breeding for Smart and Greener Rice ( Oryza sativa ) Improvement. ↗

Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.

Why it matches plant phenotyping methods高スループット表現型解析をゲノム選抜との統合という方法論的主題の一部として批判的にレビューしており、各種画像・センサープラットフォームと形質抽出を扱うため、表現型手法レビューに該当する。

abstractThis narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.

Phenology-Guided Early Prediction of Crop Damage Under Long-Duration Inundation Using Multi-Source SAR–Optical Imagery ↗

Long-duration flood inundation can substantially suppress crop growth and cause yield loss, particularly in semi-arid agricultural regions increasingly affected by extreme rainfall. Timely crop damage assessment is critical for disaster response and insurance-related decision-making, but direct yield-loss observations are often unavailable during or shortly after flooding. This study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations. The study was conducted on the Tumochuan Plateau, Inner Mongolia, China, where severe rainfall beginning on 23 July 2025 caused widespread cropland inundation. Sentinel-2 EVI time series from 2022 to 2025 were fitted using a Savitzky–Golay (SG) filter, and annual area under the EVI curve (AUC) loss in 2025 relative to the 2022–2024 historical mean was used as a proxy for flood-induced crop damage. Optical features from Landsat-8/9 and Sentinel-2, together with SAR backscatter features from Sentinel-1, Lutan-1, and Gaofen-3, were incorporated into machine learning regression models. SAR features improved pixel-wise prediction, with the Random Forest model achieving the highest R2 of 0.62 using early-period features and 0.77 using later-period features. Village-scale aggregation further improved performance, yielding an early-period R2 of 0.84 across 123 and 0.78 across 122 villages. These results demonstrate the feasibility of SAR–optical and phenology-guided regression for early crop damage assessment under long-duration inundation.

Why it matches plant phenotyping methodsSAR・光学画像とフェノロジー指標を用いて作物被害を推定する回帰手法が研究の中心であり、作物状態（洪水被害）を定量化・検証しているため。

abstractThis study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations.

A Review of Plant Leaf Disease Identification Using Deep Learning: Recent Advances, Challenges, and Future Directions ↗

Agriculture plays a pivotal role in ensuring global food security, economic stability, and sustainable development. Plant diseases significantly reduce agricultural productivity, resulting in substantial economic losses and threatening food supply worldwide. Early and accurate identification of plant leaf diseases enables timely intervention, minimizes crop damage, and enhances agricultural yield. Traditional disease diagnosis relies heavily on visual inspection by agricultural experts, making the process labor-intensive, subjective, and unsuitable for large-scale deployment. Recent advances in artificial intelligence, particularly deep learning, have transformed plant disease diagnosis by enabling automatic feature extraction and highly accurate image-based classification. This review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification. Various convolutional neural network (CNN) architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks. The paper also examines transfer learning strategies, object detection methods including YOLO and Faster R-CNN, and semantic segmentation approaches such as U-Net and DeepLabV3+. Publicly available benchmark datasets, including PlantVillage, PlantDoc, AI Challenger, Cassava Leaf Disease, and Rice Leaf Disease datasets, are discussed in terms of dataset diversity, annotation quality, and practical applicability. Furthermore, image preprocessing techniques, data augmentation methods, evaluation metrics, and deployment considerations for mobile and edge devices are comprehensively reviewed. The paper identifies current research challenges, including dataset imbalance, environmental variability, model interpretability, computational complexity, and limited real-world generalization. Finally, emerging research directions such as explainable artificial intelligence, federated learning, multimodal learning, self-supervised learning, lightweight architectures, and edge AI are discussed to provide future research opportunities. This review serves as a valuable resource for researchers, practitioners, and agricultural technologists interested in developing robust, scalable, and intelligent plant disease identification systems.

Why it matches plant phenotyping methods植物葉の病害状態を画像から識別する深層学習手法を中心に、モデル、データセット、評価、展開を体系的にレビューしており、植物表現型計測手法のレビューに該当する。

abstractThis review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification.

Maize Yield Prediction via Data Fusion of UAV Multi/Hyperspectral Imagery and In-Field Measurements ↗

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (R2 = 0.82; MAE = 6.36 q ha−1; MAPE≈7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (R2 = 0.86; MAE = 6.56 q ha−1; MAPE≈7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons.

Why it matches plant phenotyping methodsUAVマルチ／ハイパースペクトル画像と地上測定を統合し、トウモロコシ収量を予測する方法を開発・比較・検証しており、植物形質の取得・推定が研究の中心である。

abstractA series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations.

A multimodal geospatial foundation model anticipates crop stress and yield failure across climates and species ↗

Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.

Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。

abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.

The informativeness of vegetation indices as predictors of the yield of two spring wheat varieties in an experiment with a plant protection system against the background of fertilizers ↗

The use of a combined assessment of the informational significance of vegetation indices for predicting the yield of spring wheat, taking into account varietal specificity and agrotechnical factors, has been studied. The test site was the field experience in the forest-steppe zone of the Novosibirsk Priobye. In the experiment, spring wheat of the Suenga and Novosibirsk 41 varieties was cultivated using intensive agricultural technology. For the analysis, data obtained using the DJI Phantom 4 Multispectral Phantom unmanned aerial vehicle during the crop growing period in 2023–2025 were used. Vegetation index values were calculated using five spectral channels: blue (B, 450 ± 16 nm), green (G, 560 ± 16), red (R, 650 ± 16), red edge (RE, 730 ± 16) and near-infrared (NIR, 840 ± 26 nm). For the analysis of informational importance, the following indices were used as predictors of crop yield: NDVI, NDWI, GNDVI, LAI, CVI, GCI, and ChlRE. For assessing the informativeness of the indices, independent methods were used: the F-statistic of one-way regression (ANOVA F-test), evaluation of mutual information (Mutual Information, MI), and feature importance of the random forest algorithm (Random Forest, RF). Each of the scores was normalized in the range [0; 1] using the min-max normalization method, after which a combined score was calculated as a weighted sum. For the Suenga variety, the stable predictors regardless of the experimental variants were CVI (tillering) and ChlRE (stem elongation and heading), while for Novosibirsk 41, the set of informative predictors significant ly depended on the combination of plant protection and fertilizer systems. It was found that chlorophyll content indices (GCI, ChlRE) increased the predictive relationship with yield under fertilization, while the water status index (NDWI) lost informativeness when fertilizers were applied.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を抽出し、複数の統計・機械学習手法を統合して小麦収量予測における指標の有用性を評価しており、植物形質推定ワークフローが中心です。

abstractThe use of a combined assessment of the informational significance of vegetation indices for predicting the yield of spring wheat

Early Multi-Class Disease Detection in Chili Plant Leaves Using Convolutional Neural Networks: A Comparative Study ↗

Abstract Chili is an important economic and nutritional crop with a relatively limited availability of different disease-resistant varieties. Leaf diseases, including those caused by fungi, bacteria, viruses, pests, or nutritional deficiencies, significantly compromise production and crop quality. Early detection of these diseases is key to reducing yield loss; however, traditional visual examinations are limited by time constraints, human subjectivity, and low detection sensitivity at early stages of infection. To overcome these challenges, this study proposes a deep learning–based framework for early multi-class detection of chili leaf diseases using convolutional neural networks (CNNs). A real-field dataset comprising 16,392 high-resolution images of chili leaves across six disease and healthy classes was collected from multiple regions of Bangladesh. The dataset was preprocessed, augmented, and split into training and validation sets using an 80:20 ratio. Five pre-trained CNN architectures—DenseNet121, EfficientNetB3, MobileNetV2, ResNet50, and InceptionV3 were evaluated using a transfer learning strategy. Experimental results demonstrate that MobileNetV2 achieved the best performance, attaining an overall classification accuracy of 96%. The results indicate that the proposed system demonstrates strong generalization capability and effectively discriminates visually similar chili leaf diseases. This work can be considered a valuable application in precision agriculture, providing an efficient, automated, and practical approach for in situ early diagnosis of chili leaf diseases through smart farm management.

Why it matches plant phenotyping methods唐辛子葉の病害状態を画像から分類するCNN手法の開発・比較評価とデータセット構築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。

abstractA real-field dataset comprising 16,392 high-resolution images of chili leaves across six disease and healthy classes was collected from multiple regions of Bangladesh.

Sentinel-2 and Unmanned Aerial Vehicle (UAV) Imagery for Irrigation Scheduling in Fodder Maize: A Comparative Remote Sensing Approach. ↗

Accurate estimation of crop water requirements is essential to improve irrigation efficiency for forage maize production. This study compared satellite- and UAV-derived normalized difference vegetation index (NDVI) models for estimating crop coefficients (K c ) and evaluated their operational performance for irrigation scheduling. K c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season in two forage maize hybrids (N83N5 and Matador) under three irrigation strategies: conventional producer irrigation (ID1), satellite-based irrigation scheduling (ID2), and UAV-based irrigation scheduling (ID3). Both NDVI sources exhibited strong relationships with K c , with higher calibration accuracy for the UAV model (R 2 = 0.9414) than for the satellite model (R 2 = 0.8278). The UAV-based model applied 23-30% less irrigation water, maintaining high water productivity but also reducing crop growth, forage yield, and nutritional quality. In contrast, satellite-based irrigation scheduling promoted greater crop growth and produced the highest forage yield, reaching 59.8 t ha -1 in hybrid N83N5 while maintaining efficient water use. This treatment also improved forage quality by increasing dry matter and starch concentrations while reducing fiber fractions. The findings highlight the complementary potential of satellite and UAV imagery in precision irrigation and underscore the trade-offs between spatial detail, temporal resolution, and operational scalability. Furthermore, the results demonstrate that a stronger K c -NDVI relationship does not necessarily translate into improved irrigation scheduling performance. Under the conditions evaluated, the satellite-based model provided the best balance between water use, forage yield, and nutritional quality.

Why it matches plant phenotyping methods衛星・UAV画像からNDVIを用いて作物係数を推定する手法を開発し、別年・圃場条件で検証しており、植物群落状態の取得・推定が研究の中心である。

abstractK c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season

Image-Based Estimation of Blueberry Yield Incorporating External Validation and Canopy Architecture Under Field Conditions ↗

Abstract Quantifying blueberry fruit yield and maturity is important for evaluating yield potential in breeding trials, but manual measurement remains slow, labor-intensive, and costly. Object detection and classification networks offer a high-throughput solution, yet few studies validate image-based counts against hand-harvested ground truth while explicitly accounting for canopy occlusion. Hence, this study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts across 32 diverse southern highbush blueberry genotypes. Among the models evaluated, YOLOv8x achieved the highest detection performance, with an mAP50 of 0.82 and an mAP50–95 of 0.66. External validation produced F1 scores ranging from 0.74 to 0.91 for berry maturity classes. However, image-based detections systematically underestimated hand-harvested fruit counts, with R² values ranging from 0.40 to 0.61. Fruit occlusion varied widely among genotypes, from 42% to 90%, indicating that canopy structure strongly affects berry visibility. Incorporating image-derived canopy architecture, color, and texture features improved predictions of berry counts and maturity. Partial Least Squares regression provided the best performance, increasing R² values of hand-harvested fruit counts, ranging from 0.52 to 0.74. These results show that accounting for canopy occlusion improves image-based estimation of blueberry yield and supports more accurate high-throughput phenotyping.

Why it matches plant phenotyping methodsブルーベリーの収量・成熟度を画像から推定する検出パイプラインを開発し、手収穫値との外部検証および樹冠遮蔽・構造を考慮した改良を行っており、植物表現型取得法が中心である。

abstractthis study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts

Remote Sensing Applications in Sugar Beet Production: From Crop Monitoring to Precision Management ↗

Remote sensing has become an important tool for crop monitoring and precision agriculture, yet its applications in sugar beet production remain fragmented across sensing platforms, target traits and modelling strategies. This review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management. A structured search was conducted in Scopus and the Web of Science Core Collection for publications from 2003 to 2025, and 181 relevant peer-reviewed articles were retained for thematic analysis. The literature shows a clear increase in sugar beet remote sensing studies, particularly after 2015, coinciding with the availability of Sentinel-2 imagery and, from 2016 onwards, the growing use of unmanned aerial vehicle-based sensing. It also indicates a gradual shift from crop mapping and canopy monitoring towards disease detection, weed mapping, yield prediction and management-oriented applications. Current studies demonstrate the value of satellite, unmanned aerial vehicle and proximal sensing for retrieving canopy traits, assessing biotic stresses, estimating root yield and supporting field-scale management. However, sugar beet presents specific challenges because its economic value depends not only on canopy development or root biomass, but also on sucrose concentration, recoverable sugar yield, and processing quality. These quality-related traits remain less studied and are difficult to infer directly from canopy observations. Modelling approaches have evolved from vegetation-index-based empirical models towards machine learning, deep learning, multi-temporal analysis, data fusion and crop model assimilation, but issues of model transferability, ground-truth availability and operational decision support remain unresolved. Future research should strengthen multi-source observations, external validation, quality-oriented prediction and decision-support workflows to promote robust, scalable and economically meaningful remote sensing applications in sugar beet production.

Why it matches plant phenotyping methodsサトウダイコンのリモートセンシングによるキャノピー形質、ストレス、根収量などの推定手法を体系的にレビューしており、センシング基盤とモデル化・検証課題が中心的に扱われている。

abstractThis review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management.

INTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES ↗

Selecting ideal drought-tolerant wheat varieties requires a holistic synthesis of digital phenotypes, molecular markers, and agronomic indices. This study evaluated 16 wheat genotypes for drought tolerance by integrating digital phenotyping (UAV-based thermal imaging), molecular data (DREB gene expression profiles), and 12 agronomic indices. While vegetative DREB accumulation remained mostly homogeneous, the generative stage triggered pronounced transcriptional shifts, and late-stage thermal screening revealed highly significant genotypic differences during grain filling. A multivariate PCA biplot identified early canopy temperature differences during tillering (ΔCT_TL) as the most informative non-destructive selection indicator. ΔCT_TL showed a strong positive association with terminal yield stability metrics (YSI and RSI) and a marked negative relationship with the drought sensitivity index (SDI). This early canopy temperature regulation contributed to the maintenance of yield stability in the modern hexaploid variety MFTBY-T and advanced tetraploid lines OR2-T and OR4-S. In contrast, poorly adapted ancient varieties (P5-S and S3-S) exhibited high drought sensitivity accompanied by pronounced late-stage induction of DREB1 and DREB2, suggesting a delayed stress-response mechanism activated under severe tissue dehydration. Conversely, the modern tetraploid variety KZLTN-T and hexaploid landraces appeared to rely on an early vegetative molecular priming strategy. These findings suggest that breeding programs should prioritize the incorporation of vegetative transcriptional traits associated with effective canopy temperature homeostasis into elite genetic backgrounds.

Why it matches plant phenotyping methodsUAV熱画像によるキャノピー温度の非破壊測定をデジタル表現型として用い、乾燥耐性選抜指標として評価しており、表現型取得・解析が研究の主要な構成要素である。

titleINTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES

Light-FCCNet: A Compact Multi-Scale Framework for Accurate Field Crop Counting ↗

Accurate field crop counting supports phenotyping, crop monitoring, and yield-related analysis, but real field images often contain scale variation, target overlap, cluttered vegetation, shadows, and visually similar backgrounds. These factors make density-map regression difficult because weak target responses and accumulated false responses in non-target regions can both bias the final count. This study proposes Light-FCCNet, a compact multi-scale framework for accurate field crop counting under a limited parameter budget. The framework integrates three coordinated components: a lightweight feature pyramid aggregation module for compact scale-aware representation, a multi-attention fusion module for refining fused features and suppressing background-induced responses, and an FCC loss that combines pixel-level density regression, count consistency, and structural similarity. Light-FCCNet is evaluated on three public field crop counting datasets, namely GWHD, MTC, and URC. In the canonical ablation trajectory consisting of baseline, baseline_p1, baseline_p1_p2, and full configurations, the full model achieves the lowest errors, with MAE values of 13.28 on GWHD, 18.10 on MTC, and 61.23 on URC, corresponding to reductions of 18.0%, 25.3%, and 35.2% relative to the baseline. Compared with representative general counting baselines and our implementation of TasselNetV2++ under the same experimental protocol, Light-FCCNet obtains the lowest MAE on all three datasets while using only 0.92 M parameters. These results indicate that its advantage comes from the coordinated design of compact pyramid aggregation, attention-guided feature refinement, and counting-oriented supervision, rather than from any isolated module alone.

Why it matches plant phenotyping methods作物個体数を圃場画像から推定する新規CNN手法を開発し、複数データセットとアブレーションで性能検証しており、表現型取得・推定が研究の中心である。

abstractThis study proposes Light-FCCNet, a compact multi-scale framework for accurate field crop counting under a limited parameter budget.

UAV image-derived canopy traits for predicting alfalfa fall dormancy and forage yield in Mediterranean environments. ↗

Fall dormancy (FD) and forage yield (FY) are two key traits in alfalfa ( Medicago sativa L.) breeding programs. However, genetic progress has remained limited over the past decades, largely due to the complexity of alfalfa breeding and the reliance on labor-intensive phenotyping methods. High-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation. The objectives of this study were to i) estimate FD using UAV-derived canopy height and RGB vegetation indices and ii) evaluate the predictive performance of machine learning (ML) models for FY estimation. A total of 210 alfalfa populations with diverse genetic backgrounds were evaluated over two growing seasons (2023 to 2025) across seven harvests under Mediterranean conditions in central Chile. FY was measured manually, while FD was estimated using both manual and UAV-based approaches. A total of 19 RGB-derived indices (VIs) including plant height (PH) were extracted and used as predictor variables. Five complex predictive ML models were evaluated: PLS, PCR, SVM, ANN, and MLR. The results showed that UAV-derived FD was significantly correlated with FD obtained through conventional methods ( R 2 = 0.88). The automated UAV-based FD phenotyping framework demonstrated slightly higher precision ( R 2 = 0.92) and broad-sense heritability ( H 2 = 0.69) compared to manual measurements ( R 2 = 0.87–0.89; H 2 = 0.64), providing a more reliable selection tool for breeders. Among the tested ML models, SVM and ANN achieved the highest accuracy ( R 2 ≈ 0.73) for FY prediction. These findings demonstrate that integrating low-cost RGB imagery with complex modeling offers a promising avenue that could assist in refining future selection strategies for this genetically complex species.

Why it matches plant phenotyping methodsUAV画像からアルファルファの休眠性と収量関連形質を推定する高スループット表現型解析手法を開発・検証し、手動測定との比較と機械学習モデル評価を行っているため、方法が研究の中心である。

abstractHigh-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation.

Modeling production curves in a strawberry breeding program to optimize early season productivity. ↗

Florida and California produce 98% of U.S. strawberries, with Florida growers' profitability depending on high yields early in the season (November-January), when prices are the highest in the U.S. market. This study aims to model the cumulative Marketable Yield and Delta Yield curves (difference in cumulative Marketable Yield between consecutive harvest time points) of strawberry genotypes to facilitate selection for greater early season productivity. The dataset comprised thirteen seasons (2013-14 to 2025-26) of advanced selection trial data. Marketable Yield trajectories were modeled using Legendre polynomial smoothing, with optimal degree selection balancing flexibility and noise reduction. The resulting coefficients served as surrogate phenotypes for genomic prediction. Forward prediction cross-validation was implemented for five seasons (2021-22, 2022-23, 2023-24, 2024-25, and 2025-26), with each season predicted using the information from all preceding seasons. For cumulative yield, across all five seasons, reconstructed curves from the Legendre models presented a clear temporal trend, with predictive ability increasing from low early-season values to peaks around Trait Dates (weeks) 7-8. In the 2021-22 and 2022-23 seasons, Legendre models showed higher predictive ability than single time-point predictions but were comparable to single-time point predictions for the other seasons. Legendre polynomial models utilizing Delta Yield achieved moderate predictive ability across five validation seasons, with consistent advantages over single time point models particularly in earlier seasons, indicating that genetic control extends beyond total yield to the trajectory of yield accumulation. Overall, Legendre modeling effectively captured the temporal dynamics of yield development while describing the trajectory with only a few parameters.

Why it matches plant phenotyping methodsイチゴの収量軌跡をLegendre多項式でモデル化し、少数の係数を代理表現型としてゲノム予測に利用・検証しており、収量表現型の計算的抽出が研究の中心である。

abstractMarketable Yield trajectories were modeled using Legendre polynomial smoothing, with optimal degree selection balancing flexibility and noise reduction.

Sector-Specific Machine Learning Models for Short-Term Sugarcane Yield Forecasting Using NDVI at Plot Level ↗

Abstract Accurate plot-level sugarcane yield forecasting is essential for optimizing agricultural management, resource allocation, and operational planning. Existing forecasting approaches are often limited by their inability to capture temporal crop dynamics and local biophysical variability, reducing their usefulness for real-time decision-making. To develop and evaluate a Machine Learning (ML)-based framework for short-term sugarcane yield forecasting at plot level using age-segmented Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery, and to determine the earliest crop stage at which reliable yield predictions can be obtained. An integrated dataset was constructed by combining productivity records from 2,132 sugarcane plots across six harvest seasons (2016–17 to 2021–22) with NDVI time series derived from Sentinel-2 satellite imagery. NDVI observations were aggregated into phenology-based temporal intervals, from which statistical features were extracted. Ten ML regression algorithms were evaluated under two forecasting schemes: a global model trained with all observations and a sector-specific approach that developed localized models for individual production sectors. Model performance was assessed using RMSE and R² on an independent test set. The sector-specific approach outperformed the global model, achieving an RMSE of 12.48 TCH and an R² of 0.7840 on the independent test set, compared with an RMSE of 16.75 TCH and an R² of 0.5724 for the global model. Sparse Partial Least Squares (spls) and Support Vector Machines with Polynomial Kernel (svmPoly) were the most frequently selected algorithms. SHAP analysis revealed that Median NDVI was the dominant predictive feature, while the Elongation I stage was the most influential phenological period. Reliable forecasts were obtained from the fifth month of crop growth (RMSE = 14.13 TCH), and prediction accuracy improved progressively as the crop matured. The proposed framework also surpassed traditional expert estimations (RMSE = 15.47), providing earlier and more accurate yield forecasts. This study demonstrates that localized, sector-specific ML models combined with temporal NDVI dynamics can provide accurate and operationally useful plot-level sugarcane yield forecasts. The framework supports proactive agronomic management, improves planning and budgeting processes, and offers a scalable methodology for precision agriculture and sustainable sugarcane production systems.

Why it matches plant phenotyping methods圃場・区画レベルのサトウキビ収量という植物形質を、Sentinel-2 NDVI時系列と機械学習から推定する枠組みを開発・評価しており、予測手法が中心である。

abstractTo develop and evaluate a Machine Learning (ML)-based framework for short-term sugarcane yield forecasting at plot level using age-segmented Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery

A Hex-View Perspective on Plant Disease Detection Using Remote Sensing ↗

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex- view perspective to synthesise remote sensing–based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): Plant-pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): The diverse disease detection tasks and their corresponding research objectives. (3) Sensor (S): The sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): The environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot and regional scales. (5) Algorithm (A): The classical and state-of-the-art data analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): The data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production.

Why it matches plant phenotyping methods植物病害を対象としたリモートセンシングによる観察・検出法を、センサー、条件、アルゴリズム、データセットの観点から体系化する方法論レビューであり、植物状態の推定手法が中心である。

abstractThis review introduces a hex- view perspective to synthesise remote sensing–based plant disease detection within a cohesive conceptual framework.

Phenomics-assisted sparse testing for potato breeding. ↗

In recent decades, global weather patterns have shifted dramatically, introducing greater unpredictability into agriculture. A major challenge in plant breeding is developing selection strategies that remain accurate under such uncertainty. Sparse testing is a well-established approach to increase the number of genotypes evaluated in field trials while keeping costs manageable. However, incorporating image-based data into sparse testing remains challenging. We developed a strategy to integrate high-throughput phenotyping data into sparse testing in potato breeding to improve predictive performance in multi-environment trials. Our approach involved constructing an environmental kernel derived from the covariance matrix of image-based data. We assessed the predictive performance of several regression models under sparse testing, including those based on genomic or phenomic data alone and in combination. Models using only the proposed environmental kernel achieved predictive accuracies comparable to, or exceeding, those of genomic prediction models in various sparse testing scenarios. The best results were observed for tuber yield, a key trait in potato breeding. These findings highlight the potential of image-based environmental kernels to improve the efficiency and accuracy of sparse testing. This approach is cost-effective and scalable, particularly useful for breeding programs with limited resources.

Why it matches plant phenotyping methods画像ベースの高スループット表現型データを環境カーネルとして構築し、ジャガイモ育種の疎試験に統合する方法が研究の中心であるため、表現型予測手法として収載する。

abstractWe developed a strategy to integrate high-throughput phenotyping data into sparse testing in potato breeding to improve predictive performance in multi-environment trials.

UAV remote sensing for yield prediction in staple crops: a review. ↗

Accurate yield prediction for major grain and oilseed crops, including soybean, corn, wheat, and rice, is essential for food-security assessment and precision field management. This study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis. Seventy peer-reviewed studies published between 2018 and 2025 were synthesized within a "Data-Ground Truth-Model-Decision" framework. Beyond summarizing UAV platforms, sensor configurations, feature-engineering strategies, and model architectures, the review explicitly distinguishes among microplot, field, and regional prediction scales, and evaluates the characteristics and limitations of yield-label acquisition methods, including manual harvest, plot-combine harvest, and combine yield-monitor data. Existing evidence indicates that the reliability of UAV-based yield prediction depends not only on optimal image acquisition windows, multi-source feature fusion, and model architecture, but also on scale-consistent yield labels, spatially aware validation strategies, and clearly defined model outputs, such as plot-level scalar yield, field-scale yield maps, and regional yield estimates. Major bottlenecks include scale mismatch between UAV imagery and yield labels, error propagation during yield-map generation, limited cross-year and cross-region transferability, weak causal interpretability, and difficulties in deploying models under complex operational field conditions. Future research should emphasize scale-explicit benchmark datasets, quality-controlled ground-truth yield acquisition, UAV-satellite-ground data fusion, spatiotemporal deep learning, and edge-cloud collaborative systems that can translate prediction outputs into agronomic decisions. This review provides a practical pathway for developing robust, interpretable, and deployable UAV-based yield prediction systems for major grain and oilseed crops.

Why it matches plant phenotyping methodsUAV画像から作物の収量という植物形質を推定する手法を中心に、プラットフォーム、特徴量、モデル、検証尺度、グラウンドトゥルースを体系的にレビューしているため、方法レビューとして収載。

abstractThis study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis.

Double Transfer Learning-Based Capsule Network for Multi-Crop Plant Disease Classification Using Heterogeneous Leaf Image Datasets ↗

Abstract Crop disease is a major worldwide problem in agricultural production and food security, adversely affecting yield and quality for a variety of plant species. To overcome these drawbacks, this research provides a Double Transfer Learning-based Capsule Network (DTL-CapsNet) approach for automated plant disease classification with multiple crops. Based on the image pre-processing, segmentation, double transfer learning, and Capsule Networks technologies, the proposed framework extracts discriminative features of the diseases and maintains spatial relations between the leaves symptoms effectively. This double transfer learning approach involves extracting general visual features from pre-trained deep learning models and then fine-tuning these features to classify plant diseases. Capsule Networks then leverage the visual similarity of disease patterns to make the recognition more robust, while simultaneously adding hierarchical part–whole relationships in leaf structures, thereby improving the feature representation. Experiments were performed on a heterogeneous data set consisting of 21,927 leaf images belonging to 17 different healthy and diseased classes of apple, chilli, cotton, corn and potato crops. The experimental results proposed DTL-CapsNet framework is more accurate compared to the traditional CNN-based models and conventional transfer learning models. The proposed method of double transfer learning and Capsule Networks offers an efficient and scalable approach for intelligent plant disease diagnosis, offering significant potential in precision agriculture and real-time crop monitoring systems.

Why it matches plant phenotyping methods葉画像から植物病害状態を分類する画像・計算手法が研究の中心であり、提案手法の開発と既存モデルとの比較検証が行われているため。

abstractBased on the image pre-processing, segmentation, double transfer learning, and Capsule Networks technologies, the proposed framework extracts discriminative features of the diseases and maintains spatial relations between the leaves symptoms effectively.

PlaFe: an outdoor platform for crop phenotyping under progressive drought ↗

The selection of genotypes adapted to water stress requires experimental facilities that allow environmental control without compromising physiological and yield relevance. The objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe, which comprised sixty-two high-volume prismatic lysimeters arranged in rows 1.2 m long and spaced 0.6 m apart. Soil water dynamics were monitored weekly using a weighting system. To validate PlaFe, two soybean genotypes were exposed to two water scenarios for forty days from R2 + 7d, during two growing seasons. Two irrigation treatments were applied: irrigation to keep soil water content over 60–70 % of field capacity (EH0), and irrigation equivalent to 35 % of that applied in EH0 (EH1). Water consumption, crop biomass, and pod number were determined at maturity. On average, water stress reduced both biomass and pod numbers by 40 %. However, reproductive efficiency varied among genotypes. Canopy temperature increased by 0.56 °C as daily water consumption decreased, demonstrating its potential to assess drought. These results demonstrate PlaFe’s potential for the accurate evaluation of crop response and adaptation to diverse water scenarios without compromising the complex plant-environment interactions inherent to field conditions.

Why it matches plant phenotyping methodsPlaFeという屋外半制御型フェノタイピングプラットフォームを設計・検証しており、植物の水消費、バイオマス、莢数、群落温度などの表現型評価が研究の中心である。

abstractThe objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe

A density-based boundary line analysis framework using accessible spatial datasets to identify within-field limitations to crop production ↗

Context Precision agriculture can benefit from within-field boundary line analysis (BLA) to identify the most limiting factors impacting crop yield. In this study, the BLA was applied at the within-field scale using yield monitor data and key environmental factors, including evapotranspiration (ET), elevation, and the apparent soil electrical conductivity (ECa). Aims We aimed to develop and evaluate a novel BLA method to estimate Yp and quantify Yg at the within-field scale, and to assess the diagnostic potential of freely available proxy variables for identifying spatially variable yield-limiting factors in dryland wheat production. Methods We combined Mahalanobis distance-based filtering for data denoising with 2Dl kernel density estimation (KDE) and percentile thresholding to select high-density, high-yield points that define the upper yield envelope. A generalised additive model (GAM) was then used to produce the boundary line through these selected points to represent the Yp. Key results Results from the two case studies showed that this approach was robust and less sensitive to noise and outliers in fine-scale datasets. Freely available ET and elevation could be proxies to highlight the impact of some limiting factors, such as frost events or waterlogging. The ECa could identify areas where some potential soil-related factors (e.g. lower clay content reducing plant available water capacity) could be the limiting factors. Conclusions While the proxy variables effectively indicated potential limiting factors, ground-truth validation is required to confirm the underlying causal mechanisms. Implications Growers could benefit from the BLA approach to identify local yield constraints, estimate site-specific Yp and Yg, and fine-tune their inputs, leading to more efficient resource use and improved profitability.

Why it matches plant phenotyping methods作物収量という植物形質を推定する境界線分析法を開発・評価し、ノイズ除去、KDE、GAMによるワークフローを中心的に提示しているため。

abstractWe aimed to develop and evaluate a novel BLA method to estimate Yp and quantify Yg at the within-field scale

Toward Autonomous Crop Sensing: High-Frequency UAV-Based RGB and Thermal Imaging of Maize and Soybean ↗

Abstract Precision field management and high-throughput plant phenotyping increasingly rely on remote sensing to capture spatial and temporal variability in crop performance. Unmanned aerial vehicle (UAV) – based sensing offers unique advantages for field-scale data collection, including high spatial resolution, flexible deployment, and scalable throughput. However, the full potential of UAV platforms remains constrained by labor-intensive operations across flight execution, data transfer, and processing workflows. This study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment. Data acquisition was conducted over a maize irrigation trial and a soybean breeding experiment, resulting in 176 completed flights over 28 days during the growing season. High-frequency flights on selected days captured diurnal dynamics in key canopy traits, including maize leaf rolling under drought stress and genotype-dependent plot temperature variation in soybean. In the maize irrigation experiment, significant differences in diurnal canopy cover ratio (CCR) were observed among irrigation treatments. The predictive relationship between CCR and final grain yield strengthened throughout the day, with the coefficient of determination (R 2 ) increasing from 0.05 in the early morning (RMSE = 3.05 Mg ha − 1 ) to 0.65 at midday (RMSE = 1.87 Mg ha − 1 ), highlighting the importance of temporal optimization in UAV-based sensing. Temperature measurements from the onboard thermal infrared camera showed a strong overall linear correlation with ground truth measurements (R 2 = 0.85). In the soybean trial, the highest plot temperature was observed on the fast-wilting genotype. Additionally, regression models were developed to estimate key crop traits, including canopy height (CH) and leaf area index (LAI), demonstrating the platform’s quantitative sensing capability. Overall, this study demonstrates that automatic UAV systems enable high-temporal-resolution crop monitoring while substantially reducing operational cost. The results highlight their potential for precise crop management and scalable field phenotyping. Future work will focus on integrating automated data processing pipelines to support near-real-time analytics and decision-making.

Why it matches plant phenotyping methods自動UAVのRGB・熱画像センシング platform を圃場で系統的に評価し、温度・キャノピー被覆率・高さ・LAIなどの植物形質を定量化しているため、フェノタイピング手法が研究の中心である。

abstractThis study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment.

Bayesian optimized color filter: A fast method for segmentation of plant phenotypes from 3D point cloud images ↗

Abstract Multispectral three‐dimensional (3D) imaging offers substantial potential for plant phenotyping, yet manual segmentation of plant organs remains a bottleneck in breeding programs. We developed a color‐based filtering workflow for faba bean ( Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband brightness via Bayesian optimization. Rather than maximizing geometric segmentation accuracy, thresholds are selected to maximize correlations between retained points and yield‐related traits, outperforming manual filtering, reducing user effort, and standardizing decisions. Across multispectral 3D point clouds, Bayesian optimization recovered index‐specific threshold ranges that yielded strong in‐sample correlations with grain yield ( r = 0.72), bean number ( r = 0.61), pod number ( r = 0.53), and straw biomass ( r = 0.72). Peak associations occurred at harvest for straw biomass, at 41 days before harvest (DBH) for seed yield, 33 DBH for bean number, and 34 DBH for pod number. Across the season, greenness‐based indices and broadband brightness metrics consistently showed stronger links with seed yield than pigment ratio or water status indices. For straw biomass and pod number, pigment ratio indices showed consistently lower correlations. Targeting trait‐relevant canopy signals via Bayesian optimization enables reliable, nondestructive assessment of relationships between spectral signals and yield‐related traits in faba bean. By optimizing thresholds to maximize trait correlations rather than geometric accuracy, the workflow can support earlier, more cost‐efficient identification of high‐performing genotypes under drought stress and contribute to strengthening high‐throughput phenotyping in breeding programs. This enables faster identification of canopy signals most relevant to target traits.

Why it matches plant phenotyping methods植物の3D点群画像から表現型を抽出するセグメンテーション手法を開発し、ベイズ最適化による閾値選択と性能評価を行っており、フェノタイピング手法が研究の中心である。

abstractWe developed a color‐based filtering workflow for faba bean ( Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband brightness via Bayesian optimization.

Rice yield prediction using UAV-based multispectral imagery and AutoGluon across regions and field scales ↗

Introduction Accurate and transferable rice yield prediction is essential for precision agriculture and food security, yet existing remote sensing-based models often suffer from limited generalization across regions, cultivars, and field scales. Methods This study developed an interpretable automated machine learning framework for rice yield prediction using UAV-based multispectral imagery collected at the maturity stage. A total of 143 rice samples, including 79 experimental plots and 64 production fields across 15 counties in Sichuan Province, China, were investigated. 20 vegetation indices and 36 gray-level co-occurrence matrix texture features were extracted from multispectral orthomosaics, and three feature selection strategies: Pearson correlation coefficient (PCC), Random Forest feature importance (RF-I), and AutoGluon feature importance(AutoGluon-I), were systematically compared. Four regression approaches, including CatBoost, ExtraTrees, Random Forest, and an AutoGluon stacked ensemble, were evaluated using R 2 , RMSE, and MAE. Results The results showed that the AutoGluon ensemble consistently outperformed individual machine learning models, improving testset R 2 from 0.403-0.670 to 0.528-0.736. The best performance was achieved by combining Pearson correlation-based feature selection with AutoGluon, yielding a training R 2 of 0.821 and a test R 2 of 0.736, with RMSE and MAE values of 0.749 and 0.568 t ha -1 , respectively. Shapley Additive Explanations (SHAP) analysis further revealed that texture features, particularly red-band contrast and angular second moment features, contributed substantially to yield prediction, indicating the importance of canopy structural heterogeneity at maturity. Discussion Overall, the proposed PCC-AutoGluon-SHAP framework provides a lightweight, accurate, and interpretable approach for UAV-based rice yield estimation across heterogeneous field conditions, offering practical potential for scalable precision agriculture and regional yield monitoring.

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画像・特徴抽出・機械学習フレームワークを開発し、複数モデルと特徴選択法を比較検証しており、植物表現型取得・推定が研究の中心である。

abstractThis study developed an interpretable automated machine learning framework for rice yield prediction using UAV-based multispectral imagery collected at the maturity stage.

RGB-UAV-based phenotyping reveals the influence of planting density on physiological traits, biomass, and yield in buckwheat. ↗

Accurate estimation of crop yield and biomass using UAV-based remote sensing is influenced by flight altitude, plant density, canopy structure, vegetation index (VI) selection, and crop growth stage. This study evaluated how the joint optimization of these factors influences biomass and yield prediction in common buckwheat (Fagopyrum esculentum Moench var. 'Yangjeol'). Field experiments were conducted using broadcast seeding and drill seeding at four row spacings (12.5, 20, 30, and 40 cm) following the complete Latin square design. UAV RGB imagery was acquired at the third-leaf and full-flowering stages from 30, 50, and 60 m altitudes, and vegetation indices Excess Green Index (ExG), Green Leaf Index (GLI), and Normalized Green-Red Difference Index (NGRDI)were extracted. ANOVA with Tukey's HSD revealed significant variations in biomass and yield traits among sowing treatments (p < 0.005). The highest fresh weight was recorded under drill seeding at 12.5 cm spacing, while seed weight was consistently higher under all drill seeding treatments compared with broadcast seeding. The number of seeds per plant peaked under 40 cm spacing, indicating a trade-off between planting density and reproductive output. Strong and significant correlations between vegetation indices and ground-measured traits were observed (r = 0.82-0.98), but these relationships were highly dependent on growth stage, sowing configuration, and UAV altitude. The third-leaf stage under broadcast seeding and full flowering stage under drill seeding at 20 cm spacing showed the strongest and most consistent VI-trait associations. Among UAV altitudes, 50 m provided the most stable predictive performance across traits. ExG and GLI exhibited more consistent relationships with biomass and yield parameters than NGRDI. These findings demonstrate that no single UAV altitude, vegetation index, or growth stage is universally optimal. Instead, coordinated optimization of UAV operational parameters and sowing configurationsubstantially improves the reliability of UAV-based yield and biomass estimation in buckwheat.

Why it matches plant phenotyping methodsUAV RGB画像と植生指数を用いたバイオマス・収量推定の条件最適化と精度評価が研究の中心であり、植物形質の取得手法を実質的に検証している。

abstractAccurate estimation of crop yield and biomass using UAV-based remote sensing is influenced by flight altitude, plant density, canopy structure, vegetation index (VI) selection, and crop growth stage.

SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images ↗

Potato leaf diseases directly reduce yield and quality, and accurate field detection is important for precision plant protection. However, potato disease lesions are often weak in deep semantic representation, easily disturbed by complex field backgrounds, and variable in multi-scale lesion texture. To address these challenges, this study proposes an improved YOLO-based potato leaf disease detection model. The proposed model enhances the detector through three task-oriented modules. Deep Symptom Enhancement is used to strengthen deep disease feature extraction. Lesion Selection Attention based on large separable kernel attention improves the spatial selection of lesion regions. Multi-Scale Refinement Adapter uses a Mona-based C2PSA structure with two stacked Mona adapters to refine multi-scale texture and lesion-boundary information. Experiments were conducted on a potato leaf disease image dataset using mAP50, average recall (AR), parameters, GFLOPs, and FPS as evaluation metrics. The baseline YOLO26s achieved 81.31% mAP50 and 77.85% AR. The proposed SLR-YOLO model achieved 88.92% mAP50 and 83.51% AR, improving mAP50 and AR by 7.61 and 5.66 percentage points, respectively, while maintaining 118.6 FPS. The results show that the proposed framework improves detection accuracy for potato leaf disease images while retaining practical real-time performance.

Why it matches plant phenotyping methodsジャガイモ葉の病斑・病害状態を画像から推定するYOLOベース手法を開発し、データセット上で精度とリアルタイム性能を評価しており、植物表現型取得手法が中心である。

abstractthis study proposes an improved YOLO-based potato leaf disease detection model

An automated dual-module AI-based solution for early detection and classification of crop diseases and stress conditions ↗

Abstract One of the most important challenges faced by smallholder farmers in the agricultural industry is the lack of accurate, timely knowledge to predict and detect crop health issues. Crop productivity is often threatened not only by diseases but also by environmental and physiological stresses, which contribute to significant yield losses and negatively impact the national economy. Traditional detection methods are time-consuming, costly, and require expert knowledge, creating a need for automated and intelligent systems. This work proposes a dual-functional framework that combines crop disease and stress detection using advanced machine learning and deep learning techniques to accurately classify healthy and diseased leaves. This integrated system ensures early detection, reduces crop loss, improves productivity, and provides a scalable, farmer-friendly solution for sustainable agriculture.

Why it matches plant phenotyping methods葉画像から健康・病害状態を機械学習で分類する手法が研究の中心であり、植物の病害・ストレス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採用。

abstractThis work proposes a dual-functional framework that combines crop disease and stress detection using advanced machine learning and deep learning techniques to accurately classify healthy and diseased leaves.

High-Throughput Phenotyping of Faba Bean Crop Using Remote Sensing Technologies and Data Analytics: A Systematic Assessment of Status and Trends Over Past Decade ↗

Abstract Faba bean ( Vicia faba L.) is a widely cultivated legume in temperate regions, valued for human consumption, animal feed, hay production, and as a cover crop. Improving faba bean productivity requires accurate characterization of morphological and physiological traits that govern crop performance and yield. Although conventional phenotyping methods are available, they are often constrained by high cost, limited accuracy, and insufficient spatial and temporal coverage. In recent years, high-throughput phenotyping (HTP) approaches have shown potential to overcome these limitations. HTP integrates sensors, unoccupied aerial and ground vehicles, and imaging systems to enable rapid, and non-destructive monitoring of crop traits at high spatiotemporal resolution. Despite increasing adoption of HTP, no study has comprehensively compared and synthesized these methods for faba beans, therefore is the goal of this study with emphasis on advanced sensing and data analytics including machine learning (ML) and deep learning (DL). A systematic evaluation of 24 peer-reviewed articles from an initial pool of 381 publications between 2015 and 2025, identified research trends, performance benchmarks, and integration challenges with HTP-based faba bean characterization. Substantial increase in faba bean HTP studies has been noted after 2021, with ML approaches dominating current applications (37.5%). Most studies have relied on small datasets, single-season experiments, and limited environmental variability, restricting model robustness and scalability. Such limitations, research gaps, and future directions are also outlined to support reliable, and scalable phenotyping for improved faba bean production.

Why it matches plant phenotyping methodsソラマメの高スループット表現型解析手法を体系的に比較・評価し、センサー、画像、UAV・地上車両、データ解析の性能と課題を扱う方法論レビューである。

titleHigh-Throughput Phenotyping of Faba Bean Crop Using Remote Sensing Technologies and Data Analytics: A Systematic Assessment of Status and Trends Over Past Decade

Validating superior maize hybrids in all India coordinated trials using REMATTOOL-R: a decision support approach. ↗

Introduction The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consuming, and susceptible to subjectivity, highlighting the need for objective and reproducible decision-support tools. This study evaluated the effectiveness of REMATTOOL-R (Relative Maturity Adjustment Tool in R) in validating the existing hybrid advancement framework adopted under the AICRP on Maize. Methods Multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late conducted during Kharif 2020-21 across five locations representing the Central West Zone (CWZ) of India were analysed. The dataset comprised 45 entries, including 40 experimental hybrids, four commercial checks, and one filler entry. REMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits. Least-square means generated from mixed-model analysis were used to identify superior hybrids based on a predefined grain yield superiority threshold (≥5%) over the standard check while maintaining comparable maturity and agronomic performance. Results REMATTOOL-R enabled rapid visualization and integrated assessment of multiple agronomic traits, allowing objective identification of superior hybrids. Five experimental hybrids-PM 21109L (Entry 30), R8050 (Entry 35), PM 21111L (Entry 32), BIO 978 (Entry 4), and DKC 9226 (Entry 9)-recorded ≥5% higher grain yield than the standard check Bio 9682 while maintaining statistically comparable days to 50% anthesis, grain moisture at harvest, and harvested plant stand. All five hybrids identified by REMATTOOL-R corresponded with the official AICRP decisions for advancement from NIVT to Advanced Varietal Trial-I (AVT-I), while three hybrids (R8050, PM 21111L, and DKC 9226) progressed further to AVT-II during subsequent testing cycles, confirming the reliability of the analytical framework. Discussion The findings demonstrate that REMATTOOL-R provides an efficient, transparent, and reproducible framework for the simultaneous evaluation of grain yield, maturity, and adaptation-related traits in maize multi-environment trials. By complementing the existing AICRP hybrid evaluation procedure, the tool facilitates objective advancement decisions and reduces subjectivity associated with manual interpretation of complex datasets. REMATTOOL-R therefore represents a valuable decision-support approach for coordinated maize breeding programmes and has considerable potential for application in large-scale hybrid evaluation systems.

Why it matches plant phenotyping methodsREMATTOOL-Rという解析ツールを開発・評価し、収量、成熟期、収穫時水分、植立本数を統合してハイブリッドの表現型・適応性を客観的に評価することが中心である。

abstractREMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits.

Automated Plant Leaf Disease Diagnosis using Deep Learning ↗

Plant varieties are essential for the survival of human beings and animals, as they act as an alternative source of food, fiber, fodder, and other raw materials for domestic needs and industries in any society. Early identification of plant leaf diseases is very important for keeping the health of crops intact. Crops being infected can affect the overall yield of crops, which may be detrimental to the earnings of farmers. With the emergence of artificial intelligence technology, it has become possible to deploy systems for quicker identification of illnesses. This work has been carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves. For this purpose, the dataset has been created after retrieving data from the PlantVillage dataset. The clinical reliability of different deep learning models of various representational capacities has been tested while using ImageNet pre-trained parameters. The experimental results show that MobileNetV2 achieves the highest accuracy of 96.15%, outperforming deep CNN (76.92%) and medium CNN (61.54%). The test accuracy and class-wise F1-scores for the CNN are observed to be substantially very high. The generalization ability and result of DCNN and MCNN are moderate and poor, respectively, as observed. Additionally, the proposed CNN only uses the disease-affected areas on the leaf, thus making the result more interpretable

Why it matches plant phenotyping methods葉の画像から病徴を推定する深層学習手法を開発・比較し、モデル性能と解釈性を評価しているため、植物表現型取得が中心である。

abstractThis work has been carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves.

Effects of tree-stripes on crop growth in agroforestry systems using unmanned aerial systems-based analysis ↗

Abstract Agroforestry systems (AFS) offer a promising strategy to address environmental challenges while supporting rising food demands. However, the complex interactions between trees and crops complicate research, particularly regarding their effects on crop yields. This study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study. Growth parameters, namely the Normalized Difference Vegetation Index (NDVI) and plant height, were derived as proxies for yield and analyzed in relation to the distance from tree stripes. Additionally, direction-dependent regression analyses were conducted to assess whether spatial variations in the field could be attributed to the trees. Two distinct patterns emerged: first, a pronounced increase in NDVI was observed at close proximity to the trees, correlated with tree height and schematically illustrated for two representative tree stripes; second, at greater distances, fluctuations in NDVI were associated with the trees but lacked consistent directional trends. Considerable inconsistencies were also observed in plant height variations. The discussion highlights potential drivers of the close-range NDVI increase, the applicability of UAS for AFS research, and limitations in generalizing findings from a single case study. Overall, the results demonstrate that tree effects on crop growth and vitality are detectable but marginal in terms of their influence on maize yields at this site, while showcasing the utility of UAS-based approaches for field-scale analysis of AFS.

Why it matches plant phenotyping methodsマルチスペクトルUASからNDVIと植物高を抽出し、樹木からの距離に伴う作物形質を解析する手法の実質的適用が研究の中心であり、単なるルーチン測定を超える。

abstractThis study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study.

Concept and rule guided neural network for early crop leaf nutrient deficiency diagnosis ↗

Crop nutrition deficiency poses a major challenge to achieving optimal yield, particularly in smallholder farming systems where timely expert diagnosis is limited. Early detection is crucial to minimize losses and reduce unnecessary fertilizer or pesticide usage. While deep learning offers potential for automated visual diagnosis, most existing approaches operate as black boxes and lack interpretability, explainability, or actionable recommendations. In this work, we present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset. Our approach integrates a ResNet-50 backbone with a dual-head design: a classification head for deficiency prediction and a concept-prediction head that quantifies physiologically meaningful visual patterns such as yellowing, edge discoloration, spots, and vein greenness. These concept scores are combined with predefined domain rules to guide the learning of the neural component and to generate transparent, human-aligned explanations for each diagnosis. Building on the model outputs, we incorporate a Retrieval Augmented Generation (RAG)-based pipeline along with an agricultural knowledge base to generate targeted recommendations. This approach overcomes key shortcomings of pure neural models by incorporating domain knowledge in the form of differentiable fuzzy logic rules. The study demonstrates that the proposed framework improves both classification performance and interpretability compared to standard ResNet baselines. Grad-CAM analysis demonstrates that concept-guided attention aligns with symptom-specific regions, such as yellowed areas for Nitrogen deficiency or marginal discoloration for Potassium deficiency, providing visual validation of the reasoning process. Since EarlyNSD is limited in scale and visual diversity, the results are not directly comparable to large open-field datasets. Overall, our results establish a proof of concept for integrating neural detection with symbolic reasoning, enabling interpretable, actionable, and domain-informed nutrient management for practical applications.

Why it matches plant phenotyping methods葉画像から栄養欠乏状態と症状形質を推定する解釈可能な画像解析手法が研究の中心であり、植物表現型の取得・抽出に該当する。

abstractwe present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset.

Field-scale robotic phenotyping of three-dimensional wheat canopy architectural traits ↗

Assessing tools for rapid evaluation of new cultivars across large fields is essential for improving crop yields and ensuring future food security. Current phenotyping approaches remain labor-intensive and imprecise at the field scale, particularly for traits determining light interception and the light extinction coefficient ( K ). While robotic phenotyping and three-dimensional (3D) models have gained interest in estimating light interception in plant canopies, primarily at the single-plant scale or using single-plant-derived virtual canopies, applications at the field-scale canopy level remain limited. In this study, a semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years. 3D canopy architecture revealed substantial genotype-specific variation in inclination angle and K , which influenced radiation use efficiency (RUE), contrasting with the constant K commonly assumed in conventional approaches. This architectural variation was classified as two distinct architectural phenotypes, erectophiles (median angle 65°) and planophiles (59°) at pre-anthesis stages, which converged toward being homogenous by the anthesis stage. Erectophile phenotypes exhibited higher RUE (33.1%) than planophile phenotypes, leading to a higher yield. In yield prediction analyses, 3D-derived architectural traits provided comparable predictive performance to conventional measurements, however, architectural phenotype information reduced prediction error. Together, these findings highlight the value of field-scale robotic canopy phenotyping for characterizing genotype-specific canopy architectural traits and their relationship with yield.

Why it matches plant phenotyping methodsフィールド規模のロボット型3D・マルチスペクトル計測プラットフォームを用いて、コムギ群落の建築形質を取得・評価することが研究の中心である。

abstracta semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years.

AI-Driven Crop Detection and Plant Disease Prediction ↗

Crop disease identification is still a big problem in agriculture, which results in large yield losses and food lacks, especially in regions dependent on manual monitoring. Traditional methods of identifying plant diseases are often labor intensive, error-prone, and ineffective in early-stage diagnosis. To overcome these limitations, this study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data. The methodology utilizes the PlantVillage dataset, encompassing over 50,000 annotated leaf images across 14 crops. After rigorous preprocessing involving image resizing, normalization, and cleaning, Improved Deep Joint Segmentation is applied to localize disease-affected regions. Feature extraction incorporates color, texture (GLCM, LBP), and shape attributes to enhance classification accuracy. A hybrid approach integrating XGBoost for feature selection and Support Vector Machine (SVM) for classification is proposed to capture both overarching trends and intricate details. Experimental results across four major crops—potato, tomato, corn, and grape—demonstrate superior performance, with the hybrid model achieving 98.6% accuracy, 98.3% precision, 99.0% recall, and 99.1% F1-score. The model outperforms existing approaches, offering a robust, scalable, and accurate solution for early crop disease detection in precision agriculture.

Why it matches plant phenotyping methods画像から病変領域を抽出し、植物病害の分類と重症度を推定する機械学習ワークフローが研究の中心であり、植物状態の表現型計測に該当する。

abstractthis study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data.

Artificial Intelligence Adoption in Smart Agriculture: A Review of Convolutional Neural Networks for Plant Disease Detection and Agribusiness Sustainability ↗

Plant diseases remain one of the most persistent and economically damaging threats to global food security, with yield losses across major staple crops running into the tens of billions of dollars each year. The rise of artificial intelligence, and convolutional neural networks (CNNs) in particular, has opened genuinely new possibilities for detecting plant disease early, accurately, and at scale. This review critically examines the state of CNN-based plant disease detection within the wider context of smart agriculture and agribusiness sustainability. Drawing on peer-reviewed literature published between January 2016 to February 2026, the paper traces the evolution of CNN architectures, training methods, and benchmark performance across a wide range of crops and disease categories. Particular attention is given to transfer learning, data augmentation, and lightweight architecture design as responses to the recurring problem of limited annotated training data. The paper also considers how CNNs are being combined with complementary technologies, including the Internet of Things, unmanned aerial vehicles, and edge computing, and what this means for deployment in real farming conditions. Economic and sustainability dimensions are explored throughout, with attention to whether the gains from AI adoption are likely to reach smallholder farmers or remain concentrated among larger, better-resourced agribusinesses. Despite genuinely impressive results under controlled benchmark conditions, several barriers to field deployment persist: dataset bias, poor generalisation in complex agricultural environments, computational constraints, and a continuing shortfall in model interpretability. The review closes by identifying priority research directions, including cross-domain transfer learning, explainable AI, the development of field-representative datasets, and participatory approaches to tool design. Taken together, the evidence suggests that CNN-based disease detection holds real promise for agribusiness sustainability, but realising that promise will depend on sustained interdisciplinary collaboration and deployment strategies that are sensitive to local context rather than assuming one-size-fits-all solutions.

Why it matches plant phenotyping methods植物病害を画像から検出・評価するCNN手法を中心に、モデル、学習法、ベンチマーク、汎化性、データセット、実運用上の課題をレビューしており、植物状態の画像ベース推定に関する方法論的レビューである。

abstractThis review critically examines the state of CNN-based plant disease detection within the wider context of smart agriculture and agribusiness sustainability.

Drone-Based Crop Health Analysis and Precision Agriculture System ↗

Agriculture remains the backbone of global food security, yet crop diseases, nutrient deficiencies, water stress, and pest infestations cause annual yield losses estimated at 20–40% worldwide. Conventional field scouting methods are labour-intensive, time-consuming, and fail to capture the spatial heterogeneity of large farms. This paper presents a Drone-Based Crop Health Analysis and Precision Agriculture System (DBCHAPS) that integrates multi-spectral and RGB imaging drones, deep learning-based crop disease detection, vegetation index analysis, variable-rate prescription mapping, and autonomous precision spraying. A DJI Matrice 300 RTK drone equipped with a MicaSense RedEdge-MX multi-spectral camera captures high-resolution aerial imagery across five spectral bands (Blue, Green, Red, Red-Edge, Near Infrared). The captured data is processed through a custom-trained YOLOv8-based convolutional neural network (CNN) pipeline to detect 18 distinct crop diseases and stress conditions across rice, wheat, and cotton crops. Concurrently, vegetation indices (NDVI, NDRE, GNDVI, SAVI) are computed to generate prescription maps for site-specific fertilizer and pesticide application. Experimental evaluation on a 120 acre farm in Thanjavur, Tamil Nadu over two crop seasons demonstrates a disease detection accuracy of 96.3%, early stress detection 8–12 days before visible symptoms, and a 31% reduction in agrochemical usage through variable-rate application. The system achieves an end-to-end field analysis time of under 45 minutes for 100 acres. Keywords — UAV, Precision Agriculture, Crop Disease Detection, Multi-Spectral Imaging, NDVI, YOLOv8, Deep Learning, Variable-Rate Application, Remote Sensing, Smart Farming.

Why it matches plant phenotyping methodsドローンのマルチスペクトル/RGB画像とYOLOv8を用いて作物の病害・ストレス状態を推定するシステムを開発し、精度と運用性能を評価しており、植物フェノタイピング手法が中心である。

abstractThis paper presents a Drone-Based Crop Health Analysis and Precision Agriculture System (DBCHAPS) that integrates multi-spectral and RGB imaging drones, deep learning-based crop disease detection, vegetation index analysis, variable-rate prescription mapping, and autonomous precision spraying.

PLANT DISEASE IDENTIFICATION AND PESTICIDES RECOMMENDATION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK FOR PROTECTION ↗

Agriculture is one of the most important sectors contributing to economic development and global food security. However, plant diseases caused by fungi, bacteria, viruses, and other pathogens significantly reduce crop yield and quality, leading to substantial economic losses for farmers. Early and accurate identification of plant diseases is essential for effective crop management and timely application of suitable pesticides. Conventional methods of disease diagnosis rely on manual inspection by agricultural experts, which is time-consuming, labor-intensive, expensive, and often inaccessible to farmers in remote areas. Recent advancements in Artificial Intelligence (AI) and Deep Learning have provided efficient solutions for automating plant disease detection through image analysis. This project, "Plant Disease Identification and Pesticides Recommendation System Using Convolutional Neural Network (CNN) for Crop Protection," presents an intelligent system that automatically identifies plant diseases from leaf images and recommends appropriate pesticides for effective crop protection. The proposed system utilizes a Convolutional Neural Network (CNN), a deep learning model specifically designed for image classification tasks. The CNN model is trained using a large dataset of healthy and diseased plant leaf images collected from publicly available agricultural datasets. During training, the model learns to recognize disease-specific visual features such as color variations, lesion patterns, texture changes, and leaf deformities. Image preprocessing techniques, including resizing, normalization, and data augmentation, are employed to improve the quality of the input images and enhance the overall performance of the model. When a farmer uploads an image of a plant leaf through the system, the trained CNN model analyzes the image and accurately classifies it as either healthy or affected by a specific disease. After identifying the disease, the system recommends suitable pesticides, fungicides, insecticides, or biological treatments based on an agricultural knowledge database. It also provides additional information such as recommended dosage, application method, spraying schedule, safety precautions, and preventive measures to ensure responsible pesticide usage and minimize environmental impact. The proposed system offers several advantages, including rapid disease detection, high classification accuracy, reduced dependence on agricultural experts, optimized pesticide application, lower crop losses, improved productivity, and support for sustainable farming practices. Furthermore, the system can be deployed as a web or mobile application, enabling farmers to access disease diagnosis and treatment recommendations anytime and anywhere using smartphones or other digital devices. Overall, the proposed CNN-based plant disease identification and pesticide recommendation system provides a reliable, cost-effective, and intelligent solution for modern agriculture. By combining image processing, deep learning, and agricultural expertise, the system supports precision farming, enhances decision-making, reduces unnecessary pesticide usage, and contributes to increased crop productivity, environmental sustainability, and long-term food security.

Why it matches plant phenotyping methods葉画像から植物病害の状態をCNNで直接推定する手法が研究の中心であり、病害症状の画像ベース表現型計測に該当する。農薬推薦も含むが、植物病害識別というフェノタイピング要素が明示的である。

abstractpresents an intelligent system that automatically identifies plant diseases from leaf images and recommends appropriate pesticides for effective crop protection.

A multi-source remote sensing and machine learning framework for maize mapping and yield estimation in fragmented Loess gully regions. ↗

Accuracy crop distribution mapping and reliable yield estimation are essential for overcoming fragmentation and decentralization in smallholder farming systems of the Loess Plateau gully region. Multi-source remote sensing data, ancillary datasets, and machine learning techniques were integrated to map maize distribution and estimate yield. First, Sentinel-2 temporal spectral features, vegetation indices, and topographic variables were integrated to identify the optimal maize mapping model by a comparing machine learning algorithms: Random Forest (RF), Extra Trees (ET), Gradient Boosting Decision Tree (GBDT), and Histogram-Based Gradient Boosting Decision Tree (HGBDT). Subsequently, Sentinel-2 optical data and ERA5-Land meteorological data were dynamically resampled and spatiotemporally fused. A maize yield estimation model was then developed by integrating these fused predictors with in-situ measured maize yield samples. Finally, SHapley Additive exPlanations (SHAP) analysis was applied to quantify the feature contribution to both crop mapping and yield estimation, improving model transparency and interpretability. The results indicate that RF model achieved superior performance for maize identification in heterogeneous agricultural landscapes, with an overall Accuracy of 0.825, Precision of 0.849, Recall of 0.933, and F1-Score of 0.889. In the multi-source fusion-based yield estimation task, the HGBDT model yielded the highest predictive accuracy, with an R² of 0.6, RMSE of 1.07 t/ha, relative RMSE (rRMSE) of 11.49%, and MAE of 0.86 t/ha. Here, a methodological advancement is presented toward accurate and interpretable crop mapping and yield estimation in the ecologically complex and topographically fragmented Loess Plateau.

Why it matches plant phenotyping methodsトウモロコシの収量という植物形質を、リモートセンシング・気象データ融合と機械学習で推定する手法を開発・評価しており、単なる農業実験の routine 測定ではない。

abstractA maize yield estimation model was then developed by integrating these fused predictors with in-situ measured maize yield samples.

Phenomics-derived temporal maize health and environmental index enhance physiology-informed genomic prediction of yield across environments. ↗

Integrating multi-omics data, including phenomic, genomic, and environmental inputs, offers a powerful approach for enhancing maize performance and predicting grain yield. In this study, crop health was quantified using temporal NGRDI (Normalized Green Red Difference Index) trajectories collected from 16 unoccupied (unmanned) aerial vehicle or system (UAV or UAS, drones and sensors) flights (from 19 to 117 d after planting) in maize trials conducted in Texas. Crop health indices (CHIs) were calculated through area under the curve (CHIAUC) and functional principal component analysis (CHIFPCA), capturing dynamic plant health responses throughout the growing season. Heritability estimates of NGRDI fluctuated between 0.3 and 0.7, averaging 0.51 ± 0.02, reflecting consistent genetic contributions to growth dynamics. CHIs derived from a favorable (irrigated) trial in Texas effectively separated high- and low-yielding hybrids across 41 environment-tester combinations, achieving significant differentiation in 27 (CHIAUC ) and 28 (CHIFPCA1) environments. In comparison, only 21 environments were differentiated when using grain yield alone. Genomic mapping of temporal NGRDI revealed key quantitative trait loci (QTLs) linked to maize growth, containing candidate genes including br2, phyC1, wus1, mads69, cct1, rap2, miR172, and gl15, associated with canopy development, flowering regulation, and drought adaptation. Integrating multi-omics data into phenomic- and environment-informed genomic prediction models improved yield prediction accuracy by approximately 18.5%, particularly for untested genotypes in both tested and untested environments. These findings demonstrate that multi-omics integration provides a scalable framework for enhancing maize performance and advancing grain yield prediction across diverse agricultural systems.

Why it matches plant phenotyping methodsUAV画像から時系列NGRDIを抽出し、作物健康・生育動態の表現型指標を構築して検証・予測に利用しており、フェノタイピング手法の適用が研究の中心的要素です。

abstractCrop health indices (CHIs) were calculated through area under the curve (CHIAUC) and functional principal component analysis (CHIFPCA), capturing dynamic plant health responses throughout the growing season.

A review: research progress on intelligent technologies for orchard yield monitoring ↗

Accurate yield estimation and crop load monitoring are essential for precision orchard management, supporting targeted fertilization, pruning, thinning, harvest planning, and marketing decisions. However, reliable in-situ monitoring remains challenging because commercial orchards are characterized by severe canopy occlusion, fruit overlap, heterogeneous tree architecture, variable illumination, and complex backgrounds. This review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions. First, yield-related indicators are summarized, including direct phenotypic traits such as fruit number, size, volume, and spatial distribution, as well as indirect structural and physiological proxies such as canopy volume, vegetation indices, flowering intensity, and spectral maturity attributes. Second, representative sensing devices and carrying platforms are reviewed, including red-green-blue (RGB) cameras, red-green-blue-depth (RGB-D) sensors, light detection and ranging (LiDAR), hyperspectral and multispectral systems, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs). Third, the evolution of estimation methods is discussed, from traditional image processing and machine learning to object detection, instance segmentation, multi-object tracking, point-cloud analysis, remote-sensing regression, and multi-modal fusion. The review shows that no single sensor or algorithm can satisfy all orchard monitoring requirements. Ground-based vision and depth sensing are more suitable for fine-scale fruit counting and sizing, whereas UAV and spectral sensing provide advantages for regional yield mapping and quality-enhanced assessment. Future research should emphasize occlusion-aware perception, robust cross-environment generalization, lightweight edge deployment, standardized benchmarks, and integrated quantity-quality monitoring frameworks for actionable crop load management.

Why it matches plant phenotyping methods果実数・サイズ・体積・空間分布などの植物形質を対象に、センシング機器と画像解析・深層学習による収量推定法を体系的にレビューしており、フェノタイピング手法が中心である。

abstractThis review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions.

Use of machine learning techniques to detect and classify selected fungal diseases in rice crop using hyperspectral imaging ↗

Fungal diseases cause significant yield losses in rice, making early detection and accurate classification essential for effective disease management. In this study, hyperspectral imaging technique was used to acquire the spectral signatures of three major fungal diseases viz., brown spot, blast and sheath blight in rice. The acquired hyperspectral images were pre-processed using Standard Normal Variate (SNV) transformation and Savitzky-Golay filtering, followed by pixel-wise spectral data extraction. Principal Component Analysis (PCA) was used to investigate spectral variability among healthy and diseased leaf samples. Subsequently, machine learning models including artificial neural networks (ANN), support vector machines (SVM) and random forests (RF) were employed to classify these diseases based on the acquired and pre-processed spectral signature data. The results indicated that the ANN model outperform the others, achieving an accuracy of 98%, followed by SVM at 94%, and RF at 88%. Among the three models, the ANN exhibited the highest accuracy, precision and recall, making it the most effective model for disease detection and classification. Hyperspectral imaging, combined with machine learning, offers an affordable and efficient solution for large-scale detection and assessment of fungal diseases in rice crops.

Why it matches plant phenotyping methodsイネ葉の病害状態をハイパースペクトル画像から取得し、機械学習で検出・分類する手法が研究の中心であり、植物病害表現型の技術評価に該当する。

abstracthyperspectral imaging technique was used to acquire the spectral signatures of three major fungal diseases viz., brown spot, blast and sheath blight in rice.

Comparative Efficacy of Field-Level Sampling Techniques for Cotton Yield Estimation ↗

Accurate crop yield estimation is essential for food-security planning, supply-chain logistics and crop-insurance indexing. However, field-level forecasting in rainfed cotton systems can be affected by human bias, spatial error and seasonal variability. This study evaluated the comparative accuracy, statistical efficiency and predictive reliability of three field sampling protocols for cotton yield estimation under actual farming conditions. Primary data were collected from 31 commercial cotton plots in Sanglud, Morgaon Sadijan and Ural villages in Akola district, Maharashtra. The evaluated methods were the 5 x 5 m crop-cut experiment, plant-based estimation at the open-boll stage and area-based quadrat sampling, assessed across the first and second picking phases. Predictive performance was compared with ground-truth harvested yields using mean actual yield, percentage error, standard deviation and root mean square error (RMSE). The 5 x 5 m crop-cut method showed the closest agreement with actual yield, with low dispersion (s = 2.37) and RMSE (1.24), producing a +5.97% error in the first picking and a -0.29% error in the second picking. The plant-based method overestimated yield by +73.60% in the first picking and underestimated by -31.20% in the second picking, reflecting selection bias and late-season boll attrition. Quadrat sampling showed persistent overestimation (+18.30% and +30.40%; s = 3.73). The findings support the 5 x 5 m crop-cut protocol as the most reliable approach among the methods evaluated.

Why it matches plant phenotyping methods綿花収量という植物形質の推定について、複数のサンプリング手法を比較し、収穫実測値を基準に精度・誤差・RMSEを検証しているため、手法検証が中心である。

abstractThis study evaluated the comparative accuracy, statistical efficiency and predictive reliability of three field sampling protocols for cotton yield estimation under actual farming conditions.

Crop-Masked Vegetation Indices and TerraClimate for District-Level Wheat Yield Prediction in Kazakhstan: SAVI Advantage, Climate Dominance, and Temporal Transferability Limits ↗

Accurate district-level wheat yield forecasting is critical for Kazakhstan, the world’s seventh-largest wheat exporter. Prior remote-sensing studies typically compute vegetation indices over entire administrative units without isolating cropland, diluting the crop-specific signal and biassing remote-sensing–climate comparisons. A 25-year (2000–2024) dataset was assembled for 149 Kazakh districts (n = 2378 district–year observations, ~390 features), integrating crop-masked Sentinel-2/Landsat-7 optical indices, Sentinel-1 SAR, TerraClimate, and station, soil, and terrain data, and a HistGradientBoosting model was evaluated under both spatial (GroupKFold) and temporal (expanding-window) cross-validation. Ten-metre cropland masking substantially improved index–yield correlations, especially early in the season, and SAVI consistently outperformed NDVI from June onward. The best configuration—crop-masked optical indices with TerraClimate—achieved R2 = 0.646 (RMSE = 0.349 t/ha) under spatial cross-validation, whereas adding SAR yielded no significant gain. Pre-season winter-climate data (January–March) reached about 91% of full-year accuracy, enabling forecasts months before sowing. Critically, temporal cross-validation produced a markedly lower mean R2 = 0.413, a predictability gap (ΔR2 = 0.233) that provides a more representative estimate of operational forecast accuracy. Residuals showed no significant spatial autocorrelation. These results indicate that cropland masking and joint reporting of spatial and temporal cross-validation are valuable for yield prediction in semi-arid continental environments.

Why it matches plant phenotyping methods作物マスク付き光学・SARリモートセンシング指標から小麦収量を推定し、交差検証、指標比較、時空間移転性を評価しており、植物形質取得・推定手法が研究の中心である。

abstractintegrating crop-masked Sentinel-2/Landsat-7 optical indices, Sentinel-1 SAR, TerraClimate, and station, soil, and terrain data, and a HistGradientBoosting model was evaluated under both spatial (GroupKFold) and temporal (expanding-window) cross-validation.

Multimodal Deep Learning for Tapioca Yield Prediction Using Field-Collected Sensor and Imagery Data ↗

Tapioca is a vital root crop whose yield is influenced by multiple environmental, soil, and physiological factors.Traditional yield estimation methods depend upon statistical models and manual sampling, which often lack real-time accuracy and are time-consuming and labour-intensive.The combination of Internet of Things (IoT) sensor networks and Unmanned Aerial Vehicle (UAV)/satellite imagery, with deep learning (DL) models, gives a promising solution for accurate real-time yield prediction.In this study, sensor data (soil moisture, weather parameters, pH, NPK levels, temperature, and leaf chlorophyll content) are collected using devices like Davis Vantage Pro2, Decagon 5TE and SPAD-502, while image data are captured using DJI Phantom 4 Multispectral UAVs and Sentinel-2 satellite imagery.Pre-processing of sensor data involves missing value imputation, normalization, and feature selection using the Hybrid Frilled Lizard Osprey (HFLO) algorithm, while image data undergo resizing, augmentation, and filtering approach.Image based features are extracted by a position-attention DenseNet-201 model.Also, the features from image data and sensor data are fused using a concatenation mechanism.Finally, fully connected layers and improved support vector regression (ISVR) are used to identify the tapioca yield prediction.The integrated DL methods give higher accuracy than individual modalities for both modalities.The image-based model captures spatial and phenotypic variations, while the sensor-based model captures fine-grained environmental effects.The proposed approach obtained the MAE value of 0.0566, RMSE value of 0.654, and R 2 value of 99.5, demonstrating the potential of multimodal real-time data integration for precision agriculture in tapioca fields.

Why it matches plant phenotyping methodsUAV・衛星画像と環境センサーを統合し、深層学習でキャッサバの収量という植物形質を推定する手法の開発が中心である。

abstractThe combination of Internet of Things (IoT) sensor networks and Unmanned Aerial Vehicle (UAV)/satellite imagery, with deep learning (DL) models, gives a promising solution for accurate real-time yield prediction.

TDAVM-UNet: task-driven attention VM-UNet for crop disease detection from UAV imagery ↗

Crop diseases pose a serious threat to agricultural yield and global food security. Accurate detection using Unmanned Aerial Vehicle (UAV) remote sensing imagery is of great significance for precision agriculture. However, this task remains challenging due to complex field backgrounds, diverse spectral-spatial characteristics of diseased leaf regions, irregular lesion boundaries, and variable texture patterns. To address these issues, this paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery. The model integrates two task-driven attention modules: (1) Disease-Aware Dynamic Attention (DADA), which enhances the representation of diseased regions through disease feature enhancement, multi-scale dynamic channel attention, and texture-guided spatial attention; and (2) Channel-Spatial Visual State Space (CSVSS), which enables efficient long-range dependency modeling and local-global feature fusion while maintaining linear computational complexity. A hybrid loss strategy combining binary cross-entropy (BCE) loss, Dice loss, and cross-entropy (CE) loss with optimized coefficients is employed to address class imbalance and boundary delineation challenges. Extensive experiments are conducted on a self-constructed UAV crop disease unified mix dataset, comprising soybean disease images from Maharashtra, India, and rust disease images from wheat, corn, and other crops in Yangling, China, totaling 6,680 raw collected images, which after deduplication yields 5,000 images for experimentation. The results demonstrate that TDAVM-UNet achieves 26.87M parameters and 31.45 GFLOPs for 256×256 inputs, maintaining O(N) linear complexity (80% lower than TransUNet’s 156.78 GFLOPs), with 82.22% mIoU. This work provides a high-accuracy, robust, and computationally efficient method for UAV-based crop disease detection, offering significant technical support for precision agriculture applications.

Why it matches plant phenotyping methodsUAV画像から作物病害の病変領域・病害状態を推定する深層学習モデルを開発し、データセット上で性能評価しており、植物表現型取得・抽出手法が研究の中心である。

abstractthis paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery.

Adaptive spectral-thermal illumination management for protected tomato cultivation: a fused deep learning and pareto-based decision framework. ↗

This study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization to improve tomato production while reducing the carbon impact of supplemental LED lighting. The CNN-ELM model was trained using key environmental variables, including photosynthetic photon flux density (PPFD), red-to-blue light ratio, canopy temperature, CO 2 concentration, and relative humidity. Within the experimental conditions, the model achieved high predictive accuracy, with an R² of 0.976 and an RMSE of 0.712 µmol m -2 s -1 . Using these predictions, the MOEA/D algorithm generated Pareto-optimal lighting strategies, which were ranked through entropy-weighted TOPSIS and implemented via cloud-based control connected to a LoRa wireless sensor network and pulse-width-modulated LED drivers. The system was evaluated during a 110-day tomato cultivation trial and compared with single-parameter control and ambient-condition treatments. Results showed a 38.4% reduction in LED-related carbon emissions, a 22.6% increase in net photosynthetic rate, and a 31.7% improvement in harvestable yield relative to ambient conditions. Physiological analyses further indicated enhanced photosynthetic performance, radiation-use efficiency, and light utilization. Overall, the findings demonstrate that data-driven, closed-loop lighting management can simultaneously enhance productivity and reduce greenhouse gas emissions in controlled-environment agriculture when applied within the validated operational domain.

Why it matches plant phenotyping methods光合成という植物生理形質を予測するCNN-ELMモデルを中核に、センサーネットワークと閉ループ制御を統合・評価しており、単なる栽培試験ではなく形質推定手法の応用が主要内容である。

abstractThis study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization

Multi-Modal Learning with Explainable Artificial Intelligence for Crop Analysis: A Comprehensive Review ↗

In modern agriculture, artificial intelligence (AI) is doing excellent work in crop monitoring, crop disease detection, crop yield prediction, and crop stress assessment. Various techniques such as deep learning, generative models, vision transformers, explainable AI (XAI), multimodal fusion, etc., have helped in building intelligent crop analysis. This paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets. The important challenges and open issues identified are limited field datasets, class imbalance, dataset bias, high computational complexity, privacy concerns, etc. Based on these, we suggested future work that can include foundation models, digital twin techniques, federated learning, multimodal frameworks, and interpretability architecture. This review provides a review for creating reliable, scalable, and sustainable AI-driven crop analysis systems. In addition to that, the survey seeks to give researchers and AI practitioners a comprehensive analysis of the current situation.

Why it matches plant phenotyping methods作物の病害・収量・ストレス評価を対象に、AI手法、データモード、ベンチマーク、実験方法、データセットを体系的にレビューしており、植物表現型取得・推定手法のレビューが中心です。

abstractThis paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets.

Satellite data reconstruction under cloud cover for corn yield forecasting via multimodal fusion ↗

A comprehensive intelligent system for corn yield prediction based on the synergy of a Spatio-Temporal Generative Adversarial Network (ST-cGAN) and a recurrent CNN-LSTM architecture has been developed and tested. A multimodal fusion of weather-independent Sentinel-1 radar data, ERA5-Land meteorological factors, and historical Sentinel-2 optical observations was applied. The problem of "biochemical blindness" in radar signals, where radar captures physical plant structure but fails to detect photosynthetic activity, and cloud cover limitations in optical remote sensing was successfully resolved. To achieve this, an early fusion strategy was implemented. The ST-cGAN simultaneously processes current SAR data for the structural macro-state, historical optical data for the last known biochemical baseline, and a 10-day history of meteorological priors to enforce physiological constraints. Consequently, if radar indicates high biomass but meteorological data reveals severe drought, the neural network mathematically recognizes biological stress and synthesizes proportionally depressed NDVI and NDRE indices, preventing the hallucination of falsely healthy crops. Dynamic synthesis of missing NDVI and NDRE vegetation indices was conducted under prolonged continuous cloud cover (up to 21 days), achieving a high structural similarity index (SSIM = 0.89). Temporal discriminators for frame sequence analysis were introduced into the architecture, improving the edge-preserving index by 18 % and minimizing spatial artifacts at field boundaries. Pixel-level regression was performed for a 15,000-hectare test area in the Western Forest-Steppe of Ukraine based on reconstructed time series covering 15 critical phenological stages. It was established that the proposed architecture reduces the root mean square error (RMSE) to 0.48 t/ha with a coefficient of determination (R2) of 0.90. Statistical analysis proved the significant superiority of the developed method over industry-standard gap-filling algorithms (e.g., STARFM). A scalable decision-support system for optimizing harvest logistics and financial planning under any atmospheric conditions was presented. Future research directions involving neural network knowledge distillation for IoT devices and UAV data integration were outlined.

Why it matches plant phenotyping methods雲下で欠測するNDVI・NDREなど植物状態指標を再構成する深層学習手法の開発と、SSIM・RMSE・R2および既存手法との比較検証が中心であり、単なる収量予測ではなく植物表現型推定手法に該当する。

abstractDynamic synthesis of missing NDVI and NDRE vegetation indices was conducted under prolonged continuous cloud cover (up to 21 days), achieving a high structural similarity index (SSIM = 0.89).

Plant Disease Detection Using CNN and GAN ↗

Agriculture is a critical sector for global food security, but plant diseases and nutrient deficiencies remain major challenges that reduce crop yield and economic returns for farmers. Traditional diagnosis methods depend on manual observation and expert intervention, which are often time-consuming, subjective, and inaccessible in remote regions. This paper presents an intelligent crop health analysis system that automates the detection of plant diseases and nutrient deficiencies using a hybrid deep learning framework. The proposed approach integrates Convolutional Neural Networks (CNNs) for feature extraction and classification with Generative Adversarial Networks (GANs) for synthetic image generation and dataset augmentation. The CNN model learns discriminative features such as color variations, texture patterns, and lesion characteristics from leaf images, while the GAN enhances dataset diversity by generating realistic samples, thereby addressing class imbalance and limited training data. The system is trained on a dataset containing more than 55,000 leaf images across 32 classes and is deployed through a Flask-based web application. It supports two operational modes: Basic Mode for disease identification and Advanced Mode for comprehensive crop health assessment through the integration of CNN-based predictions and rule-based nutrient analysis. Experimental results demonstrate improved classification performance, robustness, and scalability under real-world conditions. Additionally, the multilingual user interface enhances accessibility for farmers from diverse linguistic backgrounds. The proposed system provides an effective and practical solution for early crop health monitoring, enabling timely intervention, reducing dependency on agricultural experts, and contributing to increased agricultural productivity and sustainable farming practices.

Why it matches plant phenotyping methods葉画像から植物病害と栄養欠乏を推定するCNN・GAN手法と運用システムが研究の中心であり、植物の病徴・健康状態を直接評価する画像ベース表現型計測に該当する。

abstractThis paper presents an intelligent crop health analysis system that automates the detection of plant diseases and nutrient deficiencies using a hybrid deep learning framework.

Robot-assisted Neural Radiance fields for plot-level cotton crop 3D reconstruction and yield estimation ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsロボット支援NeRFによる綿花の3D再構成と収量推定が題名の中心であり、植物形質の取得・推定手法を扱うため。

titleRobot-assisted Neural Radiance fields for plot-level cotton crop 3D reconstruction and yield estimation

AgroPulse: A Real-Time Field Intelligence System for Crop Disease Tracking and Notification System ↗

Agriculture faces significant challenges due to plant diseases, which directly affect crop yield, quality, and farmer income. Early detection of agricultural diseases is critical to prevent large-scale crop losses and reduce excessive use of pesticides. Traditional disease detection methods rely on manual inspection by farmers or agricultural experts, which is time-consuming, subjective, and often inaccurate, especially during early stages of infection. With the advancement of Machine Learning (ML) and Internet of Things (IoT) technologies, automated and intelligent solutions for crop disease detection have become feasible. The IoT-Based Crop Disease Recognition and Field Notification System proposes an intelligent system that combines machine learning–based image analysis with IoT-enabled monitoring to detect crop diseases at an early stage. The system uses an ESP32 microcontroller integrated with an ESP-CAM module to capture images of plant leaves. These images are analyzed using trained machine learning models to identify disease patterns and abnormalities. The detection results are communicated through an IoT platform, enabling remote monitoring and real-time alerts. An LCD display provides local status information, while a buzzer generates immediate alerts when a disease is detected. The system is designed to be cost-effective, scalable, and suitable for deployment in real agricultural environments. By enabling early disease identification and timely intervention, the proposed solution helps improve crop productivity, reduce losses, and promote smart and sustainable agricultural practices.

Why it matches plant phenotyping methods植物葉の画像を機械学習で解析し、病徴・異常を検出するシステムが研究の中心であり、植物病害状態の画像ベースフェノタイピングに該当する。

abstractThe system uses an ESP32 microcontroller integrated with an ESP-CAM module to capture images of plant leaves.

Joint Modeling of Grain Yield and Root Lodging in Maize Using Multi-Output Neural Network and Machine Learning Models Under Defined Environmental Conditions ↗

We evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions. To address model robustness, the multi-output neural network was compared with linear regression, elastic net, random forest, and XGBoost using repeated five-fold cross-validation, an 80/20 holdout split, and independent year-wise validation. Under repeated cross-validation, XGBoost provided the strongest average predictive performance for both traits, with R2 values of 0.57 for GY and 0.67 for LP. The multi-output neural network showed moderate performance, with R2 values of 0.49 for GY and 0.57 for LP. Final holdout performance for the neural network for GY and LP was R2 = 0.64 and R2 = 0.92, respectively. Year-wise validation showed weak temporal transferability because the two seasons differed not only in environmental conditions, but also in lodging mechanism. Repeated permutation importance identified ear width (EW), kernel row number (RNE), thousand kernel mass (KM1000), and kernel number per ear (KNE) as important predictors of GY, while LP prediction was most strongly associated with internode major diameter (IDmajor), ear length (EL), and the number of green leaves (NGL). Across both permutation importance and SHAP, only RNE and NGL were consistently shared between GY and LP. Supplementary ALE diagnostics indicated that RNE showed increasing model-estimated effects for both predicted GY and LP, whereas NGL showed a positive association with predicted GY but a decreasing or nonlinear association with predicted LP. These results show that joint modeling can support exploratory trait interpretation, but the predictive relationships remain environment-specific and should not be interpreted as causal or broadly transferable without further multi-environment validation.

Why it matches plant phenotyping methods穀粒収量と倒伏率という植物形質を推定する多出力ニューラルネットワーク等のモデルを開発・比較し、交差検証、ホールドアウト、年次外部検証で性能評価しており、計算的形質推定が中心である。

abstractWe evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions.

Non-destructive yield estimation of onion and garlic using UAV-based hyperspectral imaging and hybrid machine learning models. ↗

Background Accurate pre-harvest yield estimation of underground bulb crops such as onion and garlic is important for precision agriculture, harvest planning, and food-security-oriented decision-making. However, their harvestable organs develop below ground and cannot be directly observed using conventional remote sensing methods. This study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models. Method Field experiments were conducted in Muan-gun, Korea, using onion and garlic as representative underground bulb crops. UAV-based hyperspectral images, crop growth traits, and destructive live bulb weight measurements were collected during the growing period. Hyperspectral images were processed through geometric correction, radiometric correction, and Savitzky-Golay spectral smoothing. Three dimensionality reduction methods, including genetic algorithm (GA), principal component analysis (PCA), and clustering, were used to reduce spectral redundancy. Five prediction models, including random forest (RF), XGBoost, partial least squares regression (PLSR), multilayer perceptron (MLP), and residual network (ResNet), were then evaluated for live bulb weight prediction. Result Significant spectral differences were observed in the 550-680 nm and 730-800 nm bands, which were closely associated with crop yield and below-ground bulb development. GA was the most effective feature selection method for extracting yield-related spectral bands. For onion yield prediction, the GA + RF model achieved the highest predictive accuracy, with an R 2 of 0.9656 and an NRMSE of 18.55%. For garlic yield prediction, PLSR showed the best performance, with an R 2 of 0.9260 and an NRMSE of 27.20%. Conclusion The proposed UAV-based hyperspectral framework enables accurate, real-time, and non-destructive yield estimation for underground bulb crops. This approach reduces reliance on labor-intensive destructive sampling and provides a practical tool for precision crop monitoring and data-driven agricultural management.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習による地下球根の収量（生体球重）推定フレームワークの開発・比較評価が研究の中心であり、植物形質の取得・推定手法に該当する。

abstractThis study aimed to develop a non-destructive yield estimation framework by integrating UAV-based hyperspectral imaging with hybrid machine learning models.

Editorial: Plant phenotyping for agriculture ↗

Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-

Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。

titleEditorial: Plant phenotyping for agriculture

Deep Learning and Computer Vision for Crop Maturity Assessment ↗

Maturity at harvest is a critical determinant of yield, storability, market value, and nutritional quality, making accurate and objective maturity assessment essential for sustainable crop and fruit production. The agricultural sector is under pressure to satisfy rising global food demand while reducing losses and environmental impacts, yet conventional maturity assessment methods remain largely manual, subjective, and labour-intensive. Against this backdrop, computer vision and deep learning have emerged as powerful tools for non-destructive, high-throughput evaluation of maturity traits in the field and along the supply chain.​ This review consolidates recent advances in deep learning-based maturity assessment across a wide range of crops, with a particular emphasis on citrus fruits, where external colour change, internal quality, and heterogeneous orchard conditions pose distinctive challenges. The paper analyzes state of the art architectures for classification, segmentation and detection, associated datasets and imaging modalities, and the metrics used to benchmark performance. By critically examining their advantages and limitations for real-world deployment, the review outlines key research gaps and future directions toward robust, scalable, and sustainable DL-driven maturity assessment systems for both citrus and other major crops.

Why it matches plant phenotyping methods作物の成熟度という植物形質を対象に、画像・深層学習による評価手法、データセット、画像モダリティ、ベンチマーク指標を体系的にレビューしており、フェノタイピング手法が中心です。

abstractThis review consolidates recent advances in deep learning-based maturity assessment across a wide range of crops

Mobile Application for Automated Plant Disease Detection ↗

The widespread impact of plant diseases on agricultural yield demands an intelligent, accessible, and real-time detection solution. This paper presents the Mobile Application for Automated Plant Disease Detection, a lightweight smartphone-based system that leverages a fine-tuned MobileNetV2 Convolutional Neural Network (CNN) to classify plant leaf diseases in real time. The system accepts smartphone camera images, applies preprocessing including resizing to 224×224 pixels and normalisation, and classifies the plant health state as Healthy, Early Blight, Late Blight, Leaf Curl, or Powdery Mildew with associated confidence scores. A Flask API backend hosts the trained model and communicates with a React Native mobile frontend to return classification results within 1.2 seconds on a 4G network connection. Firebase Cloud Messaging delivers real-time push notifications and treatment recommendations directly to the farmer's device. The system is deployed entirely on standard Android and iOS smartphones without any specialised hardware, sensors, or wearable devices. Experimental evaluation on the PlantVillage dataset with over 54,000 annotated leaf images demonstrated classification accuracy exceeding 96%, API response latency below 800 milliseconds, and zero dependency on dedicated agricultural equipment. Usability testing with agricultural practitioners confirmed intuitive operation without prior technical training. These results confirm that the proposed application offers an efficient, portable, and institutionally deployable solution for modern precision agriculture. Keywords—Plant disease detection; MobileNetV2; convolutional neural network; deep learning; precision agriculture; smartphone application; transfer learning; PlantVillage dataset; real-time classification; push notification

Why it matches plant phenotyping methodsスマートフォン画像から植物葉の病害・健康状態を推定するCNNベースの手法とアプリを開発・評価しており、植物病害表現型の取得が中心である。

abstractThis paper presents the Mobile Application for Automated Plant Disease Detection, a lightweight smartphone-based system that leverages a fine-tuned MobileNetV2 Convolutional Neural Network (CNN) to classify plant leaf diseases in real time.

Bridging Magnetic Field Agriculture and UAV-Based Precision Monitoring: A Systematic Review and Framework for Field-Scale Validation ↗

Magnetic field (MF) technologies have been applied in agriculture for decades. However, they have not achieved mainstream adoption, partly because no validated methodology exists for evaluating their effects under realistic field conditions. UAV-based multispectral sensing represents a potential pathway to address this limitation: by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, it could provide the monitoring infrastructure through which MF treatment responses are, for the first time, systematically evaluated and validated under open-field conditions. To exploit this complementarity, however, a common evidential ground must first be established, identifying which crop physiological variables are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream systematic review of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency/nitrogen assimilation, above-ground biomass, leaf area index, and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R² = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The results reveal a complete absence of integration between the two research domains despite their strong biological and methodological compatibility. The proposed framework provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems.

Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングによる作物生理形質の推定を体系的にレビューし、地上検証と機械学習を含むフィールドスケール評価フレームワークを提案しており、植物フェノタイピング手法が中心である。

abstractUAV-based multispectral sensing represents a potential pathway to address this limitation: by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale

Leaf movements as a quantitative metric for early stress detection ↗

Early, precise, and non-destructive stress detection is essential for maintaining crop productivity, particularly in high-density plant growth systems like controlled environment agriculture (CEA), where manual monitoring is often impractical. Using plant motion as a proxy for growth and plant health, we demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis in lettuce and five other CEA relevant crops. Leaf-movement dynamics under stress were imaged with a low-cost, scalable Raspberry Pi imaging setup and quantified using a repurposed open-source motion estimation algorithm; Tracking Rhythms in Plants (TRiP). Our system detected stress-induced changes in leaf-movement within 1 hour of stress, with the timing dependent on the nature of the stress. Sustained reductions in leaf-movement coincide with decreased biomass accumulation. This approach offers a non-invasive, rapid, scalable, and cost-effective solution for continuous crop monitoring, with potential for application in both terrestrial and space farming CEA systems. Abstract Figure Graphical abstract: Quantification of leaf-movement dynamics as a high-throughput proxy for plant physiological status, enabling early stress detection and timely intervention to mitigate yield penalties in CEA settings (image made with biorender.org).

Why it matches plant phenotyping methods低コスト撮像と既存アルゴリズムを用いて葉の動きを定量化し、植物ストレス・生理状態を早期推定する方法が研究の中心である。

abstractwe demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis

Real-time detection of plant leaf diseases based on improved YOLOv13-LM in complex field environments ↗

Plant leaf diseases are a major factor leading to crop yield reduction, seriously threatening food security and sustainable agricultural development. Timely and accurate detection is crucial for scientific prevention and control. Traditional manual detection is time-consuming, labor-intensive, and highly subjective. Existing deep learning detection models still have significant shortcomings in complex field scenarios, struggling to balance detection accuracy, real-time performance, and stability. They are easily affected by background interference, differences in lesion scale, and sample imbalance, and some models are not lightweight enough to meet the needs of real-time field detection. Therefore, this paper uses YOLOv13 as the baseline model and constructs a YOLOv13-LM model through multi-module collaborative optimization. The optimization directions cover the backbone, neck, detection head, and loss function, strengthening lesion feature extraction and multi-scale fusion, reducing task interference, and improving localization accuracy. Model validation was completed in a complex farmland environment. The results show that the model’s mAP@0.5 is improved by 5.4 percentage points to 87.9% compared to the original YOLOv13, the FPS is improved by 21.1% to 46 frames/second, and the number of parameters and computational cost are reduced by 27.3% and 25.7% respectively. The overall performance is better than the mainstream YOLO models of the same scale. However, the lightweight nature of the model is still not as good as that of the ultra-lightweight model, and its generalization and interpretability need to be improved. In the future, we will focus on ultra-lightweight design, expanding the generalization ability of multiple crops and diseases, and studying the interpretability of the model to further adapt to the actual needs of field applications.

Why it matches plant phenotyping methods植物葉の病斑を画像から検出するYOLOモデルを開発・改良し、複雑な圃場環境で精度とリアルタイム性能を検証しており、病害状態の表現型取得が中心である。

abstractTherefore, this paper uses YOLOv13 as the baseline model and constructs a YOLOv13-LM model through multi-module collaborative optimization.

Precise estimation of rice leaf macro and micro nutrients from multi-spectral images using neural architecture search with polynomial approximation functions ↗

Estimating the nutritional status of rice leaves is crucial for efficient nutrient management and yield enhancement. Traditional wet lab analyses are time-consuming and labor-intensive. This study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves. The proposed framework integrates a differentiable neural search technique using polynomial function approximators and an adaptive activation mechanism, which not only provides improved predictive performance but also deals efficiently with limited training data. The model performance is evaluated across different treatments and crop growth stages using mean absolute error (MAE) and [Formula: see text] values. Experiments were conducted at the Punjab Agricultural University. The results demonstrate that the proposed model achieves MAE values in the range of 0.06-0.11 for SAS-I and 0.06-0.16 for SAS-II across eleven leaf macro/micro nutrients. To further evaluate the reliability of the predicted nutrients beyond the prediction error analysis, uncertainty estimation of nutrients is also performed. Comparative analysis shows that the proposed framework outperforms conventional deep learning baselines and machine learning methods in terms of accuracy and robustness. Furthermore, the t-SNE visualization of learned feature representations effectively clusters similar nutrient values while separating dissimilar ones. The robustness of the proposed framework is further validated through ablation studies, treatment-wise and plot-wise cross-validation, highlighting the contribution of individual components and their performance under varying field conditions. These findings highlight the proposed NAS-based framework for precise and reliable nutrient assessment in precision agriculture.

Why it matches plant phenotyping methods稲葉のマクロ・微量栄養素という植物状態をマルチスペクトル画像から推定する深層学習手法を開発し、比較検証・不確実性評価・アブレーション試験まで行っており、表現型取得・推定法が中心である。

abstractThis study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves.

Multi Class Support Vector Machine Based Plant Leaf Disease Detection from Color Texture And Shape Pictures ↗

Agriculture plays a vital role in the economy of many countries, and crop productivity is highly dependent on plant health.Plant diseases can significantly reduce crop yield and quality if not detected at an early stage.Traditional disease identification methods rely on manual inspection by agricultural experts, which can be time-consuming, expensive, and sometimes inaccurate.Recent advancements in image processing and machine learning have enabled automated systems for plant disease detection.This research presents a plant leaf disease detection system based on a Multi-Class Support Vector Machine (SVM) classifier using color, texture, and shape features extracted from leaf images.The proposed approach captures leaf images, performs preprocessing to remove noise and enhance image quality, segments the infected region, and extracts relevant features.These features are then used to train a Multi-Class SVM model capable of classifying different plant diseases.The combination of color, texture, and shape characteristics improves classification accuracy by providing comprehensive information about disease symptoms present on the leaf surface.Experimental analysis demonstrates that the proposed method can effectively identify multiple plant diseases with high accuracy while reducing the dependency on manual diagnosis.The developed system offers a cost-effective and efficient solution for farmers and agricultural professionals, helping in early disease detection and timely treatment recommendations.The proposed approach contributes to the advancement of smart agriculture and precision farming technologies.

Why it matches plant phenotyping methods葉画像から病斑領域を分割し、色・テクスチャ・形状特徴を抽出して植物病害状態を分類する画像ベースの表現型推定手法が研究の中心であるため。

abstractThis research presents a plant leaf disease detection system based on a Multi-Class Support Vector Machine (SVM) classifier using color, texture, and shape features extracted from leaf images.

Physics-informed machine learning and Vision Transformer for predicting photosynthetic traits, biomass, and grain yield in winter wheat using UAV multispectral imagery ↗

Predicting crop photosynthetic traits from UAV imagery requires frameworks that connect canopy-level spectral observations to leaf-level physiological processes. Existing approaches rely on empirical vegetation indices (VIs) and standard machine learning models, lacking physical interpretability and appropriate deep learning architectures for image data. We developed a physics-informed multi-output machine learning framework that combines PROSAIL radiative transfer model inversion-derived biophysical parameters with spectral VIs and texture features (TFs), applies two spatial deep learning architectures, a Vision Transformer (ViT) and a 2D convolutional neural network (CNN), to multispectral image patches, and introduces a hybrid architecture that fuses PROSAIL-derived features with ViT spatial embeddings. The framework was evaluated for predicting CO 2 assimilation rate ( A ), stomatal conductance ( g sw), Photosystem II efficiency ( F v’/ F m’), aboveground biomass (AGB), and grain yield in a subset of seven European winter wheat varieties selected from a larger 18-variety field experiment across two growing seasons (2022–2024). Model performance was evaluated using random hold-out tests and leave-one-variety-out (LOVO) validation with bootstrap confidence intervals. For grain yield, the best tabular models achieved R 2 = 0.92–0.96, and the ViT on image patches achieved a competitive R 2 = 0.92. ViT delivered the best performance in predicting g sw. BorutaSHAP selected PROSAIL-derived features alongside empirical VIs, confirming that physics-informed features provide complementary information. The hybrid ViT+PROSAIL model matched or outperformed ViT-only for most traits under LOVO validation, with the clearest gain observed for grain yield, indicating that physics-based features can help regularize spatial representations for improved cultivar-level transferability. This study demonstrates that integrating radiative transfer model physics with spatial deep learning advances UAV-based high-throughput phenotyping of photosynthetic traits in breeding programs.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から光合成形質、バイオマス、収量を推定する物理情報機械学習・画像解析フレームワークを開発し、複数の検証法で性能評価しており、植物表現型取得・推定法が中心である。

abstractWe developed a physics-informed multi-output machine learning framework that combines PROSAIL radiative transfer model inversion-derived biophysical parameters with spectral VIs and texture features (TFs), applies two spatial deep learning architectures, a Vision Transformer (ViT) and a 2D convolutional neural network (CNN), to multispectral image patches, and introduces a hybrid architecture that fuses PROSAIL-derived features with ViT spatial embeddings.

AGIcam: An open‐source Internet of Things–based camera system for automated in‐field phenotyping and yield prediction ↗

Abstract Continuous, high‐frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season‐long trait assessment. This study introduces AGIcam, an open‐source Internet of Things (IoT) camera system for automated and continuous in‐field plant phenotyping and yield prediction. The platform integrates solar‐powered Raspberry Pi units with a modular software stack, comprising Node‐RED, InfluxDB, Grafana, and Microsoft Azure, for automated data acquisition, transfer, and visualization. In the 2022 growing season, 18 AGIcam systems were deployed in spring and winter wheat ( Triticum aestivum ) breeding trials, maintaining an uptime of over 85% while capturing frequent red‐green‐blue and no‐infrared imagery. Time‐series vegetation indices derived from these images were used to predict yield using random forest and long short‐term memory (LSTM) models. The LSTM approach achieved the highest accuracy approximately one week after heading, with mean prediction errors of 3.41% for spring wheat and 1.62% for winter wheat. These results highlight the potential of IoT‐based platforms such as AGIcam to enable real‐time, scalable, and effective phenotyping solutions for data‐driven crop improvement. The presented work provides open‐source resources for the development and time‐series analysis of IoT data for phenotyping and precision agricultural applications.

Why it matches plant phenotyping methods植物フェノタイピング用のIoTカメラ基盤を開発・実証し、画像由来の時系列形質から収量を予測する方法が研究の中心である。

abstractThis study introduces AGIcam, an open‐source Internet of Things (IoT) camera system for automated and continuous in‐field plant phenotyping and yield prediction.

Fluorescence Lifetime Imaging in Plants: Practical guidelines for multiplexing, label-free imaging and data analysis ↗

ABSTRACT Fluorescence Lifetime Imaging Microscopy (FLIM) is becoming a key technique for live-cell multiplexing and label-free detection of endogenous fluorescence in animal systems. Its potential in plant biology, however remains largely unexploited, despite its integration into a number of commercial microscopy setups. Here, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores. Lifetime imaging of different fluorescent reporters targeted to distinct organelles (nucleus, plasma membrane, endoplasmic reticulum, etc.) and subsequent analysis of the decay curves using different modes allowed us to simultaneously discriminate up to four spectrally overlapping fluorophores solely by lifetime differences in specific subcellular compartments. Remarkably, fluorophores with lifetimes differing by as little as 0.1 ns can be reliably discriminated using one of these modes, namely Phasor-based analysis. Moreover, we show that the same fluorophores exhibit compartment-specific lifetime shifts, enabling Phasor separation of identical tags residing in different organelles. Finally, we extended the Phasor approach to label-free imaging of endogenous plant fluorescence. Together, these results establish FLIM-Phasor as a versatile, multiplex-capable tool for plant cell biology, opening new avenues for imaging strategies that yield higher content information at both cellular and tissue-level resolution.

Why it matches plant phenotyping methods植物細胞・組織の蛍光状態を取得・解析するFLIM-Phasor法を体系的に構築・検証し、マルチプレックスおよびラベルフリー植物蛍光イメージングへの応用を示した、方法中心の研究である。

abstractHere, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores.

An innovative screening method for heat stress tolerance in chickpea ( Cicer arietinum L.). ↗

Reliable field phenotyping for terminal heat stress (THS) tolerance in chickpea is constrained by conventional late-sowing approaches that confound reproductive stress with reduced vegetative growth. We developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour. Early flowers were removed to synchronize flowering and delay reproduction by 10-15 days, exposing flowering and pod set to high temperatures (>33 °C). Across two seasons and contrasting genotypes, DF maintained vegetative growth but significantly reduced pollen viability, pod set, and yield, with tolerant genotypes showing markedly lower yield penalties than susceptible ones. The method effectively discriminated reproductive thermotolerance and provides a simple, low-cost, and biologically grounded phenotyping tool for chickpea breeding under warming climates.•A DF-based field method selectively imposes reproductive-stage heat stress without compromising vegetative growth.•The approach reliably distinguishes heat-tolerant and susceptible chickpea genotypes under natural field conditions.

Why it matches plant phenotyping methods生殖期の耐暑性を選択的に評価するDFベースの圃場スクリーニング法を開発・検証しており、遺伝子型の識別に用いるフェノタイピング手法が中心である。

abstractWe developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour.

Deciphering the genetic basis of yield components in wheat by integrating hyperspectral-based phenomes. ↗

Genome-wide association studies (GWAS) have advanced crop genetics by the detection of loci controlling complex traits; however, their power is often constrained by the quality and the throughput of phenotypic data. In this study, we integrated hyperspectral and genomics data to investigate the genetic architecture of spectral signatures associated with yield components in wheat. A diverse panel of 341 soft wheat lines was evaluated over three years, and hyperspectral data were collected using a UAV-mounted sensor. Among 273 spectral bands, those most strongly correlated with grain yield (GY), thousand-grain weight (TGW), and grains per unit area (GN) were selected. Principal component analysis was used for dimensionality reduction, and the first principal component (PC1), here defined as the hyperspectral phenome, accounted for 78.9%-97.1% of overall variance. The GWAS using both manual phenotypes and hyperspectral phenomes identified 31 significant marker-trait associations (MTAs), including several pleiotropic loci shared across traits and data types. A notable SNP on chromosome 1A, associated with all three hyperspectral phenomes, was located within a gene specifying a chlorophyll a-b binding protein, a key component of photosynthesis and stress response. Additional MTAs were linked to genes involved in cytochrome P450 metabolism and LRR proteins, highlighting their roles in yield and environmental response. Overall, this study shows that hyperspectral imaging serves as a valuable, high-throughput secondary correlated trait for uncovering novel loci and dissecting the genetic basis of complex yield traits in wheat.

Why it matches plant phenotyping methods小麦の収量関連形質を推定するUAV搭載ハイパースペクトル計測と、スペクトルデータからフェノームを抽出する解析が研究の中心であり、GWASへの実質的な応用として記述されている。

abstracthyperspectral data were collected using a UAV-mounted sensor

Estimation of bread wheat yield by multiple linear regression (MLR) and artificial neural network (ANN) methods and their fair comparison ↗

Abstract This study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding. This research, conducted using 782 wheat genotypes in Rafsanjan, Iran, compares MLR and ANN methodologies. MLR, using seven traits selected via stepwise regression, achieved an R² of 0.90, the root of mean square of error (RMSE) of 14, Average absolute percentage error (MAPE) of 13.3, and Average deviation of prediction from the actual value (MAE) of 10. Key traits identified were biological weight (weight of total plant per line, WPP) and harvest index (HI). Conversely, the GA-ANN model, employing six selected traits, demonstrated superior performance with R² values of 0.94, 0.96, and 0.94 for training, testing, and combined datasets respectively. Validation metrics for the ANN model were MSE of 144.3, RMSE of 12, and MAE of 5.7. GA-ANN selected height, peduncle length, days to flowering, spike length, biological weight, and harvest index as significant predictors. The results underscore that ANN models, particularly when combined with genetic algorithms for feature selection and optimization, can improve prediction accuracy by modeling complex, non-linear relationships in agricultural data, therefore providing more precise yield prediction tools for plant breeders. This study emphasizes the need for advanced predictive techniques in achieving more accurate assessments of crop yield for sustainable agriculture.

Why it matches plant phenotyping methods小麦遺伝子型レベルの収量という植物形質を対象に、MLRとGA-ANNの予測精度を比較・検証しており、計算による形質推定手法が研究の中心です。

abstractThis study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding.

HarvestField: A Decision-Support Framework for Robot-Oriented Kiwifruit Canopy Management Using Spatial Harvestability Modelling ↗

Abstract Purpose Robotic harvesting systems are commonly evaluated in canopies treated as fixed operating environments, although kiwifruit canopy architecture is actively shaped by seasonal pruning, fruit thinning, tipping, and cane training. This study developed HarvestField, a decision-support framework for quantifying how robot-oriented canopy management affects harvesting feasibility before field operation. Methods HarvestField defines a spatial harvestability field \(\:\mathscr{H}\left(x\right)\in\:\left[\text{0,1}\right]\) as the product of reachability, visibility, clearance, and detachability sub-fields. A mass-weighted field average at fruit positions yields the Harvested Yield Index (HYI). The framework couples an L-system-based functional–structural plant model of Hayward kiwifruit with NSGA-III multi-objective optimisation over six management variables, jointly evaluating HYI, yield, and labour cost. Results Calibration against a published 12,000-fruit robotic kiwifruit harvesting benchmark produced HYI = 0.562, with an absolute deviation of 0.004 from the observed success rate of 0.558. HarvestField-optimised management increased mean HYI relative to standard management (0.642 versus 0.587). Under equal fruit counts, \(\:\mathscr{H}\)-guided thinning improved HYI by up to 0.301 compared with uniform thinning. Sobol analysis identified clearance neighbourhood radius as the dominant parameter (\(\:{S}_{T}=0.576\)). Conclusion HarvestField provides a spatially explicit and robot-specific framework for linking kiwifruit canopy management with harvesting feasibility. It supports management trade-off analysis while indicating the need for independent orchard-scale validation before deployment.

Why it matches plant phenotyping methodsキウイフルーツ樹冠の可視性・到達性・クリアランス・離脱性を統合して、果実位置ごとの収穫可能性を定量化する計算フレームワークが研究の中心であり、単なる収穫対象の検出を超えた植物構造由来の状態推定である。

abstractThis study developed HarvestField, a decision-support framework for quantifying how robot-oriented canopy management affects harvesting feasibility before field operation.

Early Crop Disease Detection using Vision Transformers ↗

Crop diseases pose a significant threat to global food security, often resulting in substantial yield losses and economic instability for farmers. Traditional methods of disease identification, which rely on manual visual inspection, are labor-intensive, subjective, and frequently prone to error. While Convolutional Neural Networks (CNNs) have established a baseline for automated detection, they occasionally struggle with capturing global context within complex leaf patterns. This paper presents a robust image-based classifier utilizing Vision Transformers (ViT) to identify crop diseases from leaf images. Leveraging the self-attention mechanism, the proposed model effectively captures long-range dependencies in image data. The system is trained and validated on the PlantVillage dataset using transfer learning techniques. Experimental results demonstrate that the Vision Transformer architecture achieves a classification accuracy of 98.4%, outperforming traditional CNN architectures such as ResNet50 and VGG16. These findings suggest that transformer-based models offer a promising avenue for precision agriculture, enabling early intervention and reduced pesticide usage.

Why it matches plant phenotyping methods葉画像から作物病害を分類するVision Transformer手法の開発・検証が中心で、植物の病害状態を直接推定しているため。

abstractThis paper presents a robust image-based classifier utilizing Vision Transformers (ViT) to identify crop diseases from leaf images.

Dynamic monitoring of rice plant height during the early growth stage using UAV-LiDAR and GWAS analysis of growth rate ↗

Plant height during the early growth stage of rice is a key indicator reflecting canopy establishment rate, tillering potential, and overall growth vigor, all of which critically determine final yield formation. Conventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics, limiting the understanding of early growth vigor and its genetic mechanisms. In this study, a UAV-based LiDAR system was employed to acquire canopy point clouds of 211 rice accessions across ten time points within 40 days after transplanting. High-resolution canopy height models (CHMs) were generated, and continuous plant height trajectories H(t) were reconstructed using piecewise cubic Hermite interpolation (PCHIP). The first derivative V(t) quantified growth rate dynamics and identified the timing of maximum growth (T max ), enabling precise differentiation of early growth patterns among geno-types. Genome-wide association analysis (GWAS) using a mixed linear model (MLM, Q+K) detected 604 significant SNPs, among which 33 were stably expressed across environments. Five candidate genes were identified within ±200 kb windows, mainly encoding proteins related to cell elongation, hormone signaling, and photosynthetic metabolism. The results highlight that LiDAR-based dynamic monitoring of plant height, coupled with genomic association analysis, provides a robust framework for quantifying rice early growth vigor and elucidating its molecular basis, offering valuable guidance for breeding high-vigor “early-establishing” rice cultivars.

Why it matches plant phenotyping methodsUAV-LiDARによるイネ草丈の時系列取得・CHM生成・成長率推定が研究の中心であり、GWASはその測定形質の応用分析。

abstractConventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics

Construction of a Virtual Sensor-Driven Digital Twin System for Plant Growth Monitoring on Rooftop Farms ↗

Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R2 values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms.

Why it matches plant phenotyping methods植物成長のLAI、地上部バイオマス、フェノロジー、収量を予測する仮想センサー・デジタルツインを開発し、精度検証しており、表現型推定手法が研究の中心である。

abstractThis study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China.

Early hyperspectral detection of Carlavirus vignae in common bean under field conditions ↗

Abstract Virus-associated diseases are among the biological stresses that affect common bean yield, such as Carlavirus vignae ( Cowpea mild mottle virus , CPMMV), transmitted by the whitefly Bemisia tabaci . CPMMV is present in different continents, and recent outbreaks have concerned Brazilian farmers and researchers. Integrated Pest Management routines for field monitoring of viral spread are laborious and may be limited to visible symptoms. We hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance. To test this hypothesis, we used a hyperspectral sensor mounted on a drone to capture images of CPMMV-inoculated and non-inoculated field plots in 2022 and 2023, across a tolerant common bean cultivar (BRS FC420 RMD) and a susceptible one (BRS FC401 RMD). Results showed that CPMMV was detected in common bean plants by hyperspectral imaging at early infection stages (~ 6 DAI) before symptom onset and at an advanced infection stage (~ 22 DAI). Reflectance within the visible light spectrum was affected by soil cover on all flights, and in most of these, also in the near-infrared region. The main differences between CPMMV-inoculated and control plants were consistent across two years of experiments, regardless of the common bean phenological stage and genotype. Fit statistics using the sum of squared errors, R 2 and AIC indicated that reflectance from 401 to 425 nm, especially near 415 nm, differed significantly between infected and healthy plants. Such changes are associated with chlorophyll degradation and disruption of the photosynthetic apparatus, and are detectable even before symptom onset. Progress in the disease severity index also differentiated the tolerant cultivar from the susceptible one. CPMMV infection significantly reduced common bean yield by ~ 21% compared with healthy plants. CPMMV detection by hyperspectral imaging enables early scouting to optimize disease management.

Why it matches plant phenotyping methodsハイパースペクトル画像を用いて、症状発現前の感染植物の反射特性と病害状態を検出し、複数年・品種で技術性能を検証しているため、植物フェノタイピング手法が中心である。

abstractWe hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance.

Aerial imagery and deep learning accurately estimate maize foliar disease severity ↗

ABSTRACT Southern leaf blight (SLB) is a foliar disease of maize (Zea mays L.) caused by the necrotrophic fungal pathogen Cochliobolus heterostrophus. Genetic resistance is the most effective control method for SLB. Developing disease resistant maize lines requires field trials during which disease phenotypes must be visually assessed. Remote sensing using drones is an emerging technology that can be leveraged for high-throughput phenotyping of disease severity that is otherwise labor-intensive and subjective. This project used a deep learning approach to estimate SLB disease severity of single-row maize plots from drone imagery. Over 26,000 plot-level images produced from flights conducted across three growing seasons were labeled with in-field visual scores taken contemporaneously by expert raters. Variation in environmental conditions contributed to a labeled image dataset that reflects the complexity of agronomic field experiments. We assessed the ability of nine deep learning models from three architectural families to estimate disease severity. The best-performing model, EVA-02-B, achieved strong cross year generalization (R 2 = 0.697). Error analysis found that performance was more strongly associated with seasonal disease progression and flight-score time offset than with image-level noise. UAV-based deep learning estimated SLB severity with comparable precision to expert raters. This study lays the groundwork for integrating automated phenotypes into genetic studies of disease resistance. PLAIN LANGUAGE SUMMARY Southern leaf blight (SLB) of maize is a disease that causes yield loss worldwide and developing resistant varieties offers the best hope for controlling the disease. Studying SLB resistance requires plant pathologists to visually score severity in the field, a labor-intensive method that requires expertise. To address these challenges, we asked whether SLB severity scoring could be automated using drone images and artificial intelligence (AI). We trained AI models using three years of image and score data then compared the results to visual scores taken by five plant pathologists. The best performing AI model showed a similar level of consistency to the experts and proved capable of scoring severity despite unpredictable and uncontrollable conditions that affect field imaging experiments such as weeds or shadows. These findings provide a validated method that improves the efficiency of maize disease research, a critical area of study for agricultural sustainability and productivity.

Why it matches plant phenotyping methodsドローン画像と深層学習により、トウモロコシの葉病害重症度という植物状態を推定し、複数年データで性能と汎化性を評価した手法研究である。

abstractRemote sensing using drones is an emerging technology that can be leveraged for high-throughput phenotyping of disease severity

AI CROP DISEASE DETECTION ↗

The rapid growth of technology in agriculture has created new opportunities to improve crop productivity and reduce losses caused by diseases. Crop diseases are one of the major challenges faced by farmers, leading to significant reduction in yield and quality. Early detection and proper diagnosis of these diseases are essential for effective treatment and prevention. This project, titled “AI Crop Disease Detection System”, aims to develop a web-based application that uses Artificial Intelligence to identify crop diseases from images. The system allows users to upload images of infected crop leaves through a simple and user-friendly interface. The uploaded image is processed using an AIbased model, which analyzes the image and predicts the type of disease. The backend of the system is developed using Python Flask, which handles image processing, model prediction, and server-side operations. The frontend is designed using HTML, CSS, and JavaScript to provide an interactive user experience. The system uses MySQL database to store user information, uploaded images, and prediction results.

Why it matches plant phenotyping methods感染葉画像から作物病害を推定するAI画像解析システムの開発が中心であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として収録する。

abstractaims to develop a web-based application that uses Artificial Intelligence to identify crop diseases from images

Spring Maize Yield Prediction and Optimal Phenological Stage Assessment in the Junggar Basin Based on UAV and Stacked Ensemble Learning ↗

Abstract Accurate prediction of maize yield is crucial for improving field management and enabling timely yield estimation. To improve the accuracy and determine the optimal timing of field-scale spring maize yield estimation in the Junggar Basin, this study focuses on spring maize in this region. In 2023, UAV-based multispectral images were acquired at three key growth stages: jointing, filling, and milk stages. Eighteen spectral features significantly correlated with yield were selected. Spring maize yield prediction models were constructed using XGBoost, CatBoost, RF, DT, SVR, GP, LR, and a stacked ensemble learning model, respectively, revealing differences in prediction accuracy across growth stages. Finally, SHAP was used for model interpretability analysis. The results show that: (1) The milk stage achieved the highest prediction accuracy (R² = 0.761, MAE = 0.067 kg·m⁻², RMSE = 0.089 kg·m⁻², MAPE = 5.055%), outperforming the jointing and early grain-filling stages, thereby resolving the uncertainty regarding the optimal timing for UAV-based yield estimation of spring maize in the Junggar Basin. (2) Compared with traditional machine learning algorithms, the stacked ensemble model exhibited stronger robustness and generalization ability. This study provides a technical reference for timely yield estimation and field management of spring maize in irrigated areas of the Junggar Basin, supporting regional food production stability.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトウモロコシ収量を推定し、複数生育段階・機械学習モデルの精度と頑健性を比較して最適推定時期を評価しており、表現型推定手法が研究の中心である。

abstractUAV-based multispectral images were acquired at three key growth stages: jointing, filling, and milk stages.

Defining critical drivers of cross-pollination for better hybrid grain set in wheat ↗

Hybrid wheat breeding offers a promising route to enhance grain yield and yield stability through heterosis, yet hybrid grain production remains constrained by limited cross-pollination efficiency due to high rates of autogamy. To achieve cross-pollination in an autogamous species like wheat, pollen must shed outside the floret. This is typically assessed by scoring visual anther extrusion (VAEX), a key floral trait that sets the foundation for cross-pollination. However, VAEX explains only part of the variation in hybrid grain set. To address this, we analyzed floral structures and reproductive processes underlying cross-pollination efficiency in wheat. From 24 elite winter wheat genotypes, we developed traits describing anther extrusion kinetics, pollen release, and floral bract architecture. These traits showed substantial genotypic variation and high heritability. While VAEX alone explained approximately 49% of the variation in hybrid grain set, combined trait analyses explained up to 77%, demonstrating that hybrid grain production is governed by coordinated floral and reproductive trait interactions. Together, our analyses define a hierarchical trait architecture linking floral bract mechanics, anther extrusion dynamics, and pollen shedding to cross-fertilization success. This establishes a systems-level phenotyping framework for improving male parent selection in hybrid wheat breeding. Highlight High cross-pollination efficiency in wheat is a multi-factorial process that requires lighter floral bract architecture combined with adequate anther extrusion and pollen release for improving hybrid grain production.

Why it matches plant phenotyping methods交雑受粉効率を評価するための葯突出動態、花粉放出、苞葉構造の形質を開発し、統合的なフェノタイピング枠組みとして解析しており、表現型取得・評価法が研究の中心である。

abstractFrom 24 elite winter wheat genotypes, we developed traits describing anther extrusion kinetics, pollen release, and floral bract architecture.

AN EFFICIENT CROP IMAGE CLASSIFICATION PROCESS USING ENSEMBLE CONVOLUTION FEATURES-INTEGRATED MULTI-SCALE AND DILATED ADAPTIVE DEEP NETWORK FOR HIGH YIELD PRODUCTION ↗

The agricultural sector is the most important asset in the country, which boosts development by reducing unemployment, food shortages, poverty, and unstable economic conditions. The recognition and classification of agricultural imagery are the fundamental requisites of contemporary farming methodologies. Thus, it helps to accurately identify the enumeration of plant growth, plant diseases and so on, leading to an increase in crop yield production. There are numerous methods for classifying crop images; however, the conventional methods struggle with issues like noise, distortion, and image quality. It is not capable of fully exploiting the rich potential of image characteristics due to the weather conditions and shooting angles. It does not have the ability to significantly extract the relevant information in the training process and enhance the negative classified outcomes. Multiple learning-based methods have been created to gain more accurate and trustworthy information from image classification. In order to overcome these challenges, a novel crop image classification model is implemented in this research work for classifying the crop images to improve yield production. The required high-quality crop images are collected from the standard datasets. These images are further fed into the feature extraction phase, the Visual Geometry Group 16 (VGG16), graph convolutional neural network (GCNN), and residual network (ResNet) models are utilized to extract the relevant primary information in the collected crop images for generating ensemble convolution features. Subsequently, the resultant features are fed into the classification process, where the multi-scale and dilated adaptive recurrent neural network (MDARNN) mechanism is utilized to effectively classify the crops. In this process, a random function improved dark forest algorithm (RFIDFA) is employed for tuning the RNN parameters, thus improving the classification process. Finally, the validation of the designed approach is performed using several performance measures and compared with the previous works to ensure the designed model’s superior performance. The designed method achieves the best outcome of 95.19% precision, 95.14% NPV, and 95.15% accuracy measures. The developed crop image classification method can continuously monitor the crop growth information to improve yield production. Also, it helps to optimally detect the disease and pest-affected crops in an earlier stage to reduce crop losses, allowing for proper treatment and preventing widespread infection. It is possible to determine the water needs of various crops to ensure efficient crop production and precise farming practices.

Why it matches plant phenotyping methods植物画像から生育状態や病害状態を分類する画像解析手法の開発・検証が中心であり、植物の状態を直接推定するため採用。

abstracta novel crop image classification model is implemented in this research work for classifying the crop images

BN-NeRF: A fast 3D reconstruction and phenotyping framework for banana plants using handheld devices ↗

High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.

Why it matches plant phenotyping methodsスマートフォン画像からの3D再構成と植物形態形質抽出を中核とするBN-NeRF手法を開発し、圃場データで精度・再現性を検証しているため。

abstractthis article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones

Integrated UAV-Based Multispectral Scouting and Variable-Rate Aerial Spraying for Enhanced Crop Yield and Input Efficiency: A Field-Validated Study ↗

Precision agriculture demands integrated systems that couple accurate crop stress detection with targeted intervention to mitigate climate volatility and input overuse. Traditional manual scouting and uniform chemical application are spatially imprecise, labour-intensive, and environmentally burdensome. This study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS). A randomized complete block design with four replications was implemented in maize (Zea mays L.) across a 2.4 ha field in Davangere Karnataka, India. Scouting flights at 25 m altitude (1.8 cm ground sampling distance) utilized a MicaSense RedEdge-P and FLIR thermal sensor, with imagery processed through a radiometrically calibrated YOLOv8-Seg pipeline to detect early-stage disease, nutrient deficiency, and water stress. Prescription maps derived from NDRE and CWSI thresholds directly controlled a DJI Agras T40 centrifugal sprayer calibrated to ASABE S572.1 standards. The integrated system achieved an AI detection F1-score of 0.91, reduced agrochemical volume by 34.2%, and improved spray deposition uniformity (coefficient of variation = 18.4%) relative to conventional blanket spraying. Grain yield increased significantly by 11.7% (p

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とAI解析により、作物の病害、栄養欠乏、水ストレスを検出する方法を開発・現地検証しており、植物状態の取得が統合システムの中心的要素である。

abstractThis study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS).

Multi-Scale and Global–Local Feature Enhanced Detection for Tobacco Plants in Complex Field Environments ↗

Accurate detection of tobacco plants in complex field environments is critical for precision agriculture, crop monitoring, and yield estimation. Traditional manual counting methods are time-consuming, labor-intensive, and susceptible to environmental and subjective factors. In this study, we propose an improved YOLO11-based framework for automated tobacco plant detection, specifically designed to address challenges such as scale variation, dense distribution, and background interference. The framework integrates four key modules: the Edge-Enhanced Feature Stem (EEFS) to strengthen low-level feature extraction, the Multi-Scale Kernel Interaction (MSKI) to capture multi-scale contextual information, the Adaptive Weighted Feature Fusion (AWFF) to optimize feature aggregation, and the Global–Local Synergistic Attention (GLSA) to enhance feature discrimination by jointly modeling local details and global context. A comprehensive UAV-based tobacco dataset was constructed, encompassing multiple lighting conditions, collection heights, and observation angles. Experimental results demonstrate that the proposed method significantly outperforms the YOLO11 baseline and achieves superior performance compared to mainstream YOLO variants. Ablation studies and heatmap visualizations confirm the effectiveness of each module. Furthermore, the model exhibits robust performance under multi-dimensional environmental perturbations, including varying illumination, scale, and camera angles. The proposed framework provides a practical and efficient solution for automated tobacco plant counting, offering potential applications in UAV-based precision agriculture and large-scale crop monitoring.

Why it matches plant phenotyping methodsUAV画像からタバコ個体を自動検出・計数する手法を開発し、専用データセット、比較実験、アブレーション、頑健性評価まで行っており、植物個体数の取得方法が中心です。

abstractwe propose an improved YOLO11-based framework for automated tobacco plant detection

UAV-based phenotyping outperforms visual canopy wilting for evaluating soybean drought tolerance and yield retention under rainfed conditions. ↗

Drought is the major abiotic stress limiting soybean growth and yield, yet accurately identifying genotypes that sustain yield under rainfed conditions remains a major bottleneck in soybean breeding. Canopy wilting scores are widely used as a proxy for evaluating plant responses to drought stress. However, most assessments rely on leaf-level visual observations that are inherently subjective and typically based on single time-point scores, providing only a snapshot of stress expression and failing to capture their relationship with yield retention under rainfed conditions. To address these limitations, this study used Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping at a single growth stage (R4/R5) as a more quantitative and objective alternative to visual scoring, with closer relevance to yield performance under drought conditions. From 2023 to 2025, a total of 85 soybean genotypes developed by soybean breeding programs in Arkansas, Missouri, Kansas, and North Carolina, along with commercial checks, were evaluated under irrigated and rainfed conditions in Stuttgart, Arkansas. Visual canopy wilting scores were recorded at R4/R5, along with vegetation indices captured using UAV-based multispectral imagery. UAV-derived indices showed significant correlations with yield ( r = 0.22 to 0.45, p<0.05) under rainfed conditions. In contrast, visual canopy wilting scores displayed weak and inconsistent associations with yield ( r = -0.28 to 0.35, p<0.05), suggesting limited ability to capture yield retention under rainfed conditions. Unsupervised k -means clustering ( n = 2) of UAV-derived vegetation indices separated genotypes into two distinct canopy response groups that were consistent across 2023 to 2025 rainfed seasons. Significant differences were observed among clusters for several vegetation indices (ARI, CIG, CIRE, GSAVI, GNDVI, GOSAVI, OSAVI, NDVI), indicating contrasting canopy stress responses. Under rainfed conditions, these UAV-defined clusters also differed for grain yield (2023: 1,925.6 vs 1,703.1 kg/ha; 2024: 1,849.9 vs 1,229.2 kg/ha; 2025: 2,056.7 vs 1,773.8 kg/ha), whereas visual wilting scores failed to distinguish yield-retaining genotypes. Overall, UAV-based high-throughput phenotyping offers a robust and yield-relevant alternative to visual wilting scores, supporting the development of drought-tolerant soybean germplasm and cultivars.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像による植生指数抽出を、目視評価と比較・検証し、干ばつ応答および収量保持に関連する表現型測定法として中心的に評価している。

abstractthis study used Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping at a single growth stage (R4/R5) as a more quantitative and objective alternative to visual scoring

Deep Learning-Based Models For Crop Disease Detection Using Leaf Images: A Comprehensive Review ↗

Crop diseases pose a serious threat to agricultural productivity and global food security. Early and accurate detection of plant diseases is essential to minimize yield losses and reduce excessive pesticide usage. Traditional disease identification methods rely heavily on manual inspection by agricultural experts, which is time-consuming, subjective, and impractical for large-scale deployment. Recent advances in deep learning and computer vision have enabled automated, image-based crop disease detection with significantly improved accuracy and scalability. This review critically examines state-of-the-art deep learning techniques employed for crop disease detection using leaf images. It analyses commonly used datasets, preprocessing strategies, neural network architectures, evaluation metrics, and deployment challenges. Furthermore, existing research gaps and future directions toward robust, real-world agricultural applications are identified.

Why it matches plant phenotyping methods葉画像から植物病害を検出する画像ベース手法を対象とした包括的レビューであり、データセット、前処理、モデル、評価指標を体系的に扱うため、植物フェノタイピング手法が中心です。

abstractThis review critically examines state-of-the-art deep learning techniques employed for crop disease detection using leaf images.

An Efficient Dynamic Deep Learning Methodology for Identification of Plant Disease and it’s classification ↗

Abstract Nowadays, estimation & identification of plant diseases (PD) pointedly shows the high impact on agricultural & food productivity. In this paper, develop the Dynamic Deep Learning Methodology-(DDLM) for Plant Disease Classification-(PDC) using the Residual Neural Network (ResNet) Architecture, enhanced with an intelligent supplement recommendation module. The procedure of present Deep Learning Methodology (ResNet) is gathering the images from the number of input sensors, create large amount of dataset that contains number of sample leaf images (both diseased & healthy) finally applying ResNet model to dataset. The model is trained (80 %) on a large dataset of plant leaf images, including healthy and diseased samples across various species. Pre-processing steps such as (R_N_A) Resizing (R), Normalization (N), and Augmentation (A) are working on development of the model to improve model generalization. Once a disease is detected, Methodology generates output including the disease name (e.g., "Tomato Late Blight") and a Recommended Supplement (e.g., "Apply Copper-Based Fungicide, Ensure Proper Drainage"). The ResNet50 model, fine-tuned using Transfer Learning (TL), achieves a classification accuracy of 97.4%, outperforming traditional CNN models. Early estimation & identification of plant diseases (PD) gives the high increases the yield of the crop. Evaluation metrics such as Confusion Matrix, Precision, Recall & F1-score validate the reliability of the model across multiple classes. By integrating accurate disease detection with actionable supplement guidance, the proposed solution empowers farmers to take immediate and informed actions, enhancing crop health and yield with supplement recommendation. When comparing with resnet50 the other methods had a less accuracy. KEYWORDS— Hybrid Machine Learning Methodology (Dynamic Deep Learning Methodology-(DDL) for Plant Disease Classification-(PDC), Transfer Learning (TL), Residual Neural Network (ResNet), Image Classification, Accuracy, Disease Detection, Precision Agriculture, Smart Farming, Transfer Learning.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。

abstractdevelop the Dynamic Deep Learning Methodology-(DDLM) for Plant Disease Classification-(PDC) using the Residual Neural Network (ResNet) Architecture

A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems ↗

Alternatives to soil-based horticulture, such as hydroponics, have been developed to respond to food distribution concerns for dense urban centers. A new system was developed to track an individual lettuce plant's growth in a hydroponic environment, utilizing streams of measured information and available models to continuously update the growth trajectory estimates for a plant. These "digital twin" models were integrated into an operating hydroponic greenhouse, with custom horticultural and sensor hardware to grow and measure relevant information. To aid in updating model parameters, plant yield was continuously measured with a custom neural network, using RGB-D images of the plants as an input. The network, trained on a collected dataset of 1300 images, was able to estimate mass within 1.5 g of the ground-truth value. After integration into the custom system, digital twin growth projections could approximate future yield between one and four days in the future, maintaining around a 2 g forecasting error.

Why it matches plant phenotyping methodsRGB-D画像から個体レタスの収量・質量を推定するニューラルネットワークと、センサー統合型の成長追跡基盤が研究の中心であり、植物形質の取得・予測手法を実質的に開発・検証している。

abstractA new system was developed to track an individual lettuce plant's growth in a hydroponic environment, utilizing streams of measured information and available models to continuously update the growth trajectory estimates for a plant.

Application of deep learning in crop research: From genomics to phenomics ↗

Abstract Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures—such as convolutional neural networks, recurrent neural networks, and transformers—across key crop genomic tasks, including gene expression prediction, alternative splicing analysis, cis ‐regulatory element identification, epigenomic profiling, and genome‐based trait prediction. In phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery, supporting yield forecasting, disease diagnosis, and stress response monitoring. We critically evaluate the performance and limitations of each model type across tasks, considering trade‐offs between complexity, accuracy, and interpretability, to offer practical guidance for crop researchers. Additionally, the review addresses major challenges in deploying deep learning—such as data scarcity, model transparency, and computational demands—and proposes future pathways to enhance model generalizability, multimodal data integration, and applications in intelligent breeding and sustainable agriculture.

Why it matches plant phenotyping methods作物フェノミクスにおける深層学習による画像からの形質抽出を中心的にレビューしており、フェノタイピング手法の方法論的整理に該当する。

abstractIn phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery

Improved plant leaf disease detection architecture using unmanned aerial vehicle images and hybrid YOLOv11 ↗

Plant diseases affect crop quality and quantity, which significantly reduces crop production. Grapes are a popular fruit, and pumpkin is a widely consumed vegetable in the world. Early detection of grape and pumpkin leaf disease is crucial to avoiding large losses in crop quality and yield. Even though the use of unmanned aerial vehicle (UAV) remote sensing can assist in reaching a broader scale for plant leaf disease identification, the work of detection is significantly hampered by the overlapping leaves, and blurring of UAV images. This paper suggests combining object detection techniques with Real-World Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) for enhancing image quality to detect grape and pumpkin leaf diseases in UAV-captured images. The Atrous Spatial Pyramid Pooling (ASPP) technique is incorporated into YOLOv11 architecture to enhance the network’s feature representation capabilities by efficiently incorporating depth-wise separable convolutions and dilated convolutions with different rates. The proposed approach has shown remarkable effectiveness. In the validation, the ASPP-YOLOv11 outperforms the baseline YOLOv11 architecture in precision achieving 84% with 5.2% increase, 94.7% with 3.3% increase in mAP50, and 74% with 0.9% increase in mAP50-95. Additionally, in the testing set the ASPP architecture significantly enhances mAP50 to 91.6%, attaining 1.6% gain and obtaining 1.6% increase in mAP50-95.

Why it matches plant phenotyping methodsUAV画像から植物葉の病害を推定する画像解析アーキテクチャを開発し、既存モデルと性能比較しており、植物病害状態の取得方法が中心である。

abstractThis paper suggests combining object detection techniques with Real-World Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) for enhancing image quality to detect grape and pumpkin leaf diseases in UAV-captured images.

RSCM: A Bayesian remote sensing-integrated crop model software framework for yield estimation ↗

RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.

Why it matches plant phenotyping methods衛星データと作物モデルを統合し、LAI・地上部乾物量・収量という植物形質を推定するソフトウェア手法を開発・検証しており、形質取得・推定法が研究の中心である。

abstractRSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization.

Observation-Constrained Agroecosystem Model Inversion Reveals Continental-Scale Variation of Winter Wheat Traits. ↗

Understanding how crop trait variability shapes genotype × environment × management (G × E × M) interactions remains a key uncertainty in predicting agricultural performance under a changing climate. Continental-scale crop models commonly rely on spatially uniform parameters, limiting their ability to represent adaptive variation in phenology, allocation, and yield formation. Here we integrate the mechanistic agroecosystem model Ecosys with a deep learning-enabled model-data fusion inversion to infer spatially explicit physiological controls (trait proxies) of U.S. winter wheat directly from observations. By constraining simulations with satellite-derived photosynthesis and county-level yield records from 2008 to 2022 across ~1000 winter wheat-producing counties, the inversion recovers coherent patterns of maturity group, reproductive capacity, harvest index, and root-shoot allocation. The optimized simulations reproduce observed carbon uptake and yield variability (gross primary productivity r = 0.76-0.88; phenology bias 90% of yields within ±20% of reports) and reveal distinct physiological profiles that align with the geographic distributions of major winter wheat market classes. The inferred controls explain class- and region-specific climate sensitivities: warmer winters reduce vernalization success in late-maturing cultivars, while elevated vapor pressure deficit causes strong yield losses in rainfed Hard Red Winter wheat. The results demonstrate that observation-constrained trait inversion within model-data fusion framework reveals biologically meaningful crop-class variation, thereby providing a scalable, physiologically grounded framework for diagnosing adaptive diversity and climate vulnerability across agroecosystems.

Why it matches plant phenotyping methods衛星由来の光合成観測と収量記録を用い、深層学習によるモデル・データ融合で冬コムギの生理・形態形質を空間的に推定する手法が研究の中心であるため。

abstractHere we integrate the mechanistic agroecosystem model Ecosys with a deep learning-enabled model-data fusion inversion to infer spatially explicit physiological controls (trait proxies) of U.S. winter wheat directly from observations.

Predicting complex phenotypes using multi-omics data in maize. ↗

Understanding and predicting complex traits in plants remains a fundamental challenge due to the emergent nature of most phenotypes and their dependence on genetic, regulatory, and environmental interactions. Accurate prediction of traits and identification of underlying genetic elements have broad applications for plant breeding, systems biology, and biotechnology. Here, we tested if multi-omic datasets could improve predictive accuracy of 129 diverse maize phenotypes across 9 environments using genomic markers, field-based transcriptomic data from 2 locations, and drone-derived phenomic data of vegetative indices. We trained and compared linear (rrBLUP) and nonlinear (support vector regression) models using single- and multi-omics inputs. Multi-omics models consistently outperformed single-omics models for most traits, with genomic and transcriptomic inputs contributing distinct biological features. Phenomic features alone yielded the lowest predictive power but improved predictions for specific trait categories like root architecture. Transcriptomic datasets enabled cross-environment prediction, demonstrating that gene expression patterns from one field site could accurately predict traits measured in another. Environment-specific expression of benchmark flowering time genes highlighted the value of transcriptomics in capturing genotype-by-environment (G × E) interactions not detectable through genomic data alone. Analysis of model feature weights further indicated that predictive signal is distributed across many genes, consistent with complex traits such as yield arising from coordinated, network-level processes rather than a small number of dominant loci. These findings demonstrate that integrating transcriptomic and phenomic data with genotypes enhances trait prediction, improves model generalizability across environments, and provides deeper insight into the genetic and regulatory architecture of agriculturally important traits in maize.

Why it matches plant phenotyping methods複数オミクスとドローン由来フェノミックデータを統合し、植物形質を予測するモデルを比較・評価しており、形質推定ワークフローが研究の中心である。

abstractWe trained and compared linear (rrBLUP) and nonlinear (support vector regression) models using single- and multi-omics inputs.

Derivation of crop yield response factor (Ky) based on satellite data and machine learning methods ↗

Deficit irrigation (DI) is a crucial strategy for optimizing water use in arid and semi-arid agriculture, yet its success depends on accurately determining the crop yield response factor (K y ). This study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators. fused Landsat 7/8/9 and MODIS data within the Google Earth Engine platform to generate high-resolution daily Crop Water Stress Index (CWSI) maps for the 2023 season in southern Iran. The Random Forest (RF) algorithm was applied to correct biases in land surface temperature (LST), achieving high accuracy (RMSE < 1.0°C, nRMSE < 3%, rMBE ≈ 0%) before CWSI calculation. Ground measurements from 12 field points, including canopy temperature and yield, were used for calibration and validation. The satellite-based CWSI showed strong agreement with field data (RMSE = 0.05, nRMSE = 11%), with values ranging from 0.18 to 0.71 at dekadal scale. Using this CWSI, K y was computed at dekadal, monthly, and seasonal scales, revealing substantial spatiotemporal variability (0.2–1.63) and an average seasonal K y of 1.05. This value is lower than the FAO-66 default of 1.2, indicating that the standard coefficient may prompt over-irrigation without yield benefits. The analysis further identified early July as the period of peak water stress sensitivity, with K y values exceeding 1.82. This ML-enhanced, satellite-based approach provides a robust tool for deriving spatially explicit K y values, offering a significant advancement for precision irrigation planning and water resource management.

Why it matches plant phenotyping methods衛星データと機械学習で作物の水ストレス状態（CWSI）を推定し、地上測定で較正・検証する手法が中心であるため、植物生理状態のセンシング型フェノタイピングとして含める。

abstractThis study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators.

Plant Disease Recognition Using ML ↗

Crop cultivation sustains the livelihoods of a substantial portion of households across developing economies, yet the crops on which those households depend are perpetually at risk from pathogenic infections that erode both yield volume and produce quality. Infected fields, when not addressed at the right time, translate into mounting financial strain that smallholder growers — who operate with limited financial reserves — are ill-equipped to withstand. The dominant method of spotting such infections today still relies on a farmer walking the field and judging leaf condition by eye, or waiting for an agronomist's visit — a workflow that is neither fast nor consistent enough for large-scale cultivation. Progress in deep learning has fundamentally changed what automated visual inspection can accomplish, and plant pathology diagnosis is one of the fields that has benefitted most visibly. Image-based pipelines can now scan a leaf photograph and return a disease classification in fractions of a second. Among the architectures driving this capability, Convolutional Neural Networks occupy a central role: their layered filter design allows them to extract and encode visually informative features — texture discontinuities, color anomalies, lesion geometry — without any manual specification of what to look for. We present a CNN-driven plant disease recognition system that operates on photographs of plant leaves and returns a disease label together with a confidence estimate. Our training corpus is the Plant Village benchmark collection, a large repository of annotated leaf images spanning healthy and diseased specimens across multiple crop varieties. The input pipeline applies spatial normalization, pixel rescaling, and augmentation strategies to condition the data before it reaches the network. A React.js browser interface connects end users to the model via a lightweight prediction API, enabling diagnosis without any specialist involvement. Validation results affirm that this deep learning approach surpasses rule-based image processing baselines on both accuracy and response time, and the system holds clear potential for adoption in early disease management program

Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNNベースの画像解析手法を開発し、既存手法との性能比較・検証を行っており、表現型取得が研究の中心である。

abstractWe present a CNN-driven plant disease recognition system that operates on photographs of plant leaves and returns a disease label together with a confidence estimate.

AgroVision- Platform Independent Crop Analysis Using Deep Learning Techniques ↗

Despite numerous efforts to incorporate emerging innovations into the agricultural domain for increasing crop yield and actively managing the state of the fields, it remains difficult for the industry to implement cutting-edge technologies in practice. This paper proposes AgroVision – a web-based intelligent multimodal system for comprehensive analysis of crops around the world. Designed using a three-layer scalable architecture, the system includes four modules – CNN-based growth stages and plant diseases recognition, AI Chatbot with LLM capabilities and RAG support, as well as the video analysis tool for detecting plant density and weeds. The key technology behind the core image analysis functionality of AgroVision is represented by the efficient Vision Mamba (ViM) architecture, which allows for analysing multiple tasks simultaneously using only one image uploaded by the user. Based on the extensive dataset called "New Plant Diseases Dataset" containing over 87 thousand images divided into 38 classes, the ViM model demonstrates exceptional results achieving weighted average F1-Score of 97.1%. Considering that the inference latency of the model does not exceed 25-40 milliseconds, the system can be deployed at the edge, providing an easy-to-use solution for farmers.

Why it matches plant phenotyping methods植物の成長段階、病害、植物密度を画像から推定するウェブ型解析プラットフォームを提案しており、表現型取得・推定が研究の中心である。

abstractThis paper proposes AgroVision – a web-based intelligent multimodal system for comprehensive analysis of crops around the world.

Design and Implementation of an IOT-Assisted Image Processing System for Early Plant Disease Identification ↗

Agriculture plays a vital role in economic development, and plant diseases significantly affect crop productivity. Early detection of plant leaf diseases is essential to reduce crop loss and improve yield. This paper proposes an Internet of Things (IoT)-based plant leaf disease detection system using image processing and deep learning techniques. The system captures realtime images of plant leaves using a camera module and processes them using convolutional neural networks (CNN) for classification. The processed results are transmitted to a cloud platform for remote monitoring. The proposed model improves accuracy and enables early detection compared to traditional manual methods. Experimental results demonstrate that deep learning-based approaches achieve high accuracy in identifying plant diseases, making the system efficient for smart agriculture applications.

Why it matches plant phenotyping methods植物葉の画像から病害を分類する画像処理・深層学習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します。

abstractThis paper proposes an Internet of Things (IoT)-based plant leaf disease detection system using image processing and deep learning techniques.

An Ensemble of EfficientNetV2B3 and EfficientNetB4 for Crop Disease Detection Using the PlantVillage Dataset ↗

Crop diseases continue to threaten global food security, causing annual yield losses of 20–40% worldwide. Farmers in developing nations often lack timely access to ex-pert diagnosis, leading to delayed interventions and reduced harvests. This study presents a deep learning-based solution that automates crop disease identification using leaf images. We trained and evaluated two state-of-the-art convolutional neural networks—EfficientNetV2B3 and EfficientNetB4—on the publicly available PlantVillage dataset, which contains 54,303 images spanning 38 disease categories across 14 crop species. To improve classification robustness, we developed an ensemble model that combines the predictions of both architectures via weighted averaging. EfficientNetV2B3 achieved 98.0% accuracy individually, while EfficientNetB4 reached 94.0%. The proposed ensemble model attained an accuracy of 98.5% and an area under the curve (AUC) of 0.98, outperforming both parent models and several established baselines, including VGG16, ResNet50, InceptionV3, MobileNetV2, and DenseNet121. Beyond model development, we deployed the ensemble inside a Flask-based web application with user authentication, confidence scoring, and a searchable disease knowledge base. This end-to-end system bridges the gap between research and practice, offering farmers an accessible tool for rapid, reliable disease diagnosis.

Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習モデルを開発・評価し、実用的な診断アプリにも実装しているため、植物の病害状態を対象とするフェノタイピング手法が中心である。

abstractThis study presents a deep learning-based solution that automates crop disease identification using leaf images.

WaveST-Yield: a novel spatio-temporal deep learning framework with frequency-domain refinement for UAV-based maize yield prediction. ↗

Accurate and generalizable plot-scale maize yield prediction is critical for precision agriculture and food security. While UAV-based multispectral remote sensing provides rich phenotyping data, existing yield prediction models often struggle with insufficient mining of complex spatio-temporal dynamics, ineffective separation of spatial details from background noise, and inadequate focus on yield-sensitive features throughout the crop growth cycle. To address these limitations, this study proposes WaveST-Yield, a novel hybrid deep learning framework tailored for multi-temporal multispectral data. The proposed model integrates three core modules: a Spatio-Temporal Phenology Encoder (SPE) based on ConvLSTM to capture the temporal dynamic patterns and spatio-temporal correlations across the entire growth period; a Multiscale Frequency-Spatial Refiner (MFSR) utilizing Haar Wavelet Downsampling (HWD) to preserve image details and decouple noise without early loss of key physiological features; and an Adaptive Yield-Sensitive Re-calibrator (AYSR) leveraging a 3D-CBAM attention mechanism to enhance the extraction of critical yield-related traits while suppressing background interference. The model was rigorously evaluated on two independent maize experimental fields using 5-fold cross-validation and cross-plot external validation. Results demonstrate that WaveST-Yield consistently outperforms traditional machine learning algorithms and single-structure deep learning models, achieving the highest prediction accuracy (Overall R² of 0.883 and 0.775 in Field 1 and Field 2, respectively) with superior error control. Extensive ablation and multi-model comparison experiments confirm that the synergistic integration of spatio-temporal encoding, frequency-domain refinement, and 3D attention mechanisms significantly improves model robustness and cross-regional generalization ability. This study provides a highly accurate, robust, and generalizable methodological framework for high-throughput crop yield monitoring.

Why it matches plant phenotyping methodsUAVマルチスペクトル時系列からトウモロコシ収量を推定する深層学習フレームワークの開発と、独立圃場・交差検証による技術評価が研究の中心である。

abstractthis study proposes WaveST-Yield, a novel hybrid deep learning framework tailored for multi-temporal multispectral data.

Estimation of Ramie Key Phenotypic Traits Based on UAV Remote Sensing ↗

UAV-based phenotyping enables efficient high-throughput measurement of field crops. Phenotypic monitoring of ramie is critical for its cultivation management and variety breeding. However, ramie exhibits characteristics including multiple annual harvests, short growth cycles and rapid dynamic growth change, all of which increase the difficulty of growth monitoring and yield estimation. This study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping. Over three ramie growth cycles, a total of 15 UAV flights were conducted over an experimental field consisting of 72 plots. The structure from motion (SfM) algorithm was applied to estimate PH. Remote sensing features derived from UAV imagery were used with background segmentation and machine learning to estimate LAI. The AGB was estimated by combining remote sensing-derived PH, LAI, and climate data. The results showed that the estimated and measured phenotypes were highly correlated, with optimal coefficients of determination of 0.961 for PH and 0.873 for LAI. Background segmentation improved LAI accuracy. Integrating climate data, remote sensing-derived PH and LAI significantly enhanced the accuracy of AGB estimation. In conclusion, this study provides a feasible method for extracting ramie phenotypes from UAV remote sensing imagery, providing methodological support for large-scale management of the crop industry and intelligent, precise monitoring of crop growth.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像、SfM、背景分割、機械学習を用いてラムーの草丈・LAI・地上部バイオマスを推定し、精度評価まで行う手法研究であり、表現型取得・抽出が中心である。

abstractThis study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping.

Towards Accurate Crop Yield Prediction: Integrating Sentinel-2 Remote Sensing with AI-Based Modelling ↗

Abstract. Accurate monitoring of vegetation health and canopy structure is essential for optimizing agricultural productivity and managing natural resources. Remote sensing technologies, combined with artificial intelligence (AI) and advanced satellite data, have revolutionized the capacity to assess crop conditions at large scales with high temporal and spatial resolution. This study leverages Sentinel-2 multispectral imagery and a novel AI-driven model approach to estimate Leaf Area Index (LAI) across multiple fields for canola. By integrating spectral reflectance data with view and solar geometry parameters, the model effectively captures the complex interactions between canopy structure and environmental factors. The methodology employs a two-layer neural network calibrated with physically based normalization to translate Sentinel-2 spectral and angular inputs into accurate LAI estimates. Validation against observed field measurements demonstrates strong agreement, underscoring the model’s robustness and reliability. Spatial analysis reveals distinct LAI patterns among the crop types, highlighting differences in canopy density and growth dynamics. Temporal profiling further illustrates crop-specific development trends, with canola showing extended canopy expansion. The results confirm that the fusion of remote sensing data with AI modelling provides a powerful tool for precision agriculture, enabling detailed monitoring of crop growth and facilitating informed decision-making. This approach offers significant potential for enhancing yield prediction, resource management, and sustainable farming practices, ultimately supporting global food security efforts.

Why it matches plant phenotyping methodsSentinel-2画像とニューラルネットワークにより、作物のLAIという明示的な植物形質を推定し、実測値で検証する手法が研究の中心である。

abstractThis study leverages Sentinel-2 multispectral imagery and a novel AI-driven model approach to estimate Leaf Area Index (LAI) across multiple fields for canola.

Smart Farming with Deep Learning: CNN-Based Crop Disease Detection Using Drone Imaging and IoT ↗

Plant diseases represent a major challenge to global food security, often resulting in severe yield reductions if not detected and controlled promptly. This work introduces an AI-based framework that integrates deep learning, drone-assisted imaging, and IoT-enabled real-time monitoring for effective disease detection and management. A convolutional neural network (CNN) is trained on crop leaf image datasets to accurately classify different disease types. The system is further equipped with a mobile application and cloud-based alert service to support timely farmer interventions. Experimental evaluation demonstrates a classification accuracy exceeding 95% and reliable alert generation, underscoring the system's potential as a scalable solution for precision agriculture and smart farming.

Why it matches plant phenotyping methods植物葉画像から病害状態を分類するCNN・ドローン画像・IoT監視システムが研究の中心であり、植物病害の表現型を直接推定する方法として評価されている。

abstractThis work introduces an AI-based framework that integrates deep learning, drone-assisted imaging, and IoT-enabled real-time monitoring for effective disease detection and management.

Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning. ↗

Bread wheat ( Triticum aestivum L.) is a major staple crop, and timely, in-season prediction of grain yield (GY) and grain quality traits, grain protein content (GP), and grain test weight (TW), is critical for informed management and field-based high-throughput phenotyping (HTP). Unmanned Aerial Vehicle (UAV) remote sensing, coupled with artificial intelligence and deep learning (DL), offers a practical pathway for rapid, plot-scale trait estimation. Here, we investigate the value of multitemporal, multispectral UAV imagery for predicting winter wheat GY, GP, and TW, and we systematically compare two modeling paradigms: (1) handcrafted feature-based workflows that use plot-aggregated spectral and texture descriptors derived from UAV imagery, and (2) image-based, end-to-end workflows that learn directly from plot-level reflectance image chips. During the 2022 growing season, multispectral UAV data were collected repeatedly over seven experimental wheat sites in South Dakota, USA. For handcrafted feature-based modeling, we evaluated Support Vector Regression (SVR) and Random Forest Regression (RFR), along with DL models including a feedforward Deep Neural Network (DNN) and a one-dimensional Convolutional Neural Network (1D-CNN). For end-to-end image-based modeling, we implemented 2D-CNN, 3D-CNN, and a hybrid 2D-CNN–LSTM architecture to leverage both spatial information and multi-date dependencies. Our results show that: 1) the image-based modeling workflow yielded comparable to slightly better performance than the handcrafted feature-based modeling workflow across wheat GY, GP, and TW predictions; 2) 3D-CNN outperformed all other methods with R 2 of 0.65, 0.61 and 0.69 for GY, GP and TW estimations, respectively; 3) multitemporal UAV data outperformed the data collected from a single growth stage; and UAV data from wheat Feekes 10 (booting) stage yielded slightly better estimation results compared to the data collected from other growing stages, with R 2 of 0.62, 0.55, and 0.62 for GY, GP, and TW estimations, respectively. The results indicate that DL applied to high-resolution multitemporal and multispectral UAV imagery holds strong promise for predicting winter wheat yield and grain quality during the growing season, while also informing HTP efforts and site-specific management.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から収量・品質形質を推定するワークフローを開発・比較・評価しており、植物表現型取得が研究の中心である。

abstractwe systematically compare two modeling paradigms: (1) handcrafted feature-based workflows that use plot-aggregated spectral and texture descriptors derived from UAV imagery, and (2) image-based, end-to-end workflows that learn directly from plot-level reflectance image chips.

UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability ↗

Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping. In this study, we applied a deep transfer learning (DTL) approach to enhance GY prediction using UAV‐derived spectral and textural traits across multiple environments and developmental stages of winter wheat ( Triticum aestivum L.). The model's transferability and generalizability were evaluated across years, locations, nurseries, and stage‐specific scenarios. We employed fine‐tuning, which involves retraining a pretrained one‐dimensional convolutional neural network (1D‐CNN) model on scenario‐specific target data to enhance generalizability. Fine‐tuning was tested with 20%, 40%, 60%, and 80% of the target data to identify an optimal balance between model accuracy and adaptation, and compared with 1D‐CNN without DTL (baseline model). In cross‐year predictions, the baseline model (from 2022) performed poorly ( R 2 = −2.9 in 2023), while DTL improved prediction to an R 2 of 0.83 with 20% fine‐tuning, demonstrating strong temporal adaptability. Similarly, in cross‐location scenarios, baseline model performance was poor ( R 2 ranging from −15.3 to −0.4) but improved to 0.29–0.69 with 40% fine‐tuning. Stage‐specific predictions benefited most at Feekes stages 10.5 and 11, where DTL achieved an R 2 of 0.78 and 0.82, respectively, compared to baseline model R 2 values near 0. These results demonstrate that DTL improves model transferability and generalizability, increasing prediction accuracy and offering a resource‐efficient tool to accelerate selection for complex and labor‐intensive traits in modern breeding programs.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から抽出した植物形質を用い、深層転移学習による穀粒収量予測の汎化性・転移性を検証しており、表現型取得・推定ワークフローが中心である。

abstractRemote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.

A lightweight YOLOv11-based model for rice false smut detection under complex field conditions ↗

Rice false smut is an important fungal infection in the rice panicle stage, which occurs only in the panicle. Rice yield and quality will be seriously threatened after the occurrence of panicle disease. Early identification of disease is very important for precise prevention and control. However, in the actual field environments, complex light changes, the dense distribution of small disease spots, panicle overlapping shading, and other factors often result in the semantic attenuation of key discriminant information in the stage of visual feature extraction, which has brought great challenges to the early detection and prevention of the disease. To resolve the above problems, this study introduces a rice false smut detection model derived from an improved YOLOv11 framework, named Rice-Smut, to bolster the resilience and stability of the network regarding the identification of rice false smut disease under complex field backgrounds. Firstly, in order to enhance the feature capture capabilities for multi-scale and densely distributed lesions, the C3SC backbone feature extraction network combining the SCConv block is integrated. This architecture can significantly suppress the spatial and channel redundancy and augment the precise characterization of the texture features of the lesion. Then, the C2PSA-SE attention module is introduced to effectively filter the background interference and improve the precise positioning of dense small targets. Finally, to address the irregular structure of rice false smut lesions, the GIoU loss function serves as a substitute for the conventional CIoU, which enhances the network's proficiency in locating the irregular shape lesions. Experimental outcomes revealed that the Rice-Smut model yielded a precision of 79.3% and mAP@50 of 75.3%, which represented a 7.6 and 4.5 percentage point improvement over the baseline model YOLOv11. The model requires 2.41M parameters, with a model size of 4.9MB, which results in low computational complexity. The preliminary validation on mobile platforms shows that the method is viable for the potential to be applied to the real-time field detection and disease monitoring of rice false smut, and can provide support for disease control decision-making and field management.

Why it matches plant phenotyping methodsイネの病徴（病斑）を画像から検出・位置推定するYOLOベース手法を開発し、複雑な圃場条件で性能検証しているため、植物病害状態のフェノタイピング手法が中心である。

abstractthis study introduces a rice false smut detection model derived from an improved YOLOv11 framework, named Rice-Smut, to bolster the resilience and stability of the network regarding the identification of rice false smut disease under complex field backgrounds.

MMIU-Net: an encoder–decoder architecture based on multimodal feature fusion for wheat yield prediction under drought stress ↗

To address the poor regression performance caused by strong spatiotemporal heterogeneity, inconsistent information scales and complex feature relationships in field-based multimodal data, this study proposes a hierarchical fusion framework embedded within an encoder – decoder network. The framework integrates multi-scale, interpretable, and cross-modal representations through coordinated modules that bridge feature discrepancy understanding and information flow regulation. A multi-scale parallel pathway structure is designed to enhance joint perception of local and global information by leveraging feature mappings with different receptive fields. An interpretable feature importance allocation strategy is further introduced to improve the backbone network’s ability to provide dynamic guidance on feature contributions. This enables the model to perform adaptive weighting and feature selection during multimodal fusion. In addition, a cross-modal dense interaction and gated fusion mechanism is constructed to regulate information flow and capture fine-grained associations across modalities. The improved feature-guided model is applied to yield regression using multimodal data collected over three consecutive years and multiple wheat varieties. Results show that, in the first year, the highest prediction accuracy reaches an R2 of 0.8112 with an rRMSE of 16.85%. Validation using data from the same planting region in the second and third years yields a highest R2 of 0.8107 and 0.7986, with corresponding rRMSE values of 17.92% and 17.27%, respectively. Compared with other deep learning models within the same year, the proposed approach improves R2 by up to 30.52%, 33.96% and 32.79% across the three years, while reducing rRMSE by up to 41.59%, 45.01% and 46.43%. The results demonstrate that the coordinated interaction among modules establishes an integrated optimization pathway that spans from feature discrepancy understanding to information flow regulation, while maintaining interpretability in the decision process. Under complex field conditions with multiple sources of uncertainty, the proposed framework achieves stable module contributions ranging from 5% to 10% based on cross-validation and t-test analyses. This effectively alleviates the difficulty of efficient multimodal feature fusion for robust yield prediction under stress conditions. The study provides a new methodological perspective for multimodal agricultural sensing and crop phenotyping. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings.Develops a weight-guided method to enhance interpretability of multimodal features.Designs a cross-modal dense interaction and gated fusion mechanism.Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings. Develops a weight-guided method to enhance interpretability of multimodal features. Designs a cross-modal dense interaction and gated fusion mechanism. Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features.

Why it matches plant phenotyping methods小麦の収量という植物形質を対象に、マルチモーダルデータ融合と収量回帰のための新規エンコーダ・デコーダ手法を開発し、複数年・品種で検証している。フェノタイピング手法が中心である。

abstractthis study proposes a hierarchical fusion framework embedded within an encoder – decoder network

MMIU-Net: an encoder–decoder architecture based on multimodal feature fusion for wheat yield prediction under drought stress ↗

To address the poor regression performance caused by strong spatiotemporal heterogeneity, inconsistent information scales and complex feature relationships in field-based multimodal data, this study proposes a hierarchical fusion framework embedded within an encoder – decoder network. The framework integrates multi-scale, interpretable, and cross-modal representations through coordinated modules that bridge feature discrepancy understanding and information flow regulation. A multi-scale parallel pathway structure is designed to enhance joint perception of local and global information by leveraging feature mappings with different receptive fields. An interpretable feature importance allocation strategy is further introduced to improve the backbone network’s ability to provide dynamic guidance on feature contributions. This enables the model to perform adaptive weighting and feature selection during multimodal fusion. In addition, a cross-modal dense interaction and gated fusion mechanism is constructed to regulate information flow and capture fine-grained associations across modalities. The improved feature-guided model is applied to yield regression using multimodal data collected over three consecutive years and multiple wheat varieties. Results show that, in the first year, the highest prediction accuracy reaches an R2 of 0.8112 with an rRMSE of 16.85%. Validation using data from the same planting region in the second and third years yields a highest R2 of 0.8107 and 0.7986, with corresponding rRMSE values of 17.92% and 17.27%, respectively. Compared with other deep learning models within the same year, the proposed approach improves R2 by up to 30.52%, 33.96% and 32.79% across the three years, while reducing rRMSE by up to 41.59%, 45.01% and 46.43%. The results demonstrate that the coordinated interaction among modules establishes an integrated optimization pathway that spans from feature discrepancy understanding to information flow regulation, while maintaining interpretability in the decision process. Under complex field conditions with multiple sources of uncertainty, the proposed framework achieves stable module contributions ranging from 5% to 10% based on cross-validation and t-test analyses. This effectively alleviates the difficulty of efficient multimodal feature fusion for robust yield prediction under stress conditions. The study provides a new methodological perspective for multimodal agricultural sensing and crop phenotyping. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings.Develops a weight-guided method to enhance interpretability of multimodal features.Designs a cross-modal dense interaction and gated fusion mechanism.Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings. Develops a weight-guided method to enhance interpretability of multimodal features. Designs a cross-modal dense interaction and gated fusion mechanism. Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features.

Why it matches plant phenotyping methodsマルチモーダル作物センシングから小麦収量を推定する encoder–decoder 手法を開発し、複数年・品種データで検証しており、表現型推定手法が研究の中心である。

abstractthis study proposes a hierarchical fusion framework embedded within an encoder – decoder network.

Fine classification of rice diseases under field conditions based on improved ConvNeXt network ↗

Abstract Rice disease identification is a critical technique for ensuring yield and quality in precision agriculture. However, complex field backgrounds, subtle lesion features, and similar symptomatic manifestations have led to low accuracy and poor robustness in traditional classification methods. To address these issues, this study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions. A high‑quality dataset containing six categories (healthy rice, rice blast, brown spot, bacterial leaf blight, bacterial leaf streak, and bacterial grain rot) was constructed from field images and public datasets, with a total of 5,663 samples. Using ConvNeXt‑Tiny as the backbone, the model integrates a Hybrid Attention Transformer (HAT) to enhance the perception of lesion regions and key channels, and introduces Spatial and Channel Reconstruction Convolution (SCConv) to reduce feature redundancy and strengthen effective information expression. Experiments show that the Improve‑ConvNeXt model achieves 96.27% accuracy on the test set, which is 4.85% higher than that of the original ConvNeXt and significantly outperforms ResNet and DenseNet. The precision, recall, and F1‑score reach 95.84%, 96.11%, and 95.95%, respectively. Confusion matrix and Grad‑CAM visualization prove that the model can accurately focus on lesion areas and effectively distinguish similar diseases. This method provides high precision and strong generalization for rice disease identification in complex field environments, and offers a reliable technical reference for intelligent monitoring and precise management of rice fields.

Why it matches plant phenotyping methodsイネの病徴画像から病害状態を分類する画像・深層学習手法を開発し、データセットと性能比較で技術的に検証しているため、植物フェノタイピング手法が中心です。

abstractthis study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions.

MMIU-Net: an encoder–decoder architecture based on multimodal feature fusion for wheat yield prediction under drought stress ↗

To address the poor regression performance caused by strong spatiotemporal heterogeneity, inconsistent information scales and complex feature relationships in field-based multimodal data, this study proposes a hierarchical fusion framework embedded within an encoder – decoder network. The framework integrates multi-scale, interpretable, and cross-modal representations through coordinated modules that bridge feature discrepancy understanding and information flow regulation. A multi-scale parallel pathway structure is designed to enhance joint perception of local and global information by leveraging feature mappings with different receptive fields. An interpretable feature importance allocation strategy is further introduced to improve the backbone network’s ability to provide dynamic guidance on feature contributions. This enables the model to perform adaptive weighting and feature selection during multimodal fusion. In addition, a cross-modal dense interaction and gated fusion mechanism is constructed to regulate information flow and capture fine-grained associations across modalities. The improved feature-guided model is applied to yield regression using multimodal data collected over three consecutive years and multiple wheat varieties. Results show that, in the first year, the highest prediction accuracy reaches an R2 of 0.8112 with an rRMSE of 16.85%. Validation using data from the same planting region in the second and third years yields a highest R2 of 0.8107 and 0.7986, with corresponding rRMSE values of 17.92% and 17.27%, respectively. Compared with other deep learning models within the same year, the proposed approach improves R2 by up to 30.52%, 33.96% and 32.79% across the three years, while reducing rRMSE by up to 41.59%, 45.01% and 46.43%. The results demonstrate that the coordinated interaction among modules establishes an integrated optimization pathway that spans from feature discrepancy understanding to information flow regulation, while maintaining interpretability in the decision process. Under complex field conditions with multiple sources of uncertainty, the proposed framework achieves stable module contributions ranging from 5% to 10% based on cross-validation and t-test analyses. This effectively alleviates the difficulty of efficient multimodal feature fusion for robust yield prediction under stress conditions. The study provides a new methodological perspective for multimodal agricultural sensing and crop phenotyping.

Why it matches plant phenotyping methodsマルチモーダル農業センシングから小麦収量という植物形質を推定するエンコーダ・デコーダ手法を開発し、複数年・品種データで検証しているため、フェノタイピング手法が中心的である。

abstractthis study proposes a hierarchical fusion framework embedded within an encoder – decoder network.

Explainable Deep Learning for Greenhouse Horticulture: Feature and Temporal Interpretability in Crop Yield and Energy Optimization ↗

Optimizing crop yield while minimizing energy consumption remains a central challenge in greenhouse horticulture. This study introduces an integrated deep learning framework that couples multi-horizon time-series forecasting with dual-layered explainability to address the critical need for spatiotemporal transparency in optimizing greenhouse crop yield and energy efficiency. Four deep learning architectures, including the One-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory Network (BiLSTM), and TinyTimeMixer (TTM), were evaluated across two varieties of capsicum. LSTM and BiLSTM achieved the highest accuracy for incremental yield prediction, whereas TTM outperformed other models in forecasting daily energy usage, reflecting the distinct temporal characteristics of biological growth and environment-driven energy demand. To uncover the factors driving these predictions, two complementary explainability methods were applied: Gradient SHapley Additive exPlanations (SHAP) for feature-level attribution and a Temporal Convolutional Network with Convolutional Block Attention Module (TCN–CBAM) attention mechanism for joint temporal-feature interpretation. Radiation and drainage-related variables consistently emerged as the dominant contributors to yield, whereas external temperature, and humidity were the primary determinants of energy usage. Temporal attention further showed that yield is influenced by both recent irrigation responses and longer-term developmental dynamics, while energy consumption is driven mainly by short-term climatic fluctuations. These findings provide actionable insights for irrigation scheduling, climate-control strategies, and energy optimization, supporting more transparent and sustainable greenhouse management.

Why it matches plant phenotyping methods温室作物の収量という植物形質を深層学習で予測し、複数モデル比較と説明可能性解析を行う計算的形質推定手法が研究の中心である。

abstractThis study introduces an integrated deep learning framework that couples multi-horizon time-series forecasting with dual-layered explainability

Research progress on rapid detection technology of soybean phenotypic indicators under saline-alkali stress. ↗

Main conclusion The progress of soybean phenotypic detection and intelligent sensing technologies has been reviewed under salt-alkali stress , and an integrated approach combining three-dimensional imaging with near-infrared spectroscopy has been proposed to construct full-spectrum three-dimensional images. The approach could provide a reference for the breeding of salt-alkali-tolerant soybean varieties and the optimization of cultivation practices. Soil saline-alkali is one of the major environmental factors limiting global agricultural development, posing a serious challenge to normal crop growth, resource use efficiency, and sustainable agricultural development. Soybeans are a vital oilseed crop and plant-based protein source, and their phenotypic traits are significantly affected by saline-alkali stress, severely limiting soybean grain yield and quality. With the rapid advancement of technologies such as intelligent sensing and big data, this progress has driven new developments in plant phenomics detection, offering fresh insights into germplasm resource evaluation, breeding, gene function, and the cultivation of salt-alkali stressed soybeans. This article introduces the impact of salinity-alkali stress on soybean "phenotype-environment-gene" information, reviews the technical progress and application fields of traditional phenotypic detection methods for obtaining various phenotypic indicators across crops, and focuses on a rapid detection method of soybean phenotype under salinity-alkali stress. This paper analyzes the current state of research on detecting phenotypic indicators of soybeans under saline-alkali stress using intelligent sensing methods, including near-infrared spectroscopy, image recognition, and three-dimensional imaging. It is anticipated that through the integration of three-dimensional imaging and near-infrared spectroscopy, forming "full-spectrum three-dimensional images" with spatial structure and spectral information, this approach will advance the breeding and cultivation of superior salt-alkali tolerant soybean varieties through "intelligent data-driven" methods.

Why it matches plant phenotyping methods植物フェノタイピング指標の迅速検出技術を中心に、画像認識、三次元 imaging、近赤外分光などをレビューし、統合的な表現型取得法を提案する方法論的レビューである。

abstractThe progress of soybean phenotypic detection and intelligent sensing technologies has been reviewed under salt-alkali stress

BerryFlowerNet: A Customized Convolutional Neural Network for Blueberry Flower Cluster Detection and Flowering Stage Prediction with a Field Phenotyping Robot ↗

Blueberry production has rapidly expanded over the past decade, accompanied by growing demand for efficient and accurate methods to monitor the flowering and fruiting phases of blueberry development, which has a direct impact on yield potential. Accurate determination of blueberry phenology enables growers to make data-driven decisions on freeze protection applications and harvest windows. In addition, objective phenology data of blueberry mapping populations will provide high-quality phenotype data for the discovery of genetic mechanisms regulating blueberry flowering and fruiting times. Traditional approaches, such as manual counting and visual ratings, are labor-intensive and subjective in capturing variation across genotypes. Recent progress in computer vision and deep learning has enabled automated flower detection, but most existing studies on blueberries remain restricted to narrow flowering windows or close-up images, limiting their application at the bush level and across the seasonal development. In this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages. A comprehensive dataset was collected on three dates using a field phenotyping robot, covering five flowering stages. The integration of CFNet, a custom module fusing shallow spatial features, and PIoU loss improved the detection performance. Additionally, the Slicing Aided Hyper Inference algorithm was employed to address small-object detection in bush-level images. Experimental results demonstrated that BerryFlowerNet outperformed the baseline YOLO model and three additional detectors, achieving an average mAP0.5 of 0.644 across five independent training runs. The model achieved an accuracy of 0.88 when predicting blueberry flowering stages, indicating its effectiveness and accuracy. Additionally, the results of the bush-level image analysis showed the capability of the model to capture genotype-level differences in flowering dynamics. Overall, this approach offers new opportunities for growers and breeders to determine blueberry phenological development that is critical for optimizing on-farm management strategies and advancing precision phenotyping to facilitate the development of climate-resilient blueberries.

Why it matches plant phenotyping methodsブルーベリーの花房検出・計数と開花ステージ推定を行うCNNおよびフィールド表現型ロボットの開発・評価が研究の中心であり、植物の生育状態を直接推定する実質的な表現型手法である。

abstractIn this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages.

The Accuracy and Efficiency of YOLO Algorithms in Identifying Plant Leaf Diseases ↗

Plant diseases are one of the biggest challenges of world agriculture, leading to enormous output losses and economic damages. Early and accurate detection of these diseases may help to increase crop yield, improve resource efficiency, decrease costs and environmental impact and assist the production of high-quality food. In recent years, deep learning (especially computer vision approaches) has become a strong tool for a number of tasks such as picture classification, segmentation and object detection. Such techniques include the You Only Look Once (YOLO) family of neural networks, a state-of-the-art technology for accurate object detection. In this work, we use YOLOv5, YOLOv7 and YOLOv8 models for citrus disease detection with the CCL’20 dataset. During training, a number of data augmentation techniques are used to improve the model performance, such as picture translation, scaling, flipping and mosaic augmentation. The model performance was evaluated using the Mean Average Precision (mAP) for Intersection over Union thresholds from 50% to 95% (mAP@50–95). The results showed that the YOLOv8 model performed better than the other variations, with significant improvements compared to the benchmarks reported in previous studies. After hyper-parameter adjustment, the improved model reached a mAP@50-95 of 96.1% on the test set for detection of the citrus diseases. The model attained the mAP@50-95 of 95.3%, 96.0% and 97.0% for Anthracnose, Melanose and Bacterial Brown Spot respectively for each disease. Furthermore, the model could reliably identify both single and many cases of the same and different diseases inside a single image, illustrating the robustness of recent YOLO architectures. Finally, the trained YOLOv8 model has been successfully installed into the Roboflow platform which is ready for practical applications in citrus disease monitoring.

Why it matches plant phenotyping methods柑橘葉の病害状態を画像から検出・分類するYOLO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。

abstractIn this work, we use YOLOv5, YOLOv7 and YOLOv8 models for citrus disease detection with the CCL’20 dataset.

Machine Learning Algorithms for Spatial Interpolation of Crop Yield at the Field Scale ↗

Abstract Crop mapping is one of the most frequent uses of yield monitor data in precision agriculture. Processing such data requires spatial interpolation to predict yields at unsampled locations. Ordinary kriging (OK) is the geostatistical interpolation method usually employed for mapping georeferenced data. In recent years, machine learning (ML) algorithms have gained attention for spatial interpolation tasks because of their ability to process large data volumes; however, their comparative performance for yield mapping at the field scale remains limited. This study compares the statistical behavior of ML methods, including quantile regression forest (QRF), generalized boosted regression models (GBM), extreme gradient boosting (XGB), and radial basis function neural networks (RBFNs) for processing yield monitor data. OK is applied as reference. To enhance the performance of ML algorithms for fine-scale yield mapping, covariates from the spatial neighborhood of sampled yield values were incorporated to predict yields at unsampled sites. Over 1,000 yield monitor datasets from multiple crop species were processed to assess algorithm performance. The ML methods—QRF, GBM, and XGB—demonstrated robust statistical performance, since they effectively handled large data volumes and improved spatial interpolation accuracy. The QRF method achieved the highest error reduction (on average, an 8.7% error reduction), was faster than OK, and generated high-quality uncertainty maps. GBM and XGB also performed better than OK. Coupled with spatial covariables, the studied ML algorithms are valuable alternatives to conventional kriging for yield mapping at the field scale.

Why it matches plant phenotyping methods作物収量という植物形質を対象に、収量モニターデータの空間補間手法を比較・評価し、未観測地点の収量推定性能を検証しているため、計算的フェノタイピング手法が中心である。

abstractThis study compares the statistical behavior of ML methods, including quantile regression forest (QRF), generalized boosted regression models (GBM), extreme gradient boosting (XGB), and radial basis function neural networks (RBFNs) for processing yield monitor data.

Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement. ↗

Abstract Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.

Why it matches plant phenotyping methods根系フェノタイピングの高スループット画像化、計算ツール、機械学習統合、データ標準化を中心に扱う方法論レビューであり、植物形質の取得・解析手法が主題である。

titleAdvances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.

Discovering genetic loci associated with rate of vegetative index gain using UAV-based phenomics in spring wheat ↗

In wheat, the pre-heading stage determines spikelet formation, floret fertility, and canopy development, making it a critical window for early stress detection and yield potential. The genetic basis of pre-heading canopy development in wheat has remained constrained by the conventional phenotyping due to the low temporal resolution. Here, we quantified the rate of vegetation index gain (RVIs) during tillering to heading stages using UAV-mounted multispectral sensor in 196 spring wheat cultivars representing 112 years of breeding history. RVIs were calculated using six vegetation indices for consecutive two growing seasons, and genome wide association study (GWAS) was performed on RVIs, grain yield (GY) and thousand grain weight (TGW) using a wheat 37K SNP array. RVIs showed significant positive correlations with grain yield (r=0.28-0.43; p<0.001) and consistently increased in the modern cultivars compared to old cultivars. This indicated that resource remobilization during pre-heading canopy development significantly contributed to GY during modern wheat breeding. GWAS identified 67 loci, including 12 Group-I loci associated only with RVIs, and 18 Group-II loci associated with both RVIs and yield traits. Two stable loci on chr1B and chr5D consistently increased GY and RVIs across environments, and the tag SNPs were converted to selectable KASP markers. The allelic distribution on global wheat collection of ∼3000 accessions showcased that favorable alleles on both loci were dominant in cultivars compared to landraces. Similarly, favorable alleles showed more frequency in winter type than spring type. Across breeding eras both alleles showed increasing trend with chr5D reaching near fixation and chr1B remaining partially enriched in modern cultivars. Our work on capturing pre-heading canopy development, discovery of two stable loci underpinning yield and RVIs, and development of KASP markers provided a strong foundation to HTP assisted genetic dissection of GY and facilitated the understanding of canopy dynamics and yield formation.

Why it matches plant phenotyping methodsUAV搭載マルチスペクトルセンサーで生育期間中のキャノピー発達を定量化するフェノタイピング手法が、研究の主要なデータ取得・解析基盤として用いられている。

abstractwe quantified the rate of vegetation index gain (RVIs) during tillering to heading stages using UAV-mounted multispectral sensor

Towards precision agriculture for assessing germination rates and density of rice seedling using hierarchical convolutional neural network on drone imagery ↗

Rice is a significant food that plays a vital part in delivering nutrition to the world's population. Hence, approaches for assessing rice yield have received considerable study. The amount of rice seedlings (density) is a main agronomic module. It is related to harvest and also plays a significant part in the survival rate. Unmanned Aerial Vehicles (UAVs) are prepared with lightweight sensors, which creates a substantial effect in the field of crop phenotyping. The UAV was effectively used to measure germination rates and density in an accurate and effective method that would otherwise be laborious and expensive to obtain when compared to manual valuation. In image processing, mainly over the applications of deep learning (DL) models, there was a notable academic search for the value of UAV images for varied agricultural monitoring tasks. This work develops a Rice Seedlings for Assessing Germination Rates and Density using Aerial Images with Hierarchical Deep Network (RSAGRD-AIHDN) model. The goal of this paper is to assess germination rates and seedling density in rice fields using remote sensing (RS) or UAV-based imaging techniques for improved crop establishment monitoring. To accomplish that, the image pre-processing stage is initially applied with dual stages, such as image acquisition and pre-processing, to ensure high-quality and consistent inputs. Furthermore, the RSAGRD-AIHDN model employs the ConvNeXt method for the feature extraction process. For rice seed detection and classification, the RSAGRD-AIHDN model implements ensemble models, namely stacked autoencoder (SAE), bidirectional temporal convolution network (BiTCN), and Deep Q-Learning (DQL). The experimental assessment of the RSAGRD-AIHDN method is performed under the aerial dataset of rice seedlings. The experimentation of the RSAGRD-AIHDN method portrayed a superior accuracy value of 98.68% over existing approaches.

Why it matches plant phenotyping methodsUAV画像と深層学習モデルを用いて、イネの発芽率と苗密度という植物形質を推定する手法を開発・評価しており、表現型取得が研究の中心である。

abstractThe UAV was effectively used to measure germination rates and density in an accurate and effective method

End to End High-Throughput Phenotyping of Sorghum Grain Weight Based on Computer Vision ↗

Accurate estimation of sorghum (Sorghum bicolor [L.] Moench) grain yield remains a major bottleneck in breeding programs across sub-Saharan West Africa, where traditional methods rely on manual counting and weighing of grains. These approaches are labor-intensive, time-consuming, and prone to-induced variability, limiting throughput and reproducibility. This study presents an end-to-end high-throughput phenotyping pipeline for automated grain detection and mass estimation using computer vision. The workflow integrates smartphone-based image acquisition, automated foreground extraction, and grain detection using YOLOv11 models, followed by count-to-mass calibration. Three YOLOv11 architectures, small, medium, and large, were evaluated under identical training conditions using transfer learning from COCO pre-trained weights. Among the tested models, the medium configuration provided the best trade-off between precision and detection performance, with precision values reaching up to 0.861. A preprocessing step based on automatic background masking was applied prior to detection, significantly improving robustness under heterogeneous field conditions. Grain count was converted to mass using a linear calibration model. For two locally relevant sorghum elite lines, Faourou and Payenne, strong linear relationships were observed between detected grain number and measured grain weight (R² > 0.998), with mass coefficients k ≈ 0.026 g/grain. To facilitate adoption, a user-friendly R Shiny application was developed, allowing users to upload images, perform automated grain detection, and estimate grain mass using user-defined calibration coefficients. This pipeline provides a scalable and reproducible approach for rapid grain phenotyping and offers strong potential to accelerate selection decisions in sorghum breeding programs under field conditions.

Why it matches plant phenotyping methodsソルガム粒の画像取得、検出、質量推定を統合した高スループット表現型解析パイプラインを開発・評価しており、方法が研究の中心である。

abstractThis study presents an end-to-end high-throughput phenotyping pipeline for automated grain detection and mass estimation using computer vision.

Total Fresh Weight Estimation Model for Herbaceous Plant Based on Improved YOLOv5 and 3D Point Clouds ↗

The real-time quantitative estimation of herbaceous plant growth status holds significant potential for investigating fertilization effects, predicting growth curves, and enhancing crop yield. This study constructed a growth quantification model using an improved YOLOv5 architecture integrated with 3D point cloud processing, with pak choi as an exemplar crop. To improve the recognition accuracy while reducing the number of parameters, we employed a lightweight YOLOv5 model enhanced with Atrous Spatial Pyramid Pooling and Ghost convolution modules for individual pak choi plant localization and growth stage classification. We also developed a segmentation method based on the HSV color space to segment leaves. To estimate the total fresh weight of individual plants, we first calculated the leaf surface area by generating a triangular mesh from the corresponding leaf point clouds and predicted the chlorophyll content using a stacking ensemble model. Subsequently, to address the leaf occlusion issues, the leaf pixel ratio in the images, leaf surface area, and mean leaf chlorophyll content were collectively used as independent variables. Finally, a multiple linear regression model was developed to accurately estimate the total fresh weight of individual pak choi plants. Experimental results demonstrate that the modified YOLOv5 architecture achieves a 3.5% improvement in mAP@0.5 (reaching 96%) and a 4.66% increase in F1-score (attaining 90.26%), while significantly reducing the computational complexity compared to the baseline model. Statistical tests verified that the fitted equation could explain 79% of the variation in the total fresh weight, with an average relative error of 12.16%. This enables non-contact and accurate measurement of the pak choi growth status.

Why it matches plant phenotyping methodsYOLOv5、3D点群、葉面積・クロロフィル推定を統合し、個体の生体重という植物形質を非接触推定する手法が研究の中心である。

abstractThe real-time quantitative estimation of herbaceous plant growth status holds significant potential

AI-Based Plant Disease Recognition System: A CNN Approach with Dual Deployment via Web and Telegram ↗

Early detection of plant diseases is crucial for improving crop yield and reducing economic losses. This paper presents a CNN-based plant disease recognition system using a hybrid dataset of over 87,000 images across 38 classes. An EfficientNet-based transfer learning model is employed to achieve high accuracy while maintaining computational efficiency. The system incorporates advanced features such as context-aware analysis using environmental data, economic loss estimation, and explainable AI through Grad-CAM. To ensure accessibility, it is deployed via a Streamlit web application and a Telegram chatbot, along with a multilingual voice interface. Experimental results show improved performance, achieving 96–97% accuracy, making the system suitable for real-world agricultural applications.

Why it matches plant phenotyping methods植物画像から病害状態を認識するCNN手法の開発・評価が中心であり、植物の病徴・病害状態を直接推定するため、植物フェノタイピング手法として適格です。

abstractThis paper presents a CNN-based plant disease recognition system using a hybrid dataset of over 87,000 images across 38 classes.

Crop Yield Prediction using Hyperspectral Imagery and Machine Learning Algorithms Deployed on Edge Computing Nodes ↗

This research work proposed the precision agriculture is supported by accurate future crop yield forecasting which allows optimal use of resources automated use of crops and planning production of food. The problems that conventional predictive systems basing on cloud computing are likely to face include longer latency unreliable network connectivity and heavy processing power requirements which are especially troublesome in the rural farm environment. To deal with these drawbacks the study proposes an Edge-AI-driven framework of crop yield estimation which entails the integration of hyperspectral imaging with real-time measurements of agricultural sensors of soil moisture, pH level, temperature, and humidity. it is based on an STM32 Edge-AI microcontroller and a hybrid deep learning architecture Conv LSTM-ViT (Convolutional Long Short-Term Memory embedded with Vision Transformer) to process spectral changes and environmental changes over time and identify the factors that influence crop growth. The model is able to make decisions in a short time track continuously and lessen cloud reliance by executing inference at the edge. The proposed Conv LSTM-ViT Edge AI model outperforms Random Forest, XG Boost, CNN, and Conv LSTM with up to 14% higher accuracy and 70–80% lower inference latency, demonstrating strong suitability for real-time smart agriculture deployments. Metadata and prediction outcomes are safely uploaded to the Blynk IoT cloud to be visualized and offer decision support to the farm levels. Field testing applications with UAVs further support that the system is energy-saving scalable and able to assist real-time smart farming processes which creates a sustainable system of future farming technologies.

Why it matches plant phenotyping methodsハイパースペクトル画像とエッジAIによって作物収量を推定する取得・解析手法を開発し、既存モデルとの性能比較と実地検証を行っており、収量推定法が中心である。

abstractthe study proposes an Edge-AI-driven framework of crop yield estimation which entails the integration of hyperspectral imaging

Dynamic-weighing grain mass flow sensing for reliable in-field yield monitoring in combine harvesters ↗

The accuracy of on-board yield monitoring for combine harvesters is limited by the difficulty of acquiring high-fidelity mass flow signals and achieving reliable cumulative integration under complex vibration and varying operating conditions. To address this challenge, this study developed a weighing-based on-board yield monitoring system (DW-ORMS), in which a flow-collecting weighing-type grain mass flow sensor (C-GMFS) served as the core sensing unit. The C-GMFS enables stable weighing observation in confined installation spaces through mechanical decoupling and a single force-transmission path. At the system level, a state-aware gating strategy and trusted update mechanism were introduced to improve the reliability of output and cumulative mass estimation during unsteady operating phases. Based on field-measured disturbance characteristics, a parameterized disturbance model was established and used to identify the valid operating window and fix the gating parameter range. Simulation results showed that the proposed method achieved band-limited interference suppression of A band ≥17.73 dB under strong disturbances while maintaining trend fidelity of PRR trend ≥41.73% and low processing delay. Field harvesting experiments were conducted at working speeds of 2–8 km·h − ¹. The cumulative strip mass showed excellent agreement with manual weighing references, with R ²=0.973 and RMSE = 0.84 kg, and the strip-level mass closure error remained within CE ≤ 5% (N = 35). No monotonic drift in error was observed across working-speed groups. These results demonstrate that the DW-ORMS provides a deployable and traceable solution for high-confidence on-board yield monitoring of combine harvesters under unsteady field conditions.

Why it matches plant phenotyping methods収量という植物由来の形質を対象に、動的秤量センサーと信号処理による圃場収量モニタリング手法を開発し、シミュレーションおよび実収穫データで検証しているため、方法が中心的である。

abstractthis study developed a weighing-based on-board yield monitoring system (DW-ORMS), in which a flow-collecting weighing-type grain mass flow sensor (C-GMFS) served as the core sensing unit.

UAV-based field phenotyping to assess yield-related traits in potato genotypes ↗

Abstract Unmanned aerial vehicle (UAV)-based phenotyping has been applied to assess potato traits, however, its use to identify canopy traits associated with tuber yield across diverse genotypes remains limited. The objective of this study was to evaluate the use of UAV-based field phenotyping, integrating RGB and thermal imaging, to identify key canopy traits associated with tuber yield and it´s agronomic components in a set of eight potato genotypes grown across two environments and two growing seasons. Despite higher seasonal rainfall in Chiloé, tuber yields were consistently greater in Osorno, underscoring that total precipitation alone is less important than its temporal distribution and effective crop water availability; this makes it necessary to supplement with irrigation during the period of highest demand. RGB-derived vegetation indices and canopy temperature successfully differentiated genotypes, although their discriminatory power varied according to developmental stage and environmental conditions, with intermediate to late growth stages generally providing the strongest genotype separation. Canopy temperature supplied complementary physiological information related to canopy water status, whereas RGB traits captured broader variation in canopy structure and greenness. These findings highlight the importance of integrating phenological stage and environmental context when interpreting remote sensing data, and demonstrate the strong potential of UAV-based HTP to support breeding and agronomic strategies aimed at improving drought resilience, yield stability, and selection efficiency in potato.

Why it matches plant phenotyping methodsUAVによるRGB・熱画像を用いた圃場フェノタイピングが中心で、ジャガイモのキャノピー形質を抽出・評価し、遺伝子型間比較や収量関連性を検討している。

abstractevaluate the use of UAV-based field phenotyping, integrating RGB and thermal imaging, to identify key canopy traits associated with tuber yield

An Integrated Deep Learning-Based System for Plant Disease Detection and Smart Agriculture ↗

Plant diseases remain a major challenge in modern agriculture, directly affecting crop yield, quality, and economic stability. Early and accurate identification of plant diseases is essential for minimizing losses and improving productivity. This paper presents an integrated deep learning-based framework for automated plant disease detection and smart agricultural decision support. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze leaf images and classify them into multiple disease categories based on learned visual patterns such as texture, color variation, and structural features. To enhance practical applicability, the system is designed to operate as part of a broader decision-support pipeline, providing insights that assist in timely intervention. The model is trained on a labeled dataset containing diverse plant disease classes and optimized using standard preprocessing and regularization techniques to improve generalization. Experimental evaluation demonstrates that the model achieves high classification accuracy under realistic conditions while maintaining computational efficiency. The proposed approach reduces dependency on manual inspection and enables scalable deployment in agricultural environments. By combining deep learning with application-oriented design, this work contributes toward the development of intelligent and accessible solutions for precision agriculture.

Why it matches plant phenotyping methods葉画像から植物病害を分類するCNN手法が研究の中心であり、植物の病害状態を直接推定・評価しているため、植物フェノタイピング手法として含める。

abstractThis paper presents an integrated deep learning-based framework for automated plant disease detection and smart agricultural decision support.

Deep Learning-Based Plant Disease Detection and Pesticide Recommendation System for Smart Agriculture ↗

Agriculture is an essential part of the worldwide economy, and initial detection of crop disease is essential to avoid substantial yield reduction. Conventional approaches of disease detection are primarily completed manually by specialists, which is expensive and frequently requires human errors. This survey aims to introduce an intelligent deep learning model to identify crop disease and suggest pesticides. This model is established on Convolutional Neural Networks (CNN) and uses the idea of transfer learning to sort the disease from the leaves of crops such as tomato and pomegranate. The proposed model works on the rule of image classification and is accomplished through image preprocessing, feature extraction, and classification employing the pre-trained model MobileNetV2. Once the disease is detected, it is mapped to the dataset.

Why it matches plant phenotyping methods作物葉の画像から病害を分類する深層学習手法が中心で、植物の病害状態を直接推定しているため収載。農薬推薦も含むが、病害検出モデル自体が主要な技術的貢献である。

abstractThis survey aims to introduce an intelligent deep learning model to identify crop disease and suggest pesticides.

Phenomic prediction in drought-stressed faba bean across spectral, structural, and fused canopy predictors ↗

Abstract Background Faba bean is an important grain legume in temperate cropping systems because it provides protein-rich seed and contributes biological nitrogen fixation. However, its productivity is highly sensitive to drought, and breeding for improved drought performance is constrained by complex genotype by environment interactions and the difficulty of measuring relevant traits at scale. This study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean, and how predictive ability changes when information is used from single dates or cumulatively across the season. Results Predictive performance was strongly trait dependent and varied with predictor set and temporal strategy. Combined VI + 3D predictors generally produced the highest and most consistent predictive ability for major traits. Total grain yield reached 0.75 under cumulative VI + 3D prediction at 93 days after sowing (DAS 93), cumulative water uptake peaked at 0.80 at DAS 97, and total straw biomass reached 0.66 at DAS 104. In contrast, some component traits were predicted equally well or better by 3D information alone, including grain number with 0.70 and pod number with 0.55 under cumulative 3D prediction. Useful prediction windows also differed among traits, with broad late-season windows for major agronomic traits but narrower, more stage-specific windows for productive tillers, thousand kernel weight, and water-use efficiency. Conclusion Phenomic prediction under drought in faba bean was strongly shaped by trait type, predictor composition, and temporal design. Combined VI + 3D predictors were most effective for integrative traits, whereas several component traits were predicted equally well or better by 3D information alone. These findings highlight the potential of scanner-based multisensor phenotyping to support drought-related selection in faba bean breeding.

Why it matches plant phenotyping methodsスキャナー由来のスペクトル指標と3Dキャノピー形質を用いたマルチセンサー表現型解析・予測が研究の中心であり、乾燥ストレス下の収量、バイオマス、水利用などの植物形質を技術的に評価している。

abstractThis study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean

Optimizing soybean breeding: High-throughput phenotyping for stink bug resistance and high yields ↗

Abstract Background: The stink bug complex is one of the most damaging pests of soybean, reducing yield and seed quality. Genetic resistance remains the most sustainable and effective management strategy, but its quantitative inheritance and labor-intensive field phenotyping make its implementation in breeding programs challenging. Objective: This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models. Methods: A population of 304 soybean lines was evaluated in alpha-lattice design trials across two seasons under natural infestations. Five resistance-related traits, grain yield (GY), healthy seed weight (HSW), number of days to maturity (NDM), tolerance (TOL), and leaf retention (LR), were manually scored and linked to UAV-derived vegetation indices (VIs) and texture indices (TIs). Three ML models (AdaBoost, SVM, MLP) were tested to predict these traits from aerial features. Results: Results showed that VIs, particularly Visible Atmospherically Resistant Index at the 25th percentile (VARI_P25), were consistently associated with resistance-related traits, while decision tree analysis highlighted TIs at 45° and 135° as complementary sources of structural information. Prediction ability was highest for GY, HSW, and NDM, especially in flights near flowering and maturity, but remained low for TOL and LR. Integrating multiple flights modestly improved accuracy, whereas cross-season predictions were unreliable. Nonetheless, indices such as VARI_P25 provided useful cross-season correlations for HSW and TOL, enabling early screening of less promising lines. Conclusion: This pioneering study demonstrates that UAV–ML pipelines can capture genetic signals of stink bug resistance in soybean, despite environmental complexity. These findings open new avenues for resistance phenotyping, supporting more efficient breeding strategies and accelerating genetic gains in soybean improvement.

Why it matches plant phenotyping methodsUAV画像と機械学習を用いて、ダイズの抵抗性関連形質や収量を推定するHTPパイプラインを技術的に評価しており、表現型取得・推定法が研究の中心である。

abstractThis study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models.

AI-Driven Crop Disease Prediction System ↗

The rapid advancement of agricultural technology has created a pressing demand for intelligent systems that can safeguard crop health and ensure sustainable farming practices. This paper presents an AI-Driven Crop Disease Prediction System, a web-based platform that assists farmers in the early detection and prevention of plant diseases through data-driven insights. The system integrates image processing and machine learning techniques to analyze leaf images, identify disease symptoms, and suggest suitable remedies. Convolutional Neural Networks (CNNs) are utilized for image classification, while data analytics modules evaluate environmental parameters such as temperature, humidity, and soil conditions to improve prediction accuracy. The backend framework employs Python Flask with TensorFlow integration and a scalable MySQL database for efficient data storage and retrieval. The proposed system minimizes crop loss, enhances yield quality, and supports timely decision-making for farmers. Future enhancements will include integration of IoT-based sensors, multilingual chatbot support, and a mobile application for real-time field monitoring. This work demonstrates how artificial intelligence can transform traditional agriculture into a smart, predictive, and resilient ecosystem.

Why it matches plant phenotyping methods葉画像から植物の病徴を画像処理・CNNで判定するシステムが研究の中心であり、植物病害状態の表現型取得・推定に該当する。

abstractThis paper presents an AI-Driven Crop Disease Prediction System, a web-based platform that assists farmers in the early detection and prevention of plant diseases through data-driven insights.

Plant Leaf Disease Classification Using Multiscale Pyramid Autoencoder with Kolmogorov–Arnold Network ↗

The plant leaf diseases classification is important for crop yield management and market value. Effective prevention and treatment depend on the rapid and accurate identification of leaf diseases and assessment of their severity. However, hyperspectral images capture detailed spectral information across hundreds of narrow bands. The full-spectrum data are high-dimensional, which increases redundancy and computational complexity. Subtle nutrient-induced biochemical changes in plant leaves produce weak spectral variations that fail to generate strong discriminative features for classification tasks. Hence, this research proposes a Multiscale Pyramid Autoencoder with a Kolmogorov–Arnold Network (MPA-KAN) for plant leaf disease classification. An autoencoder components learn a compressed representation of high-dimensional hyperspectral data, reducing dimensionality by effectively eliminating redundant information across multiple spectral bands. The KAN significantly captures both spectral and spatial features while eliminating irrelevant information, thereby enhancing the model efficiency. The proposed MPA-KAN model achieves 98.99% accuracy in the hyperspectral image and 99.98% accuracy in the PlantVillage dataset when compared with existing methods.

Why it matches plant phenotyping methods植物葉の病害状態を hyperspectral 画像から分類するモデルを提案し、複数データセットで精度比較・検証しており、病害フェノタイピング手法が中心である。

abstractthis research proposes a Multiscale Pyramid Autoencoder with a Kolmogorov–Arnold Network (MPA-KAN) for plant leaf disease classification.

High-throughput phenotypic analysis of plant and curd growth dynamics during the whole growth period of cauliflower based on instance segmentation ↗

Efficient phenotyping monitoring of cauliflower is crucial for its breeding and production. However, traditional manual measurement methods are time-consuming and labor-intensive, and existing deep learning (DL) methods mostly focus on the seedling stage, lacking systematic research covering the entire growth period. In this study, RGB images of cauliflower from seedling to harvest were collected. Through systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes. Evaluation results showed that YOLO12s-seg was the optimal model. It can achieved a segmentation mask mAP 50 of 99.4% for plants and curds in sparsely planted images and showed an advantage in identifying partially occluded early curds beneath inner leaves. Traits such as plant canopy width and curd diameter automatically extracted from segmentation results were highly consistent with manual measurements (R 2 > 0.90). Furthermore, the Richards model and Sine model were used to accurately fit the growth dynamics of leaf area and curd area, respectively. Based on growth kinetics, curds were classified into three types: mature and compact type, peak-burst type, and steady-increase type. Cluster analysis of 47 germplasms based on high-throughput phenotyping data revealed four groups and their growth characteristics: comprehensively coordinated type, mid-maturity compact type, large high-yield type, and curd-dominant type. Integrating the above functions, a platform for cauliflower growth monitoring and phenotypic analysis was developed. It provided full-process support from automatic image processing to growth dynamic analysis. This work provides an effective automated solution for high-throughput phenotyping analysis and growth dynamic monitoring of cauliflower, and offers a referable analytical framework for crop growth pattern research and intelligent breeding decision-making.

Why it matches plant phenotyping methods植物のインスタンスセグメンテーションから葉面積・草冠幅・花蕾形質を自動抽出し、手動測定との技術検証と成長動態解析、統合プラットフォーム開発を行っており、フェノタイピング手法が研究の中心である。

abstractThrough systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes.

Crop Disease Prediction in Agriculture Using Deep Learning: A Comprehensive Review ↗

Crop diseases continue to pose a serious danger to agricultural productivity worldwide, resulting in large losses in crop quality, yield, and economic value. For large-scale farming, traditional disease detection techniques, which mostly rely on specialist knowledge and manual examination, are frequently laborious, subjective and ineffective. Deep learning (DL), a branch of artificial intelligence, has become a potent method for automated and precise crop disease prediction because to developments in information technology. With an emphasis on image-based analysis and data-driven modelling, this paper provides a thorough overview of current advancements in deep learning-based methods for crop disease diagnosis and prediction. Convolutional Neural Networks (CNNs), one type of deep learning architecture, have shown exceptional performance in reliably diagnosing different crop illnesses and extracting complicated characteristics from plant photos. The detection accuracy has been further enhanced by advanced versions like ResNet, VGGNet, and EfficientNet, which frequently surpass 95 percentage under controlled circumstances. Precision agriculture techniques have been improved by the real-time monitoring and early disease identification made possible by the integration of deep learning with Internet of Things (IoT) devices, remote sensing technologies, and drone-based imaging systems. Despite these developments, a number of problems still exist, such as the requirement for sizable labelled datasets, high processing demands, overfitting problems, and restricted model generalisation in practical settings. This paper identifies these drawbacks and explores possible remedies, such as explainable deep learning methods, data augmentation, and transfer learning. Future research will focus on integrating intelligent decision-support systems, scalable deployment approaches, and edge computing. All things considered, deep learning-based crop disease prediction systems have enormous potential to revolutionise contemporary agriculture by facilitating early intervention, enhancing crop health management, and encouraging sustainable farming methods.

Why it matches plant phenotyping methods植物画像から病害状態を推定する深層学習手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。

abstractWith an emphasis on image-based analysis and data-driven modelling, this paper provides a thorough overview of current advancements in deep learning-based methods for crop disease diagnosis and prediction.

From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series. ↗

Product demand and climate variability are progressively increasing the need for real-time, scalable crop monitoring to support varietal selection and in-season input optimisation. However, producers still have limited information on the temporal and spatial variability of cotton health and performance beyond point-scale field surveying. In addition, given cotton's high phenotypic plasticity, near real-time derived metrics are essential to improve input efficiency and strengthen long-term sustainability of the cotton industry in Australia. Therefore, we proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m). Scalability was validated by applying the proposed framework to estimate and map cotton yield across commercial fields. To do this, we explored the accuracy of high-resolution multispectral imagery from two platforms (unmanned aerial vehicle (UAV) and PlanetScope (PS)) collected across two 144-plot trials for two cotton seasons. These were designed with a large range in nitrogen rates (N), shading, and two growth-regulator doses, thus, creating variable environments. Sensing metrics were obtained from UAV imagery (1.3-1.6 cm pixel size) acquired once in 2022/23 and eight times in 2023/24, while PS composites (3 m pixel size) provided near-daily coverage in both seasons. Time-series gaps were imputed using Savitzky-Golay smoothing in thermal time (GDD), enabling extraction of growth dynamic metrics (GDMs) as single-date (SD; e.g., peak canopy) and multi-date (MD; e.g., daily average growth rate) metrics. After reducing collinearity and dimensionality, random forest (RF), support vector regression (SVR), and gaussian process regression (GPR) were trained and interpreted with SHAP, for feature contribution. UAV single-date models (SD_ UAV) achieved strong accuracy for height (R 2 = 0.77), biomass (R 2 = 0.73), and yield (R 2 = 0.81). Incorporating UAV time-series metrics (MD_UAV) improved the performance R 2 = 0.87, 0.86, and 0.85 for height, biomass and yield, respectively. Application of the derived models using high resolution satellite data (MD_PS) for different farming systems showed highly significant accuracy (R 2 = 0.67) to predict cotton yield at aggregated field scale. As such, enabling the detailed spatial prediction of cotton yield within a field. It is anticipated that the proposed functional sensing framework will improve the estimation of key cotton production traits, supporting field- and within-field decision-making, ultimately contributing to more resilient and sustainable cotton production in Australia.

Why it matches plant phenotyping methodsUAV・衛星時系列画像から綿花の形態・生産性形質を推定するセンシング／予測フレームワークを開発・検証し、商業圃場へ適用しており、表現型取得手法が中心である。

abstractwe proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m).

Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants ↗

Global changes in agricultural and environmental systems will necessitate new crop research methodologies in the future years to ensure more effective use of natural resources and food security. The progress in next-generation sequencing has led to the emergence of multi-omics techniques as successful crop improvement strategies. Multi-omics studies using high-throughput techniques have been critical in understanding growth, senescence, yield, and biotic and abiotic stress responses in an array of crops. When multi-omics provide a high-resolution map of the molecular frameworks governing stress responses, advanced deep phenotyping systems can utilize advanced sensors to quantify dynamic physiological and morphological traits non-destructively. The systematic integration of these multi-layered datasets through association mapping and machine learning frameworks allows for the identification of superior alleles and regulatory hubs. Currently, the non-invasive imaging methods have effectively incorporated computer vision, machine learning, and deep learning components of AI. The use of machine learning and deep learning have progressively increased the effectiveness of data gathering and analysis. The supervised, unsupervised, and deep learning architectures have become effective tools for overcoming the genotype-to-phenotype gap, enabling more accurate predictions of yield and stress tolerance. Despite challenges related to data dimensionality, high infrastructure costs, and the need for standardized protocols, the convergence of these fields offers a robust architecture for predictive breeding. By linking microscopic molecular shifts to macroscopic field performance, integrated strategies accelerate the discovery of adaptive traits and the delivery of high-yielding, climate-smart cultivars. This review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.

Why it matches plant phenotyping methods深層フェノタイピングと非破壊センサー・画像解析を中心に、作物形態・生理形質の高スループット計測とマルチオミクス統合を論じる方法論的レビューである。

abstractThis review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.

Enhancing Crop Health and Early Detection of Tomato Leaf Diseases Using Deep Learning Techniques ↗

Background/Objectives: One of the most widely produced and consumed crops in the world, tomatoes are often threatened by various leaf diseases, including early blight, late blight, and leaf mold, which can result in significant yield losses if not detected and managed promptly. Due to reliance on manual inspection, tomato leaf diseases are often detected late, resulting in significant crop loss. The goal of this research is to develop a deep learning (DL) model that accurately classifies tomato leaf diseases. The model is trained using supervised learning on the PlantVillage dataset, which includes labelled images of tomato leaves under different conditions. Method: Gaussian Blurring-Gaussian Mixture Models (GB-GMM) are a preprocessing method used to enhance the image quality. While EfficientNet and VGGNet architectures are utilised for accurate classification, data augmentation is used to boost model robustness. A standard train-validation-test split is used to assess the model. Findings: The results demonstrate that the proposed method, implemented in Python, performs better than the others in terms of accuracy (94.3%), precision (93.7%), recall (92.5%), and F1-score (93.1%) in VGGNet architectures. These results indicate that the proposed model is very effective overall and produces balanced predictions. In the future, the system may be integrated with drone and IoT technologies for automatic disease warnings and real-time field surveillance. Novelty: The Multivariable Grey Prediction Evolution Algorithm (MGPEA) is included for illness trend forecasting to enhance predictive power further. This technology facilitates large-scale, sustainable agricultural management, minimizes human inspection, and enables prompt disease response. Keywords: Tomato Leaf Diseases, Crop Health, Gaussian Mixture Models, Multivariable Grey Prediction Evolution Algorithm, EfficientNet, VGGNet

Why it matches plant phenotyping methodsトマト葉画像から病害状態を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。

abstractThe goal of this research is to develop a deep learning (DL) model that accurately classifies tomato leaf diseases.

Real - Time Plant Disease Detection by Ai ↗

Timely and precise identification of plant diseases is essential for improving agricultural yield, reducing financial losses, and supporting sustainable farming practices. In this study, a lightweight real-time plant disease detection system intended for edge devices such as smartphones and embedded platforms is presented. The proposed framework employs an optimized convolutional neural network along with model compression methods to enable efficient offline inference on real-time field images, ensuring high accuracy with minimal latency and reduced computational demand. The system is specifically tailored for use in rural and underdeveloped areas where internet connectivity is limited, offering a reliable, practical, and scalable approach for real-time crop health monitoring and agricultural decision support.

Why it matches plant phenotyping methods植物画像から病害状態を推定する軽量CNNとモデル圧縮を中心に開発しており、植物病害フェノタイピング手法として適格。

abstracta lightweight real-time plant disease detection system intended for edge devices such as smartphones and embedded platforms is presented

Comparative analysis of explainable machine learning integrated with hyperspectral imaging for early prediction of wheat yield. ↗

Wheat is one of the most widely cultivated crops worldwide and provides essential nutrition for millions of people. Accurate and timely wheat yield mapping is critical for strategic planning and decision making to ensure global food security, particularly through early prediction at the field scale to support precision agriculture. Recently, hyperspectral imaging (HSI) integrated with machine learning (ML) techniques has emerged as a robust and reliable approach for assessing crop characteristics and predicting yield. This study combines Visible Near Infrared (VNIR) HSI data (400-1000 nm) with explainable ML models to enhance early and accurate wheat yield prediction and enable comprehensive image based agricultural analysis. The performance of Partial Least Squares Regression (PLSR), Random Forest (RF), and Convolutional Neural Networks (CNN) was compared. Hyperparameters of the CNN model were optimized using Bayesian optimization, resulting in superior performance with R 2 of 0.76, RMSE of 1022.24 gm/plot, and RPD of 2.03 compared with optimized PLSR and RF models. Three distinct explainable artificial intelligence (XAI) methods (Kernel-SHAP, Tree-SHAP and DeepExplainer) were further employed to analyze the predictions of the PLSR, RF, and CNN models and to determine the relative importance of key wavelengths. The CNN model developed using important wavelengths was further employed to visualize the spatial distribution of leaf regions most influential for yield prediction. These findings demonstrate the effectiveness of integrating HSI with explainable ML for advanced agricultural analysis and reliable early yield prediction.

Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習による小麦収量の早期推定を中心に、複数モデルの比較・性能評価・重要波長解析を行っており、植物形質取得手法が主要な貢献です。

abstractThis study combines Visible Near Infrared (VNIR) HSI data (400-1000 nm) with explainable ML models to enhance early and accurate wheat yield prediction

Research on the Application of Agricultural Big Data in Plant Growth Prediction ↗

The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment. Empirical validation across major crops (rice, wheat, maize, tomato) in diverse eco-regions of China (2023--2025) demonstrates significant improvements: the MDA-HI model achieved average RMSE reductions of 42.7% for yield prediction and 38.1% for key phenological stage prediction relative to best-in-class standalone models. A large-scale case study on rice-wheat rotation systems showed that data-driven prescriptions reduced nitrogen fertilizer use by 22.5% and irrigation water by 18.3% while increasing yield by 5.1%. The study further establishes a five-dimensional evaluation system covering accuracy, robustness, interpretability, scalability, and economic benefit. Remaining challenges include edge computing for real-time inference, federated learning for privacy-preserving collaboration, and explainability of complex ``black-box'' models. This research concludes that agricultural big data constitutes a foundational catalyst for predictive, precise, and proactive cognitive agriculture, with profound implications for global food system resilience.

Why it matches plant phenotyping methods農業ビッグデータを用いて生育・収量・フェノロジーを推定するMDA-HI手法を提案し、複数作物・地域で性能検証しており、植物形質推定手法が研究の中心である。

abstractWe propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment.

Trait-based modeling of buffalograss seed yield using UAV-derived plant height and canopy nitrogen concentration ↗

Accurate seed-yield prediction is essential for optimizing nitrogen (N) management in buffalograss seed production. However, current UAV-based approaches often rely directly on vegetation indices (VIs), which provide limited physiological insight and not transfer well across growing seasons. To address this limitation, we developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC), representing crop structural and physiological status, respectively. Field experiments were conducted from 2022 to 2024 under seven N application rates. Using data from 2022 and 2023, we calibrated a quadratic PH-CNC model and then evaluated its predictive performance with an independent 2024 dataset. We also compared this framework with a conventional direct VI-based model. The trait-based model explained 89% of the variation in seed yield during calibration and showed better cross-year predictive performance than the VI-based model (R 2 = 0.70, NRMSE = 17% versus R 2 = 0.52, NRMSE = 22%). In addition, the model captured the decline in seed yield under excessive N input, indicating that it reflected biologically meaningful crop responses. These results demonstrated that combining structural and physiological traits can provide a more robust and interpretable alternative to conventional VI-based methods for UAV-based yield prediction. This framework has practical potential for improving precise and sustainable N management in buffalograss seed production.

Why it matches plant phenotyping methodsUAV由来の草高と群落窒素濃度を統合した形質ベース予測法を開発し、独立年データで検証・従来法と比較しており、表現型取得と解析手法が中心である。

abstractwe developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC)

Iterative Motion Compensation for Canonical 3D Reconstruction From UAV Plant Images Captured in Windy Conditions ↗

Three-dimensional (3D) phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available Unmanned aerial vehicle (UAV) captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes.

Why it matches plant phenotyping methodsUAV画像から植物個体の高解像度3D形状を再構成する手法を開発しており、植物フェノタイピングの取得・抽出方法が研究の中心である。

abstractWe present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants.

Machine vision RGB phenotyping reveals divergent and real‐time responses to different watering regimes in near‐isogenic wheat genotypes ↗

Abstract This study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences under well‐watered and reduced watering conditions in genetically uniform wheat ( Triticum aestivum L.) populations. It aims to support the design of breeding populations by identifying parents with complementary coping mechanisms that can be combined in crosses to produce superior progeny. We used RGB imaging to monitor side‐projected area (SPA) in BC 2 F 6 wheat progenies under well‐watered, pre‐anthesis, and post‐anthesis reduced watering conditions. SPA was modeled with logistic growth curves per genotype to extract dynamic canopy traits, which, together with the area under the SPA‐based growth curve, were then correlated with yield, straw biomass, harvest index, and spike traits measured at maturity. Despite genetic similarity, RGB‐based imaging revealed distinct phenotypes under normal conditions and stress response strategies among wheat lines, highlighting the value of dynamic, non‐destructive phenotyping for identifying complementary response patterns. Under well‐watered conditions ( n = 36), area under the curve was strongly associated with grain weight ( R 2 = 0.76, 95% confidence interval [CI]: 0.59–0.87), but relationships weakened under reduced watering, especially post‐anthesis, indicating a reduced association of canopy size with reproductive output. The data revealed contrasting response patterns among breeding lines based on characteristics of the logistic growth curve under normal conditions, their recovery slope after pre‐flowering reduced watering, or conversion of their straw biomass into harvestable grains. RGB imaging enables real‐time, non‐destructive detection of reduced watering responses in genetically similar wheat lines and provides complementary in‐season data to design next‐generation breeding populations for climate‐resilient cultivars.

Why it matches plant phenotyping methodsRGB画像で動的なキャノピー形質を抽出し、育種利用に向けた非破壊・リアルタイム表現型解析を実質的に評価しているため。

abstractThis study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences

Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features. ↗

Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting. However, there is a scarcity of structured research on the estimation of rapeseed biomass yield. This study aims to address this gap by focusing on rapeseed in Jiangsu Province. Multispectral and RGB images captured by unmanned aerial vehicles (UAVs) were taken during key growth stages (budding, flowering, and podding stages). Using the extracted multidimensional features, we developed biomass-yield estimation models using four machine learning techniques. Subsequently, we employed ensemble learning with multidimensional, multi-stage data and used Shapley additive explanation (SHAP) for feature contribution analysis, thereby constructing a framework for predicting rapeseed harvest characteristics with high estimation accuracy and interpretability. Our analysis indicates that spectral‒texture is the most effective feature combination for biomass estimation, whereas the optimal combination for yield estimation includes three-dimensional (3D) spectral‒textural‒structural features. The synergy of these features, coupled with an ensemble learning model, significantly enhanced the accuracy of rapeseed biomass-yield estimation (biomass: coefficient of determination ( R 2 )=0.72, relative root mean square error (rRMSE)=14.35%; yield: R 2 =0.68, rRMSE=13.67%). The proposed model also achieved stable prediction results across the variety‒density interaction. Overall, this study presents an accurate and generalizable approach for estimating rapeseed biomass yield across various planting patterns, offering new insights for precision harvesting.

Why it matches plant phenotyping methodsUAV画像から抽出した多次元特徴とアンサンブル学習により、ナタネのバイオマス・収量を推定する方法が研究の中心であり、精度評価も実施しているため、植物フェノタイピング手法として適格です。

abstractUsing the extracted multidimensional features, we developed biomass-yield estimation models using four machine learning techniques.

QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data ↗

Maize ( Zea mays L.) is a globally significant crop that plays a crucial role in feeding the growing global population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, we employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding. We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds. Our findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with an R 2 of 0.67 versus 0.56 and an RMSE of 0.12 m vs . 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F 1 DH and F 2 DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. The study successfully identified and validated QTLs associated with plant height, revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.

Why it matches plant phenotyping methodsUAV搭載LiDARによるトウモロコシ草丈の高スループット推定を開発・評価し、単個体と列スケールの精度比較を行っているため、表現型取得法が研究の中心です。

abstractwe employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines

Artificial intelligence in sugarcane breeding: A comprehensive review of applications, tools, and future prospects ↗

Sugarcane is a high-value industrial crop vital for sugar and biofuel production, yet increasingly constrained by climate variability, biotic and abiotic stresses, soil degradation, and inefficient input use. Traditional breeding and crop management approaches are often slow, labour-intensive, and less precise, emphasizing the need for digital transformation in sugarcane agriculture. AI now offers powerful tools to accelerate genetic improvement, enhance stress resilience, and optimize resource-use efficiency. This review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems. ML and DL models enable automated, accurate prediction of key traits such as biomass, canopy temperature, nitrogen status, and sugar recovery using UAV, satellite, and proximal sensing data. AI-powered genomic selection approaches leveraging convolutional networks, transformers, and attention mechanisms improve prediction accuracy for yield, ratooning ability, and stress tolerance by integrating SNPs, pedigree, and multi-environment datasets. Emerging innovations such as digital twins, multimodal data fusion, reinforcement learning-based irrigation scheduling, and climate-smart advisory models further strengthen real-time crop intelligence. The integration of blockchain-enabled breeding databases, FAIR data standards, and interoperable analytics pipelines supports scalable and collaborative research. Literature analysis reveals 15-30% gains in selection efficiency, >90% accuracy in disease detection, and phenotyping cost reductions of up to 70%. Key challenges remain, including scarce annotated datasets, genotype × environment complexity, model interpretability, and adoption barriers for smallholders. A future roadmap is proposed featuring multimodal foundation models, edge-AI deployment, and explainable breeder dashboards. AI is redefining sugarcane research from reactive to predictive, enabling climate-resilient, sustainable, and profitable production systems.

Why it matches plant phenotyping methodsサトウキビ育種におけるAI応用の総説であり、高スループット表現型解析、UAV・衛星・近接センシングによる形質推定を主要な対象として扱っているため、フェノタイピング手法レビューとして適格。

abstractThis review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems.

Plant Disease Detection ↗

Agriculture remains a critical pillar of economic sustainability, particularly in developing countries where crop productivity directly impacts food security and farmer income. However, plant diseases significantly reduce crop yield and quality, leading to substantial economic losses. Traditional disease detection techniques rely on manual inspection and expert consultation, which are time-consuming, subjective, and often inaccessible in rural areas. This paper presents an advanced deep learning-based system for plant disease detection, severity classification, and outbreak prediction. The proposed framework integrates Convolutional Neural Networks (CNNs) for image-based disease identification, clustering techniques for severity classification, and Long Short-Term Memory (LSTM) models for time-series-based prediction. The system also incorporates feature importance analysis using Random Forest to enhance interpretability. A web-based interface enables real-time interaction, allowing users to upload images and receive immediate diagnostic feedback. Experimental results demonstrate high accuracy and robustness across diverse datasets. The integration of multiple learning paradigms ensures improved performance, scalability, and adaptability, making the system suitable for real-world agricultural applications

Why it matches plant phenotyping methods植物画像から病害の有無と重症度を推定する深層学習手法が研究の中心であり、病害状態のフェノタイピングに該当する。

abstractThis paper presents an advanced deep learning-based system for plant disease detection, severity classification, and outbreak prediction.

AI-Plant Disease Prediction & Cure Recommendation Model ↗

Plant diseases pose a serious challenge to global food security as they reduce crop yield, quality, and productivity. The timely detection of plant diseases is crucial to prevent large-scale agricultural losses. In rural and underdeveloped regions, farmers lack access to agricultural experts, leading to incorrect diagnosis and ineffective treatment. This research focuses on developing an AI-powered plant disease detection and cure recommendation system using Convolutional Neural Networks (CNNs). The system processes leaf images, detects diseases, and provides tailored treatment recommendations. Experimental results show that the proposed model achieves high accuracy and demonstrates its applicability for real-world deployment through web and mobile platforms. Plant diseases pose a serious challenge to global food security as they reduce crop yield, quality, and productivity. The timely detection of plant diseases is crucial to prevent large-scale agricultural losses. In rural and underdeveloped regions, farmers lack access to agricultural experts, leading to incorrect diagnosis and ineffective treatment. This research focuses on developing an AI-powered plant disease detection and cure recommendation system using Convolutional Neural Networks (CNNs). The system processes leaf images, detects diseases, and provides tailored treatment recommendations. Experimental results show that the proposed model achieves high accuracy and demonstrates its applicability for real-world deployment through web and mobile platforms.

Why it matches plant phenotyping methods葉画像から植物病害を検出するCNN手法の開発が研究の中心であり、植物の病徴・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として収録する。

abstractThis research focuses on developing an AI-powered plant disease detection and cure recommendation system using Convolutional Neural Networks (CNNs).

Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model. ↗

Water stress is a global challenge that severely impacts crop production by hindering essential physiological processes. To address this issue, proximal sensing has emerged as a promising technique for the early identification of stress in vegetables, enabling timely management interventions and optimizing yield. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to a decrease in various plant traits. The study also revealed significant differences in RGB image indices between different irrigation levels, with strong positive relationships identified for the majority of RGB image indices incorporating green components (G) and R2 reaching 0.99 for various plant traits. However, the red-blue simple ratio (RB) index, which does not consider the G, did not significantly correlate with any of the plant traits. The ANN models achieved high prediction accuracy, with high R2 values reaching 0.99 for various plant traits and yields. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under limited water conditions.

Why it matches plant phenotyping methodsRGB画像指標とANNによる植物形質・収量の定量推定が研究の中心であり、予測精度も評価しているため、画像ベース形質推定の方法適用・検証に該当する。

abstractThis study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits

Quantifying the dynamic recovery of plants through stress memory and physiological attractors. ↗

Climate change-induced weather variability poses a growing threat to global food security, yet plant resilience is still interpreted through static and reductionist models that treat stress as independent and transient. Here, we introduce a unified quantitative framework grounded in dynamical systems theory. We formalize three novel metrics: (1) the Phenological Weather Memory Index (PWMI), that quantifies exponentially decaying stress memory across developmental stages (with a decay constant α = 0.10 determined by cross‑validation); (2) the Treatment-Weather Resonance Coefficient (TWRC), which measures the alignment of agronomic interventions with favorable weather conditions; and (3) the Physiological State-Space Trajectory (PSST), which maps multi-trait plant physiology into low dimensional attractor basins. Analyzing 288 tomato plants across 24 cultivars under hot, sub-tropical conditions (mean VPD: 2.53 kPa, 65 heat days > 35 °C), we discovered that stress memory is strongly phase-dependent, remaining minimal during vegetative growth (PWMI = 0.009) but increasing sharply during reproductive phase (PWMI = 0.574). Despite the prolonged thermal stress, 97.9% of plants converged into a stable high-yield attractor basin, revealing a fundamental nonlinearity in plant performance. This convergence was driven by dynamic recovery, defined as the capacity of certain cultivars to rapidly forget the stress memory while maintaining internal physiological flexibility. Cultivars such as 'Pony Express', combined low PWMI with effective treatment-weather synchronization, enabling stable productivity under extreme conditions. Together, these results demonstrate that resilience is not a static trait of endurance, but an emergent property arising from temporal synchronization, rapid stress recovery and stable physiological organization. By quantifying stress "forgetting curves" and attractor dynamics, this framework provides a predictive, systems-based foundation for breeding and management strategies that prioritize dynamic recovery over stress tolerance alone.

Why it matches plant phenotyping methods植物のストレス記憶・回復・生理状態を定量化する新規指標と状態空間フレームワークを中心に提示しており、単なる生理測定ではなく表現型抽出・解析手法の開発に該当する。

abstractHere, we introduce a unified quantitative framework grounded in dynamical systems theory.

Phenotype imputation using high‐throughput phenotyping produces a new secondary trait for further selection modeling ↗

Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k ‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in wheat ( Triticum aestivum L.), using 2414 lines across six environments. Three multispectral vegetation indices (VIs) collected over time from aerial imagery were used as predictors for imputation of simulated missing data ranging from 10% to 70%. Statistical analyses examined the accuracy of imputed GY (IGY) as well as its reliability, heritability, and genetic correlation with GY. Imputation accuracies were highest for MLR, but accuracy differences between methods were small. Accuracies only decreased slightly as percent missing data increased. Genetic correlations between IGYs and observed GY within the environment ranged from −0.01 to 0.51, consistently greater than the corresponding genetic correlations between VIs and GY. Respectively, the reliabilities and heritabilities of IGY were 24% and 45% lower than those of GY, and like those of the VIs. Altogether, this study found that HTP data can be used to impute GY phenotypes suitable for use in further analyses; however, imputed and observed GY should be modeled as separate traits. Further research is needed to improve the heritability of IGY and to evaluate the utility of IGY in GS models.

Why it matches plant phenotyping methodsHTP由来の航空画像データを用いて小麦収量表現型を機械学習で補完し、複数手法の精度・信頼性・遺伝率を評価しており、表現型推定手法が研究の中心である。

abstractThree machine learning models: multiple linear regression (MLR), missForest, and k ‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in wheat

Sorghum plant height and yield prediction using multispectral data and sUAS ↗

Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic improvement, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, digital terrain models (DTMs), and digital surface models (DSMs). Manual plant height measurements were used for correlation and simple linear regression analyses, while biomass was predicted using random forest regression. Results. The DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE = 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE = 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. The DTMs and DSMs derived from multispectral imagery accurately predicted plant height during later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into predictive models enhanced biomass yield prediction. The findings demonstrate that sUAS-mounted sensors and multispectral indices are valuable tools for phenotyping in sorghum breeding programs in Costa Rica.

Why it matches plant phenotyping methodssUASマルチスペクトル画像とフォトグラメトリから草丈・バイオマスを推定し、実測値との相関・回帰および予測精度を評価する手法中心の研究である。

abstractObjective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights.

Integrating Multi-Source and Multi-Temporal UAV Observations to Improve Wheat Yield Prediction Using Machine Learning. ↗

Accurate yield estimation is vital for precision wheat management and breeding. Traditional methods based on single growth stages or single-source data cannot capture cumulative growth effects, limiting prediction accuracy. UAV remote sensing provides high-resolution, multi-source, and multi-temporal data, enabling improved non-destructive yield estimation. In this study, UAV-based multispectral and RGB imagery were collected at six key growth stages, and vegetation indices, texture, and color features were extracted to develop yield prediction models using RF, XGBoost, and KNN under single- and multi-temporal scenarios. The results showed that red-edge-based vegetation indices were highly sensitive to wheat yield and outperformed texture- and color-based features. Multi-feature fusion further improved prediction accuracy at key growth stages, particularly during booting and flowering (R 2 = 0.53-0.67). Compared with single-temporal models, multi-temporal data fusion significantly enhanced yield estimation accuracy, achieving a maximum R 2 of 0.72 by integrating data from the late-jointing, booting and flowering stages. Among the algorithms, XGBoost and KNN exhibited superior accuracy and stability across most growth stages. Overall, these results demonstrate that integrating UAV-based multi-source and multi-temporal remote sensing data effectively improves the accuracy and robustness of wheat yield estimation, providing valuable technical support for precision agriculture and phenotyping-assisted breeding.

Why it matches plant phenotyping methodsUAV画像から特徴量を抽出し、機械学習でコムギ収量を推定する手法が研究の中心であり、マルチソース・マルチテンポラル統合の技術評価も行っている。

abstractUAV remote sensing provides high-resolution, multi-source, and multi-temporal data, enabling improved non-destructive yield estimation.

Smart Breeding: Integrating AI, Genomics and Phenomics for Next-Generation Crops: A Review ↗

The convergence of artificial intelligence (AI), genomics and phenomics is ushering in a new era of smart breeding a paradigm that promises to dramatically accelerate genetic gain while reducing the time and cost associated with developing elite crop varieties. Conventional plant breeding, though enormously successful over the past century, is increasingly challenged by a rapidly changing climate, a growing global population projected to reach nearly 10 billion by 2050 and the biological complexity of quantitative traits. Smart breeding leverages exponential growth in genomic data, high-throughput phenotyping platforms and the analytical power of machine learning and deep learning algorithms to navigate these challenges. This review synthesizes the current state of knowledge across three interdependent pillars: AI and machine learning for genomic selection, trait prediction and decision support; next-generation sequencing and multi-omics tools that have transformed our understanding of crop genetic architecture; and field and controlled-environment phenomics platforms that bridge the genotype phenotype gap. Further discuss integration through digital twins, knowledge graphs and federated learning frameworks and examine applications in gene editing, stress tolerance and yield improvement. Key challenges data standardization, interpretability of black-box models, regulatory frameworks and equitable access are critically assessed and a roadmap for the next decade of smart breeding is proposed. Another point highlighted in this review is the need to conduct collaborative and interdisciplinary research to achieve the full potential of smart breeding technologies. It emphasizes the necessity of capacity-building, data sharing systems and policy support to provide sustainable and inclusive agricultural growth. Moreover, the paper highlights the importance of new innovations in developing resilient and productive and future-oriented crop systems.

Why it matches plant phenotyping methodsAI・ゲノミクス・フェノミクスを統合するレビューであり、高スループット表現型解析プラットフォーム、形質予測、機械学習による表現型解析を主要な構成要素として扱っているため、方法レビューとして採択。

abstractSmart breeding leverages exponential growth in genomic data, high-throughput phenotyping platforms and the analytical power of machine learning and deep learning algorithms

Plant-Leaf Disease Detection Based on Texture Enhancement Using ATD-Net ↗

Early plant leaf disease detection and timely control is important for agricultural yield and stability. Yet, it is difficult for manual labor to monitor the health of the plant leaf 24 h a day. Existing detection approach cannot meet the demands of texture enhancement features. Therefore, this paper proposes a new detection approach which undergoes three-layer transformations: convolutional layer, attention mechanism layer and loss function layer. Firstly, ADown is used to extract fine-grained texture features from suspected leaves to reduce computational load. Secondly, Gabor texture enhancement is proposed to extract and enhance the contour and the directional texture of suspected areas using multi-directional filtering, followed by a combination Transformer to enhance the global context modeling capability. Thirdly, a dynamic boundary loss function (DBL) is employed to dynamically adjust the probability distribution of bounding box regression through adaptive temperature coefficient and information entropy, thereby improving the positioning accuracy of the detection box. The experiments show that ATD-Net achieved an average accuracy of 87.42% (mAP50) and an accuracy of 85.96%, with a computational complexity of 6.5 GFLOPs. The visualization results and ablation experiments show that the collaborative work of the proposed modules significantly improves the detection robustness in complex backgrounds, early diseases, and small target scenes. Compared to the original model, ATD-Net achieves a performance improvement of 1.1% at mAP50 and a speed increase of 17.7%. The model size remains almost unchanged, at 5.2 MB. It is an efficient and promising solution for future real-time disease recognition in complex agricultural environments.

Why it matches plant phenotyping methods植物葉の病害状態を画像から検出・認識する新規モデルを開発し、実験・アブレーションで性能検証しており、植物フェノタイピング手法が中心である。

abstractthis paper proposes a new detection approach which undergoes three-layer transformations

A Deep Learning Approach for Enhancing Crop Disease Detection and Pesticide Management ↗

The rapid growth of the global population and the intensification of agricultural practices have significantly increased the vulnerability of crops to diseases and pest infestations, leading to substantial yield losses and economic instability in the agri-food sector. Traditional crop disease identification and pesticide application methods are largely manual, time-consuming, and prone to human error, often resulting in delayed intervention and excessive or inefficient pesticide usage. In recent years, deep learning has emerged as a powerful paradigm for automated crop disease detection due to its superior capability in learning complex visual patterns from largescale image data. This research presents a deep learning–based approach for enhancing crop disease detection and pesticide management by leveraging advanced convolutional neural networks and intelligent decision-support mechanisms. The proposed approach aims to achieve accurate and early-stage disease identification while facilitating targeted and optimized pesticide recommendations, thereby minimizing chemical overuse and environmental impact. By integrating image-based disease recognition with intelligent inference models, the system supports precision agriculture objectives, including improved crop health monitoring, sustainable pest control, and increased agricultural productivity. The study synthesizes recent advances in deep learning architectures, dataset augmentation strategies, and evaluation metrics relevant to real-world agricultural deployment. The findings underscore the potential of deep learning–driven systems to transform crop protection practices by enabling scalable, real-time, and cost-effective disease detection and pesticide optimization.

Why it matches plant phenotyping methods植物画像から病害状態を検出する深層学習手法が研究の中心であり、農薬管理は付随する応用であるため、植物フェノタイピング手法として含める。

abstractThis research presents a deep learning–based approach for enhancing crop disease detection and pesticide management by leveraging advanced convolutional neural networks and intelligent decision-support mechanisms.

ParaLeafNet-Severity: A Parallel Deep CNN with Squeezeand-Excitation Attention for Automated Plant Disease Severity Assessment and Agricultural Health Monitoring ↗

Accurate and timely detection of plant diseases is a critical component of modern agricultural biosecurity, directly impacting crop yield, food safety, and the health of farming communities. Plant diseases cause annual agricultural losses that exceed $220 billion worldwide, yet accurately quantifying how severely a plant is infected remains one of the least-addressed problems in automated crop monitoring. This paper presents ParaLeafNet-Severity, a deep parallel convolutional neural network designed to perform disease identification and four-level severity grading simultaneously within a single forward pass. The architecture draws complementary feature representations from two lightweight backbones — MobileNetV2 and MobileNetV3Small — running in parallel, fuses their outputs through channel-wise Squeeze-and-Excitation (SE) attention, and routes the resulting shared embedding to two task-specific output heads. An optional K-Means clustering branch operates on the shared feature space to discover natural severity groupings without requiring additional expert labels. Experimental evaluation on the adapted PlantVillage dataset demonstrates that the proposed framework achieves strong performance in disease identification and severity classification, outperforming existing baseline approaches while maintaining robustness across multiple classes. The integration of an unsupervised clustering module further confirms that the learned feature representations preserve meaningful severity-related structure consistent with expert annotations. In addition, the model maintains a compact design and efficient inference characteristics, making it suitable for deployment on resource-constrained devices such as smartphones and edge platforms. Interpretability analysis using Grad-CAM indicates that the model focuses on pathologically relevant regions of the leaf, supporting transparent and reliable decision-making. Furthermore, the extended ParaLeafNet framework incorporates deployment-oriented optimization techniques that enhance performance without requiring modifications to the core architecture.

Why it matches plant phenotyping methods葉画像から植物病害の重症度を自動推定するCNN手法が研究の中心であり、植物状態の表現型計測に該当する。

abstractThis paper presents ParaLeafNet-Severity, a deep parallel convolutional neural network designed to perform disease identification and four-level severity grading simultaneously within a single forward pass.

Crop Disease Prediction and Yield Prediction using Machine Learning (Sugarcane) ↗

Abstract— Sugarcane cultivation faces significant challenges due to biotic stresses such as fungal and viral diseases, as well as abiotic factors including climate variability and soil nutrient imbalance. Accurate disease diagnosis and yield estimation are critical for improving productivity and ensuring sustainable agricultural practices. This study proposes an integrated machine learning framework for automated sugarcane disease classification and yield prediction.A Convolutional Neural Network (CNN)-based deep learning model is implemented for image-based disease detection, enabling automatic feature extraction and high-accuracy classification of major sugarcane diseases. For yield prediction, supervised regression algorithms including Random Forest Regressor and Gradient Boosting are employed to model the relationship between environmental parameters, soil properties, and historical yield data.Experimental evaluation demonstrates improved predictive performance in both classification and regression tasks. The proposed system provides a data-driven decision support tool for farmers, enhancing crop management efficiency and reducing agricultural losses. Keywords: Sugarcane, Disease Prediction, Yield Prediction, Machine Learning, Convolutional Neural Network (CNN), Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Precision Agriculture, Crop Monitoring.

Why it matches plant phenotyping methodsCNNによるサトウキビ病害の画像ベース分類は、植物の病徴・状態を観測から推定する中心的手法であり、方法適用として収録対象です。収量予測も含まれますが、環境・土壌・履歴データに基づく農業予測で、病害画像分類部分が主な適格要素です。

abstractThis study proposes an integrated machine learning framework for automated sugarcane disease classification and yield prediction.

Unmanned aerial vehicle–based spatiotemporal phenotyping and growth modeling for forecasting potato yield ↗

Abstract Monitoring spatial variations in plant growth and forecasting yield before harvest provides valuable insights for optimizing agronomic decision‐making in potato ( Solanum tuberosum L.) cultivation. Although unmanned aerial vehicle (UAV)‐based remote sensing has recently enabled the development of tuber fresh weight (TW) estimation models, their integration into practical yield‐forecasting systems remains limited. In this study, we developed machine learning models to estimate tuber weights at multiple preharvest time points using RGB and multispectral UAV imagery. Image‐derived features were extracted from the orthomosaic and digital surface model images for each plot, and a random forest regression model was trained for TW estimation. The estimated values were subsequently used to fit the Gompertz growth curves, which were then used to forecast the yield at the expected harvest time. The correlation between the estimated and observed values was strong in the UAV‐based TW estimation, with correlation coefficients exceeding 0.8 and coefficients of determination ( R 2 ) above 0.6 at all time points. Yield forecasts based on fitted growth curves achieved a correlation of 0.78 and an R 2 of −0.17 in 2023 and 0.70 and an R 2 of 0.47 in 2024. These results demonstrate that UAV‐based sampling combined with machine learning is a feasible approach for monitoring spatiotemporal variations in tuber growth and forecasting potato yield at the plot level prior to harvest.

Why it matches plant phenotyping methodsUAV画像からジャガイモ塊茎重量を推定し、機械学習と成長曲線で収量を予測する手法が研究の中心であり、推定精度も検証している。

abstractwe developed machine learning models to estimate tuber weights at multiple preharvest time points using RGB and multispectral UAV imagery.

Apple Plant Disease Detection System using Leaf Images ↗

The cultivation of apples is affected by various apple plant diseases. These diseases, if not identified and treated on time, may lead to considerable losses in yield. Early detection is highly essential in order to provide early warnings to farmers and to help in identifying diseases at an early stage so that further action can be done to prevent the spread of disease as these diseases cannot be identified through naked eyes in their early stages. This leads to less wastage of yield. This paper proposes a comparison among the deep learning models such as LetNet, AlexNet, VGG, Resnet, Inception Net, and DensNet, for the efficient classification of leaf diseases of the apple plant. The model is trained on the Plant Village Dataset (Updated) taken from kaggle, which contains both healthy and diseased leaf images of apple plants. In this, images go through various preprocessing techniques like resizing, normalizing, and augmenting images in order to increase the robustness of the model. AlexNet attained the maximum classification accuracy in initial trials but had the largest number of parameters, didn't use modern regularization, and hence got at risk of overfitting at some early stage. The paper improved their performance by using a hybrid architecture which consisted of MobileNetV3 and ResNet50 because MobileNetV3 offered efficient extraction of features with little computational expense. It was further complemented by the depth features offered by ResNet50. The main aim for proceeding with the idea of hybrid architecture was not only to improve generalization but also to prevent overfitting. The hybrid model is implemented using streamlit. This web interface allows the users to upload images of leaves and get real-time results predicting whether the leaves are affected by a disease or not. The system demonstrates high classification accuracy and effective differentiation among visually similar diseases. However, the model's performance in terms of empirical data analysis is influenced by dataset quality, computational resource demands, and its limited ability to generalize in the presence of sparse data. Despite these challenges, the proposed solution provides a scalable and accessible tool to assist farmers and agricultural experts in early disease detection and management.

Why it matches plant phenotyping methodsリンゴ葉の画像から病徴・病害状態を推定する画像ベースの植物フェノタイピング手法を開発・比較しており、分類モデルと実装が研究の中心である。

abstractThis paper proposes a comparison among the deep learning models such as LetNet, AlexNet, VGG, Resnet, Inception Net, and DensNet, for the efficient classification of leaf diseases of the apple plant.

AgriViT-NLP: A Multi-Modal Framework for Plant Disease Detection and Farmer Query Understanding ↗

Abstract- Plant diseases pose a serious threat to global food security by reducing crop yield and quality. To improve detection, a multi-modal diagnostic framework is proposed that combines Vision Transformers (ViTs) for image-based disease classification with Natural Language Processing (NLP) for symptom description and treatment recommendations. The system supports multilingual interaction and generates automatic disease reports, making it accessible to diverse farming communities. By integrating ViT and NLP, the model offers higher diagnostic accuracy and interpretable, farmer-friendly support. Designed for real-world use, it can be deployed on mobile and IoT platforms, enabling smart, interactive decision-making in precision agriculture. Keywords: Plant Disease Detection, Vision Transformers, NLP, Multi-Modal Learning, Precision Agriculture.

Why it matches plant phenotyping methods植物画像から病害を分類するマルチモーダル診断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、フェノタイピング手法として採用する。

abstracta multi-modal diagnostic framework is proposed that combines Vision Transformers (ViTs) for image-based disease classification

UAV-Based Multispectral Phenotyping and Machine-Learning Modeling Reveals Early Canopy Traits as Strong Predictors of Yield and Weed Competitiveness in Oat (Avena sativa L.) ↗

Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R2 = 0.90) and weedy yield (MAE = 258, R2 = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からキャノピー形質を高スループットに抽出し、機械学習による予測性能も評価することが研究の中心であるため、植物フェノタイピング手法の実質的応用・検証に該当する。

abstractThis study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars

AI Crop Disease Detection Using Mobile Camera ↗

Abstract-The paddy farming industry suffers a lot due to a number of diseases that are capable of producing a crop yield of 20-70 per cent. Conventional disease surveillance systems are slow, costly and need a person who is skilled and hence not accessible to small-scale farmers. The proposed AI-based system in this paper will involve the detection of the paddy disease through the remote sensing data of the Bhuvan and Bhoomi systems of the Indian Space Research Organization and the mobile camera. The given system uses a deep convolutional neural network (CNN) model that is mobile-oriented with an accuracy of 96.8 percent to recognize the major paddy diseases such as bacterial leaf blight, blast disease, brown spot, and sheath blight. The system is based on MobileNetV2 structure to perform efficient on-device inference with a mean processing time of 85ms per image. Combination with satellite Bhuvan imagery and Bhoomi land records allows monitoring of disease on a multi- scale; focusing on individual plants down to the area level. The process of field validation on 250 farmers confirmed the user satisfaction and the high rate of early disease detection increased considerably. The suggested system is a viable, economical, and accessible system of precision agriculture and food security. Index TermsPaddy disease detection, Mobile AI, Deep learn- ing, CNN, MobileNetV2, Remote sensing, Bhuvan, Bhoomi, Precision agriculture. Keywords: Paddy Disease Detection, Mobile AI, Deep Learning, MobileNetV2, Precision Agriculture.

Why it matches plant phenotyping methodsモバイルカメラ画像とCNNによりイネの病害状態を直接推定する手法を開発し、精度・処理時間・圃場検証を報告しており、植物フェノタイピング手法が中心である。

abstractThe proposed AI-based system in this paper will involve the detection of the paddy disease through the remote sensing data of the Bhuvan and Bhoomi systems of the Indian Space Research Organization and the mobile camera.

Identification of self-incompatibility in macadamia (Macadamia SPP.) using field-bagging and fluorescence microscopy ↗

Self-incompatibility (SI) significantly reduces crop yield, often far below its genetic potential. Developing self-compatible varieties is the most effective strategy for overcoming SI in crops. Most macadamia ( Macadamia SPP.) species exhibit SI or partial self-incompatibility (PSI), so the efficient identification of self-compatible germplasms has emerged as a crucial topic. To characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility. That is, the degree of SI was based on the final self-incompatibility index (F_SI), which was calculated based on the open-pollination final nut set per raceme (OP_FNS) and self-pollination final nut set per raceme (SP_FNS) values (strong SI: F_SI ≥ 0.7, medium SI: 0.4 ≤ F_SI 35%). Through comprehensive analysis of the field-bagging and fluorescence-microscopy observations, thirteen varieties with strong SI (816, 778, 842, Special, 812, D, 820, 246, 772, A16, A4, 951, and 695), six varieties with moderate SI (851, 828, 508, 936, O.C, and D4), and four varieties with weak SI (915, HY, 836, and 814) were identified. Our study provides a theoretical foundation and technical support for advancing germplasm resource innovation, and the genetic improvement and breeding of self-compatible macadamia varieties.

Why it matches plant phenotyping methods自家不和合性という植物状態を対象に、圃場袋掛けと蛍光顕微鏡観察を用いた標準化分類体系を確立しており、表現型の取得・評価法が研究の中心である。

abstractTo characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility.

PlantPathNet: a novel network based on cross-layer feature integration for plant disease classification ↗

Introduction Reliable identification of plant diseases from leaf images is essential for effective crop monitoring and the prevention of yield deterioration. With the growing adoption of deep learning in agricultural applications, convolutional neural network–based classifiers have demonstrated notable success in visual plant disease recognition. Methods In this study, we propose PlantPathNet , a purpose-built deep learning architecture for plant disease classification. Input images are transformed from the RGB to the HSV color space to enhance the representation of disease-related visual features. A novel Cross-layer Feature Integration Module (CFIM) is introduced to effectively aggregate discriminative features across multiple network depths. Additionally, an efficient channel attention mechanism based on ECANet is incorporated to emphasize disease-relevant representations. The model is optimized using a composite loss function combining modified softmax loss and center loss to address class imbalance and improve feature separability. Results Extensive experiments conducted on the PlantVillage dataset demonstrate that PlantPathNet outperforms several state-of-the-art models, including ResNet-50, Inception-V3, DenseNet121, VGG16, and Vision Transformer-based approaches. The proposed model achieves an overall accuracy of 99.57%, precision of 99.52%, recall of 99.54%, F1-score of 99.53%, and an AUROC of 99.84%. Discussion The results indicate that the integration of HSV-based preprocessing, CFIM, and channel attention significantly enhances classification performance. The proposed framework provides a robust and efficient solution for automated plant disease diagnosis and has strong potential for real-world agricultural applications.

Why it matches plant phenotyping methods葉画像から植物病害状態を推定する深層学習手法を新規開発し、既存モデルとの比較検証も行っているため、植物フェノタイピング手法が中心である。

abstractwe propose PlantPathNet , a purpose-built deep learning architecture for plant disease classification.

AI Based Crop Disease Detection and Pesticide Recommendation ↗

ABSTRACT Agriculture plays a vital role in the economy of many countries, especially India. Crop diseases significantly affect agricultural productivity and lead to economic losses for farmers. Early detection of crop diseases and providing appropriate pesticide recommendations can minimize damage and improve crop yield. This project proposes an Artificial Intelligence-based system that detects crop diseases from leaf images using Convolutional Neural Networks (CNN) and recommends suitable pesticides along with preventive suggestions. The system uses image processing and deep learning techniques to classify healthy and diseased crops accurately. The proposed solution is cost-effective, user-friendly, and can assist farmers in making informed decisions. Keywords: Crop Disease Detection, Convolutional Neural Network, Image Processing, Pesticide Recommendation, Artificial Intelligence.

Why it matches plant phenotyping methods葉画像から植物病害状態をCNNで分類する手法が研究の中心であり、植物の病害表現型を直接推定しているため。農薬推薦も含むが、病害検出自体が主要な技術的貢献である。

abstractThis project proposes an Artificial Intelligence-based system that detects crop diseases from leaf images using Convolutional Neural Networks (CNN)

Impact of Sowing Space and Depth on Canopy Architecture and Vertical Leaf Traits in Dryland Wheat ↗

Sowing space and depth critically influence wheat canopy architecture, yet their layer-specific effects remain poorly understood. This two-year field study evaluated the effects of three sowing spaces (1.5, 3.0, 4.5 cm) and three sowing depths (2, 3, 6 cm) on canopy projection area, leaf inclination angle, leaf area distribution, and leaf area index (LAI) of dryland wheat (Triticum aestivum ‘Ningmai 13’) in Luhe, Nanjing, China, using image-based phenotyping with manual validation. Narrow spacing (1.5 cm) with intermediate depth (3 cm) produced the largest canopy projection area (0.239–0.245 m2) and an increase in leaf erectness in the middle canopy layer (+23% above average). The highest LAI values (4.23–4.28 m2 m−2) were achieved with narrow spacing (A1B1, A1B2), demonstrating that dense canopies can be established under dryland conditions. Grain yield (g/plant) was measured as a supporting agronomic indicator; the highest yield per plant (14.36 g/plant) was observed in A3B1. Image-based measurements showed excellent agreement with manual methods (R2 > 0.97 for all traits), validating the phenotyping pipeline. These findings contribute to a deeper understanding of how sowing parameters shape wheat canopies in dryland systems.

Why it matches plant phenotyping methods画像ベースの表現型測定パイプラインを用いてキャノピー形態・葉形質を抽出し、手動測定との一致性も検証しており、フェノタイピング手法が中心的です。

abstractusing image-based phenotyping with manual validation

Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials ↗

Abstract Improving selection for multiple disease resistance (MDR) and yield in maize ( Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years in near‐isogenic inbreds, near‐isogenic hybrids, and a diverse hybrid panel. VIs showed lower coefficients of variation but broad‐sense heritability ranging from 0.09 to 0.95, compared with 0.30 to 0.99 for visual disease scores and 0.21 to 0.97 for yield. Correlations between VIs and ground traits were strongest in near‐isogenic hybrids, particularly for early‐season yield prediction ( r = 0.98–0.99 in 2018; r = 0.87–0.90 in 2019), and moderate for total disease severity (e.g., r = −0.61 to −0.68 in 2018). Associations were weaker and less consistent in the diverse hybrid panel (yield r = 0.11–0.28). Disease‐specific signals were temporally structured: NLS correlated most strongly with early‐season VIs ( r = −0.59 to −0.75), whereas ATD was best detected mid‐season ( r = −0.63 to −0.66) along with NLB ( r = −0.66 to −0.75). Overall, multispectral VIs captured meaningful canopy variation related to MDR and yield, with predictive performance depending on germplasm structure and flight timing. These findings highlight the potential of drone‐based temporal phenotyping to complement visual assessments and improve selection efficiency in maize breeding programs.

Why it matches plant phenotyping methodsドローン multispectral による作物キャノピー形質の取得・予測性能を検証し、病害抵抗性と収量予測への適用を評価しており、フェノタイピング手法が中心である。

abstractWe evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models ↗

Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.

Why it matches plant phenotyping methodsUAV空撮とフォトグラメトリによる作物表面モデル（CSM）を用いたトウモロコシ生育差・収量変動の検出時期を検証しており、表現型取得法が研究の中心である。

abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models ↗

Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.

Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動の検出時期を評価しており、植物表現型の取得方法の技術的適用と評価が中心である。

abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.

Comprehensive Survey of Plant Disease Detection and Classification based on Machine Learning and Deep Learning Algorithms ↗

Plant diseases are a major problem worldwide, affecting crop yield and quality and impacting food security. Early identification and accurate diagnosis of plant diseases is crucial to minimizing crop damage and maximizing eco-friendly farming practices. Traditional methods of plant disease detection include manual diagnosis by experts, which is time consuming to arrive at the final diagnosis and is prone to human errors. In recent years, Machine Learning (ML) and Deep Learning (DL) algorithms have been used for the automatic detect plant diseases, with no intervention needed by experts. These algorithms are used to detect diseases by analysing images of plant stems, leaves and other parts, differentiating between healthy and diseased plants. Step by step procedures are used to diagnose plant diseases by employing techniques such as image pre-processing, feature extraction, and classification to enhance image quality for accurate disease classification and prediction. This paper details the application of various existing ML and DL algorithms in the detection, classification and prediction of plant diseases, where image-based techniques are also adopted for accurate and swift diagnosis. Performance metrics such as sensitivity, accuracy, and specificity elaborate on the efficacy of ML and DL algorithms, acting as reliable tools for accurate plant disease detection. The outcomes of this study illustrate that these ML and DL models are well suited for the identification and classification of plant diseases.

Why it matches plant phenotyping methods植物画像から病害状態を検出・分類する機械学習手法を体系的に扱うレビューであり、植物フェノタイピング手法が中心です。

abstractThis paper details the application of various existing ML and DL algorithms in the detection, classification and prediction of plant diseases, where image-based techniques are also adopted for accurate and swift diagnosis.

CNN-Assisted Growth Monitoring and Stress Management of Cucumber in Semi-Transparent PV Greenhouses for Agrivoltaics ↗

Currently operating commercial photovoltaics (PV) systems integrated with agricultural production (Agrivoltaics) offer immense potential for the dual harvest of renewable energy and agro-products. Within controlled-environment agriculture (CEA), the use of semi-transparent photovoltaics (ST-PV) and the ability to control the microclimate and shading are beneficial for the production of high-value crops such as cucumbers. The objective of this research was to commence the cultivation of cucumbers under evolving CEA-PV systems by combining greenhouse experiments with computer vision (CV) based driven phenotyping to create an analytical framework and system control framework for the cultivation of cucumbers in an evolving CEA-PV system. The method involved using the monitored plant vigor to control in real time the irrigation and shading of the cucumber plants. The control of irrigation and shading was based on the monitored plant vigor as determined by a U-Net++ implementation for canopy segmentation, an EfficientNet-B3 implementation for stress detection, and a CNN regressor for growth trait estimation. Within the greenhouse, uniform environmental and fertigation conditions were established to evaluate the effect of four shading regimes (0%, 20%, 40%, 60%) on the cucumbers. Simulated, yet representative results predicted cucumber yields to be stable (within ±4% of full yield) with a 20% shading and a 15-20% reduction in water use compared to full sun. Yield was also observed to drop by 10-14% under higher shading of 40 to 60% due to insufficient photosynthetic activity for fruiting. The CNN based models were robust, (segmentation IoU 0.91, stress-class F1 0.92, LAI regression R²≈0.93), allowing for precise and comprehensive monitoring in an annual non-invasive fashion. The greenhouse's annual photovoltaic (PV) output was estimated to be 1,550 to 1,750 kWh/kWp which is able to exceed the energy demand resulting to a net energy surplus. The outcome demonstrates that the cucumber crop can be successfully combined with controlled environment agrovoltaic systems with moderate shading for optimum cucumber yield. Moreover, informed supervision through Artificial Intelligence (AI) helps to navigate closed-loop systems and enhance the water-use efficiency and yield stability.

Why it matches plant phenotyping methodsCNNによるキャノピー分割、ストレス検出、成長形質推定を中核とする植物フェノタイピングおよび閉ループ制御フレームワークであり、性能指標も報告されているため。

abstractcombining greenhouse experiments with computer vision (CV) based driven phenotyping to create an analytical framework and system control framework for the cultivation of cucumbers

Classifying the Ripeness of Oil Palm Fresh Fruit Bunches Using a Mixed‐Order Relation‐Aware Recurrent Neural Network Approach for Enhanced Agricultural Monitoring and Optimized Crop Management in Agriculture ↗

ABSTRACT Oil palm production has increased rapidly since 2012, particularly in Guatemala, Malaysia, and Indonesia. Accurate Fresh Fruit Bunch classification is vital for oil yield and quality, but manual grading is inefficient and existing deep learning methods are computationally intensive. To overcome these challenges, this study proposes an optimized Oil Palm Fresh Fruit Bunch Ripeness classification framework based on a Mixed‐Order Relation‐Aware Recurrent Neural Network (OP‐FFBR‐MORA‐RNN). The proposed methodology integrates Confidence Partitioning Sampling Filtering (CPSF) for effective image localization, cropping, and resizing, followed by Revised Tunable Q‐Factor Wavelet Transform (RTQFWT) to extract discriminative features. These features are subsequently classified using a Mixed‐Order Relation‐Aware Recurrent Neural Network (MORA‐RNN), with its parameters optimized via the Fractional Pelican African Vulture Optimization (FPAVO) algorithm to enhance classification accuracy. The model categorizes FFBs into five ripeness classes: overripe, ripe, abnormal, empty fruit, and under‐ripe. Experimental evaluation on an oil palm dataset demonstrates that OP‐FFBR‐MORA‐RNN achieves 97.8% accuracy, 97.6% precision, and a low error rate of 2.4%, outperforming existing methods such as Oil Palm Fresh Fruit Bunch Ripeness categorization on mobile devices using Convolutional Neural Network (OP‐FFBR‐MD‐CNN), Machine Vision for maturity classification using Artificial Neural Network (MVM‐OP‐FFBR‐ANN), and Object Detection for Oil Palm Fruit Bunches using You‐Only‐Look‐Once (OB‐OPFBR‐YOLOv7). These results confirm the framework's effectiveness for reliable, scalable, and efficient ripeness classification, supporting improved agricultural monitoring and optimized crop management.

Why it matches plant phenotyping methods油ヤシ果房の熟度という植物器官の状態を画像から分類する手法を提案・評価しており、特徴抽出、分類モデル、精度比較が研究の中心である。

abstractthis study proposes an optimized Oil Palm Fresh Fruit Bunch Ripeness classification framework based on a Mixed‐Order Relation‐Aware Recurrent Neural Network (OP‐FFBR‐MORA‐RNN).

Interpreting Yield–Spectral Relationships in Wheat and Cotton Using a Harmonised Sentinel-2 Indicator Framework ↗

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of in-dividual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterra-nean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield varia-bility (up to R² ≈ 0.82) under controlled analytical conditions. In contrast, cotton yield var-iability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.76. Recurrence analysis confirmed the stability of these indicator families across analytical stages. Overall, the results indicate that parsimonious, physiologically interpretable indi-cator combinations can account for a substantial proportion of yield variability without reliance on black-box modelling, supporting crop-aware indicator selection for precision agriculture applications.

Why it matches plant phenotyping methodsSentinel-2のスペクトル・熱指標から作物収量を推定する統一ワークフローを構築・比較し、指標の安定性と予測性能を検証しており、収量フェノタイピング手法が中心です。

abstractThis study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework

Sarv Sampoorna Kisan Mitra - AI Based Crop Disease Detection and Management System ↗

The contemporary challenge of sustainable agriculture necessitates the deployment of robust, data-driven decision support systems. This review paper details the architecture and efficacy of an integrated AI-based system, termed 'Sarv Sampoorna Kisan Mitra,' for comprehensive crop disease identification and management. The system is predicated on two core technological pillars: the use of advanced Deep Learning (DL) models, specifically Convolutional Neural Networks (CNNs), for rapid, visual diagnosis of crop diseases; and the implementation of a Knowledge Graph (KG) for prescriptive management recommendations. The paper explores the full lifecycle, from data acquisition via remote sensing and IoT, through predictive modeling for yield forecasting, and culminating in the generation of actionable, customized advice for fertilizer application and pest control. By transforming raw diagnostic data into contextualized, actionable knowledge, this integrated AI-KG framework offers a scalable solution to enhance precision farming efficiency and minimize resource wastage.

Why it matches plant phenotyping methods植物病害を画像から診断する手法と統合システムの構成・有効性が中心であり、病害状態の画像ベース推定を扱うレビューとして収録対象。

abstractThis review paper details the architecture and efficacy of an integrated AI-based system, termed 'Sarv Sampoorna Kisan Mitra,' for comprehensive crop disease identification and management.

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models ↗

Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.

Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動検出について、観測時期と技術性能を評価しており、植物表現型取得法が中心である。

abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.

UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming. ↗

Aeroponic vertical tower farming is a cost-effective, sustainable method for optimizing the food crop-Lactuca Sativa (lettuce-a greeny leaf vegetable); yet accurate biomass prediction of the lettuce crop remains challenging due to the non-linear relationship between the climatic conditions and the variable lettuce growth parameters. To address this challenge, a robust machine learning model called UniTriRob regression model has been developed. This model primarily focuses on mitigating the effects of outliers and heteroskedastic errors across key growth-related parameters, including pH, total dissolved solids (TDS), temperature, electrical conductivity (EC), turbidity, humidity, light intensity and growth. The experimental validation highlights the model's capability with high R-squared value of 97.8386% and the minimized error rate of 0.46, that outperforms the conventional forecasting methods. Hence, the model presents a viable alternative for maximizing aeroponic lettuce production efficiency and increasing yield forecast accuracy, contributing to sustainable agricultural practices.

Why it matches plant phenotyping methodsレタスのバイオマス・収量という植物形質を予測する機械学習回帰モデルの開発と実験的検証が研究の中心であり、単なる農業実験のルーチン測定ではない。

abstractTo address this challenge, a robust machine learning model called UniTriRob regression model has been developed.

Artificial Intelligence-Driven Plant Disease Detection and Diagnosis: A Comprehensive Review of Deep Learning Approaches, Multimodal Sensing Technologies, and Future Perspectives in Precision Agriculture ↗

Plant diseases remain a major threat to global food production, causing significant yield losses and economic impact worldwide. Early and precise disease detection is crucial for effective crop management, yet conventional diagnostic approaches are often slow, labor-intensive, and rely on specialized expertise that may not be widely accessible. Recent advances in artificial intelligence (AI), particularly deep learning–based image analysis, offer scalable and automated solutions for plant disease recognition. This review critically examines forty-one peer-reviewed studies published between 2008 and 2025, selected following PRISMA guidelines from major scientific databases. We summarize key methodological developments, including convolutional neural networks, vision transformers, transfer and few-shot learning, and multimodal sensing approaches, highlighting their reported performance and limitations. Although many models achieve high accuracy in controlled datasets, their effectiveness often decreases under real-field conditions due to environmental variability, limited training data, and practical deployment constraints. We discuss existing challenges and propose future research directions, emphasizing improved robustness in field environments, development of lightweight and explainable models suitable for edge deployment, and integration with precision agriculture systems. This review aims to guide the design of reliable, practical, and scalable AI-driven plant disease detection strategies.

Why it matches plant phenotyping methods植物病害の画像認識・マルチモーダルセンシング手法を体系的にレビューしており、病害状態の表現型推定法が中心である。

abstractThis review critically examines forty-one peer-reviewed studies published between 2008 and 2025, selected following PRISMA guidelines from major scientific databases.

Maize yield estimation from Sentinel-2 multi-temporal imagery and CANbus data integration: a non-parametric regression approach ↗

In precision agriculture, the assessment and estimation of key crop parameters are crucial aspects for the optimisation of input usage and, as an ultimate goal, for the improvement of yield quality and quantity. In this context, a reliable prediction of yield by remotely sensed imagery is an enabling technology for optimisation. In this work, an innovative method for estimating yield in maize cultivation is presented, which exploits multi-temporal and multispectral Sentinel-2 satellite imagery with supervised Machine Learning (ML) techniques. For model training and validation, yield ground truth experimental data from combine harvesters was used, enabling the yield estimation at sub-field scale. The investigation, which was conducted on five case study plots, involved a preliminary comparison of four ML-based algorithms, trained with raw spectral bands. An assessment of the effect of the training dataset on the yield prediction accuracy was then performed. A set of Vegetation Indices (VIs) and Two Band Indices (TBIs) was also considered for this purpose. Finally, a multi-temporal analysis was conducted, in which the temporal evolution of crop spectral data over the maize growing season was exploited using imageries acquired in different epochs. The obtained results proved that an accurate estimation of maize yield can be reached using a Gaussian process regression model, exploiting multi-temporal features directly provided by the raw spectral bands. The model showed a high accuracy in the estimation of maize yield, even when fed with data acquired during only the maize vegetative phase, thus proving its capacity as a prediction tool.

Why it matches plant phenotyping methodsSentinel-2画像と機械学習を用いてトウモロコシ収量という植物形質を推定する手法を開発・比較・検証しており、フェノタイピング手法が研究の中心である。

abstractan innovative method for estimating yield in maize cultivation is presented, which exploits multi-temporal and multispectral Sentinel-2 satellite imagery with supervised Machine Learning (ML) techniques.

Exploring the potential role of multi-source remote sensing data during different growth stages in crop yield prediction. ↗

Accurate prediction of grain yield is essential for enhancing food security, particularly in the context of climate change. Although remote sensing indices have been extensively utilized to monitor vegetation growth and estimate crop yields, there has been limited research comparing their effectiveness for predicting grain yield, especially across different growth stages. This study examined the performance of multi-source indices, such as normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR V ), and solar-induced chlorophyll fluorescence (SIF), in predicting grain yield at various growth stages at Shangshan Rice Research Station in Zhejiang Province, China. The results indicated that SIF exhibited the strongest and most consistent correlation with grain yield ( R 2 = 0.34 to 0.75), followed by NIR V ( R 2 = 0.34 to 0.71). SIF also demonstrated advantages in capturing the dynamic changes of GPP during the reproductive period. During both the vegetative and reproductive stages, leaf area index (LAI) showed significant correlations with NDVI, NIR V , and SIF, whereas leaf chlorophyll concentration exhibited comparatively weaker associations with these indicators. These findings provide valuable insights for improving crop yield forecasts using remote sensing, thereby contributing to enhanced agricultural management and food security strategies under climate change.

Why it matches plant phenotyping methods作物収量を推定するマルチソースリモートセンシング指標の成長段階別性能比較・検証が研究の中心であり、植物の収量やLAIなどの形質推定手法を評価している。

abstractThis study examined the performance of multi-source indices, such as normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR V ), and solar-induced chlorophyll fluorescence (SIF), in predicting grain yield at various growth stages

Leaf and cluster spectral signatures reveal trait-dependent prediction performance for grapevine cluster architecture and juice quality ↗

Abstract Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R² = 0.53), berry diameter (R² = 0.79), and total acidity (R² = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. Highlight Spectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.

Why it matches plant phenotyping methodsブドウの器官反射スペクトルとPLSRを用いて、房構造および果汁品質形質の予測性能を評価することが中心であり、スペクトル表現型解析手法の検証・応用に該当する。

abstractHigh-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits

Deep Learning Approach for Cotton Plant Disease Identification and Severity Evaluation ↗

Cotton productivity plays a crucial role in the global agricultural economy; however, various leaf diseases significantly threaten crop yield and fiber quality. Early and accurate disease detection is essential for effective crop management, yet traditional inspection methods are time-consuming, labor-intensive, and dependent on expert knowledge, often leading to inconsistent results. Conventional machine learning approaches also face limitations in real-world agricultural environments due to variations in lighting conditions, complex backgrounds, and similarities between disease symptoms. To address these challenges, this research proposes an intelligent framework called Cotton Plant Disease Identification Using ResMobNet with Attention-Guided Localization and Severity Analysis (CPDI-RMN). The proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework. Initially, cotton leaf images are collected from a comprehensive dataset and preprocessed through image resizing, noise removal, contrast enhancement, and Min–Max normalization to improve visual quality and ensure stable model training. Data augmentation techniques such as rotation, flipping, zooming, and brightness adjustment are applied to enhance dataset diversity and improve model robustness against overfitting. For feature enhancement, contour visualization and geometric feature representation are combined with texture analysis using the Gray-Level Co-occurrence Matrix (GLCM) and Laplacian filtering. The core of the framework is the ResMobNet hybrid architecture, which integrates ResNet-50, EfficientNet-B3, and MobileNet-V2 to capture multi-scale spatial and texture features while maintaining computational efficiency. Gradient-Weighted Class Activation Mapping (Grad-CAM) is employed to generate attention maps for disease localization, followed by segmentation to isolate infected regions. Disease severity is then quantified by calculating the percentage of infected leaf area and classifying it into mild, moderate, and severe categories. Experimental results using five-fold cross-validation demonstrate that the CPDI-RMN model achieves 98.85% classification accuracy, outperforming CNN, ANN, ResNet, and MobileNetV2 models. Additionally, the attention-based localization achieves 96.8% Intersection over Union and 98.0% Dice Score, indicating highly accurate disease region detection. Overall, the proposed framework provides a reliable and scalable solution for intelligent cotton disease monitoring and supports precision agriculture through data-driven crop management.

Why it matches plant phenotyping methodsワタ葉画像から病変領域を抽出し、感染面積率に基づいて病害重症度を定量化する画像解析手法が研究の中心であるため、植物フェノタイピング手法として含める。

abstractThe proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework.

Deep Neural Networks for Plant Disease Detection: Insights from VGG16, U-Net, and ResNet ↗

Plant diseases must be identified promptly and accurately in order to achieve sustainable growth and a consistent yield. Although manual observation and identification is the traditional diagnostic approach, it is frequently laborious, slow, and prone to human error. Even though deep learning has advanced quickly and is now used as an automatic disease detection method for plants, it is still a probabilistic approach that is susceptible to human technology. Despite the rapid advancements in deep learning, automated plant disease detection systems have not kept pace. The major goals to be realized by this study are two. Our contributions are twofold: I worked on two problems; (1) identifying features that allow for disease classification and (2) precisely locating diseased regions within images. When we were doing the feature extraction, vgg16 was showing more accuracy score of 95.2% showing that it is able to grasp the minute details of texture and pattern of the plants in the images. For Resnet, we reach 92.1% accuracy rate; for u-net, we get 93.5%. For image segmentation, u-net fared better on other architectures, obtaining 91.6% accuracy, outperforming fully convolutional networks and segnet. Each architecture’s strength is unique, as the research points out. By our analysis, vgg16 achieves a better feature extraction because of its deep hierarchical structure, while u-net does better on segmentation because of its encoder decoder design. But resnet had great performance, just slightly bad for other models. In addition to accuracy, the study thoroughly evaluates the models' precision, sensitivity, and f1 score. These demonstrate model reliability and dependability on real world agriculture application. This is indicative of how deep learning approaches can change plant disease identification, into more efficient, less expert driven and feasible solution for common agricultural practices. Significant value is added to the growing field of agricultural technology with this study.

Why it matches plant phenotyping methods植物画像から病変領域を抽出し、病気状態を分類・局在化する深層学習手法を比較評価しており、植物病害フェノタイピング手法が中心である。

abstractidentifying features that allow for disease classification

Insights into biomass accumulation and challenges in grain yield prediction of elite breeding materials using UAV‐based vegetation indices in soft red winter wheat ↗

Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)‐based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above‐ground biomass, serving as a ground truth for the HTP‐derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi‐trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro‐environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV‐based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield.

Why it matches plant phenotyping methodsUAV植生指数によるバイオマス推定を中心に、実測値を用いて検証し、遺伝解析・収量予測への利用可能性を評価しているため、植物フェノタイピング手法の実質的な適用・検証に該当する。

abstractCumulative vegetation indices were found to be a reliable method to infer biomass accumulation.

Plant Leaf Disease Detection and Pesticide Recommendation System using Deep Learning ↗

An Agriculture plays a crucial role in the economy, yet crop productivity is significantly affected by plant leaf diseases that often go undetected at early stages. Farmers, especially in rural areas, face challenges in accurately identifying diseases and selecting appropriate pesticides, leading to reduced yield and increased costs. Existing solutions are either manual, timeconsuming, or lack intelligent decision-making capabilities. This paper presents a deep learning-based plant leaf disease detection and pesticide recommendation system designed to address these challenges in an end-to-end manner. The system employs a Convolutional Neural Network (CNN) model trained on a large dataset of plant leaf images to accurately classify diseases across multiple crops. The trained model achieves high accuracy in identifying both healthy and diseased leaves under varied environmental conditions. Once a disease is detected, the system integrates a recommendation module that suggests suitable pesticides and preventive measures based on the identified disease. The complete solution is implemented as a userfriendly web application where users can upload leaf images and receive instant results. The system is designed for real-time usage, ensuring accessibility and ease of use for farmers without requiring technical expertise. By combining computer vision and deep learning with practical agricultural knowledge, this system provides an efficient, scalable, and cost-effective solution for early disease detection and crop management. It has the potential to reduce crop losses, improve productivity, and support sustainable farming practices.

Why it matches plant phenotyping methods葉画像から植物の病害状態をCNNで推定する手法が研究の中心であり、植物表現型（病徴・病害状態）の画像ベース推定に該当する。農薬推薦も含むが、病害検出モデルと実用システムが主要な技術的貢献である。

abstractThis paper presents a deep learning-based plant leaf disease detection and pesticide recommendation system designed to address these challenges in an end-to-end manner.

Combining phenomic and genomic selection for pea breeding improvement ↗

Abstract Pea ( Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops. Phenomic selection, based on near‐infrared spectroscopy data, is a cost‐effective alternative demonstrated in various crops, but not yet undertaken in pea. This study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield‐related traits in a panel of elite spring pea lines evaluated across 12 environments. Three cross‐validation scenarios were implemented to simulate predictions across different years and locations. Our results show that phenomic selection is as effective as genomic selection at predicting yield. The integrative model, combining spectral and molecular data, consistently achieved the highest accuracy for most traits, particularly for complex traits such as seed yield and seed protein yield. In temporal prediction scenarios, the most accurate predictions were obtained using the spectra data from the same year as phenotyping. In spatial prediction scenarios, predictive accuracy varied by site and year, nevertheless, integrative phenomic‐genomic models consistently outperformed univariate approaches. These findings confirm the potential of phenomic selection in pea and underscore the added value of combining near‐infrared spectroscopy and genotyping data to improve the prediction of complex traits in breeding programs. In the face of increasing environmental variability, the integrative approach offers a valuable tool for accelerating genetic gain.

Why it matches plant phenotyping methods近赤外分光データを用いるフェノミック選抜と予測モデルの性能評価が研究の中心であり、収量関連形質の推定・検証を行っているため。

abstractPhenomic selection, based on near‐infrared spectroscopy data, is a cost‐effective alternative demonstrated in various crops, but not yet undertaken in pea.

Modified RefineNet with Attention-Based Fusion for Multi-Class Classification of Corn and Pepper Plant Diseases ↗

Early and precise detection of plant diseases is essential for safeguarding crop yield and ensuring sustainable agricultural practices. In this study, we propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of corn (maize) and Pepper leaf diseases. Unlike the original RefineNet, which was segmentation-oriented and computationally heavy, MoRefNet-AF is redesigned for lightweight and discriminative classification. The modifications include replacing standard convolutions with depthwise separable convolutions for efficiency, adopting the Mish activation function for smoother gradient flow, redesigning the multi-resolution fusion module with concatenation and shared convolution for richer cross-scale integration, and incorporating Squeeze-and-Excitation (SE) blocks for adaptive channel recalibration. Additionally, Chained Residual Pooling (CRP) with atrous convolutions enhances contextual representation, while global average pooling with dense layers improves classification readiness. When evaluated on a curated six-class dataset combining PlantVillage and Mendeley leaf disease repositories, MoRefNet-AF achieved 99.88% accuracy, 99.74% precision, 99.73% recall, 99.95% F1-score, and 99.73% specificity. These results outperform strong baselines including ResNet152V2, DenseNet201, EfficientNet-B0, and ConvNeXt-Tiny, while maintaining only 0.3 M parameters. With its compact design and TensorFlow Lite (v2.13) compatibility, MoRefNet-AF offers a robust, lightweight, and real-time deployable solution for precision agriculture and smart plant disease monitoring.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類する軽量深層学習手法を開発し、複数データセットとベースラインで性能評価しているため、植物フェノタイピング手法が中心である。

abstractwe propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of corn (maize) and Pepper leaf diseases.

Geo-Referenced Factor-Graph SLAM for Orchard-Scale 3D Apple Reconstruction and Yield Estimation ↗

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture.

Why it matches plant phenotyping methods果実の三次元再構成から体積・質量・収量を推定する画像・計算パイプラインが研究の中心であり、植物器官の形態および収量形質を技術的に抽出している。

abstractThis paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization.

A Heuristic Approach to Ensemble-Based Deep Learning Models for Plant Disease Classification and Farming Decision Support ↗

Leaf based plant diseases detection is one of the significant factors affecting crop yield and productivity. Most of the current techniques used for disease prediction are trained using observational records featuring numerous plant image parameters, with a higher frequency of diseased images compared to blight-free images. Hence, discriminating against the crucial insights from irrelevant and redundant images has been a crucial and challenging study. This research inspects the suitability of machine learning models in disease prediction focusing both specific and wide range of plant leaf images. Also, most classical methods are pretentious by various issues such as the format of image statistics, computation, and representation. To address this crucial setback in the present prediction methods, the proposed system develops a hybrid model utilizing stacked ensemble learning, which enhances the detection of plant disease attacks beyond what conventional learning methods. The proposed stacked ensemble-based disease prediction framework is designed to identify both misclassified and correctly classified images. This approach features a two-tier classification mechanism that involves a base learner (Level 0) and a meta learner (Level 1). It considers both image datasets and image features as inputs to facilitate the two-tier classification process. It also focuses on extracting internal features from the damaged leaves. The proposed model was trained with over 30,000 images at various levels. The experimental results revealed that the stacked ensemble learning technique outperformed with a prediction accuracy of 99.93%.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定する分類手法を開発し、アンサンブル学習による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。

abstractthe proposed system develops a hybrid model utilizing stacked ensemble learning, which enhances the detection of plant disease attacks beyond what conventional learning methods

Hybrid CNN-Based Plant Disease Detection for Smart Agriculture: A Review ↗

Abstract Plant diseases are a major challenge in agriculture, leading to reduced crop yield and economic losses. Traditional methods of disease detection rely on manual inspection, which is time-consuming and less accurate. With the advancement of Deep Learning, Computer Vision, and Internet of Things, automated plant disease detection systems have been developed to improve accuracy and efficiency. Recent research shows that deep learning models, especially Convolutional Neural Networks (CNNs), are widely used for plant disease classification due to their ability to automatically extract features from images and achieve high accuracy [3], [34] . Transfer learning techniques using models such as ResNet and VGG further enhance performance, particularly when datasets are limited [9], [41] . In addition, lightweight models like MobileNet and ShuffleNet are being developed for deployment on mobile and IoT devices [2], [12] . Many studies use benchmark datasets such as PlantVillage, which provide high accuracy under controlled conditions. However, real-world applications face challenges such as varying lighting conditions, complex backgrounds, and limited dataset diversity [13], [15] . To address these issues, researchers are exploring advanced techniques such as data augmentation, object detection models like YOLO, and multimodal approaches that combine image data with environmental sensor data [5], [10] . IoT-based systems are also gaining importance as they enable real-time monitoring of crop conditions using sensors and smart devices [21], [26] . Furthermore, Explainable AI techniques such as Grad-CAM are being used to improve model transparency and help users understand the prediction results [25] . Despite significant progress, challenges remain in terms of model generalization, computational complexity, and real-time deployment. Future research should focus on developing lightweight, efficient, and scalable models, along with the use of large real-field datasets and edge computing technologies. Keywords Plant Disease Detection; Leaf Image Analysis; Convolutional Neural Network (CNN); Deep Learning; Transfer Learning; Computer Vision Internet of Things; Lightweight Models; Object Detection (YOLO); Multimodal Data Fusion; Explainable Artificial Intelligence (XAI)

Why it matches plant phenotyping methods植物病害を葉画像から推定するCNN・画像解析手法を体系的に扱うレビューであり、植物の病態を観測・推定するフェノタイピング手法が中心です。

titleHybrid CNN-Based Plant Disease Detection for Smart Agriculture: A Review

Evaluation of Downy Mildew Resistance in the USDA Hemp Germplasm Repository Using High-Throughput and Traditional Phenotyping Methods ↗