Plant phenotyping methods.

High-throughput phenotyping platform for facility crops based on optical sensing technology: A review ↗

High-throughput acquisition of crop phenotypic information is one of the key technologies for achieving intelligent facility agriculture and precision breeding. Traditional phenotypic data collection methods suffer from low efficiency and strong subjectivity, making it difficult to achieve multi-scale continuous monitoring and meet the demands of modern research and production. This paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops. First, starting from optical sensing technologies, a comparative analysis highlights the advantages and limitations of RGB, multi-/hyperspectral, thermal infrared, and LiDAR sensors in phenotypic perception. Second, the characteristics and applicable scenarios of stationary, rail-mounted, mobile robot, and unmanned aerial vehicle (UAV) platform architectures are summarized. Furthermore, the evolution of phenotypic data processing methods is examined, focusing on the shift from traditional feature engineering to deep learning-driven approaches. Finally, key challenges such as multimodal data fusion, system cost, and real-time performance are discussed, along with the future direction of phenotypic platforms toward intelligent closed-loop decision-making systems. This article systematically reviews the facility agriculture phenotyping platforms driven by optical sensing technology, and also incorporates representative research progress in field phenotyping studies. These advances provide transferable sensing technologies, methodological frameworks, and platform design concepts that can facilitate the development of phenotyping platforms for controlled-environment agriculture.

Why it matches plant phenotyping methods施設作物の光学センシング型ハイスループット表現型解析プラットフォームを体系的にレビューしており、センサー、プラットフォーム構成、データ処理を中心に扱うため、方法論レビューとして明確に適格です。

abstractThis paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops.

Hyperspectral–Region Aggregation Network for Maize Leaf Nitrogen Content Estimation via Spectral–Regional Joint Modeling ↗

Abstract Accurate estimation of maize leaf nitrogen content is important for improving nitrogen-use efficiency and supporting precision crop management. However, leaf-level hyperspectral modeling is challenged by high spectral redundancy and heterogeneous spectral responses among local leaf regions. This study proposes a Hyperspectral–Region Aggregation Network (HSRAN) for maize leaf nitrogen content estimation from region-level hyperspectral spectra. HSRAN consists of a Spectral Adaptive Recalibration Encoder (SARE) and a Context-Aware Gated Aggregation Module (CAGM). SARE performs band-wise residual recalibration and extracts regional spectral representations, whereas CAGM models contextual dependencies among regional features and performs gated attention-based aggregation for leaf-level prediction. Field experiments were conducted in 2024 and 2025 at the jointing, silking, and maturity stages. HSRAN was evaluated against PLSR, RF, XGBoost, SVR, 1D-CNN, MLP, and Transformer1D models. Across the stage-specific and pooled datasets, HSRAN achieved the highest R² and the lowest RMSE while maintaining competitive MAE values. On the pooled full-growth-period dataset, HSRAN achieved an R² of 0.84, an RMSE of 3.63 g kg⁻¹, and an MAE of 2.59 g kg⁻¹. At the jointing, silking, and maturity stages, the corresponding R² values were 0.56, 0.76, and 0.72, respectively. Ablation experiments indicated that integrating SARE and CAGM improved R² from 0.80 to 0.84. To interpret regional contributions, the learned attention weights were mapped back to the original leaf coordinates recorded during regional sampling. Regions near leaf veins, tips, and margins often received relatively higher attention weights, suggesting that their local spectra provided informative cues for model prediction. These findings indicate that spectral–regional joint modeling can improve leaf-level hyperspectral estimation of maize nitrogen content. HSRAN provides a practical framework for non-destructive nitrogen assessment in maize.

Why it matches plant phenotyping methodsトウモロコシ葉の窒素含量という植物形質を非破壊推定するためのハイパースペクトル深層学習手法を開発し、複数手法との比較・アブレーション検証を行っており、フェノタイピング手法が中心である。

abstractThis study proposes a Hyperspectral–Region Aggregation Network (HSRAN) for maize leaf nitrogen content estimation from region-level hyperspectral spectra.

Wheat biomass estimation across crop development using UAV LiDAR structure–intensity fusion alongside multispectral and thermal data ↗

This study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework. Among the evaluated single sensor approaches, LiDAR features consistently provided the strongest performance, demonstrating the complementary value of crop height, vertically distributed canopy density, and normalized LiDAR intensity for characterizing canopy structure and within-canopy variability. Multi-layer GF improved AGB estimation relative to conventional ground-based GF approaches, highlighting the importance of incorporating the vertical distribution of canopy density. Multi-sensor fusion produced only modest additional improvements, indicating limited benefits relative to the increased acquisition and processing requirements. Temporal analysis showed that structural LiDAR features were most informative during early crop development, whereas normalized intensity, spectral reflectance, and thermal observations became increasingly valuable during canopy maturation and senescence. Comparisons with destructively measured plant area index (PAI), leaf area index (LAI), green leaf area index (GLAI), and green fraction of LAI further demonstrated that normalized LiDAR intensity (903 nm) was more closely associated with green canopy components than purely structural LiDAR metrics. Overall, the results demonstrate that fully exploiting both the structural and spectral information contained within LiDAR observations can substantially improve UAV-based biomass estimation, while multispectral and thermal observations provide complementary information whose contribution varies with crop development and monitoring objectives.

Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトル・熱画像とANNを用いた小麦バイオマス推定手法を系統的に比較・評価しており、植物形質推定の取得・解析方法が研究の中心である。

abstractThis study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework.

Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning ↗

Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.

Why it matches plant phenotyping methods継続的な植物センサー、ドローン画像、樹冠構造解析、果実表現型計測を統合し、NDVIや樹冠表面粗さなどのストレス指標を抽出する方法が研究の主要部分であるため。

abstractWe conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping

A Cost-Effective Approach to Estimate Quinoa Aboveground Biomass Volume Combining UAV RGB Data with Sentinel-1 and Sentinel-2 Satellite Imagery ↗

This study assessed the integration of Unmanned Aerial Vehicles (UAVs) and satellite images (Sentinel-1 and Sentinel-2) in advanced machine-learning techniques to monitor the ABV of quinoa crops (Jacha Grano variety) across the Bolivian Altiplano. The proposed method follows a two-step procedure. First, UAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs at a 10 m spatial resolution from the crop canopy 3D model and classification, respectively. Secondly, several spectral and polarization/texture indices derived from Sentinel-2 and -1 images were integrated into three decision-tree-based machine-learning models (Random Forest-RF, Gradient Boosting-GB, eXtreme Gradient Boosting-XGB), and one CNN-based machine-learning model to estimate ABV. Additionally, a Stacking Model (STM) build on top of the three decision-tree-based models was considered for comparison. Model evaluation was also performed in a two-step approach. First, a 10-fold cross-validation strategy was used to highlight ABV sensitivity to Sentinel-2 and Sentinel-1 alone and in combination. Secondly, a Leave-One-Plot-Out Cross-Validation (LOPOCV) strategy was used to avoid autocorrelation between the training and evaluation dataset and therefore provided more insight into ABV mapping potential. The results showed that the combination of Sentinel-1 and Sentinel-2 features in the CNN model achieved the best predictive performance with R2 and RMSE values of 0.64 and 0.39 m3 ∙ 100 m−2, respectively. These findings highlight the potential of integrating multi-source information in advanced artificial intelligence algorithms for quinoa ABV monitoring, offering new insights toward the identification of sustainable practices across remote regions with complex socio-economic contexts.

Why it matches plant phenotyping methodsUAV画像・衛星センサー・機械学習を統合し、キノアの地上部バイオマス体積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中心である。

abstractUAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs

AgriIDIA: Early Detection of Plant Diseases Using Transfer Learning on 24-Channel Multispectral Stacks with EfficientNet-B0 ↗

Plant diseases pose a serious threat to agriculture, causing yield losses of 20 to 40 percent each year, resulting in more than 220,000 million dollars in economic damage and significantly affecting the global food supply. Traditional plant health monitoring practices involve visual inspection of plant tissue and can only detect the presence of disease once visual symptoms are already evident. This article presents AgriIDIA, an early-detection plant disease recognition system trained on datasets of 24-channel multispectral images derived from six optical filters (BlueIR, Hotmirror, K590, K665, K720, and K850) and six vegetation indices (NDVI, GNDVI, NDRE, EVI, REI, and SAVI). First, an exploratory data analysis is conducted on the diagnostic capability of the described 24-channel data representation, using 1,266 image stacks labeled with six classes (diseased/healthy papaya, diseased/healthy potato, diseased/healthy tomato). Next, using the results of the exploratory data analysis, the manuscript describes the training and cross-validation performance of AgriIDIA, with a macro-F1 score of 83.91 ± 3.42% and an accuracy of 84.00 ± 3.17% on the validation set. Finally, the performance of the trained model is evaluated on the reserved test set (N=190), demonstrating an accuracy of 81.05%, a macro-F1 score of 0.7398, and a weighted ROC-AUC of 0.9383. The results of this study suggest that the 24-channel multispectral representation has significant diagnostic potential for the early detection of plant diseases and can be used to design accessible phytosanitary methods for small-scale farmers.

Why it matches plant phenotyping methods多チャネルマルチスペクトル画像から植物病害状態を推定する認識システムの開発・検証が研究の中心であり、植物表現型として病害状態を直接評価している。

abstractThis article presents AgriIDIA, an early-detection plant disease recognition system trained on datasets of 24-channel multispectral images

Pinus pinaster Seedling Detection in Coastal Dune Plantations Using a UAS Multispectral Point Cloud and Point Transformer V3 ↗

Early detection of tree-seedling establishment is essential for monitoring regeneration success in coastal-dune plantations, where conventional field assessments remain labour-intensive and spatially limited. This study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds. Field surveys in the Quiaios National Forest, Portugal, mapped approximately 1500 seedlings using RTK GNSS positioning, biometric measurements, and field photographs. Multispectral imagery acquired with a DJI Mavic 3 Multispectral platform was processed through Structure-from-Motion to generate calibrated orthomosaics, terrain products, and dense point clouds. Training-data preparation combined pine-centred buffers, spectral conditioning, manual refinement and point-cloud class assignment. Point Transformer V3 models were trained in ArcGIS Pro and evaluated using field-mapped buffers withheld from model training within plantation-line areas. The Baseline high-recall model achieved 88% object-level recall at the operational threshold of at least three classified Pine-Seedling points per buffer. The refined hard-negative model retained 84% recall while reducing off-buffer detections from 243 to 41. False-negative analysis showed that omissions were associated with reduced crown diameter and limited branch development under the adopted buffer-based retrieval framework. These results support transformer-based multispectral point-cloud classification for scalable monitoring of early-stage pine regeneration in heterogeneous coastal environments.

Why it matches plant phenotyping methodsUASマルチスペクトル点群とPoint Transformer V3により、マツ幼苗の存在・定着状態を植物個体レベルで推定する手法を開発・評価しており、検出性能も検証しているため、植物フェノタイピング手法が中心である。

abstractThis study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds.

Lightweight Near-Infrared Spectral Reconstruction from Red UAV Imagery Using Artificial Intelligence for Low-Cost Remote Sensing ↗

Near-infrared imagery is essential for vegetation monitoring, precision agriculture, and environmental remote sensing, but multispectral UAV systems remain significantly more expensive and less accessible than conventional RGB imaging platforms. This study presents a lightweight artificial intelligence framework for reconstructing the NIR spectral band exclusively from the red spectral band acquired by a UAV. The proposed methodology formulates the reconstruction task as a pixel-wise nonlinear regression problem and employs a compact multilayer perceptron (MLP) containing only 609 trainable parameters, without exploiting spatial neighborhood information. The framework was developed and evaluated using 280 synchronized multispectral UAV image sets acquired with a DJI Phantom 4 Multispectral platform over a heterogeneous agricultural landscape in the Republic of Moldova. Of these, 252 image sets were used for model development, and 28 were reserved as a held-out within-mission test subset. Quantitative evaluation on a held-out test dataset from the same acquisition mission yielded a mean squared error of 0.010329, a root mean squared error of 0.101632, a mean absolute error of 0.079883, a coefficient of determination of 0.253383, and a Pearson correlation coefficient of 0.683637 between measured and reconstructed normalized NIR digital intensities. The results indicate that the model captures part of the red–NIR relationship under the evaluated acquisition conditions; however, the moderate coefficient of determination suggests that the reconstructed values are an approximation rather than a replacement for measured NIR observations. An illustrative NDVI-based assessment showed that broad spatial vegetation patterns remained identifiable. Rather than introducing a new neural network architecture, this work establishes a compact empirical baseline to investigate the practical performance and limitations of pixel-wise NIR reconstruction from a single red-band value with minimal model complexity.

Why it matches plant phenotyping methodsUAV画像からNIRを再構成し、NDVIを含む植生状態の推定に用いる計算・センシング手法が研究の中心で、独立データによる技術評価も行っている。

abstractThis study presents a lightweight artificial intelligence framework for reconstructing the NIR spectral band exclusively from the red spectral band acquired by a UAV.

Physiology-informed hyperspectral retrieval of leaf Vcmax across wheat and maize ↗

Leaf hyperspectral reflectance can provide a scalable way to estimate photosynthetic capacity (Vcmax), but models trained in one species or measurement context often lose accuracy in another. This transfer problem limits the use of spectral approaches in multi-species crop phenotyping and carbon-cycle applications. Here, we tested physiology-informed inputs for leaf-level Vcmax25 retrieval using paired gas-exchange and reflectance data from wheat (C₃; n = 198) and maize (C₄; n = 81) grown under contrasting nitrogen supply. The four input configurations were raw spectra (Mod1), spectra scaled by a PPFD–absorptance proxy (Mod2), scaled spectra augmented with radiative-transfer-derived traits (Mod3), and scaled spectra augmented with a spectral coordination proxy (Mod4). Within datasets, the best models reached R² = 0.82 in wheat, 0.41 in maize, and 0.76 in the combined dataset. In a matched comparison with a common random-forest learner, the spectral coordination proxy Mod4 improved accuracy only slightly over Mod2 in wheat (RMSE −0.51%; p = 0.0058) and maize (RMSE −1.26%; p = 0.0011) but not in the combined dataset (RMSE −0.15%; p = 0.074), and the trait-based Mod3 showed no consistent benefit. When wheat models were tested on measurement dates not used in training, accuracy remained moderate (R² = 0.563; RMSE = 15.07 µmol m⁻² s⁻¹). Despite this within-dataset performance, models applied to the other species without calibration failed in both directions (negative R²), and adding source-species data did not improve prediction even when a few samples of the new species were used for calibration. These results show that physiology-informed input design provides at most small within-dataset gains, and that reliable prediction across C₃ and C₄ crops requires calibration data from the target crop.

Why it matches plant phenotyping methods葉のハイパースペクトル反射から光合成能力Vcmaxを推定する手法を開発・比較・検証しており、植物形質取得が研究の中心です。

abstractLeaf hyperspectral reflectance can provide a scalable way to estimate photosynthetic capacity (Vcmax)

Differentiation of Plant-Pathogenic Fungi in Soybean Seeds Using Hyperspectral Sensors ↗

Hyperspectral sensors have emerged as a promising approach in the study of plant diseases. The objective was to distinguish between healthy and inoculated seeds, and also to distinguish between genera of plant-pathogenic fungi in soybean seeds, using hyperspectral sensors combined with machine learning. The experimental design was a fully randomized factorial design with six algorithms (Simple Logistic Regression, Support Vector Machine, Artificial Neural Network, Random Forest, REPTree and J48 decision trees) and four phytopathogens (Sclerotinia sclerotiorum, Macrophomina phaseolina, Rhizoctonia solani, and Colletotrichum sp.) plus the control. Spectral analysis of the seeds was performed using a spectroradiometer (Ocean Optics) consisting of two sensors: NIR and Flame, covering the spectrum from 350 to 2500 nm. It was possible to distinguish between healthy and inoculated seeds, as well as identify the type of phytopathogen, based on each spectral signature. The Simple Logistic Regression and Support Vector Machine algorithms performed best. Hyperspectral sensors combined with machine learning constitute a promising tool for the detection of phytopathogens in seeds, enabling rapid and non-destructive analysis. This promising tool could serve as a complementary alternative to traditional diagnostic methods, which, although accurate, are time-consuming and rely on specialized labor.

Why it matches plant phenotyping methods種子の健全・感染状態を非破壊的に推定するハイパースペクトルセンシングと機械学習が研究の中心であり、感染植物器官の状態を直接測定する方法として扱える。

abstractThe objective was to distinguish between healthy and inoculated seeds, and also to distinguish between genera of plant-pathogenic fungi in soybean seeds, using hyperspectral sensors combined with machine learning.

UGV-based multimodal RGBD–multispectral fusion framework enables high-quality 3D phenotyping of greenhouse lettuce seedlings ↗

High-throughput phenotyping of lettuce seedlings is highly prone to background confusion because the seedlings are small, have weak textural features, and exhibit spectral reflectance similar to that of the substrate. Traditional single-visual-modality approaches struggle to achieve reliable structural and physiological characterization simultaneously under the repetitive backgrounds and dense arrangements typical of greenhouse tray cultivation. To address these challenges, we establish a multimodal 3D phenotyping framework tailored for controlled agriculture environments, enabling the quantification of structural and physiological characteristics of lettuce seedlings. This framework is based on an unmanned ground vehicle (UGV) platform integrating a RGBD camera and a quad-band multispectral sensor which are rigidly coupled and synchronously triggered. An alignment module based on established feature matching algorithm is introduced to register the misalignment between source multispectral and RGBD images. Subsequently, we design a novel dual-backbone instance segmentation network, MS-SegNet, to enhance segmentation accuracy by hierarchically fusing geometric information with multispectral features. A robust 3D metric pose estimation pipeline, incorporating standard SfM initialization, scale recovery, and generalized ICP refinement, is constructed to generate 3D point clouds with spectral attributes and semantic labels. Finally, key structural and physiological phenotype parameters of each seedling are calculated based on the 3D semantic multispectral point clouds. Experiments demonstrate that MS-SegNet achieves significant advantages in instance segmentation of lettuce seedlings with mAP@50:95 = 0.854. The metric 3D pose estimation pipeline exhibits reliable performance under complex controlled conditions. The quality of the 3D reconstructions is indirectly validated through downstream structural trait extraction. The estimated seedling height and crown width show high correlation with manual measurements, achieving R 2 values of 0.8379 and 0.918, and RMSE values of 10.94 mm and 11.56 mm, respectively. Overall, by systematically integrating these adapted components with the novel segmentation architecture, this framework achieves stable performance improvements in 3D reconstruction, instance segmentation, and phenotypic analysis under greenhouse conditions. It provides a scalable, integrated technical solution for non-destructive, high-throughput phenotyping of crop seedlings in controlled environments.

Why it matches plant phenotyping methodsRGBD・マルチスペクトル・UGVを統合した3Dフェノタイピング基盤を開発し、分割・再構成・構造／生理形質抽出を検証しており、フェノタイピング手法が研究の中心である。

abstractwe establish a multimodal 3D phenotyping framework tailored for controlled agriculture environments, enabling the quantification of structural and physiological characteristics of lettuce seedlings.

Deep spatial-spectral fusion of UAV RGB and hyperspectral imagery for potato plant disease detection ↗

Accurate plant disease detection remains challenging when using single-modality data, which fails to capture comprehensive disease-related features. However, many existing studies rely on pixel-level classification or prior plant segmentation and lack explicit modeling of cross-modal interactions, limiting their ability to distinguish between healthy and diseased plants. This study presents a spatial–spectral fusion deep learning framework (S2-PDD) that fuses uncrewed aerial vehicles (UAV)-based RGB imagery and hyperspectral-derived feature representations for plant-level detection of potato diseases (blackleg and Potato virus Y (PVY)), employing early fusion (modalities combined at the input stage) and middle fusion (features integrated at intermediate stages within the model backbone) strategies. The multimodal fusion models were compared against single-modal models and an existing S2ADet model. Model performance, assessed through five-fold cross-validation, demonstrated that multimodal models integrating RGB and vegetation index features achieved the highest mAPs of 86.65 ± 1.71 (%; E-RV model) and 85.74 ± 1.96 (M-RV), respectively. These mAPs were higher than those of all single-modal models, including the RGB-only (83.21 ± 1.46) and hyperspectral-only models (PCA features: 79.71 ± 1.45; vegetation index features: 85.31 ± 2.36). They also exceeded mAPs of multimodal models combining RGB with PCA features (early fusion: 83.00 ± 2.81; middle fusion: 83.11 ± 2.46; S2ADet: 84.04 ± 2.73), regardless of the fusion strategy. The superior performance highlights that vegetation index features provide strong class separability compared to other hyperspectral representations. The proposed models achieved strong plant-level detection performance, with AP of 78.65 ± 4.14 (E-RV) and 77.40 ± 3.46 (M-RV) for blackleg disease, as well as 84.82 ± 3.59 (E-RV) and 83.28 ± 3.86 (M-RV) for PVY. These results demonstrate the potential of UAV-based multimodal sensing for disease monitoring in cropping systems. A potato plant disease detection dataset was constructed and made publicly available, containing paired RGB and hyperspectral image tiles with bounding box annotations. The code is available at https://github.com/Tim-Agro/S2-PDD.

Why it matches plant phenotyping methodsUAV RGB・ハイパースペクトル画像からジャガイモ個体の病害状態を推定する融合モデルを開発・比較検証し、公開データセットも構築しており、病害表現型の取得・抽出手法が中心である。

abstractThis study presents a spatial–spectral fusion deep learning framework (S2-PDD) that fuses uncrewed aerial vehicles (UAV)-based RGB imagery and hyperspectral-derived feature representations for plant-level detection of potato diseases (blackleg and Potato virus Y (PVY))

Deep Learning-Based Monitoring of Tea Plant Growth and Nitrogen Status Using UAV Multisource Remote Sensing Features ↗

Accurate and efficient monitoring of tea plant growth parameters via remote sensing is essential for precision plantation management. However, spectral indices relying solely on reflectance often exhibit limited sensitivity in capturing complex tea canopy characteristics. This study developed a data-driven framework integrating spectral reflectance, frequency-domain harmonic components, and spatial texture features to construct tri-feature fusion indices (TFIs) and establish machine learning and deep learning models for tea growth monitoring. Ten-band multispectral imagery was acquired using a UAV alongside synchronous field measurements of leaf and plant biomass and nitrogen accumulation. TFIs were constructed through exhaustive feature combinations and optimized via a data-driven search strategy. Subsequently, random forest (RF), multilayer perceptron (MLP), convolutional neural network (CNN), and transformer models were evaluated using a leave-one-site-out cross-validation (LOSO-CV) strategy. The selected TFIs showed strong associations with tea growth parameters within the investigated dataset, with R2 values up to 0.63 and 0.62 for leaf dry matter and leaf nitrogen accumulation, respectively. Models incorporating selected TFIs achieved cross-validated R2 values of 0.56 for leaf dry matter (MLP), 0.59 for plant dry matter (MLP), 0.73 for leaf nitrogen accumulation (MLP), and 0.68 for plant nitrogen accumulation (CNN). These models exhibited competitive predictive performance comparable to RF, although no statistically significant differences in mean absolute error were observed under site-held-out evaluation. Furthermore, model-derived spatial maps provided insights into fine-scale spatial heterogeneity and potential interannual variations in tea growth parameters across representative plantations from 2024 to 2025. Overall, this study provides a UAV-based framework for tea growth parameter estimation by integrating multi-domain information without requiring additional environmental observations.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から茶植物の乾物量・窒素蓄積を推定する特徴量融合および機械学習・深層学習フレームワークを開発し、サイト外交差検証で評価しており、植物形質の取得・推定手法が中心である。

abstractThis study developed a data-driven framework integrating spectral reflectance, frequency-domain harmonic components, and spatial texture features to construct tri-feature fusion indices (TFIs) and establish machine learning and deep learning models for tea growth monitoring.

Remote Spectral Detection of Canopy Functional Dimensions Varying Within and Across Forest Types. ↗

Global trait axes reveal overarching dimensions of plant functional variation. However, how these dimensions are spatially organized within and across forest types remains unclear. We combined drone-based full-range imaging spectroscopy with crown-level measurements of 16 physiological, morphological and biochemical traits across temperate, subtropical and tropical forests in China to enable spatially-explicit trait mapping. Through site-training scenario, leaf-to-canopy scaling and spectral-domain modelling tests, we find that reliable canopy trait retrieval depends not only on trait and spectral coverage, but also on preserving trait-spectral relationships across sites and scales. Spectral predictions recovered observed multivariate covariation, summarizing crown variation into a leaf-economics dimension and two additional biochemical dimensions related to hydro-thermal regulation and defence/metabolism. Mapping these dimensions revealed distinct community-level trait organization alongside substantial species- and crown-level variation within forests. These findings link remotely sensed trait retrieval to environmental filtering and plant functional differentiation, providing a scalable framework for monitoring forest functional diversity.

Why it matches plant phenotyping methodsドローン分光画像と冠レベル形質測定を用いた植物形質の空間マッピング手法が中心で、スケーリングおよびスペクトルモデルの検証も行っている。

abstractWe combined drone-based full-range imaging spectroscopy with crown-level measurements of 16 physiological, morphological and biochemical traits across temperate, subtropical and tropical forests in China to enable spatially-explicit trait mapping.

Destructive harvest validation of high-throughput measurements show that water use efficiency is unaffected by moderate drought in tobacco ↗

ABSTRACT Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle. PLAIN LANGUAGE SUMMARY We grew tobacco plants under either a drought or high-water treatment and harvested a portion of the plants every few days for a total of eight harvests. Throughout the experiment, we collected 3D images of the plants and continuously measured pot weight to track plant growth and water use across different developmental stages. Destructive biomass served as the gold-standard measurement. We then compared biomass and water-use estimates generated from the digital measurements with the destructive measurements. The digital approach provided accurate estimates of plant biomass and water use while requiring little hands-on labor and no plant destruction. These nondestructive methods could help plant breeders identify water-efficient plants earlier in the breeding process, accelerating the development of crops that use water more efficiently.

Why it matches plant phenotyping methods3D画像による非破壊バイオマス推定と連続的な重量測定からWUEを推定する手法を、破壊収穫と比較して検証しており、植物表現型取得法が中心である。

abstractRecent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies.

Autonomous Quadcopter Flight Path Generation via MAVLink and Ground Control Station Architecture for Precision Agricultural Crop Monitoring ↗

This paper presents an integrated system design for autonomous quadcopter flight path generation using the MAVLink protocol and a custom Ground Control Station (GCS) for precision agricultural crop monitoring. The system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment. The 2.8 kg quadcopter (450 mm frame, 4-cell LiPo) achieves 22–25 minutes of flight time. Across five field sizes (0.5–10 ha), the Energy-Optimized path achieved 96.5% coverage efficiency with 4.2% overlap and a 12.4% energy reduction over the Boustrophedon baseline. NDVI-based crop segmentation achieved pixel accuracy of 92.5% (maize), 94.1% (rice), and 90.8% (wheat), and four-class crop-health classification achieved a weighted F1-score of 90.0%. MAVLink 2.0 command latency averaged 15.8 ms with 99.3% packet delivery at ranges up to 800 m. An ablation study showed additional gains of 1.5–3.1% coverage from wind compensation and 2.1–2.8% from terrain-following.

Why it matches plant phenotyping methods自律ドローン、マルチスペクトル撮像、NDVIセグメンテーションによる作物健康状態推定を統合し、飛行・画像解析性能を定量評価しているため、植物状態の取得・抽出が技術的に実質的な構成要素である。

abstractThe system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment.

Combining 3D-multispectral and hyperspectral imaging to identify environmental stress treatments imposed during plant growth ↗

Abstract Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments – including different growth irradiances, heat treatment and drought stress – with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.

Why it matches plant phenotyping methods3Dマルチスペクトル画像・ハイパースペクトル画像と機械学習を統合し、植物形態・スペクトル形質を非破壊かつ高スループットに取得・検証する方法が研究の中心である。

abstractNon-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function

Systematic evaluation of hyperspectral imaging workflows for predicting pigment and nutrient traits in tomato leaves under varying nitrogen supply levels ↗

Abstract Purpose Rapid and non-destructive detection of pigment and nutrient traits in tomato ( Solanum lycopersicum L.) leaves is essential for precision fertilization and greenhouse management. However, most existing studies focus on individual traits (e.g., chlorophyll or nitrogen) with isolated models, limiting the establishment of robust analytical workflows across growth stages and cultivation conditions. This study systematically evaluated hyperspectral imaging workflows for estimating pigment and nutrient traits in tomato leaves. Methods Hyperspectral images were collected at the stages of flowering-fruiting, ripening and harvest from tomato plants supplied with nitrogen at 0, 210, 300 and 390 kg N ha⁻¹. Total contents of chlorophyll, total nitrogen and nitrate were measured by standard biochemical assays for model calibration and validation. Result After sample partition with four strategies, seven spectral preprocessing methods were evaluated, namely moving average (MA), Savitzky-Golay smoothing (SG), Gaussian filtering (GF), median filtering (MF), normalization, baseline correction and standard normal variate (SNV), with normalization, MA and SNV yielded the best predictive performance for chlorophyll, total nitrogen and nitrate, respectively. For feature wavelength selection, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used with CARS yielding the best performance for total chlorophyll and nitrate prediction, while SPA was optimal for total nitrogen prediction. By application of the above optimal methods, random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost) and convolutional neural network (CNN) models were developed to predict pigment and nutrient indicators. Conclusion The SVM performed best for chlorophyll ( R c ²=0.823, R p ²=0.431) prediction, while the CNN achieved higher accuracy for total nitrogen ( R c ²=0.826, R p ²=0.780) and nitrate ( R c ²=0.851, R p ²=0.753) prediction. Overall, leaf nitrogen-related traits were predicted more reliably than total chlorophyll, for which validation performance remained limited. Impact These findings demonstrate that sample partitioning, spectral preprocessing, wavelength selection and model selection should be optimized for each target trait rather than applied uniformly. This study provides a methodological basis for non-destructive assessment of tomato leaf N status and precision fertilization management.

Why it matches plant phenotyping methodsトマト葉の色素・養分形質を対象に、ハイパースペクトル画像処理、波長選択、機械学習モデルを体系的に比較・検証しており、植物形質取得手法が研究の中心である。

abstractThis study systematically evaluated hyperspectral imaging workflows for estimating pigment and nutrient traits in tomato leaves.

Harnessing Vitis diversity to dissect and predict adventitious rooting traits in grapevine ↗

Adventitious root formation is critical for the cost-effective propagation of grapevine rootstocks, and poor rooting limits the adoption of new rootstocks derived from underutilized Vitis L. species. We evaluated 308 accessions representing 18 Vitis species over three growing seasons, scoring rooting at two developmental stages, callus-stage and post-transplant, together with root biomass, cutting weight, and a derived transplant- response index. Phenotypic variation was extensive within and among species, and species rankings depended on the trait considered: V. riparia , V. rupestris and V. californica ranked among the top five species for all four rooting traits, whereas V. arizonica and V. acerifolia rooted well at the callus stage but only intermediately after transplanting. Repeatability was moderate to high for root weight and callusstage rooting and lower for post-transplant rooting. Between-species differences accounted for most of the genetic variance in callus-stage rooting but little of that in cutting weight. After removing differences among species, accessions originating from wild sites with lower dry-season precipitation rooted better and produced more root biomass. Genome-wide association analysis of 3.4 million single-nucleotide polymorphisms identified 54 significant markers resolving into 18 independent loci across four traits. Candidate genes implicate auxin-linked cell proliferation, cell wall and lignin remodeling, and solute transport. Genomic and phenomic prediction achieved moderate accuracies across traits and seasons, including for previously unevaluated accessions, and combining spectral with genotypic data improved performance; accuracy was essentially flat between 5,000 and 50,000 markers. These results provide a framework for broadening the germplasm base of grapevine rootstock breeding. Plain Language Summary Grapevines are almost always grown as two plants joined together: a fruiting variety grafted onto a rootstock that supplies the root system. Nurseries build these plants from dormant cuttings, so a rootstock is only useful in practice if its cuttings root easily. Almost all commercial rootstocks descend from just three wild North American grape species, in part because cuttings of other species are believed to root poorly. We grew cuttings from 308 wild and cultivated grapevines representing 18 species over three years and measured how well each one rooted, first in the callusing room and again after the young plants were transplanted. Rooting ability varied a great deal, and several species outside the usual three rooted as well as the standards. Vines originally collected from places with drier summers tended to root best. We also located regions of the grape genome linked to rooting, and showed that the rooting ability of a vine can be predicted from its DNA or from light reflected by its leaves. Together, these results give breeders a way to screen a much wider range of wild grapevines for rootstock development before testing them in a nursery. Core Ideas Rooting ability varies widely across 18 Vitis species, well beyond the three used in rootstock breeding. Callus-stage and post-transplant rooting behave as genetically distinct stages of root formation. Accessions from collection sites with drier summers rooted better and produced more root biomass. GWAS resolved 18 loci implicating auxin signaling, cell wall remodeling, and solute transport. Genomic and phenomic prediction reached moderate accuracy and was insensitive to marker density.

Why it matches plant phenotyping methods発根形質を対象に、スペクトル情報を用いたフェノミック予測を実施し、遺伝子型情報との統合や予測精度を評価しているため、形質取得・推定手法が研究の主要な技術的要素です。

abstractGenomic and phenomic prediction achieved moderate accuracies across traits and seasons, including for previously unevaluated accessions, and combining spectral with genotypic data improved performance

UAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures ↗

Introduction Accurate estimation of aboveground biomass (AGB) is essential for monitoring pasture productivity and supporting sustainable management of integrated crop–livestock (ICL) systems. We hypothesized that integrating multispectral, thermal, and canopy-structural information derived from unmanned aerial vehicles (UAVs) would improve AGB prediction relative to spectral information alone, and that Generalized Additive Models for Location, Scale and Shape (GAMLSS) would accommodate seasonal heteroscedasticity while maintaining predictive performance comparable to Random Forest (RF) and Support Vector Machine (SVM) models. Methods We collected 280 destructive biomass samples from two ICL paddocks and one continuously grazed pasture in the Brazilian Cerrado between 2022 and 2024. Twenty-four UAV-derived predictors, including spectral bands, vegetation indices, canopy surface temperature, and canopy height, were evaluated using repeated five-fold cross-validation. Model transferability was assessed by withholding one management paddock at a time. Results and discussion Under repeated five-fold cross-validation, GAMLSS achieved the lowest prediction error (R² = 0.69 ± 0.01; RMSE = 2.15 ± 0.04 Mg ha⁻¹), followed closely by SVM (R² = 0.68 ± 0.01; RMSE = 2.19 ± 0.03 Mg ha -1 ); RF showed lower accuracy (R 2 = 0.53 ± 0.01; RMSE = 2.63 ± 0.02 Mg ha -1 ). In the paddock-transferability assessment, GAMLSS also showed the lowest error (R 2 = 0.63 ± 0.04; RMSE = 2.34 ± 0.26 Mg ha -1 ). For GAMLSS, the complete multisensor configuration reduced RMSE by 6.2% compared with the spectral-only configuration. The selected model was used to generate spatially explicit maps of AGB and standing aboveground biomass carbon, estimated from the mean measured carbon concentration of forage biomass. Integrating multispectral, thermal, and structural UAV data with distributional regression improves AGB estimation and enables spatial monitoring of tropical pastures under contrasting management conditions.

Why it matches plant phenotyping methodsUAVのマルチセンサーデータと統計モデルを用いて牧草の地上部バイオマスを推定し、交差検証と圃場間移 transferability 評価を行っており、植物形質の取得・推定手法が研究の中心である。

titleUAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures

Video-rate label-free molecular mapping in living plant tissue with a deployable optical encoder ↗

Abstract Living tissues contain dynamic biochemical information that is difficult to capture with conventional hyperspectral microscopes because sequential spectral acquisition is poorly matched to in vivo molecular processes that evolve during measurement. Here we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue. The system integrates a passive spectral encoder, implemented here as a low-angle scattering LDPE layer, into a 22-mm miniaturized probe and learns a supervised mapping from ultraviolet-excited autofluorescence measurements to biomolecular abundance maps. Unlike conventional pipelines that first reconstruct hyperspectral datacubes and then perform spectral unmixing, the deployed system directly estimates endogenous molecular contrast associated primarily with lignin and chlorophyll in poplar tissue, with additional suberin-associated contrast evaluated in suberin-rich tissue. This reframing makes the measurement task biomolecular inference rather than spectral reconstruction, enabling biochemical mapping under low-photon autofluorescence conditions while reducing data burden and computational latency. In living poplar stems, the platform captures autofluorescence-derived videos of embolism propagation and wound-induced biochemical remodeling, dynamic processes for which sequential spectral acquisition can introduce temporal mixing because the molecular contrast evolves during the scan itself. The system also resolves genotype-dependent reductions in lignin-associated autofluorescence in engineered poplar lines. Direct molecular inference improves biomolecular estimation relative to a reconstruction-based pipeline, while probabilistic decoding provides uncertainty estimates. These results show that compact passive spectral encoding, when optimized for biological inference rather than datacube recovery, enables deployable, label-free molecular videography of living plant tissue dynamics after task-specific calibration.

Why it matches plant phenotyping methods生体植物組織の生化学的状態を動画取得・推定する光学センシング手法を開発し、校正、比較評価、不確実性推定まで行っており、表現型取得法が中心である。

abstractHere we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue.

Time course sensor‐based phenotyping can predict Ascochyta blight disease severity in Cicer species ↗

Abstract Ascochyta blight is a widely occurring chickpea fungal disease that can cause severe yield loss. Breeding for crop resistance benefits from high‐throughput evaluation of plant–pathogen interactions in genotypes which can serve as sources of resistance. Current practice for the evaluation is human visual scoring of disease symptoms, which is limited in throughput and precision. Here, we developed open‐source sensor‐based phenotyping methods using red, green, blue (RGB) and multispectral imaging to measure resistance components and predict disease severity classes in chickpea and wild relatives grown outdoors over three seasons. Pots were imaged at multiple time points with a ground‐based platform, providing 86,792 RGB and 8199 multispectral images. Lesion count was estimated with YOLOv5 (You Only Look Once version 5) object detection (F1 score = 0.27–0.30), fractional green canopy cover was estimated from RGB images, and vegetation indices were extracted from multispectral images. A model trained on growth rates of fractional green canopy cover normalized to control genotypes could predict disease severity classes with an accuracy of 65% –81 % ( 0.43–0.59) on unseen data from three different seasons. The developed methods provide a pathway to predict visual disease severity scores and support the breeding of crops for disease resistance. They may also be used to characterize disease progression, to find underlying resistance mechanisms, and for early disease detection.

Why it matches plant phenotyping methodsRGB・マルチスペクトル画像と地上センサープラットフォームを開発し、病斑数、緑色キャノピー被覆率、病害重症度を推定・予測する手法が研究の中心であるため。

abstractHere, we developed open‐source sensor‐based phenotyping methods using red, green, blue (RGB) and multispectral imaging to measure resistance components and predict disease severity classes in chickpea and wild relatives grown outdoors over three seasons.

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.

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases ↗

Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

Why it matches plant phenotyping methods植物病害の画像ベース検出・予測手法を対象とする系統的レビューであり、植物の病徴・病害状態を観測から推定するフェノタイピング手法のレビューとして中心的です。

titleMultimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases

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).

Long-term monitoring of winter wheat phenology using a 30 m Landsat framework integrating curve reconstruction and machine learning in northern Henan and southern Xinjiang ↗

Accurate long-term monitoring of winter wheat phenology is important for crop growth assessment and irrigation management, but 30 m Landsat-based phenology retrieval remains challenging because of sparse observations, cloud contamination and sensor differences. This study developed and evaluated an integrated Landsat-based workflow for monitoring winter wheat phenology in the People’s Victory Canal (PVC) Irrigation Area of northern Henan and the Alar Irrigation Area of southern Xinjiang from 2000 to 2024. Winter wheat areas were mapped using temporally stacked NDVI/EVI features and a CART classifier. Vegetation-index trajectories were reconstructed using locally adjusted cubic-spline capping combined with Savitzky–Golay filtering, and green-up, jointing, heading and maturity were extracted using threshold- and derivative-based detection. The CART-based mapping achieved an overall accuracy of 89.51%, with higher accuracy in Alar (91.45%) than in PVC (84.35%). Compared with S-G-only, Whittaker and TIMESAT-like approaches, LACC + S-G reduced phenological-date errors, especially for green-up and maturity, with RMSE values within 3.1 d against agro-meteorological observations. Phenological stages generally occurred later in Alar than in PVC, and spatial autocorrelation confirmed significant clustering. Agro-meteorological analysis suggested stronger thermal associations in PVC and stronger moisture-related associations in Alar. These results provide practical 30 m phenological information for regional winter wheat monitoring and irrigation scheduling analysis.

Why it matches plant phenotyping methodsLandsat時系列から冬コムギの生育ステージを抽出するワークフローを開発し、複数手法との比較および農業気象観測による精度検証を行っており、植物フェノタイピング手法が研究の中心である。

abstractThis study developed and evaluated an integrated Landsat-based workflow for monitoring winter wheat phenology

A breeding-oriented UAV phenotyping framework for scalable lodging assessment and candidate gene identification in soybean ↗

Lodging is a major yield-limiting factor in soybean, but efficient large-scale phenotyping and genetic dissection of this complex trait remain challenging for breeding programs. To bridge this gap, this study developed an integrated, breeding-oriented framework that links UAV-based high-throughput phenotyping with candidate gene identification. Field experiments involving 741 diverse soybean genotypes were conducted over two years, with UAV remote sensing performed at key reproductive stages (from R5 to R7). We identified UAV-derived structural (relative plant height), textural (homogeneity, dissimilarity, correlation), and spectral (NDVI, EVI, NDRE) features as the most sensitive indices for retrieving lodging severity. The fusion of these complementary features, coupled with the XGBoost algorithm, achieved high classification accuracy (0.81–0.92) across genotypes, growth stages, and years. This reliable phenotyping pipeline enabled the precise selection of contrasting genotypes (lodging-resistant vs. lodging-prone) for transcriptomic analysis. Transcriptome sequencing revealed 13,447 differentially expressed genes, with significant enrichment in phenylpropanoid and starch–sucrose metabolic pathways. Moreover, the haplotype analysis within a natural population identified superior allelic variants of two candidate genes ( Glyma.19G249100 and Glyma.05G142200 ) significantly associated with soybean lodging resistance. This work can effectively bridge the gap between scalable field phenotyping and the discovery of functionally validated breeding targets, providing an efficient and translational framework to accelerate the development of lodging-resistant soybean varieties.

Why it matches plant phenotyping methodsUAV画像・リモートセンシング特徴量とXGBoostを統合し、ダイズの倒伏重症度を大規模に推定・検証する育種向け表現型解析パイプラインが研究の中心である。

abstractthis study developed an integrated, breeding-oriented framework that links UAV-based high-throughput phenotyping with candidate gene identification

Identification of spectral biomarkers for early fungal decay in navel oranges by Vis-NIR hyperspectral imaging and multi-scale feature fusion. ↗

Early detection of latent fungal decay caused by Penicillium italicum(P. italicum) and Penicillium digitatum(P. digitatum) remains challenging due to the absence of visible symptoms. In this study, a Vis-NIR hyperspectral imaging framework was developed to characterize early biochemical alterations in navel oranges. To address sample scarcity, a generative modeling approach (WGAN-GP) was employed to capture the intrinsic physiological variability of infected tissues. The successive projections algorithm (SPA) identified 20 key wavelengths associated with water redistribution (OH), carbohydrate depletion (CH), and chlorophyll degradation. These wavelengths were expanded into continuous ROI windows (W = 17), enabling integration of narrow-band pigment signals and broad-band absorptions related to water and carbohydrates via a multi-scale mixture-of-experts (MS-MoE) network. The framework achieved a classification accuracy of 97.10% and an F1-score of 0.9666. These results demonstrate that specific spectral absorption windows can serve as reliable, chemically interpretable spectral biomarkers for detecting early pathological changes in citrus fruit.

Why it matches plant phenotyping methodsVis-NIRハイパースペクトル画像と解析モデルを開発し、柑橘果実の初期病変をスペクトル特徴から推定する方法が研究の中心であるため、植物病害表現型の計測手法として含める。

abstracta Vis-NIR hyperspectral imaging framework was developed to characterize early biochemical alterations in navel oranges.

Comprehensive Review of Plant Disease Detection: Advancements in Imaging Sensors, AI Techniques, and Future Directions in Smart Agriculture ↗

Food safety globally is threatened by crop disease, which creates a major obstacle to yield losses, so there is an urgent need for rapid, precise, and large-scale diagnostic methods for all the global risks crops are exposed to from disease. While imaging sensors, as well as Artificial Intelligence (AI), have made great strides in recognising plant disease, most literature does not have a comprehensive analysis that combines methods, technology, and implementation. Therefore, a systematic literature review follows PRISMA methods; we review 61 excellent studies published within the last five years that outline the advancement of imaging modalities (Red, Green, Blue (RGB), multispectral/ hyperspectral, thermal), deep learning architectures, augmentation of data, explanation methods and IoT (Internet of Things)-edge-cloud for managing intelligent agriculture. These modern AI-based systems (AI systems) have consistently produced accurate results above 98%. However, there are problems with the generalisability (across hybrid plant species), robustness (when exposed to environmental stresses), and interpretability of the results presented to consumers. This review represents the first compilation of using imaging sensors, artificial intelligence models, Internet of Things architecture (IoT-edge), and robotics into one comprehensive framework for the detection of plant disease in the next generation. In addition, this review suggests future research directions, including lightweight edge-deployable models, multimodal sensor fusion, interpretable AI, larger validated datasets, and autonomous robotic systems for scalable and sustainable smart agriculture.

Why it matches plant phenotyping methods植物病害の画像・センサーによる検出手法を体系的にレビューしており、病害状態のフェノタイピング手法が中心です。

titleComprehensive Review of Plant Disease Detection: Advancements in Imaging Sensors, AI Techniques, and Future Directions in Smart Agriculture

Predicting LiDAR-Derived Canopy Leaf Area Index in Loblolly Pine Plantations with Sentinel-2 Imagery Using a Convolutional Neural Network Approach ↗

Canopy Leaf Area Index (CLAI) is a stand attribute containing information on the real-time health and growth potential of managed pine plantations. Current remote sensing techniques for quantifying CLAI rely on simple linear models applied to satellite multispectral imagery, or on techniques based on light detection and ranging (LiDAR) data that are costly and less frequently collected. This study demonstrates a convolutional neural network (CNN) approach to retrieving CLAI from 10 m Sentinel-2 multispectral imagery with a model trained on gridded LiDAR-based CLAI estimates. We demonstrate large gains in accuracy with the CNN compared to traditional linear models based on vegetation indices (e.g., Simple Ratio), but also clear shortfalls in model skill when predicting “blind” in some spatial domains that were completely excluded during model training. Pixel-scale root mean squared error ranged from 0.34 to 0.64 by domain when exposed to CLAI training data from all available spatial domains, but rose to 0.58–1.74 when predicting without prior domain-specific training. Prediction accuracy was consistently lower when applied to completely unobserved USGS LiDAR-based CLAI estimates. Traditional linear models, in contrast, had the advantage of usually lower prediction error across unobserved spatial domains (0.43–1.98), but with lower maximum accuracy. These results demonstrate a potential route for deploying more complex models for LiDAR “mimicry”, e.g., between data acquisitions widely separated in time, but advocate for the development and use of more stable generalized approaches for use in unobserved managed pine stands.

Why it matches plant phenotyping methodsLiDARで得た林分の葉面積指数をSentinel-2画像とCNNから推定する手法を開発・比較検証しており、植物キャノピー形質の取得が研究の中心です。

abstractThis study demonstrates a convolutional neural network (CNN) approach to retrieving CLAI from 10 m Sentinel-2 multispectral imagery with a model trained on gridded LiDAR-based CLAI estimates.

Construction and Validation of a High-Fidelity Virtual Scene for Low-Stature and High-Biodiversity Ecosystems—Simulating Multi-Modal Sensing Approaches ↗

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m2) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms.

Why it matches plant phenotyping methods植物群落の種判別・多様性指標を対象に、物理ベースの仮想シーンとマルチモーダルセンシングを開発し、実測データで検証しているため、植物状態の取得・推定法が中心である。

abstractWe therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring.

Spectral-Consistency-Aware Evaluation of Deep Super-Resolution Methods for UAV Five-Band Multispectral Crop Imagery ↗

Unmanned aerial vehicle (UAV)-based multispectral imaging enables flexible, non-destructive crop monitoring. Although UAV imagery offers much higher spatial resolution than satellite platforms, its effective spatial detail at typical operational flight altitudes can still be insufficient for plant-level interpretation and fine canopy structure, which can reduce vegetation-index reliability. Most super-resolution (SR) research targets RGB or satellite imagery and emphasizes perceptual or pixel-wise quality, leaving the spectral fidelity of reconstructed UAV multispectral imagery under-examined. This study benchmarked an SR evaluation framework for UAV-based five-band crop imagery (Blue, Green, Red, Red-edge, and near-infrared) using the open-source AI Hub cabbage dataset, with low-resolution inputs generated by controlled downsampling at ×2, ×3, and ×4. Nine methods (bicubic, SRCNN, EDSR, RCAN, SwinIR-based, ESRGAN-based, HAT-based, DAT-based, and DRCT-based SR) were compared under joint five-channel and band-wise reconstruction on 2170 test scenes using image-quality, spectral-angle, vegetation-index (NDVI, GNDVI, NDRE), band-wise, and efficiency metrics. EDSR and RCAN gave the most balanced performance. At ×4, band-wise reconstruction was strongest for per-band spatial fidelity, where EDSR reduced RMSE by 12.6%, and RCAN lowered near-infrared RMSE by about 21% relative to bicubic, whereas joint reconstruction with its spectral-angle and vegetation-index losses best preserved spectral relationships (spectral angle and vegetation-index errors). Learning-based gains were clearest at ×4. The recently proposed HAT-based, DAT-based, and DRCT-based attention models achieved the strongest pixel-wise RMSE and PSNR but did not surpass EDSR or RCAN on spectral angle or vegetation-index preservation under the equalized training budget. These results indicate that UAV multispectral SR should be assessed by spatial fidelity together with spectral consistency and agricultural index reliability.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像の超解像法を比較・ベンチマークし、スペクトル一貫性や植生指数の信頼性を評価する研究であり、植物キャノピー形質の画像取得・抽出基盤が中心である。

abstractThis study benchmarked an SR evaluation framework for UAV-based five-band crop imagery

Deep Learning for Plant Disease Detection: A Systematic Review ↗

Plant diseases remain a threat to global agricultural productivity, food security and livelihoods, especially in developing countries where the availability of experts in agriculture is still limited. The recent progress in AI, particularly deep learning and computer vision, has ushered in new possibilities for automated plant disease diagnosis, especially for plant image-based systems. This paper provides a systematic review of the deep learning methods employed for plant disease diagnosis, highlighting CNN-based methods, the application of transfer learning, explainable AI (XAI) methods and deployment issues. In the framework of PRISMA 2020, the relevant peer reviewed literature from 2016 to 2025 was systematically identified, screened and analysed on the most important academic databases. The review compared some of the most popular architectures such as GoogLeNet, DenseNet-121, MobileNetV2, EfficientNet, Attention-CNNs and Vision Transformers. Results showed very high classification accuracy in controlled lab conditions with DenseNet-121 achieving ~99.75% accuracy with good computational efficiency. But it also revealed a big gap between the lab and the field, mainly due to environmental variations, domain shifts, and dependence on datasets. Some innovative and emerging technologies like explainable AI, hyperspectral imaging, few-shot learning, and lightweight mobile architectures showed promise of enhancing the interpretability, early detection of disease, and the use of smart phones in low-resource agricultural settings. In conclusion, the study suggests that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability. The results enrich the existing knowledge on precision agriculture and serve as useful information for researchers, agricultural technologists, and policymakers working on the creation of AI-based systems for crop protection.

Why it matches plant phenotyping methods植物病害を画像から診断する深層学習手法を体系的に比較・レビューしており、植物の病徴・病害状態の推定方法が中心である。

abstractThis paper provides a systematic review of the deep learning methods employed for plant disease diagnosis

Hyperspectral Visual SLAM for Autonomous UAV Crop Stress Detection: A Reinforcement Learning Approach to Precision Agriculture ↗

Localized soil-moisture deficits, that is, irregular sub-field patches where crops experience water stress well before visible wilting, are a leading cause of yield variability in row-crop agriculture. These zones are difficult to detect at the spatial resolution and revisit frequency required for timely irrigation response. This paper presents a reinforcement-learningguided autonomous quadrotor unmanned aerial vehicle (UAV) platform that fuses onboard Visual Simultaneous Localization and Mapping (Visual SLAM) with a pushbroom hyperspectral imaging payload to construct georeferenced, canopy-registered maps of a Crop Water-Stress Index (CWSI) in near real time. Rather than flying a fixed lawnmower survey, the platform is guided by an adaptive-sampling policy trained with Proximal Policy Optimization (PPO) that reallocates flight time and sensor dwell toward regions of emerging water stress as evidence accumulates mid-flight. We present the complete engineering pipeline: airframe and sensor design, a keyframe-based Visual SLAM front and back end that provides centimeter-scale geolocation without continuous reliance on Real-Time Kinematic (RTK) GNSS lock, a hyperspectral preprocessing and spectralindex chain (NDVI, NDRE, NDWI/NDMI) used to derive CWSI through a learned regression, the partially observable Markov Decision Process (POMDP) formulation and reward shaping used to train the sampling policy, and the fused system architecture tying these subsystems together. In simulated field trials over a 0.8-hectare test plot, the reinforcement-learning-guided policy achieved a 92% water-stress-zone detection rate versus 61% for a fixed-grid baseline, while reducing mission flight time by approximately 32%. We further report an ablation study isolating the contribution of SLAM-derived canopy structure to CWSI accuracy, a sensitivity analysis across field complexity, and a full error budget for the fused pipeline. We close with a discussion of validation limitations, broader scientific and agricultural impact, and a roadmap toward multi-UAV fleet deployment for whole-farm monitoring

Why it matches plant phenotyping methodsUAV、Visual SLAM、ハイパースペクトル画像、機械学習を統合し、作物の水ストレス状態を推定・地図化する技術パイプラインを開発・評価しており、植物表現型取得が中心である。

abstractThis paper presents a reinforcement-learningguided autonomous quadrotor unmanned aerial vehicle (UAV) platform that fuses onboard Visual Simultaneous Localization and Mapping (Visual SLAM) with a pushbroom hyperspectral imaging payload to construct georeferenced, canopy-registered maps of a Crop Water-Stress Index (CWSI) in near real time.

Monitoring phases of plant stress in juvenile commercial forest cuttings using contemporary nursery sensor technologies ↗

Abstract Visual assessments of growing forest nursery plants are time-consuming and often result in a lack of information at a physiological level. There exists a need for health screening in nurseries, that is fast and efficient, to improve overall health monitoring and nursery productivity. Rapid handheld sensors such as rapid thermal devices, leaf porometers and moisture meters, can provide regular information at a physiological level, that can improve the understanding of the impact of stress on young plant cuttings and their decline in health over time. This paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions. Furthermore, to assess whether thermal sensors could be used as an indicator, in conjunction with other variables such as soil water content or stomatal conductance, is needed operationally for fast screening during limited planting windows. Near Infra-Red Analysis (NiRA) data was collected to understand detailed plant functions at a finer reflectance level. A relationship was found where the increase in thermal signals reflects a depletion of water content, resulting in an eventual decline in stomatal conductance and, ultimately, plant mortality. Several algorithms were used in a preliminary test, using RapidMiner software, to discriminate between the four phases of plant health decline using physiological variables and NiRA data. Both Gradient Boosting Trees (GBT) and Deep Learning (DL) showed the best performances, achieving favourable accuracies of 96.8% and 91.2% without NiRA data, 84.6% and 88.2% with NiRA data, with shorter training times. Using thermal technology weighted amongst the highest of the best performing variables using GBT, the utility and accuracy showed good discrimination between the stages of plant decline and is encouraged for future research in this field.

Why it matches plant phenotyping methods植物のストレス段階を熱センサー、ポロメータ、含水率計、NiRAおよび機械学習で測定・識別する方法の有用性と信頼性を評価しており、表現型取得・判定手法が中心である。

abstractThis paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions.

GCT-BCLN: a bidirectional closed-loop network for nondestructive detection of rice seed vigor using hyperspectral imaging. ↗

Rice seed vigor is a key determinant of germination performance and final crop yield, making its rapid and non-destructive assessment essential for seed quality evaluation. Conventional vigor detection methods are often destructive, labor-intensive, and time-consuming. Hyperspectral imaging provides a promising non-destructive alternative, but hyperspectral data are typically high-dimensional, redundant, and susceptible to noise and scattering interference. Moreover, existing models still have limited ability to discriminate subtle spectral differences among seed vigor levels. To address these challenges, this study proposes a gated recurrent unit (GRU)-guided closed-loop CNN-Transformer network (GCT-BCLN) for accurate, non-destructive identification of rice seed vigor. The model establishes bidirectional information flow between CNN and Transformer via the GRU, enabling dynamic and synergistic optimization of local spectral features and global spectral representations. In addition, a combined preprocessing strategy integrating adaptive iteratively reweighted penalized least squares (AirPLS), Savitzky-Golay (SG) smoothing, and multiplicative scatter correction (MSC) was adopted to improve spectral quality. Experimental results showed that GCT-BCLN achieved a test accuracy of 0.9795 for hybrid indica rice, outperforming the CNN-Transformer fusion model by 1.37%. The model also achieved accuracies of 0.9793 and 0.9758 on conventional japonica rice and glutinous japonica rice, respectively, showing consistent performance across the three evaluated variety-specific datasets under the controlled experimental protocol. These results support the feasibility of GCT-BCLN for laboratory-scale, non-destructive discrimination of aging-induced rice seed categories under controlled conditions, while practical application requires further external validation.

Why it matches plant phenotyping methodsイネ種子の活力という植物状態を、ハイパースペクトル画像と新規深層学習モデルで非破壊推定する手法開発が研究の中心である。

abstractthis study proposes a gated recurrent unit (GRU)-guided closed-loop CNN-Transformer network (GCT-BCLN) for accurate, non-destructive identification of rice seed vigor.

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.

Computer Vision from Tea Cultivation to Quality Evaluation. ↗

Existing reviews on AI in tea production are either agriculture-generic or limited to isolated tasks. This review thoroughly compares vision technologies (RGB, hyperspectral, near-infrared, thermal, Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicle (UAV)) and establishes a task-oriented algorithm selection framework for the tea industry. For small-sample or near-linear problems, traditional machine learning (ML) (support vector machine (SVM); partial least squares regression (PLSR)) remains effective. For unstructured field tasks, deep learning achieves superior performance: pest detection accuracy exceeds 97%, tea bud detection reaches 96.8% with RGB images, and hyperspectral imaging predicts nitrogen content with R 2 > 0.90 and tea polyphenols with R 2 up to 0.925. Algorithm choice further differentiates by task granularity: lightweight convolutional neural networks (CNNs) balance speed and accuracy for edge deployment at 16 fps; You Only Look Once (YOLO) series detectors enable real-time localization on mobile platforms at 93.1% accuracy, 24 ms per target. No single algorithm dominates all tea tasks; selection is a trade-off among accuracy, speed, data availability, and computational constraints. These findings outline a structured analysis of the challenges and pathways for transitioning computer vision (CV) from laboratory research toward field-deployable tools.

Why it matches plant phenotyping methods茶作物の画像センシング技術と解析アルゴリズムを体系的に比較し、害虫検出や窒素含量予測など植物状態・形質の推定方法を扱う方法論レビューである。

abstractThis review thoroughly compares vision technologies (RGB, hyperspectral, near-infrared, thermal, Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicle (UAV)) and establishes a task-oriented algorithm selection framework for the tea industry.

Spectral network analysis illuminates coordinated plant traits across a climate gradient ↗

Summary Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a powerful high‐throughput solution, simultaneously capturing leaf biochemistry, water content, and structural properties across hundreds of wavelengths. We present a framework combining hyperspectral data, inverse modeling, and network analysis to investigate population‐level variation in Streptanthus tortuosus . Using a common garden experiment with four populations, we apply supervised methods (partial least square discriminant analysis; ridge regression) to identify which spectral features differ among populations, and an unsupervised spectral network approach to characterize how wavelength correlations are organizationally structured within each population, where we treat coordination architecture itself as a population‐level phenotype that can vary with environment. The framework detects distinct, heritable spectral signatures across populations, population differences in anthocyanins, carotenoids, Chl, water content, and population‐specific network architectures. Thermally variable environments were associated with greater spectral modularity, demonstrating that trait coordination architecture varies with climate of origin. This approach addresses the phenotyping bottleneck in evolutionary ecology, providing a scalable, high‐throughput tool for characterizing genetically based population differences in both individual traits and their coordination, with broad applications for monitoring plant population responses to climate change.

Why it matches plant phenotyping methodsハイパースペクトル計測、逆モデリング、ネットワーク解析を統合し、葉の機能形質と形質協調構造を植物表現型として抽出する手法が研究の中心である。

abstractHyperspectral reflectance offers a powerful high‐throughput solution, simultaneously capturing leaf biochemistry, water content, and structural properties across hundreds of wavelengths.

Phenomics and High-Throughput Phenotyping of Photosynthetic Traits for Improving Abiotic Stress Resilience in Wheat and Rice ↗

Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production.

Why it matches plant phenotyping methods植物の光合成形質を対象に、HTP技術やセンサー手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 critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding

Climate change, water quality, and water diversion are associated with the shifts of aquatic vegetation structure and phenology in five temperate regulating lakes of eastern China. ↗

Aquatic plants are vital for lake ecosystem functioning and water-quality stability, yet their community dynamics and phenology rhythms remain insufficiently understood, due to the lack of effective strategies for fine-scale species mapping and phenology extraction. In this study, based on Sentinel-2 MSI imagery, we developed an integrated framework combining machine learning and a priori ecological knowledge to quantify the spatiotemporal changes in aquatic plant distribution, species composition and phenological dynamics for eight dominant species in five regulating lakes along the Eastern Route of the South-to-North Water Diversion Project in China. Results showed that the proposed framework enabled accurate aquatic plant identification, achieving an overall classification accuracy of 96.16% and over 90% accuracy for each species. Since 2016, aquatic vegetation coverage has substantially declined in most lakes, mainly due to the retreat of submerged vegetation. Community structure has shifted from submerged-plant dominance to emergent and floating-leaved dominance in two of them. Phenologically, we found that most aquatic vegetation exhibited a longer growing season, characterized by earlier growth onset (-0.28 days/year) and peak timing (-0.67 days/year) and delayed senescence (0.54 days/year). Correlation analysis indicated that aquatic vegetation dynamics was associated with climate variation, nutrient enrichment, turbidity, and water diversion, with warming and solar radiation likely promoting the growth of some emergent species, while nutrient enrichment and turbidity could be linked with submerged vegetation decline and earlier phenological shifts. Overall, this study provides an effective framework for species-level mapping and phenological monitoring of aquatic vegetation, offering valuable support for the management and conservation of lake ecosystems.

Why it matches plant phenotyping methodsSentinel-2画像と機械学習等を統合した、植物種分布・構成・フェノロジーを抽出する手法を開発し、精度検証と大規模適用を行っているため、植物フェノタイピング手法が中心である。

abstractwe developed an integrated framework combining machine learning and a priori ecological knowledge to quantify the spatiotemporal changes in aquatic plant distribution, species composition and phenological dynamics for eight dominant species in five regulating lakes

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)

Detection of nitrogen content in wheat leaves based on visible/near-infrared spectroscopy sensing. ↗

Nitrogen is an important element present in vital substances such as plant proteins and chlorophyll, the content of which directly reflects the nutrient status of crops, and provides a theoretical basis for crop nutrient diagnosis, growth monitoring and yield potential prediction. Taking the chip-level visible/near-infrared spectral sensor AS7263 as the data acquisition module and the Arduino Uno single-chip microcomputer development board as the control module, a portable crop leaf spectral sensing system was designed in this study. The spectral reflectance and nitrogen content of wheat leaves were obtained through field experiments. Principal Component Analysis (PCA) was used to eliminate abnormal spectral data. Combined with pretreatment algorithms including Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), the prediction models for wheat leaf nitrogen content were established based on Partial Least Squares (PLS), Support Vector Machine (SVR), Random Forest (RF) and a Back Propagation (BP) neural network. The results showed that compared with SNV, the model performance based on the spectral data after MSC pretreatment was better. The test set R 2 values of PLS, SVR, RF and BP models were 0.61, 0.75, 0.83, and 0.89, and the root mean square errors (RMSEs) were 4.62 mg g -1 , 4.38 mg g -1 , 3.39 mg g -1 and 3.27 mg g -1 , respectively. The MSC-BP prediction performance was the best, and the non-destructive and accurate detection of nitrogen in wheat leaves was realized, which verified the feasibility of micro-spectral sensing technology in crop nutrition diagnosis.

Why it matches plant phenotyping methods小型可见/近红外光谱传感系统及预测模型是论文核心，用于无损估计小麦叶片氮含量这一植物生理性状，并报告了模型比较与验证性能。

abstracta portable crop leaf spectral sensing system was designed in this study

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.

Multi-scale reconstruction of snow-avalanche frequency and vegetation structure using dendrogeomorphology, satellite imagery, and UAV photogrammetry ↗

This study presents a multi-scale framework for reconstructing snow avalanche (SA) frequency and assessing vegetation structural responses in data-scarce mountain environments. The approach integrates dendrogeomorphological reconstructions, satellite-based spectral disturbance detection, and UAV-based Structure-from-Motion (SfM) photogrammetry, complemented by field data, and was applied to two avalanche paths in the Piatra Craiului Mountains (Southern Carpathians, Romania). Tree ring analyses allowed reconstruction of spatially explicit minimum avalanche chronologies for the 1980–2025 period. These reconstructions were combined with a DEM-based upslope algorithm to derive spatially variable avalanche return periods, revealing the highest frequencies in release and upper-track sectors and progressively longer return periods toward lower-track zones. Sentinel-2 imagery was used to assess the surface footprint of a reconstructed avalanche event in 2018. Among the tested spectral indices, the Moisture Stress Index (MSI) showed the most spatially coherent response, while the combined MSI-NDMI-NBR approach reduced index-specific noise. UAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states. Vegetation was classified using a machine-learning-based object-oriented approach (Random Forest) integrating spectral, geometric, structural, and textural parameters. The multi-parameter feature set yielded very high classification accuracy (Cohen’s Kappa ≈ 0.95). Across avalanche return-period gradients, both UAV-derived and field-based metrics showed a systematic associations between tree height and avalanche frequency, whereas tree age and stem diameter exhibited more variable, path-dependent responses. The proposed framework provides a transferable basis for linking avalanche disturbance regimes with vegetation structure and surface stability in mountain landscapes lacking long-term observational records.

Why it matches plant phenotyping methodsUAV-SfMと機械学習による植生構造・樹高の高解像度推定が研究枠組みの主要部分であり、分類精度も評価しているため、植物状態の画像ベース表現型計測として含める。

abstractUAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states.

High‐throughput assessment of plant stand establishment, seedling vigor, and light interception in peanut using UAV‐based RGB and multispectral imagery ↗

Abstract In peanut ( Arachis hypogaea L.), plant stand establishment, seedling vigor, and canopy growth are key determinants of crop performance; yet traditional ground‐based assessment methods can be destructive, labor‐intensive, and limited in throughput. This study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut. Six runner‐type peanut cultivars were evaluated in 2024 and seven in 2025, with each cultivar represented by two seed size classes (small and large), to generate variation in these traits. Within‐row vegetation discontinuity‐based plant stand ratings for estimating plant stand count ( R 2 = 0.81–0.90), together with canopy coverage for assessing seedling biomass ( R 2 = 0.77–0.82) and light interception ( R 2 = 0.96–0.98), were the best‐performing vegetation metrics. These vegetation metrics provided similar or greater cultivar separation compared with ground‐based measurements. In contrast, several vegetation indices exhibited strong correlations with ground‐based measurements but provided inconsistent cultivar rankings and statistical groupings. MS imagery outperformed RGB imagery for plant stand and seedling biomass assessment. Overall, these results demonstrate that UAV‐derived canopy metrics provide reliable, high‐throughput tools for early‐ to mid‐season crop assessment and offer scalable alternatives to traditional ground‐based approaches for agronomic, crop physiological, and plant breeding research.

Why it matches plant phenotyping methodsUAV画像から植物体の出芽・苗勢・バイオマス・光 interception を推定する植 phenotyping 手法を開発・評価しており、取得指標の性能検証が研究の中心です。

abstractThis study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut.

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.

Advances in Multi-Scale Remote Sensing and Machine Learning for Canopy-to-Root Phenotyping of Drought Adaptation in Sorghum: A Systematic Review ↗

Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C4 carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment.

Why it matches plant phenotyping methodsソルガムの干ばつ適応に関するセンシング型フェノタイピング手法を体系的にレビューし、形質推定の精度・移植性・検証、およびモデルと機械学習の統合を扱うため、方法論が中心である。

abstractThis systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement.

Estimation of Potato Plant Nitrogen Content Using Hyperspectral Indices and Machine Learning Models ↗

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

Why it matches plant phenotyping methodsハイパースペクトル計測と機械学習によりジャガイモ植物の窒素含量を推定する手法が題名上の中心であり、植物状態の非破壊的な表現型推定に該当する。

titleEstimation of Potato Plant Nitrogen Content Using Hyperspectral Indices and Machine Learning Models

Utilizing Vegetation Indices Derived from VNIR-SWIR Hyperspectral Data to Characterize Growth, Maturation, and Senescence in Wheat and Barley ↗

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions.

Why it matches plant phenotyping methodsRGB画像、赤外線サーモグラフィー、VNIR–SWIRハイパースペクトルを統合した高スループット表現型解析ワークフローが中心的に記述され、複数の植物形質・状態を定量化している。

abstractA high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence.

Trait-Specific Contributions of UAV Multispectral, RGB and Structural Features to Soybean SPAD and Plant Height Phenotyping ↗

Unmanned aerial vehicle (UAV) imagery can support plot-scale crop phenotyping, but spectral, RGB and structural predictors may contribute differently to different traits. We compared six predefined feature groups for predicting soybean SPAD and plant height (PH) in a 1.3 ha field experiment in Sanya, China. The field contained 6197 soybean planting plots, of which 234 had paired SPAD and PH measurements. Multispectral bands, vegetation indices (VIs), RGB descriptors and digital surface model (DSM) metrics were extracted from DJI Mavic 3 Multispectral imagery. Six regression algorithms were evaluated using random fivefold cross-validation, spatial block cross-validation and nested spatial cross-validation. Under random cross-validation, ExtraTrees with multispectral bands, VIs and RGB descriptors produced the numerically highest SPAD performance (R2 = 0.589; RMSE = 6.66), while BayesianRidge with multispectral bands, VIs and DSM metrics produced the highest PH performance (R2 = 0.760; RMSE = 7.14 cm). Nested spatial cross-validation yielded R2 = 0.473 and RMSE = 7.56 for SPAD and R2 = 0.690 and RMSE = 8.13 cm for PH. G4 was selected in four of the five outer folds for SPAD, although the selected algorithm varied, and G5 was selected in all five outer folds for PH. VIs improved prediction of both traits relative to the original bands. Adding RGB descriptors produced only a small and model-dependent improvement for SPAD, whereas adding DSM metrics produced a larger and more consistent improvement for PH. The complete feature set did not outperform G4 for SPAD or G5 for PH. The retained models were applied to all 6197 plots to map SPAD, PH and their field relative combinations. Because all of the validations used one field and one UAV acquisition date, the results describe performance within this experiment and do not establish transferability to other sites, years or growth stages.

Why it matches plant phenotyping methodsUAVマルチスペクトル・RGB・構造特徴からSPADと草丈を推定する特徴抽出および回帰手法を、複数の空間交差検証で比較・評価しており、植物表現型取得が研究の中心である。

titleTrait-Specific Contributions of UAV Multispectral, RGB and Structural Features to Soybean SPAD and Plant Height Phenotyping

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna ↗

Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.

Why it matches plant phenotyping methodsUAV SfMフォトグラメトリから植物群落の canopy height を抽出し、地上部バイオマス予測性能を評価しており、植物形質取得法の技術的適用・検証が中心である。

abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna ↗

Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.

Why it matches plant phenotyping methodsUAV-SfMによるキャノピー高とスペクトル指標から植物群落のバイオマスを推定し、手法の予測性能を比較評価しており、植物形質取得が研究の中心です。

abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.

Multispectral imaging-based detection of Acidovorax citrulli: from colony identification to infested seed discrimination ↗

Abstract Bacterial fruit blotch (BFB) caused by Acidovorax citrulli , is a destructive seed-transmitted disease that seriously threatens global cucurbit production. To address the need for detecting A. citrulli -infested seeds, this study developed a colony identification model and a seed infestation detection model based on multispectral imaging. The combined nMahalanobis and nCDA colony identification models achieved a high recall of 0.999 and a low false-positive rate of 0.149 when tested on samples. For infested melon seed detection, we evaluated and compared the classification performance of seven machine learning models. The results showed that LDA, logistic regression, and MLP exhibited stable performance on artificially infested seed samples. Furthermore, multi-cultivar modeling improved model generalizability and demonstrated the feasibility of using multispectral imaging to identify naturally infested seeds. When a qPCR Ct threshold of 37 was used to define seed infestation status, the logistic regression model achieved a validation accuracy of 0.82. Overall, these findings demonstrate the potential of multispectral imaging for colony identification and seed infestation detection, providing a new technical approach and a scientific basis for seed health testing of bacterial fruit blotch in cucurbit crops.

Why it matches plant phenotyping methodsマルチスペクトル画像と機械学習により、感染種子という植物器官の状態を検出する手法を開発・比較・検証しており、表現型取得が研究の中心である。

abstractthis study developed a colony identification model and a seed infestation detection model based on multispectral imaging.

A hybrid PROSAIL inversion framework for winter wheat LCC using hyperspectral data and transfer learning. ↗

Leaf chlorophyll content (LCC) is a key indicator for assessing the photosynthetic capacity and nutritional status of winter wheat. Among traditional LCC estimation methods, empirical models lack a physical basis and have poor generalisability, while physical models are widely applicable but suffer from ill-posed inversion problems. Hybrid inversion methods, which integrate radiation transfer models such as PROSAIL with machine learning, offer both the interpretability of physical models and the efficiency of machine learning; however, they are still affected by the domain shift between simulated and measured data, which limits their generalisation performance. Transfer Component Analysis (TCA), a domain adaption method, can effectively alleviate this problem. In this study, hyperspectral and LCC data were collected in the field, and simulated data were generated using the PROSAIL model; a sensitivity analysis was conducted to identify LCC-sensitive bands. A genetic algorithm was applied to the measured data for band selection and, together with the results of the sensitivity analysis, yielded an optimal set of 30 characteristic bands for subsequent modelling. Three datasets were constructed: measured data only, a direct mixture of measured and simulated data, and a TCA-fused mixture of measured and simulated data. Four models-gradient boosting regression (GBR), random forest (RF), support vector regression (SVR) and deep neural network (DNN)-were developed for each dataset. The results show that: (1) the LCC-sensitive bands are concentrated in the 450-660 nm and 680-720 nm ranges; (2) the model built on the TCA-fused data (R² = 0.722, RMSE = 6.792) outperformed those built on the measured-only data (R² = 0.682, RMSE = 7.259) and the directly mixed data (R² = 0.616, RMSE = 7.976); (3) for the TCA-fused data, the four models differed considerably in accuracy, with SVR performing best (R² = 0.723, RMSE = 5.363), followed by RF (R² = 0.630, RMSE = 6.201) and GBR (R² = 0.575, RMSE = 6.650), whereas the DNN performed worst (R² = 0.388, RMSE = 7.947), probably owing to the limited sample size.

Why it matches plant phenotyping methods冬小麦の葉緑素含量という植物形質を、ハイパースペクトルデータとPROSAIL・機械学習・転移学習で推定する手法を開発・比較しており、形質取得とモデル性能評価が研究の中心である。

abstractHybrid inversion methods, which integrate radiation transfer models such as PROSAIL with machine learning, offer both the interpretability of physical models and the efficiency of machine learning

Physics-Informed Transfer Learning Reduces Simulation to Reality Gaps for Winter Wheat Traits Retrieval from Hyperspectral Observations ↗

Accurate retrieval of crop structural and physiological traits from remote sensing data remains challenging due to limited field observations and poor cross-platform generalization of data-driven models. This study develops a physics-informed transfer learning framework to quantify the contributions of improving simulated data fidelity and increasing model complexity to retrieving winter wheat leaf area index (LAI) and canopy chlorophyll content (CCC) from hyperspectral observations. Two PROSAIL-D datasets with default and physically optimized leaf angle distributions were generated to represent different levels of simulation fidelity. Four dual-branch deep learning architectures (CNN, CNN–SE, CNN–Transformer, and CNN–SE–Transformer) integrating spectral bands and vegetation indices were pretrained on simulated datasets and transferred to real observations using progressive fine-tuning. Model performance was assessed using ground-based and unmanned aerial vehicle (UAV) hyperspectral datasets, and SHapley Additive exPlanations (SHAP) analysis was applied to interpret feature contributions. Results demonstrated that transfer learning substantially improved cross-domain generalization, while enhancing simulation fidelity provided greater performance gains than increasing network complexity. The CNN–Transformer model pretrained on physically optimized simulations achieved the highest accuracy and robustness for both LAI and CCC retrieval. At ground and UAV scales, it achieved LAI estimation accuracies of R2 = 0.55 (RMSE = 0.63) and R2 = 0.53 (RMSE = 0.62), respectively. For CCC estimation, the model obtained R2 = 0.59 at both scales, with RMSE values of 36.12 μg cm⁻2 and 37.56 μg cm⁻2 for ground and UAV observations, respectively. SHAP analysis indicated that physically optimized simulations shifted model attention toward physiologically relevant vegetation indices, whereas default simulations induced stronger dependence on unstable visible wavelengths. Physically informed simulation design combined with transfer learning effectively reduces simulation to reality discrepancies, whereas increasing deep model complexity alone provides limited improvement. The proposed framework offers an accurate, interpretable, and scalable solution for cross-platform crop trait retrieval from hyperspectral observations.

Why it matches plant phenotyping methodsハイパースペクトル観測から冬コムギのLAIと群落クロロフィル含量を推定する物理情報付き転移学習フレームワークを開発し、地上およびUAVデータで性能評価しており、植物形質取得・推定手法が中心である。

abstractThis study develops a physics-informed transfer learning framework to quantify the contributions of improving simulated data fidelity and increasing model complexity to retrieving winter wheat leaf area index (LAI) and canopy chlorophyll content (CCC) from hyperspectral observations.

Remote Sensing and Machine Learning for Monitoring Soil Nitrogen Dynamics and Crop Nitrogen Status in Field Conditions ↗

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with nitrogen losses. However, the high spatial and temporal variability of soil nitrogen dynamics and crop nitrogen status makes field-scale monitoring challenging, while conventional soil and plant sampling methods are labor-intensive, destructive, and provide limited spatial coverage. Recent advances in remote sensing technologies and machine learning (ML) offer promising alternatives for high-throughput, non-destructive monitoring of crop nitrogen status and related nitrogen dynamics in agroecosystems. This review synthesizes current progress in the use of proximal and remote sensing platforms, including unmanned aerial vehicles (UAVs), satellites, and ground-based sensors for assessing crop nitrogen status and inferring soil nitrogen availability. We examine spectral, thermal, and structural indicators, together with emerging sensor-fusion and time-series approaches. We also evaluate ML algorithms, including emerging foundation model approaches, for estimating crop nitrogen status and inferring soil nitrogen indicators, highlighting their performance, limitations, and transferability across environments. Particular emphasis is placed on field-scale applications in heterogeneous and water-limited systems, where nitrogen-water interactions critically influence crop responses. Finally, we discuss current challenges, including data scarcity, model generalization, and operational constraints, and outline future directions toward integrated, real-time decision support systems for precision nitrogen management. Overall, this review provides a comprehensive framework for leveraging remote sensing and data-driven approaches to improve nitrogen monitoring and enhance nitrogen use efficiency in diverse cropping systems.

Why it matches plant phenotyping methods作物の窒素状態という植物形質を対象に、リモートセンシングと機械学習による推定手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 current progress in the use of proximal and remote sensing platforms, including unmanned aerial vehicles (UAVs), satellites, and ground-based sensors for assessing crop nitrogen status and inferring soil nitrogen availability.

Nanosensors and Geospatial Technologies for Early Crop-stress Detection in Precision Agriculture: A Critical Multiscale Synthesis ↗

Crop stress develops through a sequence that begins with molecular and biophysical perturbation, progresses through physiological dysfunction, and only later becomes visually apparent. Precision agriculture therefore requires sensors that can shorten the interval between stress onset and actionable diagnosis while preserving spatial context. This critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection. Literature published from 2000 to 1 June 2026 was selected through live searches of accessible scholarly indexes, DOI registries, publisher records, institutional repositories, and citation networks, with foundational studies retained where necessary. The evidence shows that nano-enabled interfaces can measure early biochemical, ionic, volatile, electrical, and microclimatic signals at high temporal resolution, whereas geospatial technologies reveal the distribution, persistence, and management relevance of stress across canopies and fields. Optical nanotube sensors, surface-enhanced Raman probes, electrochemical microneedles, ion-selective wearables, and flexible leaf sensors have demonstrated biologically meaningful signals before visible symptoms in controlled or pilot field settings. Yet most remain constrained by sparse sampling, crop-specific calibration, bio-interface effects, power and communication burdens, uncertain durability, and limited agronomic validation. Geospatial methods are operationally more mature, particularly thermal and multispectral imaging for water stress and hyperspectral imaging for disease and nutrient-related changes, but they often infer stress through non-specific proxies that are confounded by canopy structure, atmosphere, soil background, phenology, and co-occurring stresses. The strongest future architecture is therefore not a contest between nanoscale and landscape-scale sensing. It is a multiscale system in which physiologically specific plant sensors anchor and interpret spatial imagery, while remote sensing directs where high-specificity measurements and interventions are most valuable. Progress depends on prospective field trials, reference measurements, uncertainty-aware data fusion, interoperability, lifecycle safety assessment, and decision thresholds linked to economic and agronomic outcomes.

Why it matches plant phenotyping methods植物ストレス状態の検出に用いるナノセンサー、ウェアラブルセンサー、熱・マルチスペクトル・ハイパースペクトル画像などを中心に批判的に統合した方法レビューであり、単なる農業応用紹介ではなく、センサー性能、校正、検証、データ融合を論じている。

abstractThis critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection.

Machine learning-based estimation of leaf chlorophyll content in greenhouse-grown muskmelon using portable hyperspectral reflectance measurements ↗

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

Why it matches plant phenotyping methods携帯型ハイパースペクトル測定と機械学習により、葉のクロロフィル含量という植物形質を推定する手法が題名上の中心であるため。

titleMachine learning-based estimation of leaf chlorophyll content in greenhouse-grown muskmelon using portable hyperspectral reflectance measurements

Deep learning combined with UAV to assist in leaf color breeding of Chinese cabbage ↗

Abstract Leaf color is an important trait affecting vegetable quality, yield, and market value. However, traditional methods for leaf color assessment are often subjective or destructive, which limits accurate and high-throughput phenotyping. In this study, an unmanned aerial vehicle (UAV)–based multispectral imaging platform was used to collect phenotypic data from 214 Chinese cabbage inbred lines at the rosette stage. A multispectral UNet model was applied to segment individual plants, and a membership function was used to quantify leaf color on a continuous scale. Based on these high-throughput phenotypic data, a genome-wide association study was used to identify two candidate genes, BrEMB976 and BrGSH2, on chromosome A06. Subsequent virus-induced gene silencing analysis showed that silencing these genes altered leaf color. In addition, a deep learning-based genomic selection model, BrDeepGS, was developed for leaf color prediction, which achieved a Pearson correlation coefficient of 0.853. These results demonstrate the potential of integrating UAV-based high-throughput phenotyping, candidate gene analysis, and genomic prediction for leaf color evaluation and selection in Chinese cabbage breeding.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像、個体分割、葉色の連続量化による高スループット表現型取得が研究の中心であり、育種への応用も行っている。

abstractan unmanned aerial vehicle (UAV)–based multispectral imaging platform was used to collect phenotypic data from 214 Chinese cabbage inbred lines

Phenotyping maize stay green traits via in situ leaf hyperspectral reflectance sensing ↗

Advancements in stay-green phenotyping are increasingly utilizing hyperspectral sensing technology to assess crop response under extreme environmental conditions. Yet, the effectiveness of different spectral features in explaining stay green remains to be fully elucidated. This includes identifying which bands and spectral indices are more effective in capturing the genotypic differences in stay-green traits. The main objective of this study was to evaluate hyperspectral leaf reflectance as a means to estimate stay-green visual scores (SGVS) as an indicator of drought tolerance and to further understand whether chlorophyll absorption-band spectral indices can differentiate SGVS classifications during post-flowering stages of maize. The experiment was conducted over two growing seasons in Germany, comprising 18 maize genotypes under two contrasting water availability conditions. We measured leaf hyperspectral reflectance using a spectroradiometer in the second, fourth, and sixth week after flowering, along with stay-green traits measurements. We employed raw spectral reflectance, hyperspectral vegetation indices (VIs) in combination with random forest (RF) and ANN models to predict SGVS. Results showed that drought stress significantly affected stay-green-related traits and led to a 43.5% decrease in grain yield in the inbred lines. The grain dry yield (GDY) was positively correlated with stay-green visual scores (SGVS), with higher SGVS associated with higher GDY. Stay-green traits were correlated with various VIs, with the best correlation observed for the Chl_NDI (r = 0.91). Stay-green groups were successfully classified using the selected VIs, with the water-absorption band VIs performing better than the chlorophyll-absorption band VIs and other VIs. Similarly, for predicting the SGVS, the water absorption band indices (R² = 0.79 ± 0.04 and RMSE = 0.12 ± 0.01) outperformed the chlorophyll absorption band indices when using RF. Leave-one-out-location/year cross-validation revealed pronounced variation in model transferability driven by environmental and temporal domain shifts. RF consistently outperformed ANN, showing greater robustness to inter-site heterogeneity and interannual variability, whereas performance degraded most in spectrally distinct environments or atypical seasons. Interestingly, RDIS_3b (1280, 1250, 1180 nm), NDIS_2b (2190, 1510 nm), and NDWI2 (860, 1241 nm) were identified as the most critical predictors in the RF models, across merged and separated datasets. These findings demonstrate the potential of spectral signatures, particularly water-absorption band spectral indices, for quantitative phenotyping of stay-green as a proxy for drought tolerance in maize breeding programs; however, multisite, multiyear calibration is needed to enhance generalizability.

Why it matches plant phenotyping methodsトウモロコシのstay-green形質を対象に、葉のハイパースペクトル反射を用いた形質推定・分類モデルを評価し、交差検証で転移性と頑健性も検証しているため、センサー型表現型計測手法が中心である。

abstractThe main objective of this study was to evaluate hyperspectral leaf reflectance as a means to estimate stay-green visual scores (SGVS)

Hyperspectral–machine learning framework enables early and non-destructive prediction of plant resistance to pest ↗

The brown planthopper ( Nilaparvata lugens ) is one of the most destructive pests of rice and poses a threat to yield stability and food security. Although host-plant resistance is the most sustainable strategy for BPH management, conventional resistance phenotyping remains labor-intensive, destructive, and poorly suited for large-scale breeding. Here, we combined hyperspectral reflectance profiling of 50 rice varieties with an interpretable machine learning framework to enable non-destructive prediction of resistance phenotypes. Using post-infestation spectral profiles, we established classification models that captured resistance states shaped by constitutive traits and inducible defense responses. Among 13 evaluated algorithms, a radial basis function support vector machine achieved the best performance on full-spectrum data within the sampled variety panel, with an average accuracy of 0.939 ± 0.015 and a maximum of 0.972. Predictive wavelengths were concentrated in the green, red-edge, and near-infrared regions, corresponding to variation in pigment regulation, canopy structure, and water status. Spectral and network analyses showed that resistant genotypes exhibited more complex but less stable spectral co-occurrence networks, consistent with physiological trade-offs associated with defense. We also tested whether resistance could be predicted before pest infestation. Pre-infestation spectra retained significant predictive power, with accuracies of 0.572 ± 0.021 for five-class classification and 0.667 ± 0.021 for binary classification, indicating that constitutive defense-associated physiological states are optically detectable before visible damage occurs. Together, our results show that hyperspectral reflectance encodes both inducible responses after infestation and constitutive defense baselines present beforehand. This work establishes a scalable, non-invasive phenotyping strategy for early resistance screening within evaluated germplasm panels, while future validation across independent and variety-level held-out populations will be required before broader deployment.

Why it matches plant phenotyping methodsイネの害虫抵抗性という植物状態を、ハイパースペクトル計測と機械学習で非破壊推定する方法を開発・評価しており、表現型取得が研究の中心である。

abstractwe combined hyperspectral reflectance profiling of 50 rice varieties with an interpretable machine learning framework to enable non-destructive prediction of resistance phenotypes.

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

Early detection of plant pathogens in the asymptomatic phase: A scoping review of hyperspectral imaging combined with machine learning ↗

• First PRISMA-ScR mapping of 79 HSI-ML asymptomatic detection studies (42 species, 74 pathogens) • Controlled-to-field accuracy gap quantified: 91.4% vs. 86.3% (5.1 pp, p = 0.0163 ) • 56.8% of studies omit temporal sampling documentation (CV = 139%) • SWIR underutilization (11.8%) reflects economic, not scientific, barriers • DBVS proposed as standardized temporal metric for cross-study comparability Plant disease management requires non-invasive detection methods capable of identifying infections before visible symptom manifestation, thereby enabling timely intervention. Hyperspectral imaging combined with machine learning and deep learning (HSI-ML) achieves 90.2% classification accuracy in controlled environments for asymptomatic plant detection; however, systematic characterization of methodological practices across this rapidly expanding field remains absent. This PRISMA-ScR compliant scoping review mapped 79 peer-reviewed studies (2010–2025) encompassing 42 plant species and 74 pathogenic agents using a Population-Concept-Context framework. Visible-near-infrared (VNIR) systems dominated deployment (61.8%, n = 49 ), while short-wave infrared (SWIR) systems remained substantially underutilized (11.8%, n = 9 ) due primarily to economic rather than scientific constraints. Among 67 unique algorithms identified, machine learning methods accounted for 30.7% (SVM, random forests, and PLS-DA predominant), whereas deep learning represented 28.4% (2D-CNN, 3D-CNN, and hybrid architectures). Critical methodological gaps emerged: 56.8% of studies omitted temporal sampling documentation (detection latency range: 1–56 days post-inoculation; coefficient of variation = 139%). Platform-stratified analysis revealed controlled environments achieved 91.4% ± 6.6% classification accuracy ( n = 48 ) versus 86.3% ± 9.1% for field/UAV deployments ( n = 26 ), representing a significant 5.1 percentage-point performance decrease ( p = 0.0163 ). Detection accuracy exhibited a weak negative correlation with detection timing ( ρ = − 0.33 , p = 0.067 ), though this association did not reach conventional statistical significance. Methodological heterogeneity—rather than algorithmic limitations—constitutes the primary barrier to field operationalization. Adoption of Days Before Visible Symptoms (DBVS) as a standardized temporal metric could resolve an estimated 40–50% of cross-study variance currently attributed to inconsistent asymptomatic-phase definitions.

Why it matches plant phenotyping methods植物病害の無症状感染をHSIと機械学習で検出する手法群を対象に、79研究の方法、精度、時間指標、標準化課題を体系的に評価したレビューであり、フェノタイピング手法が中心です。

titleEarly detection of plant pathogens in the asymptomatic phase: A scoping review of hyperspectral imaging combined with machine learning

Field-scale crop growth stage mapping using multispectral images and deep hierarchical segmentation ↗

Accurate, field-scale mapping of crop growth stages is critical for supply-sensitive vegetable production, where timely harvests require detailed phenological information. Consecutive growth stages often involve rapid and subtle morphological changes and are influenced by challenging open-field conditions, which frequently result in misclassification when stages are treated as independent, discrete categories. To address this issue, CropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery. CropMap incorporates the structured biological progression of crop development into the learning objective through tree-based label constraints, allowing the model to recognize phenological continuity and reduce confusion between adjacent stages. The framework is evaluated on the publicly available National Information Society Agency of Korea (NIA) field crop growth-stage dataset, a large-scale, multi-institutional UAV dataset containing 337,665 multispectral patches across six hierarchically related growth stages of Chinese cabbage and radish, curated by the NIA. CropMap achieves a test-set mean Intersection over Union (mIoU) of 0.5382, representing a 5% relative improvement over the best-performing transformer baseline (SegFormer; mIoU = 0.5124). Performance varies across classes: background separation is strong (IoU = 0.9128) and the rosette stage is well distinguished (IoU = 0.6354), while the leaf expansion stage remains the primary challenge (IoU = 0.3541), reflecting the inherent difficulty of mapping this spectrally and morphologically transitional class. These findings indicate that hierarchy-aware learning reduces inter-stage confusion for most phenological classes, but transitional growth stages remain a significant limitation for field-scale deployment. The framework provides a foundation for stage-resolved crop monitoring to support harvest timing and supply forecasting in high-value vegetable systems.

Why it matches plant phenotyping methods作物の生育段階という植物状態を、マルチスペクトルUAV画像と階層型セマンティックセグメンテーションで推定する手法が研究の中心であり、公開データセット上で性能評価も行っている。

abstractCropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery.

Cover crop biomass estimation using UAV-based multispectral feature fusion and machine learning ↗

Cover crops offer essential agroecosystem benefits, including reduced soil erosion, weed suppression, and improved soil health. Aboveground biomass (AGB) is a key indicator of these benefits; however, field-based quantification is often limited, which hinders effective cover crop management decisions. This study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas. Ground-truth and imagery data were collected over three years (2023–2025) for winter rye ( Secale cereale L.) in Lamesa and two years (2023–2024) for winter wheat ( Triticum aestivum L.) in Chillicothe under varying irrigation regimes. Five ML algorithms, random forest, support vector regression, extreme gradient boosting, partial least squares regression (PLSR), and artificial neural network (ANN), were evaluated across four individual and eleven feature fusion datasets. The ANN model consistently achieved the highest predictive accuracy, particularly when vegetation indices were combined with structural features (R² = 0.87, RMSE = 9.08 g m - ²), while PLSR showed the weakest performance. Grouped validation (leave-one-year-out, leave-one-species-out, and leave-one-treatment-out) revealed reduced model performance compared to random (70/30) splitting of pooled data, yet the ANN maintained moderate predictive ability, indicating reasonable generalizability across years, species, and management conditions. Shapley additive explanations (SHAP) revealed key predictors in the ANN model, including plant height, chlorophyll vegetation index, chlorophyll sensitive index, blue band reflectance, modified chlorophyll absorption in reflectance index, dissimilarity, correlation, and enhanced green vegetation index. These findings demonstrate the effectiveness of UAV-ML integration for accurate AGB estimation and highlight the potential for scalable, data-driven cover crop monitoring in water-limited environments and beyond.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を統合し、植物の地上部バイオマスを推定する手法を開発・比較検証しており、表現型取得が研究の中心である。

abstractThis study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas.

Spatio-spectro-temporal characterisation and correction of dark current in a snapshot hyperspectral sensor ↗

Low-cost uncooled snapshot hyperspectral sensors mounted on UAV platforms offer new opportunities for field-scale remote sensing high-throughput phenotyping, but their reliability is constrained by sensor-intrinsic artefacts, particularly dark current. In this study, we present the first spatio-spectro-temporal characterisation and correction of dark current in the Senop HSC-2 dual-CMOS Fabry-Perot snapshot hyperspectral camera. Six controlled dark experiments (∼6,100 image cubes) revealed that dark current in the sensor is highly structured and reproducible, exhibiting CMOS-specific baseline offsets, monotonic temporal drift, pixel-wise dark signal non-uniformity (DSNU) with wavelength-dependent structure, and persistent hot pixels, and exposure-dependent baseline shifts that do not scale linearly with integration time. These results confirm that conventional single-frame dark subtraction is insufficient for quantitative analysis in uncooled snapshot hyperspectral sensors. Building on this characterisation, a modular correction framework was developed to stabilise the dark signal across spatial, spectral, and temporal domains. Across laboratory datasets, the framework reduced temporal drift by 70-85%, DSNU variance by approximately 37.5%, and suppressed >99.9% of persistent hot pixels, substantially improving radiometric stability. Corrected data exhibited simultaneous spatial uniformity, temporal stability, and spectral integrity, enabling downstream radiometric processing without introducing spectral distortion. Application of Senop HSC-2 to UAV-acquired wheat canopy imagery demonstrated effective transfer to field conditions, improving spectral continuity and robustness of vegetation indices after dark current correction. These results establish a transferable calibration approach for affordable snapshot hyperspectral sensors and demonstrate that rigorous dark current correction is essential for achieving quantitative radiometric performance in UAV-based phenotyping and precision agriculture applications without active thermal control, extending calibration principles traditionally applied in satellite hyperspectral systems to low-cost UAV snapshot sensors.

Why it matches plant phenotyping methodsUAVハイパースペクトルセンサの暗電流を空間・スペクトル・時間的に補正する手法の開発と検証が中心であり、圃場のコムギ群落画像への適用も行っているため、植物フェノタイピング手法として採用する。

abstractIn this study, we present the first spatio-spectro-temporal characterisation and correction of dark current in the Senop HSC-2 dual-CMOS Fabry-Perot snapshot hyperspectral camera.

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

IoT and Edge-AI Enabled Autonomous Agri-Robot for Precision Irrigation and Early Plant Disease Diagnosis Using Attention-Guided Lightweight CNN and Fuzzy Logic Control ↗

This paper presents an IoT and edge-AI enabled autonomous agricultural robot that performs early plant disease diagnosis and precision irrigation on a single mobile platform. Unlike earlier automated farming systems that rely on visible-spectrum (RGB) imagery and simple threshold-based watering, the proposed system fuses RGB and nearinfrared (NIR) imagery to compute the Normalised Difference Vegetation Index (NDVI), enabling detection of physiological plant stress several days before visible lesions appear. Leaf images are classified using a lightweight attention-guided convolutional neural network that combines a MobileNetV3 backbone with a Convolutional Block Attention Module (CBAM), allowing the network to focus on lesion-relevant channels and spatial regions while remaining compact enough for real-time inference on an ESP32-S3 edge controller. Irrigation and pesticide-spray decisions are no longer governed by a rigid binary threshold; instead, a Mamdani-type fuzzy inference engine fuses soil moisture, ambient temperature, and the NDVI-derived stress index to compute a proportional, continuously variable actuation signal, reducing both water wastage and false triggering. The robot streams sensor readings, classification results, and actuation logs to a cloud dashboard over Wi-Fi/MQTT so that farmers can monitor crop health and irrigation status remotely and receive real-time alerts. Experimental evaluation on a prototype platform shows that the proposed attention-guided model improves disease-classification accuracy over a baseline CNN, the NDVI-assisted pipeline detects stress earlier than colour-only analysis, and the fuzzy irrigation controller reduces water consumption relative to the binary threshold scheme while maintaining optimal soil-moisture levels. The results indicate that combining multispectral sensing, attention-based lightweight deep learning, and fuzzy control on a single autonomous platform is a practical and scalable route towards sustainable, resource-efficient precision agriculture.

Why it matches plant phenotyping methodsRGB/NIR画像からNDVIによる植物ストレスを推定し、葉画像から病徴を分類する取得・解析手法をロボット上で開発・評価しており、植物表現型の測定が中心である。

abstractthe proposed system fuses RGB and nearinfrared (NIR) imagery to compute the Normalised Difference Vegetation Index (NDVI), enabling detection of physiological plant stress several days before visible lesions appear.

Using hyperspectral reflectance to explore the responses of rice canopy chlorophyll fluorescence to water stress ↗

【Objective】Chlorophyll fluorescence is a physiological indicator reflecting crop photosynthesis and water stress. Non-destructively monitoring the changes in chlorophyll fluorescence under water stress is critical for improving irrigation management. This paper explores the applicability of canopy hyperspectral reflectance for elucidating the response of rice canopy chlorophyll fluorescence to water stress.【Method】The experiment was conducted in pots and the measurements were taken during the booting stage of rice. Three water treatments were set, including continuous flooding irrigation (CK), mild drought (MS) and severe drought (HS). Canopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform, from which we analysed the responses of chlorophyll fluorescence traits to soil water change. Prediction models were developed to estimate chlorophyll fluorescence traits using partial least squares regression (PLSR) and backpropagation neural network (BPNN), based on characteristic spectral bands.【Result】①The chlorophyll fluorescence traits Fv/Fm, Y(II), qL and Y(NPQ) varied with water stress, with significant changes observed 3-4 days after cessation of irrigation, and detectable variation identified up to day 6 after terminating irrigation. On day 6 after irrigation cessation, the HS treatment reduced Fv/Fm, Y(II) and qL by 41.3%, 46.9% and 53.1%, respectively, whereas increased Y(NPQ) by 117.5% compared with CK. ②Savitzky-Golay smoothing and multiplicative scatter correction (MSC) preprocessing effectively reduced the scattering effects on canopy hyperspectral data induced by structural variation. The characteristic spectral bands selected from the hyperspectral data were mainly distributed in the blue (400-500 nm), red and near-infrared regions. ③Compared with PLSR, the BPNN was more effective in capturing the nonlinear relationships between hyperspectral data and chlorophyll fluorescence traits. The BPNN was most accurate for estimating Y(NPQ) and qL, with the associated R2 values being 0.867 and 0.845, respectively, and less accurate for estimating Fv/Fm.【Conclusion】Canopy hyperspectral data can be used to estimate rice chlorophyll fluorescence traits. This approach provides a rapid, cost-effective, and non-destructive method for monitoring crop physiological responses to water stress.

Why it matches plant phenotyping methodsイネのクロロフィル蛍光という生理形質を、キャノピー分光反射から推定するセンサー計測・予測モデルを開発し、精度評価しており、フェノタイピング手法が中心である。

abstractCanopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform

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).

Quantitative morphology, machine learning, and hyperspectral interface phenotyping of Trichoderma–Colletotrichum antagonism across host-associated isolate panels ↗

The efficacy of biological control agents is often inconsistent across pathogen isolate panels, yet conventional dual-culture screening often reduces antagonism to single endpoint measurements such as radial growth or colony area. Here, we developed a quantitative phenotyping framework to evaluate interactions between three Trichoderma antagonists and Colletotrichum isolates associated with coffee and cacao. Dual-culture assays were used to quantify antagonist and pathogen morphology after 96 h, and the combined morphology dataset was analyzed using machine learning to test whether host-associated isolate panels could be classified from colony-level interaction traits. To address potential information leakage and basal-growth confounding, we evaluated control-only pathogen morphology models and leave-one-pathogen-isolate-out validation. Under random 5-fold validation, Random Forest models achieved similar balanced accuracy using control-only pathogen morphology and full dual-culture interaction morphology, 0.840 and 0.864, respectively. Under the more conservative leave-one-pathogen-isolate-out validation, performance decreased but the full dual-culture model, balanced accuracy = 0.725, outperformed the control-only model, balanced accuracy = 0.578, indicating that basal pathogen morphology contributes to host-associated differences while interaction-level traits add information beyond basal growth alone. Hyperspectral imaging was then used as a proof-of-concept, non-invasive tool to characterize selected interaction interfaces. In the complete 11C-65-1 × P24-83/P24-192 subset, VNIR reflectance residuals showed interface-specific deviations from within-plate colony-side spectral mixing axes. These residual wavelength features are presented as candidate spectral correlates rather than validated biochemical mechanisms. Overall, morphology-based machine learning and hyperspectral interface phenotyping provide a scalable framework for controlled biocontrol screening, while emphasizing the importance of isolate-aware validation and cautious spectral interpretation.

Why it matches plant phenotyping methods植物コロニーの形態を定量化し、機械学習とハイパースペクトル画像で相互作用表現型を抽出する枠組みを開発・検証しており、表現型取得と解析手法が研究の中心である。

abstractHere, we developed a quantitative phenotyping framework to evaluate interactions between three Trichoderma antagonists and Colletotrichum isolates associated with coffee and cacao.

Novel imaging approaches for visualizing root-mycorrhizal fungal interactions. ↗

Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding their interactions can benefit from advanced imaging techniques capable of visualizing nutrient exchange and structural colonization at subcellular resolution across large sample sizes. This review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses. Several techniques can now visualize and characterize mycorrhizal fungi and associated root structures non-destructively and in three dimensions, for example X-ray computed tomography (micro-CT), X-ray fluorescence (XRF), and X-ray absorption near edge structure (XANES) spectroscopy. Metabolic processes and nutrient exchange can be tracked through positron emission tomography (PET), fluorescent nanoparticles (FNPs), and the monitoring of electrical signalling. Artificial intelligence (AI)-powered image processing software is enabling high-throughput analysis of complex images generated from a range of sources. Mycorrhiza systems are also able to be tracked in-field at multiple scales: hyperspectral imaging can detect mycorrhizal associations at the kilometre scale, while portable MRI imagers can detect changes at the tissue scale. These converging technologies enable the direct, continuous measurement of structural and metabolic root-mycorrhizal fungi interactions, paving the way for a mechanistic understanding of these vital symbiotic partnerships and their impact on plant health and ecosystem functioning.

Why it matches plant phenotyping methods植物根と菌根の構造・代謝・栄養交換を画像およびセンサーで直接測定する手法を扱うレビューであり、植物状態の取得技術が中心である。

abstractThis review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses.

Machine Learning-Based Crop Growth Diagnosis System Using Spatiotemporal Relative Analysis of Vegetation Indices via a Quartile-Based Method ↗

Japanese agriculture faces pressing challenges, including a declining and aging farming population and the need to adapt to climate change. To address these issues, Smart Agriculture is being introduced to improve production efficiency. Among these, unmanned aerial vehicles (UAVs) have gained attention for their ability to rapidly monitor entire fields. We proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values. The experimental site consisted of five paddy fields within an 80 m × 50 m plot in Iwate Prefecture, Japan, equipped with weather and water sensors. Ground-truth data (overall length, culm length, panicle number, and stem number) were collected approximately one week before harvest. UAV monitoring was conducted four times using a multispectral camera, and growth analysis was performed with six VIs. Correlation analysis revealed a positive relationship between crop growth and the daily average water level during the drainage period, and a negative relationship with the daily temperature range in mid-June. A combined cluster-label representation, constructed from clustering results of all VIs for each mesh, enabled integrated analysis and visualization of multi-index patterns. Grid size optimization showed no significant differences in correlation trends between 1 m × 1 m and 5 m × 5 m resolutions. For non-crop area removal, a comparison of three image segmentation methods demonstrated that the Otsu Method achieved the highest performance. Finally, to facilitate practical use in the field, we prototyped a report interface for the diagnosis system. Future work will focus on developing a comprehensive field diagnosis system to clarify field environments, with the aim of addressing fragmentation and enclaves in Japanese farms.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数、画像分割、クラスタリングを統合した作物生育診断システムの開発・評価が中心であり、作物形質との相関検証や実用インターフェースも扱っている。

abstractWe proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values.

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

Crop residue biomass estimation using UAV multispectral imagery: A comparison of feature selection methods and machine learning models ↗

Crop residues support soil health by reducing erosion, improving water retention, and contributing to carbon sequestration. Accurate estimation of crop residue biomass is essential for understanding residue distribution patterns and improving sustainable land management practices. Remote sensing, especially high-resolution UAV-based imaging, is a powerful tool for monitoring residue over agricultural fields, and many studies use remote sensing datasets for mapping residue cover (a 2D metric). However, few studies have evaluated residue biomass using remote sensing, despite biomass being more ecologically informative. This study uses high-resolution UAV multispectral imagery to predict crop residue biomass using feature selection and machine learning. Candidate predictors included raw bands, spectral indices, texture metrics, and digital-surface-model-derived topographic variables. Three feature selection methods—recursive feature elimination with cross-validation, Pearson correlation screening, and least absolute shrinkage and selection operator regression, were applied on the training set to identify informative predictors. Four machine learning models (Random Forest Regression, Support Vector Regression, CatBoost, and k-Nearest Neighbors [kNN]) were evaluated individually and in combination using simple averaging, weighted averaging, and stacked ensemble strategies. Results show that Pearson-selected features paired with kNN achieved the best performance (R 2 = 0.61, RMSE = 188.71 g m -2 ). Ensemble approaches did not outperform the best individual model, suggesting limited benefit from meta-learning under small-sample conditions. Across selection methods, red- and blue-band-related predictors were consistently retained, while textural and topographic variables were selected more selectively, indicating context-dependent contributions. Overall, simpler models with targeted feature selection can outperform more complex ensembles for UAV-based crop residue biomass estimation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物残渣バイオマスを推定し、特徴選択法と複数の機械学習モデルを比較・評価することが中心で、植物由来バイオマスという明示的な状態量を技術的に推定している。

abstractThis study uses high-resolution UAV multispectral imagery to predict crop residue biomass using feature selection and machine learning.

Evaluation of a Plant Quality Monitoring System Based on Visible and Near-Infrared Spectroscopy Sensors in a Controlled Environment ↗

The increasing demand for agricultural production requires reliable and non-destructive methods for monitoring plant physiological conditions in real time, particularly in controlled environments. Spectral sensing in the visible to near-infrared (VIS–NIR) region offers a promising approach; however, the performance of low-cost sensors is often limited by calibration accuracy, wavelength-dependent sensitivity, and insufficient validation against plant physiological indicators. This study aims to evaluate the performance of a VIS–NIR plant monitoring system based on the AS7265x multispectral sensor in a controlled hydroponic environment. Lettuce (Lactuca sativa L.) was grown under two nutrient concentrations (600 ppm and 1200 ppm), and spectral reflectance data were collected across the 410–760 nm range. Sensor measurements were calibrated and validated against a LI-COR LI-180 reference spectrometer using linear regression, with performance assessed using the coefficient of determination (R²), root mean square error (RMSE), and spectral response consistency. The results show strong calibration performance, with wavelength-specific R² values ranging from 0.9682 to 0.9870 and RMSE values between 0.26965 and 5.19772. Although a systematic offset was observed, the AS7265x sensor preserved key spectral patterns, particularly in the green (510–560 nm) and red-edge (705–730 nm) regions. Differences in nutrient concentration were consistently reflected in both spectral responses and SPAD measurements, indicating sensitivity to plant physiological variations. These findings demonstrate that the AS7265x sensor provides reliable spectral information for relative plant monitoring and has strong potential as a cost-effective tool for plant quality assessment in controlled environments.

Why it matches plant phenotyping methodsVIS–NIR植物モニタリングシステムの校正・検証が研究の中心であり、植物の生理状態を推定するセンサー手法を評価している。

abstractThis study aims to evaluate the performance of a VIS–NIR plant monitoring system based on the AS7265x multispectral sensor in a controlled hydroponic environment.

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.

A high-resolution (500 m) dataset for mapping key agronomic growth stages of maize in Northeast China. ↗

Accurate monitoring of agronomic phenology is essential for yield estimation and food security assessment. However, currently available maize phenology datasets usually represent only a limited number of growth stages, restricting their application in process-based crop modeling and stage-specific agricultural management. Here, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution. By coupling MODIS spectral information with meteorological drivers in an energy-driven XGBoost framework, we retrieved eight key agronomic stages: Emergence, Three-leaf, Seven-leaf, Jointing, Flowering, Silking, Milking, and Maturity. Validation against observations from 91 agrometeorological stations during 2009-2024 demonstrates robust performance, with an overall RMSE of less than 5 days and R² values greater than 0.63 across all stages. Beyond overall accuracy, the dataset shows strong spatial consistency and temporal stability, preserves coherent regional phenological gradients, and captures interannual variations over the 24-year period. This long-term, multi-stage dataset provides a valuable benchmark for crop model calibration, climate change impact assessment, and the development of adaptive agricultural strategies in one of the world's major maize-producing regions.

Why it matches plant phenotyping methodsMODISスペクトル情報と気象データ、XGBoostを組み合わせてトウモロコシの8つの生育段階を推定し、観測データで検証した高解像度フェノロジーデータセットであり、植物形質の取得・抽出手法とベンチマークが中心です。

abstractHere, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution.

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.

Assessing the suitability of a developed photogrammetric and multispectral method for detecting biostimulant effects on plants ↗

The present study validates a custom multi-sensor system for high-resolution, non-phenotyping. The photogrammetric workflow was optimised by evaluating image density, algorithms, and camera calibration. Beyond validation, a case study demonstrated the system’s capacity to monitor early plant development following biostimulant treatment. Technical assessment revealed that prior internal camera calibration was unnecessary for the optics used. A reduced dataset of 120 images yielded reconstruction accuracies statistically comparable to full 360-image sets ( p > 0.05), confirming potential for maximised throughput efficiency by reducing processing time by approximately 66% without compromising data integrity. Analysis demonstrated that algorithmic settings determined reconstruction accuracy. Active parameter tweaks were essential for maximising surface area precision across all plant species ( R 2 ≥ 0.98). Conversely, volumetric accuracy required deactivation of these tweaks to maintain mesh consistency. While surface area estimation remained robust, volumetric precision showed species-specific variability driven by morphological complexity. Baseline parameters for accurate plant health scaling were established by defining species-specific vegetation index ranges (e.g., 0.38–0.82 for C. sativus ). The platform’s robustness was validated through a longitudinal study evaluating four treatments: yeast autolysate (A), a fungal biostimulant (F), their combination (AF), and a control (C). The system captured distinct morpho-physiological responses, demonstrating that autolysate-based treatments (A and AF) significantly enhanced biomass growth. The developed multi-sensor system recorded surface area expansions of 122% and 102% relative to the control ( p < 0.001), alongside a 110% increase in biological height. Fidelity of these 3D reconstructions was substantiated by a strong correlation ( R 2 = 0.96) between the 3D-derived leaf area index and ground-truth measurements. A key innovation of the pipeline is the integration of vertical distribution metrics as descriptive statistical tools, enabling high-resolution characterisation of canopy architecture. The plant health status metric evidenced enhanced physiological resilience in variants A and AF. Gravimetric analysis corroborated the non-destructive findings, confirming significant increases ( p < 0.001) in dried shoot weight of 77% (A) and 79% (AF). The validated system decoupled structural biomass from physiological health, offering broad utility across diverse phenotyping tasks. Such functionality streamlines the valorisation of industrial by-products into biopreparations, driving progress in sustainable agriculture.

Why it matches plant phenotyping methodsフォトグラメトリとマルチスペクトル計測による植物表現型取得システムの最適化・技術検証が研究の中心であり、植物形態・生理状態の測定性能を評価している。

abstractThe present study validates a custom multi-sensor system for high-resolution, non-phenotyping.

A dual-branch perception and hybrid attention integrated framework for temporal remote estimation of wheat leaf biomass. ↗

Introduction Wheat leaf biomass is a key indicator of crop growth, nitrogen status, and yield potential, and its accurate estimation is essential for precision agriculture. Unmanned aerial vehicle (UAV) remote sensing provides multi-stage phenological observations for non-destructive biomass monitoring. However, existing approaches often fail to capture the superimposed temporal patterns inherent to crop phenology, including short-term physiological fluctuations driven by management events and long-term seasonal growth trends, as well as the cumulative causal effects of early-stage conditions on final biomass accumulation. Methods This study proposed a dual-branch perception and hybrid attention integrated framework (DBAFN) for temporal estimation of wheat leaf biomass from UAV multi-temporal observations across key growth stages. Results and discussion Experimental results demonstrated that the DBAFN achieved the best performance, with the coefficient of determination (R²) of 0.87, root mean square error (RMSE) of 38.41 g/m², mean absolute error (MAE) of 27.77 g/m², and relative RMSE (RRMSE) of 17.29%. Overall, the proposed framework provided an effective solution for temporal biomass estimation and demonstrated strong generalization capability, as further validated by independent experiments across different ecological regions and wheat genotypes (R² = 0.816-0.820). Compared with conventional machine learning models, the DBAFN showed consistently higher accuracy and lower prediction error. Multi-source feature analysis indicated that the combination of reflectance, vegetation indices, and canopy height provides the most accurate estimation. Ablation experiments further confirmed the effectiveness of each module in improving model performance. The SHapley Additive exPlanations (SHAP) analysis revealed that the canopy height and key spectral features contribute most to biomass prediction, highlighting the importance of integrating structural and physiological information. This study demonstrates that integrating multi-scale temporal dynamics, hybrid attention mechanisms, and transformer-based dependency modeling significantly improves the reliability of UAV-based biomass estimation. It offers a practical, data-driven pathway for intelligent crop monitoring and precision nitrogen management.

Why it matches plant phenotyping methodsUAVマルチ時期リモートセンシングから小麦葉バイオマスという植物形質を推定する手法を提案し、独立地域・遺伝子型で検証しているため、フェノタイピング手法が中心である。

abstractThis study proposed a dual-branch perception and hybrid attention integrated framework (DBAFN) for temporal estimation of wheat leaf biomass from UAV multi-temporal observations across key growth stages.

Hyperspectral phenotyping and GWAS identify novel QTLs for soybean photosynthetic rate. ↗

Enhancing photosynthesis is an important approach to improve crop yields. Photosynthesis, as a key factor determining crop yield, is an important approach to increasing crop production and addressing global food security issues. Improving its efficiency is crucial in this regard. However, traditional photosynthetic phenotyping has long been a bottleneck in crop breeding due to time-consuming data collection. In this study, we simultaneously measured the spectral reflectance and the net photosynthetic rate (Pn) of soybean leaves to develop a high-precision model for estimating Pn based on hyperspectral data. By applying this model, we evaluated Pn in 219 soybean materials. A multi-environment genome-wide association study (GWAS) based on multi-environmental prediction Pn was carried out using the 3VmrMLM method, and 24 significant quantitative trait loci (QTLs) and four suggestive QTLs were identified. Among them, 24 QTLs overlapped with multiple previously reported QTL related to photosynthesis, chlorophyll content, quality, etc., or with genes related to key agronomic traits such as yield. Additionally, four new QTLs were discovered, and four candidate genes potentially associated with Pn were identified. Further, haplotype analysis identified their optimal haplotypes. This study presents a robust and nondestructive hyperspectral model for estimating the photosynthetic rate in soybeans, which is successfully applied to genetic analysis, yielding stable and biologically meaningful results. The approach offers an effective means to explore the genetic basis of photosynthesis and provides a solid theoretical foundation for large-scale, monitoring of soybean photosynthetic physiology.

Why it matches plant phenotyping methods大豆葉のハイパースペクトルデータから光合成速度を推定するモデルを開発し、検証・大規模適用しており、植物フェノタイピング手法が研究の中心である。

abstractwe simultaneously measured the spectral reflectance and the net photosynthetic rate (Pn) of soybean leaves to develop a high-precision model for estimating Pn based on hyperspectral data.

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.

Spatio-Temporal Monitoring of the Invasive Plant Alternanthera philoxeroides in a Narrow River Using Sentinel-2 Time-Series Data ↗

Alternanthera philoxeroides, an invasive alien species, spreads rapidly in river systems via vegetative propagation from stem fragments, requiring river-system-scale monitoring to understand its expansion dynamics and habitat preferences. This study used multi-temporal Sentinel-2 data to analyze spatio-temporal variations in fractional vegetation cover (FVC) within a 3.5 km river reach. FVC estimates derived from vegetation indices were validated against high-resolution aerial images, with an EVI-based model achieving the highest accuracy (RMSE = 9.2%), enabling reliable monitoring even in narrow (~24 m) channels. Time-series analysis from 2019 to 2024 revealed downstream expansion beginning in 2022. Annual maximum FVC (Cmax) was used to assess relationships with removal records and bank structures, showing that removal effects were temporary and more pronounced in the first year, while steel sheet-pile banks limited vegetation growth compared to concrete revetments. These results demonstrate that Sentinel-2 data can provide an effective and accessible tool for evaluating invasive plant dynamics and management effectiveness in low-flow river systems where A. philoxeroides dominates the floating vegetation community.

Why it matches plant phenotyping methodsSentinel-2時系列から侵入植物の植生被覆率を推定し、航空画像で精度検証しており、植物状態の取得・評価手法が中心です。

abstractFVC estimates derived from vegetation indices were validated against high-resolution aerial images, with an EVI-based model achieving the highest accuracy (RMSE = 9.2%)

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

Near-Infrared Spectroscopy Non-Destructive Detection Modeling for Starch Content in Kernels of 58 Rainfed Corn Varieties. ↗

Traditional methods for determining starch content in corn kernels are labor-intensive, destructive, and inefficient. To overcome these challenges, this work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging, applied to 58 rainfed corn varieties. A spectral preprocessing scheme combining wavelet transform, multiplicative scatter correction, and standard normal variate transformation was employed to enhance spectral quality. A two-stage wavelength selection framework was established using competitive adaptive reweighted sampling and sparrow search algorithm optimization. From the selected optimal wavelengths, four predictive models, namely partial least squares regression, artificial neural network (ANN), convolutional neural networks, and gradient boosting decision tree, were established, implemented, and systematically compared. The results identify 14 key wavelengths (1020.65-1647.71 nm) strongly correlated with starch content, with clear assignments to specific chemical bonds and good physical interpretability. Among these models, the ANN exhibited the best performance. The R 2 , RMSE, and RPD of the test set were 0.826, 0.759%, and 2.40, respectively, indicating favorable prediction accuracy and generalization ability. These key wavelengths provide a foundation for developing portable detection instruments. This work supports corn quality grading, breeding of high-starch varieties, and rapid raw material screening, thereby enhancing the quality and efficiency of the corn industry.

Why it matches plant phenotyping methodsトウモロコシ穀粒のデンプン含量という植物器官形質を、近赤外ハイパースペクトル画像と予測モデルで非破壊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中心である。

abstractthis work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging

Different Perspectives on the Same Target: Field and Laboratory Spectroscopy for Estimating Nitrogen Content in Sugarcane Leaves ↗

Proper nitrogen (N) management is essential for increasing the productivity of sugarcane (Saccharum spp.) and reducing the economic and environmental impacts associated with excessive fertilizer use. This study compared the performance of two portable spectroradiometers, FieldSpec 3 and HandHeld 2, in estimating foliar nitrogen content based on hyperspectral data in the visible and near-infrared regions, obtained throughout the crop cycle. The experiment was conducted in Piracicaba, São Paulo, Brazil, under four N rates: 0, 60, 120, and 180 kg ha−1. Spectral measurements were taken at the foliar and canopy levels at eight evaluation times, accompanied by laboratory determination of N content. Partial Least Squares Regression (PLSR) and Random Forest (RF) models were fitted using the spectral data and days after cutting (DAC), included as a categorical factor and evaluated using 10-fold internal cross-validation, based on the metrics R2, RMSE, MAE, and Willmott’s refined agreement index (dr). The foliar data performed better with PLSR (R2 = 0.727; RMSE = 1.381 g kg−1; MAE = 1.109; dr = 0.917) than canopy data (R2 = 0.591; RMSE = 1.489 g kg−1; MAE = 1.157; dr = 0.866). PLSR also outperformed RF at both acquisition levels. The green (~550 nm) and red edge (~740 nm) regions were the most relevant for N estimation. Under the evaluated conditions, model performance was associated with the spectral acquisition level and conditions, the instrumental configuration, and the modeling strategy employed.

Why it matches plant phenotyping methodsサトウキビ葉の窒素含量を分光計とPLSR/RFで推定し、取得レベル・機器・モデル性能を比較検証しており、形質取得手法が中心である。

abstractThis study compared the performance of two portable spectroradiometers, FieldSpec 3 and HandHeld 2, in estimating foliar nitrogen content based on hyperspectral data in the visible and near-infrared regions

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

Graft incompatibility in fruit trees in early detection: integrating physiological, molecular, and technological approaches. ↗

Main conclusion This review highlights that integrating physiological, molecular, imaging, and AI-based approaches enables early and reliable detection of graft incompatibility, improving rootstock-scion selection, orchard sustainability, fruit productivity, and long-term tree performance. One of the most serious problems in fruit growing is the breaking, weakening, or dying of the tree at the graft union, either within a short period of time or after 10-15 years. This condition is often triggered by environmental factors; however, it is certainly not solely caused by environmental conditions. This problem is defined as graft incompatibility. Graft incompatibility refers to the failure of successful anatomical and physiological integration between a rootstock and a scion, primarily due to biochemical, molecular, and genetic mismatches that impair vascular reconnection and long-term stability of the graft union. Graft incompatibility remains a significant constraint in fruit tree production, resulting in reduced longevity, yield, and quality of orchards. This review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility, with special emphasis on Prunus species such as sweet cherry. Physiological and biochemical markers, including phenolic accumulation, antioxidant enzyme activities, and isozyme patterns, serve as early indicators of incompatibility. At the molecular level, transcriptomic, metabolomic, and epigenetic analyses have revealed differentially expressed genes (DEGs) and post-translational modifications associated with stress signaling, vascular reconnection, and callus formation. Imaging-based non-destructive technologies such as micro-CT, MRI, terahertz, and hyperspectral imaging now allow real-time visualization of graft-union structures without damaging plant tissues. The integration of artificial intelligence and machine learning with multi-omics datasets and imaging tools offers unprecedented potential for predictive diagnosis and compatibility assessment. Collectively, these multidisciplinary advances are reshaping the detection and management of graft incompatibility, enabling faster, more reliable, and sustainable rootstock-scion selection in fruit tree breeding.

Why it matches plant phenotyping methods果樹の接ぎ木不親和性という植物状態の早期検出法を、画像・生理・分子・AI技術の観点から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 integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility

Fpga-accelerated IoT Deployment of a Causal- Attention Multi-modal Deep Learning Network for Precision Crop Disease Monitoring ↗

Abstract Outbreaks of plant diseases are major threats to world food security particularly in areas where real-time monitoring and quick decision support are constrained by low-power edge gadgets and untrustworthy connectivity. In order to overcome these issues, this paper presents a FPGA-Accelerated IoT implementation of a Causal-Attention Multi-Modal Deep Learning Network, named EpiFusionNet-Edge, that can be applied to monitor crop diseases with real-world farming scenarios with high precision and scalability. The framework incorporates five data modalities that are complementary in nature and they include RGB leaf pictures, microscopic foldscope images, UAV hyperspectral signatures, microclimate IoT sensor measurements and region-specific pathogen/pest pressure indexes giving a complete picture of the health of the plant. Dual causal-attention mechanism is proposed to simulate both spatial and temporal environmental factor activation, which helps to detect and make predictions at the early stage and provides an explanatory logic behind the decisions. Multi-task learning enables classification of diseases, quantification of their intensity at the level of a micro-prediction and prediction of outbreaks in the short term (1–30 days). In order to achieve deployability in resource-constrained settings, the proposed deep learning architecture is ensemble-distilled, structurally pruned, and INT8-quantized, and hardened on a Xilinx Zynq-7000 FPGA platform. The FPGA accelerator is 43.2x faster inference, 88 percent less power usage, and less than 10 ms latency, which allows real-time execution of continuous field monitoring with IoT sensors. Cross-condition assessment on multi-domain datasets shows that there are great improvements on cross-environment generalization rates with 98.6% classification accuracy, 92.7% severity estimation accuracy and less than 3.5% degradation with domain shift. Grad-CAM + + and causal feature traceability further add interpretability with the focus of the model and the pathological indicators proven by experts. The findings show the promise of using a combination of IoT sensing, multi-modal AI fusion, and FPGA hardware acceleration to develop a deployable and scalable and transparent system with regard to precision agriculture. This paper creates a roadmap to a new generation of smart farming systems that are able to conduct disease surveillance and actively protect crops at the periphery in an autonomous manner.

Why it matches plant phenotyping methods植物病害の画像・センサー観測から病害強度を定量化するマルチモーダル・エッジ推論基盤を開発し、精度・速度・消費電力・ドメインシフトを評価しているため、植物フェノタイピング手法が中心である。

abstractthis paper presents a FPGA-Accelerated IoT implementation of a Causal-Attention Multi-Modal Deep Learning Network, named EpiFusionNet-Edge

Robot-based 3D-multispectral monitoring of soybean in a spatially heterogenous agrivoltaic environment ↗

Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.

Why it matches plant phenotyping methodsカスタマイズしたロボット搭載3Dマルチスペクトル画像システムを実装し、植物形態・スペクトル・ストレス状態を高精度に取得することが研究の中心である。

abstractthe present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns

Artificial Intelligence and Machine Learning for Genomic Prediction, High-Throughput Phenotyping and Climate-Adaptive Breeding In Maize and Rice: A Comprehensive Review ↗

Climate change is intensifying abiotic stresses such as drought and heat, posing significant threats to global food security and the productivity of staple crops including maize (Zea mays L.) and rice (Oryza sativa L.). Conventional breeding approaches are often constrained by the complex genetic architecture of stress-adaptive traits and lengthy breeding cycles, highlighting the need for more efficient, data-driven strategies. This review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice. We discuss the applications of machine learning architectures, including multilayer perceptron (MLP), convolutional neural networks (CNN), random forest (RF), deep neural networks (DNN), gradient boosting methods, and explainable artificial intelligence (XAI), in improving genomic selection and capturing complex genotype–environment interactions. The review further explores the integration of AI with HTP technologies, including autonomous robotic platforms, drones, hyperspectral imaging, and LiDAR, to enable rapid, accurate, and non-destructive phenotypic assessment. In addition, we examine the role of AI-driven predictive models in identifying stress-responsive genes, improving trait prediction, and accelerating the development of climate-resilient crop varieties. Current challenges, including data heterogeneity, computational demands, model interpretability, and biological validation, are also discussed alongside emerging solutions such as multi-view learning, transfer learning, and intelligent precision design breeding. Overall, the convergence of AI, ML, multi-omics, and advanced phenotyping technologies represents a transformative framework for next-generation crop improvement, offering new opportunities to accelerate sustainable breeding programs and strengthen global food security under changing climatic conditions.

Why it matches plant phenotyping methodsAI・MLを用いた高スループット植物表現型解析と、ロボット、ドローン、ハイパースペクトル、LiDARによる表現型評価を中心的にレビューしているため。

abstractThis review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice.

Multi dimensional variable influence mechanism analysis for wheat biomass estimation using fused UAV spectral and canopy height data and machine learning. ↗

Introduction Accurate and non-destructive estimation of wheat biomass is essential for crop growth monitoring, yield prediction, and precision agriculture. Unmanned aerial vehicle (UAV)-based remote sensing, integrating both spectral and structural information, has shown great potential for biomass estimation. However, the mechanisms by which different types of variables contribute to biomass prediction remain poorly understood, especially when using machine learning models. Methods In this study, we fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages (jointing, booting, heading, and filling). Four machine learning algorithms-XGBoost, Random Forest Regressor (RFR), Support Vector Regressor (SVR), and LASSO-were employed and compared. Results and discussion The results showed that XGBoost achieved the highest accuracy (R 2 = 0.919, RMSE = 102.43 g/m², MAE = 77.43 g/m², RRMSE = 19.71%). Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that canopy height (CH) was the most important variable, followed by spectral indices such as R842 and GNDVI. The univariate and global contribution analyses demonstrated that structural and spectral variables played complementary roles in biomass estimation. This study provides a mechanistic understanding of variable contributions and offers a robust framework for UAV-based wheat biomass estimation.

Why it matches plant phenotyping methodsUAVスペクトル・キャノピー高データから小麦バイオマスを推定し、複数機械学習法を比較・評価する方法論的研究であり、植物形質取得が中心である。

abstractwe fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages

Multi-annual Multispectral Image Dataset of Chardonnay Grapevine Leaves with Yellowing disease and Easily Confused Symptoms ↗

Grapevine yellows diseases, including Flavescence Dorée (FD) and Bois Noir (BN), severely affect vineyards, making reliable detection methods essential. Detecting symptoms, particularly in Chardonnay variety, remains challenging due to year-to-year vine variability and the presence of symptoms that can be misleading. We present a dataset of multispectral images of grapevine leaves collected over four years (2021–2024). The dataset includes healthy leaves, grapevine yellows - overwhelmingly BN, and three other diseases (Esca, Discoloration, and Leafroll) with visually similar symptoms particularly for Leafroll, providing a diverse benchmark for disease recognition. Ground-truth labels were assigned based on field inspections conducted by Comité Champagne expert viticulturists who selected the vines before acquisitions and analyzed the images after acquisition. Images were acquired under laboratory conditions using a DJI Phantom 4 Multispectral camera from leaves collected in the Plumecoq Comité Champagne experimental vineyards. Leaves were collected on designated vines and placed on polystyrene boards on a 2x2 grid. One RGB image and five multispectral images corresponding to the Blue (B - 450 nm ± 16 nm), Green (G - 560 nm ± 16 nm), Red (R - 650 nm ± 16 nm), Red-Edge (RE - 730 nm ± 16 nm), and Near-Infrared (NIR - 840 nm ± 26 nm) bands were captured using the same acquisition system (camera-to-board distance of approximately 90 cm) over the four years.. For the vast majority, ambient light was used; for some acquisitions, indirect halogen spotlights were used. The 512x512 leaf images were manually cropped from the acquired original images with the same offsets between the bands; offsets were already present due to the parallax phenomenon. The dataset is organized into six folders (B, G, R, RE, NIR, and RGB). Each leaf is represented by one RGB image and five corresponding multispectral band images, enabling multimodal analysis and machine learning for grapevine disease detection. Each image filename encodes acquisition and annotation information as follows: Characters 1–2: acquisition year (21 = 2021, 22 = 2022, 23 = 2023, 24 = 2024). Character 3: image type/band (0 = RGB, 1 = B, 2 = G, 3 = R, 4 = RE, 5 = NIR). Character 4: class (J = Grapevine Yellows, T = Healthy, S = Esca, E = Leafroll, D = Discoloration). Character 5: illumination condition (0 = ambient light, G = additional left halogen spotlight, D = additional right halogen spotlight, 2 = additional both left and right halogen spotlightsl). Characters 6–9: image identifier, numbered sequentially from 0000. Character 10: relabelling status. After image acquisition, all leaves were independently reviewed by expert viticulturists on the recorded RGB images. A value of 1 indicates that the expert confirmed the original field label, 0 indicates that the original label was considered incorrect based on the information visible in the RGB image, and D indicates that the sample was reclassified as Discoloration. This dataset complements the "Multi-annual spectral data of Chardonnay grapevine leaves" dataset published on Recherche Data Gouv. While the previous dataset contains spectral measurements, the present dataset provides multispectral images acquired from the same grapevine leaves, enabling multimodal analyses that combine spectral signatures with image-based information. Note, however, that there is no bijection between the respective files.

Why it matches plant phenotyping methodsブドウ葉の病徴をマルチスペクトル画像で取得し、専門家ラベル付きの疾病認識用データセット／ベンチマークとして提供しており、植物状態の画像ベース表現型計測が中心である。

abstractWe present a dataset of multispectral images of grapevine leaves collected over four years (2021–2024).

SST-MAE: Learning Spectral-Spatio-Temporal Representations from Plant Hyperspectral Time Series to Discover Complex Genotype-Phenotype Relations ↗

Abstract Understanding the link between genetic variation and observable traits is key to crop breeding. Hyperspectral imaging captures physiological and biochemical profiles, but current supervised methods require costly trait annotations and treat each observation as a static snapshot, ignoring the temporal dynamics of plant development. We introduce SST-MAE, a self-supervised framework that learns genotype-discriminative representations from plant hyperspectral developmental trajectories, without requiring phenotypic labels. The model learns to reconstruct masked information, capturing multiple growth trajectories. Validated on 194 field-grown lettuce genotypes across eight time points, the frozen encoder serves as a feature extractor for downstream genotype classification. SST-MAE outperforms raw spectral and linear baselines, achieving AUROC > 0.89 for anthocyanin pigmentation SNPs and 0.77 for leaf serration. The learned features are highly label-efficient, attaining near-full performance with only 30–50% of labeled data, offering a scalable pathway toward high-throughput genetic screening from image-based phenotypes.

Why it matches plant phenotyping methods植物のハイパースペクトル時系列から表現型関連表現を抽出する自己教師あり手法を開発し、複数遺伝子型・時点で検証しているため、表現型取得・解析手法が中心です。

abstractWe introduce SST-MAE, a self-supervised framework that learns genotype-discriminative representations from plant hyperspectral developmental trajectories, without requiring phenotypic labels.

Improving the prediction of water stress-related traits in open-field tomato using multivariate models and variable importance-based indices ↗

The assessment of water stress levels in plants should be essential part of precise irrigation management, and a good and quick method is useful in plant phenotyping. There are many options for this task, but the effectiveness varies between crop species and environments. Apparently open field applications face the most difficulties. This study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components. Data of the abovementioned variables was collected during three consecutive growing seasons in processing tomato cultivated under different water supply regimes to provide data with varying water stress levels. Then the relation of the measured variables to 226 VIs was tested created according to the formulas collected in the Index DataBase (IDB Project, indexdatabas.de ). New VIs were also developed derived from the most important variables of the ML algorithms, customised to tomato water stress assessment. Standard normal variate and its combination with Savitzky-Golay first derivative were used for pre-processing the spectra and principal component regression (PCR), partial least squares regression (PLSR), elastic net (ENET), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGB) algorithms were tested. The newly developed indices outperformed the existing formulas, except in the case of β-carotene. The most reliable index was developed for RWC estimation; that was the difference of the reflectance on the 986 and 701 nm wavelengths. The ENET and SVR algorithms produced the best models depending on the pre-processing method. The blue, near-infrared (NIR) and green regions, respectively, were the most important regarding all models according to the variable importance analysis. The model with the best metrics was developed for chlorophyll-a (R 2 =0.82, nRMSE=11%, RPIQ=2.41), followed by RWC (R 2 =0.72, nRMSE=14%, RPIQ=2.53).

Why it matches plant phenotyping methodsハイパースペクトル反射データと多変量・機械学習モデルを用いて、トマトの水ストレス関連生理形質を推定し、新規指標も開発・評価しているため、表現型取得・推定手法が中心である。

abstractThis study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components.

Apple Crop Health Detection Based on Vegetation Indices at Shalimar, Kashmir: North-Western Himalayas ↗

Apple orchard health monitoring is important for supporting timely crop management under the temperate conditions of Kashmir Valley. The present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices, namely the Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Modified Soil Adjusted Vegetation Index and Enhanced Vegetation Index, in a 2 ha apple orchard at SKUAST-K, Shalimar, Kashmir, during the 2020 growing season. Sentinel-2 imagery acquired from April to September was processed using SNAP and QGIS, and mean vegetation index values were extracted for the orchard area. Monthly weather data, including average temperature and rainfall, were obtained from the on-campus meteorological observatory. Ground observations at 20 georeferenced points were used to support the interpretation of canopy development, phenological stage and visible plant health condition. All four vegetation indices showed a seasonal increase from April to July-August, followed by a decline during September-October, corresponding to canopy development, fruit maturation, harvest and senescence. Based on the monthly values presented in the study, temperature showed positive associations with the vegetation indices, with the strongest relationship observed for MSAVI, followed by NDVI and SAVI. Rainfall showed weak and non-significant associations with the indices during the study period. The results indicate that Sentinel-2-derived vegetation indices can reflect seasonal canopy dynamics in apple orchards under the studied conditions. MSAVI appeared particularly useful for representing canopy development, while field observations remained necessary for interpreting pest, disease and phenological effects.

Why it matches plant phenotyping methodsSentinel-2画像から植生指数を算出し、リンゴ樹冠の季節動態・健康状態を評価する測定ワークフローが研究の中心であり、植物状態の推定に直接用いられている。

abstractThe present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices

Multispectral and anatomical assessment of chromium and nickel accumulation in urban weeds. ↗

Early detection of heavy metal stress in plants is essential for effective environmental monitoring, particularly in contaminated urban areas. This study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species. Five species were selected: Trifolium pratense, Rumex acetosa, Alcea rosea, Amaranthus retroflexus, and Plantago lanceolata. Visible plant injuries were assessed using Evans Blue staining and image-based anatomical analysis, which enabled distinguishing between living, partially damaged, and dead cells. Multispectral observations using a MicaSense RedEdge-M camera allowed calculation of the Normalized Difference Vegetation Index (NDVI) to detect stress-related changes in photosynthetic apparatus. The studied species differed in their capacity to accumulate and translocate Cr and Ni. Metal bioaccumulation was low in all species (bioconcentration factor < 1), with the highest Ni accumulation observed in Plantago lanceolata. Translocation of both metals was the greatest in Trifolium pratense and Amaranthus retroflexus. Hydrogen peroxide levels increased in roots and leaves of all species, particularly in Alcea rosea. Despite the absence of visible injuries, microscopic anatomical changes were detected in T. pratense and R. acetosa, while NDVI values differed between sites. In summary, this study indicates that no simple relationship was found between physiological stress parameter values and NDVI. It is important to emphasize the need for continued research under controlled conditions with specific doses of PTEs salts. This should clearly demonstrate the relationship between plant physiological responses to stress and the results of multispectral observations.

Why it matches plant phenotyping methodsリモートセンシング、画像ベースの解剖診断、NDVIを用いた植物ストレス検出法の評価が研究目的として明示されており、植物状態の取得・推定が中心的です。

abstractThis study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species.

Leaf- and canopy-level hyperspectral sensing of wheat-Fusarium head blight-Trichoderma gamsii interactions ↗

Fusarium head blight (FHB) is a major mycotoxigenic disease of wheat, causing yield and quality losses and deoxynivalenol contamination. Rapid, non-destructive tools are needed to detect FHB, monitor wheat physiological responses, and evaluate sustainable management strategies, including biological control agents. Although vegetation spectroscopy is widely used for high-throughput phenotyping, most spectral studies focus on binary disease detection, while the capacity of hyperspectral data to capture concurrent host–pathogen–biocontrol responses across leaf and canopy scales remains underexplored. Here, we tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085. Two cultivars, Bingo and Rebelde, with higher and lower FHB susceptibility, respectively, were treated with a chemical fungicide (Chem) or T. gamsii T6085 (Bioc) under FHB pressure. Leaf- and canopy-level spectra were acquired at 2, 5, and 14 days post-inoculation, alongside gas exchange, water status, and chlorophyll measurements. Permutational multivariate analysis of variance (PERMANOVA) tested whole-spectrum effects, partial least squares discriminant analysis (PLS-DA) explored class separability, and partial least squares regression (PLSR) estimated physiological traits. PERMANOVA detected genotype × inoculation × treatment interactions from 5 days post-inoculation at leaf and canopy levels. PLS-DA revealed treatment- and cultivar-dependent spectral fingerprints, but overall low-to-fair validation performance indicates that these class-specific patterns should be interpreted as exploratory and not as evidence of operational treatment discrimination. PLSR provided high accuracy for chlorophyll content and osmotic potential, moderate accuracy for CO 2 -assimilation traits, and poor accuracy for transpiration and leaf water potential. While the workflow is scalable as an experimental and analytical framework, its operational deployment will require broader validation across sites, seasons, cultivars, disease-pressure conditions, and sensing platforms.

Why it matches plant phenotyping methods小麦のFHB・生物防除応答を対象に、葉・群落ハイパースペクトル取得、分類、検証、形質推定を統合したフェノタイピング枠組みが中心である。

abstractwe tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085.

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

Hyperspectral imaging and dynamic selective peak transformer for early-stage classification of lettuce heat responses. ↗

Introduction Early-stage detection and classification of lettuce heat responses are essential for non-destructive phenotyping, yet conventional assessment mainly relies on visible symptoms and manual observation. Methods This study constructed a lettuce hyperspectral dataset comprising heat-sensitive and heat-tolerant varieties under control and high-temperature treatments, and proposed the Dynamic Selective Peak Transformer (DSPformer). DSPformer integrates edge-enhanced feature extraction, dynamic multi-scale spatial-spectral representation, Peak-k selective attention, and a confusion-aware dynamic focal loss to enhance discriminative features while reducing spectral redundancy, class imbalance, and inter-class confusion. Results Under the patch-level evaluation protocol, DSPformer achieved 96.22% accuracy, 95.55% recall, 96.35% precision, and 95.95% F1-score, outperforming the compared CNN- and Transformer-based models. Day-wise evaluation showed that DSPformer reached 82.61% accuracy on Day 1 and 96.55% on Day 3, before visible heat-stress symptoms appeared on Day 6. Under a plant-level partition protocol, DSPformer maintained robust performance with 93.76 +/- 0.49% accuracy. Additional evaluation on the Indian Pines benchmark further demonstrated the applicability of DSPformer to general hyperspectral image classification. Discussion These findings suggest that hyperspectral imaging can capture heat-stress-sensitive information beyond visual phenotypes, and that DSPformer provides a promising framework for early, non-destructive lettuce heat-response screening and hyperspectral phenotyping-assisted breeding.

Why it matches plant phenotyping methodsレタスの熱ストレス応答を非破壊的に早期分類するため、ハイパースペクトル画像データセットと新規Transformer手法を開発・評価しており、植物表現型取得・抽出が中心である。

abstractMethods This study constructed a lettuce hyperspectral dataset comprising heat-sensitive and heat-tolerant varieties under control and high-temperature treatments, and proposed the Dynamic Selective Peak Transformer (DSPformer).

A Review on Artificial Intelligence Methods for Plant Disease and Pest Detection ↗

Artificial intelligence (AI) has emerged as a transformative tool for plant health monitoring, offering new opportunities for scalable, timely, and data-driven pest and disease management in agriculture. This review provides a comprehensive synthesis of AI-based methods for pest and plant disease detection, systematically organizing existing literature across sensing modalities, learning paradigms, and deployment scales. We distinguish between population-level pest monitoring, plant-centric visual inspection, and field-scale surveillance, as well as between post-symptomatic disease recognition and pre-symptomatic detection enabled by spectral imaging technologies. Beyond summarizing recent advances, this work places strong emphasis on critical analysis, discussing fundamental limitations related to data scarcity, domain shift, generalization under field conditions, and the challenge of disentangling biotic from abiotic stress factors. The review further examines the distinction between correlation-driven AI predictions and causal disease understanding, positioning AI as a complementary decision-support tool alongside established diagnostic methods. Building on these insights, we outline key future research directions, including multimodal sensor fusion, explainable and trustworthy AI, edge-based deployment for real-time monitoring, and the development of foundation models for unified agricultural intelligence. This review aims to serve as both an accessible entry point and a critical reference for advancing AI-driven plant health management.

Why it matches plant phenotyping methods植物の病害・害虫状態を画像・スペクトルなどで検出するAI手法を対象としたレビューであり、植物状態の取得・推定手法が中心である。

titleA Review on Artificial Intelligence Methods for Plant Disease and Pest Detection

Deep Learning for Early Detection of Crop Pathogens: A Multimodal Fusion Framework Leveraging Hyperspectral Imaging and Climate Data in Precision Agriculture ↗

This research introduces a multimodal deep learning framework for early detection of plant pathogens to capture pre-symptomatic biochemical changes in plants while simultaneously modeling the environmental drivers of disease development. A hybrid fusion architecture combines 3D convolutional neural networks for spatial-spectral feature extraction from HSI cubes with transformer-primarily based temporal modeling of climate sequences. Cross-modal attention mechanisms dynamically weight discriminative features, which includes chlorophyll degradation bands and humidity thresholds, to permit joint representation learning. The framework achieved 94.5% accuracy in pathogen detection, outperforming unimodal HSI (84.1%) and climate- only (76.5%) baselines by 10-18 percentage points. Moreover, it detected fungal infections 5-7 days before visual symptom onset and had a 12.3% higher F1-rating compared to the current methods. Field simulations showed that precision application resulted in 41% reduction in fungicide use. By connecting proximal sensing with climatic analytics, this research contributes to precision agriculture by providing timely and eco-friendly pest control of diseases. The multimodal fusion framework is introduced to overcome the limitations of unimodal approaches. It integrates the most appropriate data sources, thus allowing the earliest and most accurate detection of plant pathogens.

Why it matches plant phenotyping methods植物の病害状態をハイパースペクトル画像から抽出するマルチモーダル手法の開発・評価が中心であり、単なる病原体診断や農薬施用試験ではない。

abstractThis research introduces a multimodal deep learning framework for early detection of plant pathogens to capture pre-symptomatic biochemical changes in plants

Defective wheat kernel classification using dual-range hyperspectral imaging and an interpretable spectral-spatial fusion convolutional neural network. ↗

The rapid and non-destructive screening of defective wheat kernels is essential for quality assurance and process control, yet reliable identification remains challenging due to subtle spectral and spatial differences between defective and sound wheat kernels. In this study, a spectral-spatial fusion convolutional neural network (SSFCNN) was developed to integrate complementary spectral and spatial information from hyperspectral images for the classification of five wheat kernel categories. An end-to-end fusion framework was constructed, in which squeeze-and-excitation (SE), shuffle attention (SA), and efficient channel attention (ECA) were integrated for spectral channel recalibration, spatial feature refinement, and fusion feature enhancement, respectively. The results demonstrated that the SSFCNN with deep feature fusion outperformed a support vector machine (SVM) and a convolutional neural network (CNN) constructed using conventional feature fusion. The highest overall accuracies of 96.48% in the visible and near-infrared (Vis-NIR) and 95.61% in the short-wave infrared (SWIR) were achieved, together with consistently improved precision, recall, specificity, and F1-score across all wheat kernel categories. Moreover, visualization of classification outputs on the external validation set indicated improved spatial coherence and decision reliability of the SSFCNN. Overall, this study provided a validated and interpretable spectral-spatial fusion framework for hyperspectral screening of defective wheat kernels, offering a methodological basis for future intelligent grading and online quality control applications after further validation under real sorting-line and cross-domain conditions.

Why it matches plant phenotyping methods小麦粒の欠陥状態をハイパースペクトル画像から分類する手法を開発・検証しており、植物器官の状態推定が研究の中心である。

abstracta spectral-spatial fusion convolutional neural network (SSFCNN) was developed to integrate complementary spectral and spatial information from hyperspectral images for the classification of five wheat kernel categories.

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.

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network. ↗

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (R p 2 =0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Why it matches plant phenotyping methods油糠菜葉の鉛含量を蛍光ハイパースペクトル画像とニューラルネットワークで非破壊推定する手法の開発・比較検証が研究の中心であり、植物の化学的ストレス状態を定量するため。

titleNon-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

HSGAN-based near-infrared hyperspectral reconstruction from characteristic wavelengths images for apple bruise detection. ↗

Hyperspectral images contain richer spectral and spatial information than multispectral images, yet traditional equipment suffers from limitations such as bulky size and complex data processing. This study constructs a task-oriented near-infrared (NIR) hyperspectral reconstruction and detection framework for the precise detection of early apple bruises. First, apple samples were collected using a hyperspectral imaging system. The Weight Extremum Method was employed to screen seven characteristic wavelengths (976.4 nm, 1064.8 nm, 1175.8 nm, 1192.5 nm, 1295.4 nm, 1449 nm, and 1631.6 nm), which were further reduced to three key wavelengths (1064.8 nm, 1175.8 nm, and 1449 nm). Based on the datasets constructed from these bands, the HSGAN framework was used as the reconstruction backbone to reconstruct hyperspectral images ranging from 866 nm to 1701 nm. Results demonstrated that reconstruction performance was optimal with seven input bands (PSNR = 37.81, SSIM = 0.973) and remained favorable with three bands (PSNR = 34.70, SSIM = 0.950). Finally, YOLOv11n was used to detect bruises on both original and reconstructed images. Detection accuracy using reconstructed spectra from the 7-band input approached that of the original images (mAP50 = 0.994), while the 3-band input also maintained high precision (mAP50 = 0.992, Recall = 0.993). These results demonstrate that reconstructing 254 NIR bands from just three characteristic wavelengths is feasible. This framework significantly reduces data acquisition costs while enabling high-precision early bruise detection, offering a practical solution for agricultural quality control.

Why it matches plant phenotyping methodsリンゴ果実の打撲（植物器官の状態）を対象に、少数波長画像からNIRハイパースペクトル画像を再構成し、検出性能を評価する画像・計算フェノタイピング手法が研究の中心である。

abstractThis study constructs a task-oriented near-infrared (NIR) hyperspectral reconstruction and detection framework for the precise detection of early apple bruises.

Estimating maize canopy water content using UAV-based multispectral-thermal infrared imagery and canopy signal distributional features. ↗

Introduction Canopy water content (CWC) is an important indicator of crop water status **and** supports precision irrigation decision-making. Plot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined. Methods In this study, spring maize at the Shiyanghe site was monitored using UAV-based multispectral and thermal infrared imagery. Mean, percentile, and dispersion features were extracted from effective canopy pixels within each plot. RFECV feature selection, 50 repeated random train-test splits, paired statistical tests, simulated spatial aggregation, and four regression models were used to evaluate the stage- and scale-dependent contribution of these features. Results and discussion Water stress affected both overall spectral-thermal responses and within-plot signal distributions. Before tasseling, percentile and dispersion features were frequently selected and provided complementary information, especially for tree-based models and finer aggregation scales. After tasseling, mean features generally showed more stable performance, although some distributional features still contained CWC-related information. The supplementary Xinxiang site-internal analysis suggested that, under weak water-gradient and small-sample conditions, distributional features may be frequently selected but may not consistently improve prediction accuracy. Overall, the contribution of distributional features was growth-stage-, scale-, and model-dependent.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像からトウモロコシ群落の水分含量を推定する特徴抽出・選択・回帰手法を中心に、反復分割や統計検定で技術的に評価しているため。

abstractPlot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined.

Hyperspectral imaging combined with texture features for maize hybrid purity detection: a multi-model comparison based on machine learning and SHAP interpretability study. ↗

Hybrid maize performance depends strongly on the genetic purity of hybrid seeds, but female self-pollinated seeds and target hybrid seeds are difficult to distinguish by conventional visual inspection because of their highly similar phenotypes. This study developed a nondestructive and interpretable maize hybrid purity detection framework by integrating hyperspectral imaging and RGB-derived texture features. Hyperspectral images were acquired from the embryo and endosperm sides of five female parents, one common male parent, and their corresponding hybrids. Texture-based, full-band spectral, characteristic-band spectral, and texture-spectral fusion models were systematically constructed and compared. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and Synchronous Two-Dimensional Correlation Spectroscopy (Sync2D) were used for characteristic wavelength selection. The results showed that the embryo side provided more stable and discriminative spectral information than the endosperm side. Texture-only models showed limited ability to distinguish hybrids from female self-pollinated seeds, whereas embryo-side texture-spectral fusion models combined with CARS or SPA and Support Vector Machine (SVM) or Partial Least Squares Discriminant Analysis (PLS-DA) achieved average test accuracies of 0.99-1.00, meeting the national maize hybrid seed purity requirement of 97%. In the optimal low-dimensional models, the retained high-dimensional spectral variables were compressed to 28-69 key features, corresponding to a dimensionality reduction ratio of approximately 88%-95%. SHAP analysis identified mean saturation and seed size as important texture features; among spectral intervals, the 450-462 nm region appeared among the top-ranked embryo-side SHAP features in all five maize lines and showed the highest embryo-side mean absolute SHAP magnitude (0.0107 ± 0.0028). Overall, the proposed framework provides a high-throughput, low-dimensional, and interpretable solution for maize hybrid seed purity detection.

Why it matches plant phenotyping methodsハイパースペクトル画像、RGBテクスチャ、特徴選択、機械学習を統合し、トウモロコシ種子のハイブリッド純度を非破壊推定する手法の開発・比較が研究の中心である。

abstractThis study developed a nondestructive and interpretable maize hybrid purity detection framework by integrating hyperspectral imaging and RGB-derived texture features.

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.

UAV-Derived Multispectral Datasets and Index-Guided Segmentation for Maize Water Stress and Common Rust Detection Under Real Field Conditions ↗

The segmentation model achieved Mean IoU values of 0.7723 for Water Stress 2025, 0.9164 for Common Rust 2025, and 0.9531 on the benchmark dataset. The classifier achieved 99.54% accuracy for the five-class task; however, the improvement over the strongest baselines and the RGB + multispectral configuration was limited. Therefore, the classification component is not presented as a substantially superior classification-only model. Instead, it is interpreted as an exploratory multimodal analysis that quantifies the contribution and limitation of RGB, multispectral, Wavelet, and GLCM branches under the adopted UAV dataset protocol. For classification-only deployment, simpler alternatives such as DenseNet201 or the RGB + multispectral configuration may be more practical because they provide comparable accuracy with lower architectural or preprocessing complexity. Ablation, modality-controlled, and 21-run stability experiments showed reproducible segmentation results and clarified the behavior of the classification branches. RGB and multispectral branches mainly provided the peak classification accuracy, whereas Wavelet and GLCM branches mainly affected offline convergence rather than final accuracy. RGB, NDVI, and NDRE visualizations were also added for qualitative support. Since direct physiological ground measurements were not available for all samples, the masks are interpreted as adaptive index-guided labels rather than direct physiological ground truth. Overall, the main evidence of practical benefit is associated with UAV-based dataset construction, adaptive index-guided segmentation, and field-scale stress/disease mapping, while the classification experiments should be interpreted as modality-contribution and convergence analyses rather than proof of a practically superior complex classifier.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるトウモロコシの水ストレス・病害状態のセグメンテーション、データセット構築、再現性評価が研究の中心であり、植物表現型の取得・抽出手法として実質的です。

abstractOverall, the main evidence of practical benefit is associated with UAV-based dataset construction, adaptive index-guided segmentation, and field-scale stress/disease mapping

Comparing Unsupervised and Supervised Classifiers on Multispectral UAV Data to Detect Crop Water–Nitrogen Co-Limitation ↗

This study compared unsupervised and supervised machine learning, and deep learning (U-Net) classifiers on Unmanned Aerial Vehicle (UAV) multispectral imagery to identify nitrogen status in potato crops under nitrogen (N) fertilization treatments, irrigation (I), and their interaction (N × I). The U-Net model outperformed all other methods, achieving accuracies for crop nitrogen status of 65–99% in N, 84–100% in I, and 41–82% in N × I treatments, with variation due to different input data. Supervised machine learning also performed well, with Support Vector Machine achieving 53–87, 66–86, and 32–66% respectively, and Random Forest 61–96, 70–81, and 33–65%. Unsupervised K-means yielded the lowest accuracies (47–58, 9–65, and 8–34%), demonstrating necessity of substantial supervision to delineate crop nitrogen and water status. These findings were confirmed by repeated analyses of UAV imagery acquired later in the growing season with consistent results. Comparable classification performance was observed for crop water status and leaf area index at both time points. Despite being demonstrated in a single-field, single-crop framework, the results provide proof of concept for applying deep learning classifiers to detect subtle nitrogen and water stress under field conditions in precision agriculture. Future research could test diverse agroecosystems and growing seasons, alternative deep learning algorithms, and sensor data fusion to improve classification accuracies.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からジャガイモの窒素・水分状態およびLAIを推定する分類手法を比較・検証しており、植物状態の取得と手法性能評価が研究の中心である。

abstractThis study compared unsupervised and supervised machine learning, and deep learning (U-Net) classifiers on Unmanned Aerial Vehicle (UAV) multispectral imagery to identify nitrogen status in potato crops

From Phenomics to Genomics: Macro-GWAS of Almond Morphology and Quality ↗

Abstract In plant breeding and genetics, recent advances in high-throughput phenotyping are beginning to meet the growing demand for large-scale, high-quality phenotypic data that emerged after the development of next-generation sequencing technologies. Recent developments in phenomics have been incorporated into almond breeding programs, facilitating the large-scale acquisition of quantitative phenotypes and the dissection of the genetic architecture underlying morphological and quality-related traits. The implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations and the identification of 567 robust marker–trait associations across 66 traits. These analyses revealed two major genomic hotspots on chromosomes 2 and 5 associated with morphological and quality-related traits. These regions harbored biologically relevant candidate genes, including genes associated with OVATE family proteins, brassinosteroid signaling, protein ubiquitination, and acyl-CoA metabolism, as well as other regulators of organ growth, cell proliferation, hormone signaling, and seed development. Furthermore, a novel candidate gene encoding a COMT-like O-methyltransferase involved in lignin biosynthesis was identified and proposed to contribute to shell hardness, a major genetically controlled trait in almond. Together, these findings demonstrate the potential of integrating high-throughput phenomics and genomics to dissect complex traits, identify candidate genes, and accelerate genomics-informed breeding in almond.

Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像を統合した高スループット表現型解析プラットフォームの実装と大規模形質取得が研究の主要部分であり、単なる形質のルーチン測定ではない。

abstractThe implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations

UAV Remote Sensing for Drought-Adaptive Sesame Breeding: Flight-Altitude Benchmarking, Predictive Modelling, and Composite Stress Tolerance Indexing ↗

Early-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments. A DJI Phantom 4 Multispectral UAV was flown at 40, 80, and 120 m above ground level (AGL) over 588 M2 genotypes under full irrigation (ENV1) and terminal drought (ENV2; irrigation withheld from reproductive onset) on four dates (July–September 2025). Structure-from-motion canopy height models were compared with ground measurements, and four spectral reflectance indices—Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), and Leaf Chlorophyll Index (LCI)—were derived from 40 m imagery. Ordinary least squares (OLS), Random Forest, and Gradient Boosting were evaluated under leave-one-genotype-out (LOGO), leave-one-environment-out (LOEO), and leave-one-date-out (LODO) cross-validation; genotypic repeatability was quantified by intraclass correlation (ICC), and drought performance was ranked by a composite Stress Tolerance Index (STI) validated against an independent breeder assessment. The 40 m altitude gave the highest height accuracy (R2 = 0.812 in ENV1; 0.663 in ENV2). LOGO accuracy (R2 ≈ 0.83) fell to R2 ≈ 0.55 under LODO—the operationally relevant figure for a new phenological stage—and the full structural–spectral OLS model collapsed (R2 = −0.203) where tree ensembles remained stable. Spectral-index repeatability was up to ~2-fold higher under stress (ICC(3,4) > 0.84). The composite STI flagged 38 elite genotypes (7.6% of 498); 10 of its top 30 were confirmed in the breeder’s 48-best selection from all 588 rows—a 4.1-fold enrichment over chance (hypergeometric p = 4.5 × 10−5).

Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル形質を抽出し、飛行高度、予測モデル、再現性、交差検証を体系的にベンチマークしているため、植物表現型取得法が中心である。

abstractEarly-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments.

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.

First Field Validation of a New VNIR–SWIR-Based Six-Band Multi-Camera System for UAVs over Winter Wheat ↗

Abstract. Shortwave infrared (SWIR) UAV imaging remains uncommon despite its sensitivity to canopy water and protein. We report, to our knowledge, the first field validation of a six-band, simultaneously exposed VNIR/SWIR multicamera for plot-scale winter wheat. The payload used narrow bandpass filters at 910, 980, 1100, 1200, 1510, and 1650 nm (FWHM 10–12 nm) and was flown at 30 m AGL, yielding 4 cm GSD. Radiometric calibration used in-flight empirical line calibration with an in-scene gray panel set, followed by independent validation on a material-distinct gray set. ASD spectroradiometer measurements were convolved with Gaussian proxy spectral response functions matched to the nominal filter passbands. Empirical line fits were near-perfect (R2 ≈ 1.000; RMSE = 0.003–0.009). Independent panel validation showed near-unity slopes for five bands from 980–1650 nm (R2 = 0.998–0.999; RMSE = 0.005–0.013). Across 36 canopy plot ROIs, camera-to-ASD agreement remained strong for five bands, with slopes of 0.943–1.079, R2 = 0.58–0.85, and RMSE = 0.010–0.023. Two SWIR normalized ratio indices showed tight cross-sensor agreement: NRI[1100,1200] (R2 ≈ 0.93; RMSE ≈ 0.010) and NRI[1650,1510] (R2 ≈ 0.90; RMSE ≈ 0.017–0.018). Post-hoc filter transmittance measurements revealed secondary long-wavelength throughput in the 910 nm channel, causing compressed slopes and elevated error (MAPE ≈ 33%); this band was excluded from accuracy claims. Panel-anchored, bandpass-aware calibration enables quantitative UAV SWIR reflectance and robust SWIR indices for precision agriculture applications. The workflow also identifies hardware-specific failure modes, supporting reproducible validation and informed redesign of filter-reconfigurable SWIR payloads.

Why it matches plant phenotyping methods冬小麦キャノピーの定量的な反射率・SWIR指数取得を目的に、UAVマルチカメラの校正、独立検証、センサー間比較、故障モード評価を中心的に実施しており、植物フェノタイピング手法の技術検証に該当する。

abstractWe report, to our knowledge, the first field validation of a six-band, simultaneously exposed VNIR/SWIR multicamera for plot-scale winter wheat.

FewShotCropNet: Real-Time Detection of Emerging Crop Diseases with Limited Labels Using Spectral-Temporal Attention Prototypical Networks ↗

The acquisition of labelled data for new or emerging plant diseases is difficult due to the high cost and logistical complexity of ground-truth collection, challenges that are further compounded by the limited infrastructure available to smallholder farmers in sub-Saharan Africa. This study presents FewShotCropNet, a few-shot learning model based on Spectral-Temporal Attention Mechanisms and Prototypical Networks that utilises multispectral time-series data from Sentinel-2 to classify crop diseases. Two principal innovations are incorporated in the proposed model: (1) a spectral attention mechanism based on the Squeeze-and-Excitation approach to learn disease-relevant spectral band weights; and (2) a temporal attention pooling mechanism to identify the most discriminative growth stages for disease classification. The model employs a two-phase training strategy comprising supervised pre-training followed by episodic meta-learning, enabling the generation of optimal feature representations under extreme label scarcity. Crop disease detection experiments were conducted in Edo State, Nigeria on cassava and maize using monthly Sentinel-2 composites from 2024 (10 spectral bands and five vegetation indices across twelve temporal steps). Under a 4-way 5-shot classification scenario with 100 GPS-validated labelled samples (25 per-class), FewShotCropNet achieved a mean accuracy of 98.15% with a 95% confidence interval of ±0.58%. An equitable comparison was enabled by introducing a Pre-trained Simple Prototypical Network, a variant sharing the same two-phase training strategy as FewShotCropNet but without the attention modules—which achieved 97.75% (±0.63%). FewShotCropNet exceeded the Pre-trained Simple ProtoNet by +0.40 percentage points (t = 1.52, p = 0.13), with the attention module contribution verified as positive though not statistically significant on the current dataset. Statistically significant improvements over models trained without pre-training were observed: FewShotCropNet outperformed the Relation Network (94.40%), Matching Network (94.40%), and the Optimised Baseline Convolutional neural networks (CNN) (86.65%, pre-trained backbone with 5-shot linear probe), with p

Why it matches plant phenotyping methods植物病害状態を対象に、Sentinel-2時系列データから病害を分類するFewShotCropNetを開発し、比較評価しているため、病害フェノタイピング手法が中心である。

abstractThis study presents FewShotCropNet, a few-shot learning model based on Spectral-Temporal Attention Mechanisms and Prototypical Networks that utilises multispectral time-series data from Sentinel-2 to classify crop diseases.

Pasture Biomass Monitoring in Queensland Rangelands with UAV and Satellite Cascades ↗

Operational satellite monitoring of pasture biomass demands models that extrapolate beyond the range of properties on which they were calibrated. We show that the dominant failure mode of Sentinel-2 pasture-biomass models in tropical Australian rangelands is the saturation and phenological inversion of greenness-based vegetation indices across sites, and that this failure can be substantially repaired with open climate and topsoil covariates from public archives. The work builds on a hierarchical pipeline that scales clip-and-weigh ground truth (n=1120 tare-corrected samples across eleven sites on five Queensland properties) through UAV digital-surface-model imagery to Sentinel-2 predictions, using TabPFN – a pre-trained transformer foundation model for small tabular data – as the regressor at all three nested spatial scales, and a seven-class deep-learning pasture mask (overall accuracy 98.6 %) to suppress mixed-pixel noise. Under a leave-one-site-out (LOSO) cross-validation protocol on twenty site-date aggregates across nine sites, spectral-only models failed to transfer across sites (R2=−0.21, RMSE=4.79 t ha-1). Appending open climate (Open-Meteo ERA5) and soil (SoilGrids 2.0) covariates, and switching to a gradient-boosted regressor on log-transformed biomass, lifted LOSO R2 to +0.07 and reduced RMSE to 4.19 t ha-1. A leaf-nitrogen growth trajectory, predicted by the TabPFN nitrogen regressor developed in our earlier pasture chemistry work, reduced LOSO error by a further 11 % relative to greenness-only growth features. Three additional covariate classes – BARRA-R2 reanalysis climate, three independent fractional-cover products, and Sentinel-1 C-band SAR backscatter – were tested and rejected, all hitting the same RMSE floor. The symmetric negative results suggest that the residual LOSO ceiling at the current nine-property footprint is a sample-size and sensor-saturation limit rather than a feature-engineering one, and that the most tractable operational path forward is to stratify the production model by climatic zone and Queensland Land Type rather than pursue further covariates within a single global learner. Expanding UAV calibration footprints and integrating open climate, soil and plant-chemistry data are complementary, not competing, investments for operational rangeland remote sensing.

Why it matches plant phenotyping methodsUAV・衛星画像、マスク処理、回帰モデルを組み合わせて牧草バイオマスを推定する測定パイプラインを構築し、サイト外交差検証で性能評価しているため、植物形質取得手法が中心である。

abstractThe work builds on a hierarchical pipeline that scales clip-and-weigh ground truth (n=1120 tare-corrected samples across eleven sites on five Queensland properties) through UAV digital-surface-model imagery to Sentinel-2 predictions

Year classification of high-oleic peanut seeds based on hyperspectral hybrid bands selection method. ↗

Seeds storage-year have a significant impact on high-oleic peanut seed vigor and quality. Therefore, it is essential to identify different storage-year seeds for planting, direct consumption, industrial processing, and marketing. In this study, hyperspectral images with 616 spectral bands (from visible light to near-infrared) were employed to classify different storage-year peanut seeds. To extract characteristic information for classification, we proposed a hybrid band selection (HBS) method based on the successive projection algorithm (SPA) by fusing the color-sensitive bands and moisture-sensitive bands. Then three classifiers, support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbors (KNN), were selected for storage-year classification. The experimental results demonstrated that the features extracted with the HBS method can obtain higher classification accuracy than other methods'. Specifically, the HBS-ELM model achieved the highest classification performance, with accuracy of 90.22%.

Why it matches plant phenotyping methodsハイパースペクトル画像から落花生種子の貯蔵年を推定するバンド選択法を開発・比較しており、種子の状態・品質の表現型抽出が研究の中心である。

abstracthyperspectral images with 616 spectral bands (from visible light to near-infrared) were employed to classify different storage-year peanut seeds.

Multisource data fusion for estimating cotton leaf nitrogen: A small-sample modeling perspective with neural network and interpretable deep forest architectures ↗

Accurate monitoring of nitrogen nutrition is critical for optimizing cotton production. Traditional machine learning-based inversion models have limited effectiveness for precision monitoring. Multisource fusion models for small samples were developed in this study to achieve enhanced accuracy through fitting and data complementarity. Cotton plants subjected to different nitrogen treatments were investigated. A two-year pot experiment was conducted to collect main-stem leaf images to construct an image pretraining dataset for model transfer. In a field experiment conducted over one year, main-stem leaf data were collected using hyperspectral, chlorophyll fluorescence, and digital camera sources, thereby providing a multisource dataset for training monitoring models. Two architectures—a neural network (NN) and an interpretable deep forest (DF), which are suitable for small-sample spectral, fluorescence, image color, and texture-sequence features—were constructed to improve the accuracy of nitrogen content inversion. Additionally, a two-dimensional sliding-window processing method was introduced into the DF multigranularity scanning module, and a transfer-learning-based two-dimensional convolutional NN was employed to directly model small-sample two-dimensional images. Building upon the outcome, multilayer fusion models were constructed, with corresponding fusion strategies designed for homogeneous sequence inputs and heterogeneous image–sequence inputs. The results showed that NN and DF can effectively handle limited sample sizes and outperform traditional machine learning models. Among the fusion models, the optimal secondary decision-level fusion model achieved an R² of 0.926 on the independent test set, indicating good performance under small-sample conditions. This study provides a methodological reference for the precise monitoring of crop phenotypic parameters under small-sample conditions.

Why it matches plant phenotyping methods綿花葉の窒素含量という植物形質を、画像・ハイパースペクトル・蛍光データの融合と深層学習で推定する手法を開発・評価しており、形質取得・推定法が研究の中心である。

abstractMultisource fusion models for small samples were developed in this study to achieve enhanced accuracy through fitting and data complementarity.

Hyperspectral Estimation of Layer-Specific Leaf Nitrogen Content in Potato Canopy by Integrating Fractional-Order Derivatives and Three-Band Spectral Indices. ↗

To address the insufficient characterization of vertical heterogeneity in potato canopy leaf nitrogen content (LNC), this study developed a layer-specific LNC estimation framework based on canopy hyperspectral reflectance, fractional-order derivative (FOD) transformation, and two-band and three-band optimized spectral indices. Partial least squares regression (PLSR) was then used to evaluate the predictive ability of the selected spectral indices for Top, Middle, and Bottom LNC. Field experiments were conducted from 2022 to 2023 in the semi-arid region of Yulin, Shaanxi Province, China. Canopy hyperspectral reflectance from 350 to 1830 nm and LNC measurements of upper (Top), middle (Middle), and lower (Bottom) leaves were synchronously acquired during the tuber formation stage. The results showed that potato canopy LNC exhibited a clear vertical gradient, following the order Top LNC > Middle LNC > Bottom LNC. Traditional vegetation indices were significantly correlated with LNC, but their correlations decreased with increasing canopy depth, with the highest correlation for Bottom LNC being only 0.524. Compared with traditional vegetation indices, FOD-based two-band indices showed stronger Pearson correlations with layer-specific LNC. Under FOD1.5, the maximum absolute Pearson correlation coefficients (|r|) between the selected two-band indices and LNC reached 0.855, 0.849, and 0.814 for Top, Middle, and Bottom LNC, respectively. The three-band optimized spectral indices further enhanced spectral information extraction, with maximum |r| values of 0.893, 0.885, and 0.852, respectively. However, cross-year validation produced substantially lower R 2 values, indicating limited temporal transferability of the selected indices and the need for further validation before broader application. Compared with the traditional vegetation index model, it increased the testing-set R 2 for Bottom LNC by 0.279 and reduced RMSE from 0.159 to 0.113. These results suggest that FOD1.5-integrated three-band optimized spectral indices can improve the indirect estimation of layer-specific LNC from canopy reflectance, particularly for Bottom LNC, where the reflectance-LNC association is affected by canopy signal attenuation and mixing. The findings provide a methodological reference for describing canopy vertical nitrogen status and functional heterogeneity in potato, while their broader applicability requires further validation across growth stages, cultivars, sites, and nitrogen management conditions.

Why it matches plant phenotyping methodsジャガイモ群落のハイパースペクトル反射から層別葉窒素含量を推定する手法を開発し、相関・予測性能・年次検証で評価しており、植物形質取得・推定法が研究の中心である。

abstractthis study developed a layer-specific LNC estimation framework based on canopy hyperspectral reflectance, fractional-order derivative (FOD) transformation, and two-band and three-band optimized spectral indices.

Using UAV Multispectral Imagery to Predict Leaf SPAD Dynamics During Maize Growth Under Different Plant Densities ↗

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD (Soil and Plant Analyzer Development) method, though easy to operate, is inefficient, destructive, and unsuitable for high-throughput field monitoring. UAV (Unmanned Aerial Vehicle) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and eight texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the growth period. The correlations among SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms, i.e., RF (Random Forest), PLSR (Partial Least Squares Regression), and SVR (Support Vector Regression), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R2) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during the entire maize growth stage based on UAV data.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と回帰モデルにより、トウモロコシの葉緑素量（SPAD）を非破壊推定する手法を構築し、複数アルゴリズムの精度比較も行っており、表現型取得手法が中心である。

abstractThis study established a non-destructive monitoring framework for chlorophyll content during the entire maize growth stage based on UAV data.

Comparative evaluation of precision planter performance via UAV remote sensing: A workflow for maize emergence monitoring ↗

To overcome the inefficiency and subjectivity of manual seedling surveys, this study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing. Centimeter-level multispectral imagery was captured to reconstruct 3D point clouds using SfM and MVS techniques. At the algorithmic level, an improved unsupervised pipeline was developed: the Otsu method was employed for plant segmentation, followed by a Fourier Transform to extract 2D spatial frequency features for precise crop row identification and automated spacing measurement. Subsequently, the Combined Entropy Uniformity (CEU) index was developed using Shannon entropy, and a proxy for canopy closure (CCP) was derived using a porosity model, thereby enabling the simultaneous relative quantification of seedling height consistency, spatial distribution uniformity, and canopy geometric structure variability. At the application level, the framework was validated through field trials involving 19 precision planters of diverse configurations. Performance was assessed using indices such as qualified spacing, miss-sowing, and the Coefficient of Variation of Plant Spacing (PSCV). Results indicate that: (1) Vacuum-type planters exhibited optimal stability at speeds of 7–9 km/h, achieving an average qualified spacing rate of 76.7% and a PSCV of approximately 24%, whereas finger-pickup planters were more sensitive to seed size variation and mechanical vibration. (2) The results from the Generalized Additive Model (GAM) suggest a possible nonlinear relationship between seeding rate and certain uniformity indices, indicating that appropriately adjusting operational parameters could help balance operational efficiency and seeding quality; however, this trend requires further validation with larger sample sizes and repeated observations. (3) Point cloud CEU metrics and canopy structure proxies based on the Gap Fraction model showed statistical correlations with certain manually collected indicators, indicating that this method has the potential for rapid screening of seeding quality and relative evaluation of seedling population structure at the field scale under the current experimental conditions.

Why it matches plant phenotyping methodsUAV画像・3D点群から作物の出芽、草丈均一性、空間分布、群落構造を抽出する解析ワークフローを開発し、19種のプランターで検証しており、植物表現型取得法が中心である。

abstractthis study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing.

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.

Research progress in multi-source and multi-scale intelligent sensing technology and equipment for crop phenotyping ↗

Crop phenotyping serves as a fundamental basis for crop breeding, precision cultivation, and smart agriculture. In recent years, it has evolved toward multi-modal integration and multi-scale coordination. This paper analysed indoor and outdoor phenotyping platforms across diverse application scenarios, and reviewed sensing technologies including RGB imaging, multi-spectral imaging, hyperspectral imaging, thermal imaging, fluorescence imaging, LiDAR, and nuclear magnetic resonance (NMR). The applications of these technologies were summarized in capturing crop morphological traits, physiological status and biochemical components. The phenotyping acquisition methods and intelligent analytical techniques were also analyzed at different scales such as plant cells, tissues and organs, individual plants, population plot and field. Additionally, the advancements were explored in high-throughput phenotyping technologies and their integration with crop gene function analysis, providing a reference for future phenotyping research.

Why it matches plant phenotyping methods作物表現型センシング技術・装置、取得法、解析技術、プラットフォームを主題とする包括的レビューであり、表現型手法が中心です。

abstractThis paper analysed indoor and outdoor phenotyping platforms across diverse application scenarios, and reviewed sensing technologies including RGB imaging, multi-spectral imaging, hyperspectral imaging, thermal imaging, fluorescence imaging, LiDAR, and nuclear magnetic resonance (NMR).

Early detection and firmness prediction of apple fruit infested by Bactrocera dorsalis using hyperspectral imaging combined with 1D convolutional neural network ↗

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

Why it matches plant phenotyping methodsリンゴ果実の硬度という植物器官形質と食害状態を、ハイパースペクトル画像および1D CNNで推定する手法が題名上の中心である。

titleEarly detection and firmness prediction of apple fruit infested by Bactrocera dorsalis using hyperspectral imaging combined with 1D convolutional neural network

Regional-Scale Estimation of Maize Plant Moisture Content in Arid Regions Integrating Multi-Source Remote Sensing and Machine Learning ↗

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R2 of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R2 increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.

Why it matches plant phenotyping methodsマルチソースリモートセンシングと機械学習により、トウモロコシの植物含水量という明示的な植物状態を地域スケールで推定・検証する手法開発が中心である。

abstractthis work develops a regional PMC estimation approach by combining multi-source remote sensing data.

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.

Real-Time Crop Stress Monitoring and Early Warning System for Paddy and Maize Using Multi-Temporal Sentinel-2 Data and Deep Learning in Semi-Arid Regions ↗

Semi-arid regions with a high potential for rice and maize cultivation have become some of the most actively farmed areas. They now face the challenge of achieving food security despite the threats of crop water stress, nutrient loss, and environmental changes. In this paper, we develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models in Mahabubabad district, Telangana, India. Different types of crop stresses such as water stress, nutrient deficiency, and phenological anomalies were detected and classified using a hybrid CNN-LSTM architecture with an attention mechanism. The methodology was based on 874 field polygons with extensive in-situ data collection during 2023-24, incorporating multi-temporal spectral indices (NDVI, EVI, NDWI, REP), weather variables, and soil characteristics. The total classification accuracy reached 89.4% for paddy and 87.2% for maize over all stress types, showing that stress detection from satellite images is quite reliable. Water stress was the category that was detected most accurately (92.1% for paddy and 89.8% for maize), followed by nutrient stress (88.7% and 86.3%) and phenological stress (85.2% and 83.9%). The warning system made it possible to identify the problem 15-25 days before there were visible symptoms, making it possible for the farm management to respond in time. Activities of the farm that were most vulnerable to detection were air and water temperatures, precipitation, and crop growth stages for water stress 45-60 days after sowing, 30-45 days for nutrient stress, and during the reproductive phase for phenological stress. The system could be extended for industrial crop stress monitoring across the semi-arid agricultural systems which might lead to precision agriculture and climate-resilient farming practices.

Why it matches plant phenotyping methods衛星画像と深層学習を用いて作物の水ストレス・栄養ストレス・生育異常を直接推定し、精度検証と早期検出性能を評価しているため、植物表現型取得法が中心である。

abstractwe develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models

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.

Unraveling the environmental drivers of wheat grain quality: A chemometric framework for processing NIR spectral variance ↗

Wheat grain quality is highly susceptible to environmental fluctuations. This study proposes a complementary two-stage chemometric framework combining Analysis of Variance-Simultaneous Component Analysis (ASCA) and ANOVA-Common Dimensions (AComDim) with near-infrared (NIR) spectroscopy to systematically evaluate spatiotemporal impacts on wheat. A total of 179 samples collected across four Austrian locations over a three-year period were analyzed. In the first stage, ASCA was utilized as a computationally efficient pre-screening tool to systematically evaluate 27 preprocessing protocols, aimed at maximizing target-factor variance and minimizing background noise. In the second stage, AComDim was implemented as the core engine for orthogonal variance decomposition. By embedding ANOVA into a multiblock framework, AComDim successfully preserved joint multivariate variations among variance blocks, overcoming the independent-block limitations of traditional ASCA. By mathematically decoupling distinct sources of variance directly from the untargeted spectral fingerprints, the results revealed that harvest year was the predominant driver of spectral variation (captured in Common Component 1, explaining 45.1% of the total variance). Furthermore, the year-by-location interaction (CC2, 14.5%) and the main effect of growing location (CC3, 14.1%) were successfully and orthogonally extracted. Although their global F -values did not meet the strict 95% statistical significance threshold, they exhibited structured, deterministic spectral patterns clearly distinguishable from random background noise. Loading analysis of broad spectral regions (e.g., 1100–1200 nm and 1350–1450 nm) highlighted environmentally induced macroscopic shifts in carbohydrates, lipids, proteins, and moisture status. This research provides a robust, high-throughput phenotyping framework for quantifying the spatiotemporal sensitivity of cereals, offering essential insights for stabilizing grain quality under changing environmental conditions.

Why it matches plant phenotyping methodsNIRスペクトルから小麦粒の品質・環境応答を抽出する chemometric 手法が研究の中心であり、単なる品質測定ではなく、高スループット表現型解析フレームワークとして開発・適用されている。

abstractThis study proposes a complementary two-stage chemometric framework combining Analysis of Variance-Simultaneous Component Analysis (ASCA) and ANOVA-Common Dimensions (AComDim) with near-infrared (NIR) spectroscopy to systematically evaluate spatiotemporal impacts on wheat.

Decoupling Crop Stressors in the Bărăgan Plain: A Multi-Sensor Remote Sensing Framework ↗

Abstract With agriculture moving to a more performant and autonomous-driven sustainability, advanced digital monitoring is becoming the key to resilient land management. This study investigates hydro-climatic conditions and urban air pollution in 2024 in the Bărăgan Plain, Romania’s primary grain-producing region. Our approach uses the Google Earth Engine (GEE) platform, high-resolution multispectral imagery (Sentinel-2), and atmospheric trace gas data from Sentinel-5P TROPOMI to map spatiotemporal interactions between agricultural health and urban air pollution. To find the most robust approach we compared three machine learning algorithms: Multiple Linear Regression, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), the Enhanced Vegetation Index (EVI) was used as a predictor of the Normalized Difference Vegetation Index (NDVI). Although the models that included the EVI achieved higher accuracy (R2 of 93%), the non-EVI Random Forest model performed better (R 2 of 86%) and revealed moisture availability (NDWI) as the primary regulator of crop vigor, with an importance of 81.8%. To isolate the effect of atmospheric chemistry alone, spatial residuals from the optimized RF model were extracted and plotted. Negative residuals that produced anomalies pointed to a unique type of crop stress in which the plants were underperforming even when water was adequate. These anomalies spatially align the urban pollution plume trajectories. Even if the NO2 has a rapid distance-decay effect, the secondary pollutants (O3) reach over the agricultural land through the photochemical titration effect. Our results show that while water determines regional agricultural baselines, atmospheric chemistry from urban sources can independently cause significant crop stress even in the absence of drought. This study provides a robust proof of concept on the separation of climatic and anthropogenic stressors and lays a basic pathway for a multi-sensor diagnostic framework in the domain of remote sensing.

Why it matches plant phenotyping methods衛星マルチセンサー画像と機械学習を統合し、作物の活力・ストレスを推定する診断フレームワーク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。

abstractOur approach uses the Google Earth Engine (GEE) platform, high-resolution multispectral imagery (Sentinel-2), and atmospheric trace gas data from Sentinel-5P TROPOMI to map spatiotemporal interactions between agricultural health and urban air pollution.

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.

Estimating Crop Nitrogen Uptake from UAV-Based Imagery Using Machine Learning Techniques ↗

Unmanned Aerial Vehicle (UAV)-based remote sensing using high-throughput spectral imaging has emerged as an effective non-destructive alternative for large-scale agricultural monitoring. This study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola. Field trials were conducted at irrigated and non-irrigated sites in southern and central Alberta, Canada, respectively, over three growing seasons (2023–2025). Coincident with ground-truth tissue sampling, aerial imagery was collected and processed to train and validate six machine learning models, using ~520 matchups per crop. All models successfully estimated nitrogen uptake across years and locations, although performance varied by sensor and data types. For canola, ANN produced the highest MSI-based accuracy (R2 = 0.83, RMSE = 0.5%), whereas HSI data improved prediction performance, with SVR achieving the best results (R2 = 0.90, RMSE = 0.40%). In wheat, ANN yielded the highest accuracy for both MSI and HSI data (R2 = 0.77, RMSE = 0.54% for MSI; R2 = 0.8, RMSE = 0.48% for HSI). These findings demonstrate that UAV-based spectral imaging combined with machine learning provides a reliable and scalable approach for non-destructive nitrogen uptake estimation. Although MSI sensors produced strong predictive performance, the enhanced spectral resolution of HSI data consistently improved estimation accuracy for both crops across varied growing conditions.

Why it matches plant phenotyping methodsUAVマルチスペクトル・ハイパースペクトル画像と機械学習により、作物の窒素吸収量という植物形質を推定し、複数モデル・センサーの性能を評価しているため、フェノタイピング手法が中心である。

abstractThis study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola.

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.

Drought tolerance classification using unmanned aerial systems based on RGB and multispectral data. ↗

Drought poses a global threat to food security and demands intensified efforts from breeding programs. Yet the lack of efficient methods for selecting this trait increases the cost and time required to develop new cultivars. The goal of this work was to assess the feasibility of using spectral data from RGB or multispectral sensors for drought-tolerance classification across various machine-learning models under the most practical cross-validation scenarios typical in breeding programs. The genotypes were assessed during trials conducted under either optimal (irrigated) or drought-stress conditions across two years, and evaluated using up to 10 field traits to determine their drought-tolerance classification based on membership function values related to drought. RGB and multispectral vegetation indices collected during several flights throughout the crop cycle were used to train machine learning models. We found that drought trials offer the best training data. Specificity was the metric most affected by sensor type and the nature of the training data. The multispectral sensor outperformed the RGB sensor on most evaluation metrics in both years. AdaBoost and linear discriminant analysis models demonstrated the strongest consistency across all prediction scenarios. Together, they achieved an overall accuracy, specificity, and F1-Score of 0.71, 0.56, and 0.77, respectively. The most influential vegetation indices for model performance consistently included the NIR band. Spectral information, such as vegetation indices, is a useful tool for plant researchers to complement drought tolerance evaluations in the field. This data-driven approach facilitates automation, paving the way to speed genetic gains by including early assessments of drought tolerance in breeding pipeline, and improves resource utilization efficiency.

Why it matches plant phenotyping methodsUASのRGB・マルチスペクトルデータと機械学習を用いて干ばつ耐性を分類し、センサー比較や交差検証を行う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適格です。

abstractThe goal of this work was to assess the feasibility of using spectral data from RGB or multispectral sensors for drought-tolerance classification across various machine-learning models under the most practical cross-validation scenarios typical in breeding programs.

The role of hyperspectral imaging in forest seedling phenotyping ↗

In recent years, hyperspectral imaging has been widely adopted in agriculture and plant phenotyping, while its application in forestry has been increasing. From that point onward, hyperspectral imaging has become a valuable tool for plant phenotyping, enabling the assessment of a broad range of plant traits. Given that seedlings of forest trees are one of the most widely used types of forest planting stock, advancements in hyperspectral technology have created new possibilities for improving seedling quality assessment. High-quality forest seedlings are important for the successful establishment of forest stands, especially after outplanting within restoration initiatives. Even though hyperspectral imaging brings numerous advantages, continued technological improvements are necessary to address its several limitations and challenges. Despite its widespread use in agricultural phenotyping, applications in forest nursery production remain limited. Therefore, this review focuses on research involving hyperspectral imaging in forest seedling production and its potential for assessing seedling quality parameters.

Why it matches plant phenotyping methods森林苗木の品質形質評価に用いるハイパースペクトル画像解析を中心に扱うフェノタイピングレビューであり、方法論的役割が明確。

titleThe role of hyperspectral imaging in forest seedling phenotyping

Genetic dissection of dynamic leaf area index variation in maize using UAV-based phenotyping and time-series genome-wide association studies ↗

Abstract The leaf area index (LAI) is a key determinant of canopy architecture and yield potential in maize, primarily through its influence on photosynthetic efficiency. Although unmanned aerial vehicle (UAV) technology has greatly advanced field-based phenotyping, its potential for deciphering the genetic mechanisms underlying dynamic and complex trait development remains underexplored. In this study, multispectral UAV images were collected from a diverse maize panel across eight developmental stages in four environments over two consecutive years. Using multi-temporal data, a random forest model accurately predicted LAI (R² = 0.82–0.83), significantly outperforming models based on single time-point data. By integrating high-throughput phenotypic predictions with time-series genome-wide association studies (GWAS), 36 dynamic SNPs associated with LAI variation were identified. Principal component analysis (PCA) of temporal LAI data revealed two principal components that together explained 84.2–86.5% of the total phenotypic variance. GWAS based on these components identified an additional 51 SNPs, seven of which overlapped between the two analytical approaches. Among the 72 candidate genes identified, Zm00001d048615 exhibited significant variation in both phenotype and expression among different inbred lines. The heterologous overexpression of Zm00001d048615 in Arabidopsis induced leaf curling and a significant reduction in leaf size, indicating its potential role in regulating leaf development. Collectively, these findings establish a robust framework that integrates UAV-based phenomics with temporal GWAS to identify key genes regulating complex dynamic traits. This approach provides valuable insights and genetic targets for improving maize canopy architecture and yield potential through molecular breeding.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と時系列データからLAIを推定するモデルを開発・評価し、高スループット表現型解析に中核的に用いているため。

abstractmultispectral UAV images were collected from a diverse maize panel across eight developmental stages in four environments over two consecutive years.

Automated, high-throughput hyperspectral imaging enables early detection of grapevine downy mildew and monitoring of vineyard spray program performance. ↗

Grapevine downy mildew (GDM), caused by Plasmopara viticola, is managed largely through repeated fungicide applications, yet evaluating spray-program performance is difficult because field infections are spatially heterogeneous. However, host physiological changes can precede visible symptom development. We tested whether standardized, high-throughput VNIR hyperspectral imaging of field-grown grapevine leaf discs can capture early optical changes associated with infection and discriminate program-linked mitigation. Leaves were collected from a 2025 vineyard trial in Geneva, NY (cv. La Crescent) managed under three spray programs (conventional fungicides, biofungicides, and untreated control). Leaf discs (≈87 per program) were excised, inoculated with P. viticola, and imaged at 0.5, 1.5, and 3.5 days post inoculation (dpi) using a custom built, automated, hyperspectral imaging microscopy platform (400-980 nm). No visible sporulation occurred at 0.5 or 1.5 dpi; sporulation was first observed at 3.5 dpi and occurred only in the untreated control, whereas no discs from either treated program sporulated. Multivariate spectral analyses showed significant separation by spray program and dpi, and disease-related spectral indices exhibited program-dependent trajectories, with treated discs showing attenuated optical change relative to the control. A random-forest classifier trained on pre-sporulation spectra (0.5 and 1.5 dpi; N = 169) predicted subsequent sporulation with 0.78 accuracy and 0.77 ROC-AUC (95% CI 0.68-0.85), highlighting informative bands across visible, red-edge, and near-infrared regions. Together, these results support outcome-linked, replicate-rich hyperspectral phenotyping of vineyard spray programs using field-derived material under standardized acquisition conditions.

Why it matches plant phenotyping methods自動化VNIRハイパースペクトル撮像で、発病前のブドウ葉の光学的変化と将来の胞子形成を推定する手法を中心に検証しており、植物病害状態の表現型取得・分類に該当する。

abstractWe tested whether standardized, high-throughput VNIR hyperspectral imaging of field-grown grapevine leaf discs can capture early optical changes associated with infection and discriminate program-linked mitigation.

UAV-Based Multi-Sensor Fusion for Leaf Age Detection in Maize Inbred Line Population Seedlings ↗

Accurate and rapid detection of maize seedling growth is critical in early breeding decisionmaking, smart management, and yield improvement. Traditional leaf age detection still relies heavily on labor-intensive and low-efficiency manual field surveys, underscoring the urgent need for high-throughput phenotyping. Integrating multisource sensor data from unmanned aerial vehicle (UAV) with measured information such as crop height can further enhance the estimation accuracy of crop phenotypic parameters. Accurate field plot segmentation is critical for field-scale phenotypic analysis. However, current approaches remain largely dependent on slow, manual segmentation. Automating this step would greatly reduce the workload of agronomists. This study used UAV RGB and multispectral imagery collected over maize inbred line population plots before the canopy closure stage to perform automatic plot segmentation on field orthophotos and combined measured plant height with relative flight dates to achieve high-throughput detection of leaf age during the maize seedling stage. First, this study proposed a maize plot automatic segmentation method based on orthophotos. Then, it extracted texture features, RGB, and multispectral vegetation indices of each plot. Combined with relative flight date and plant height, four datasets were constructed. Support vector regression (SVR), random forest regression (RFR), and automatic machine learning (AutoML) regression algorithms were used to build the leaf age detection model. The results showed that the orthomosaic from March 23 achieved the best plot-segmentation performance, with minimum intersection over union (IoU), mean IoU, and IoU standard deviation of 14.67%, 96.47%, and 8.65%, respectively. Incorporating relative flight dates and plant-height measurements improved model performance, and the AutoML demonstrated the greatest robustness, achieving a validation R2 of up to 0.862 and an RMSE as low as 0.715. This study proposed a leaf age estimation method that offers practical technical support for field-based maize seedling assessment and reduces manual labor demands.

Why it matches plant phenotyping methodsUAV RGB・マルチスペクトル画像、圃場区画 segmentation、特徴抽出、回帰モデルを統合し、トウモロコシの葉齢という植物形質を推定する方法を開発・評価しており、フェノタイピング手法が研究の中心である。

abstractThis study used UAV RGB and multispectral imagery collected over maize inbred line population plots before the canopy closure stage to perform automatic plot segmentation on field orthophotos and combined measured plant height with relative flight dates to achieve high-throughput detection of leaf age during the maize seedling stage.

Wheat growth parameters prediction based on dual output Bayesian neural network using multi-modal information ↗

Introduction eaf area index (LAI) and leaf nitrogen accumulation (LNA) are key indicators of wheat growth and nitrogen nutritional status. However, existing prediction methods predominantly rely on single-modal information and single-output models, limiting their ability to characterize the complex structural and physiological traits of crops. This study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA. Methods Spectral, image, and canopy structural features were extracted from wheat canopies across different cultivars, nitrogen treatments, and growth stages. A canopy height correction-based preprocessing method was developed to improve the extraction of structural features. A Dual-Output Bayesian Neural Network (DO-BNN) was then constructed to simultaneously predict LAI and LNA. In addition, an Extreme Sample Mining (ESM) strategy and a joint loss function were introduced to strengthen the learning of complementary information across modalities and the intrinsic correlation between the two target variables. Results The DO-BNN achieved its best predictive performance when all feature modalities were fused. The coefficients of determination (R²) for LAI and LNA were 0.89 and 0.77, respectively, while the corresponding relative root mean square errors (RRMSEs) were 0.15 and 0.35. Compared with single-modal and conventional single-output approaches, the proposed method provided more accurate and robust predictions of both wheat growth parameters. Discussion The results demonstrate that integrating spectral, image, and structural information can improve the characterization of wheat canopy traits. By jointly modeling LAI and LNA, the DO-BNN effectively exploited the physiological relationship between crop growth and nitrogen accumulation. The proposed framework provides a promising approach for the high-accuracy, collaborative monitoring of wheat growth and nitrogen nutritional status.

Why it matches plant phenotyping methods小麦キャノピーのスペクトル・画像・構造情報からLAIと葉窒素蓄積を推定するマルチモーダル手法を開発し、前処理、ニューラルネットワーク、性能比較まで中心的に扱っているため。

abstractThis study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA.

UAV-Based Assessment of Pre- and Post-Harvest Water Stress Dynamics in Vineyards Using NDRE and GNDVI Spectral Responses ↗

Abstract Climate change and increasing drought frequency are intensifying water stress risks in viticultural systems, particularly in semi-arid regions where water availability directly influences grapevine productivity and quality. This study investigated pre-harvest and post-harvest water stress dynamics in a commercial Ağın Beyazı vineyard located in Eastern Türkiye using UAV-based multispectral imagery. High-resolution orthomosaics (2.31 cm pixel⁻¹) were acquired with a DJI Mavic 3 Multispectral platform and used to generate Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Red Edge Index (NDRE) maps for both phenological periods. Water stress responses were evaluated through pixel-based change detection, descriptive statistics, spatial heterogeneity metrics (Local Mean and Local Standard Deviation), and Global Moran’s I spatial autocorrelation analysis. Results revealed measurable spectral differences between pre-harvest and post-harvest periods, indicating changes in canopy physiological activity associated with water stress and post-harvest vine responses. GNDVI exhibited a higher relative change (+ 10.49%) and stronger standardized response ratio, suggesting greater sensitivity to overall canopy vigor and photosynthetic activity. In contrast, NDRE showed the highest heterogeneity response (+ 64.97%), demonstrating superior capability for identifying localized stress variability and fine-scale physiological differences within the vineyard. Spatial heterogeneity analyses indicated increasing local variability after harvest, while consistently positive Moran’s I values (≈ 0.257) revealed that stress patterns remained spatially organized rather than randomly distributed. These findings suggest that vineyard water stress is controlled not only by vine physiology but also by persistent spatial factors such as soil conditions, micro-topography, and water availability. Overall, the combined use of GNDVI and NDRE provided complementary information for vineyard water stress assessment. The proposed framework demonstrates the potential of UAV-based multispectral monitoring for precision irrigation management, early stress detection, and climate-resilient viticulture in water-limited environments.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からGNDVI・NDREを抽出し、ブドウ樹の水ストレスと空間変動を評価する測定・解析フレームワークが研究の中心である。

abstractThis study investigated pre-harvest and post-harvest water stress dynamics in a commercial Ağın Beyazı vineyard located in Eastern Türkiye using UAV-based multispectral imagery.

Non-destructive assessment of soluble solids content and firmness in tomatoes using dual-mode hyperspectral imaging technology. ↗

Background Non-destructive assessment of tomato internal quality, including soluble solids content (SSC) and firmness, is important for grading and postharvest management. However, the varying capabilities of reflectance and transmittance hyperspectral imaging for predicting biochemical and mechanical quality attributes have not been sufficiently compared. Results In this study, a dual-mode hyperspectral imaging system covering 500-950 nm was developed to evaluate SSC and firmness in 160 'Yuan Wei No. 1' tomatoes. Four preprocessing methods, including Savitzky-Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), and orthogonal signal correction (OSC), and three feature-wavelength selection strategies, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and UVE-CARS, were compared using partial least squares regression. Transmittance spectra outperformed reflectance spectra for SSC prediction. The CARS filtered transmittance model achieved the best performance, with R p = 0.9256 and residual predictive deviation (RPD) = 2.4208. Firmness prediction was less accurate; the best model was obtained using reflectance spectra combined with SG-SNV preprocessing and UVE-CARS feature selection, yielding R p = 0.8008 and RPD = 1.6696. Conclusion Dual-mode hyperspectral imaging is effective for non-destructive SSC prediction in tomatoes, whereas firmness prediction remains limited because mechanical quality attributes are less directly represented by visible-near-infrared spectral information. The results provide a basis for tomato quality assessment and suggest that future firmness prediction may benefit from multi-modal data fusion. © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methodsトマトのSSCと硬度という植物器官形質を、デュアルモード・ハイパースペクトル画像で非破壊推定するシステムを開発・比較評価しており、形質取得手法が研究の中心である。

abstracta dual-mode hyperspectral imaging system covering 500-950 nm was developed to evaluate SSC and firmness in 160 'Yuan Wei No. 1' tomatoes.

Early Detection of Muskmelon Powdery Mildew Using Time-Series 3D Multispectral Point Clouds ↗

Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, which often lack sufficient model generalization. This study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology. An Artificial Neural Network (ANN) model for 3D spatial light field distribution was developed based on a hemispherical white reference to achieve precise reflectance calibration of the multispectral point clouds. Post-calibration, the coefficient of variation (CV) for the spectral reflectance of the hemispherical reference in 3D space was reduced to less than 2.4%. On this basis, an early classification model for melon powdery mildew was constructed using Partial Least Squares Discriminant Analysis (PLS-DA) based on the mean reflectance spectra of individual plant point clouds. The results demonstrate that the average recognition accuracy reaches 85.94% from 4 days post-inoculation onwards, enabling disease early warning three days in advance. This research provides critical theoretical support and technical reference for the non-destructive early monitoring and precision smart plant protection of crops in facility agriculture.

Why it matches plant phenotyping methodsメロン個体の病徴状態を対象に、時系列3Dマルチスペクトル点群の再構成・反射率校正と早期病害分類を開発しており、植物表現型取得手法が中心である。

abstractThis study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology.

Assessing plant water status: Part 2 – Non‐destructive and remote sensing approaches ↗

Precise, real time and non-destructive assessment of plant water status is important for advancing plant physiological understanding, optimizing water usage, improving crop resilience and supporting precision agriculture in the face of increasingly variable climatic conditions. Classical methods for measuring plant water status reviewed in Part 1 of this two-part review have significant limitations for field level applications, providing only discrete, single-point measurements and potentially altering plant physiology through destructive sampling. This second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations. We review techniques such as ZIM-probe, terahertz spectroscopic techniques, microwave remote sensing, infrared transmission sensor, microtensiometers, dendrometers and leaf thickness sensors, light detection and ranging (i.e. LiDAR), imaging spectroscopy, NMR relaxation, spectroscopy based on equivalent water thickness, spectral indices, derivative spectra, post-continuum removal indicators, visible and near-infrared spectroscopy, and infrared thermography. These emerging techniques facilitate high-resolution, real-time monitoring of water status across leaf, canopy and ecosystem scales. This comprehensive comparison provides guidance for selecting most appropriate technique based on experimental objectives, guiding applications ranging from single leaf to canopy scale ecosystem assessment. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Why it matches plant phenotyping methods植物の水分状態を非破壊・遠隔センシングで測定する手法を体系的に比較・評価したレビューであり、植物フェノタイピング手法が中心です。

abstractThis second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations.

Monitoring Forest Landscape Restoration Success Using Sentinel-2 NDVI and Field Measurements in Timor-Leste ↗

Abstract Forest Landscape Restoration (FLR) has become an important strategy for reversing land degradation and improving ecosystem resilience in tropical drylands. However, quantitative evaluations of restoration effectiveness remain scarce in Timor-Leste, particularly those integrating satellite-based monitoring with field observations. This study assessed vegetation recovery following reforestation activities within a 148-ha restoration site in Balak, Manatuto, Timor-Leste, using multi-temporal Sentinel-2 imagery and field-based ecological measurements. Vegetation dynamics were evaluated using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 images acquired in May 2022 and May 2026. NDVI differencing was applied to quantify vegetation change, while field data collected from 29 monitoring plots were used to assess seedling survival and growth performance. Pearson correlation and linear regression analyses were employed to examine relationships between vegetation growth indicators and restoration performance. The results indicated substantial vegetation improvement across the restoration area. Mean NDVI increased from 0.288 in 2022 to 0.475 in 2026, representing a 65% increase in vegetation greenness. Approximately 77.6% of the sites experienced moderate to significant vegetation recovery based on ΔNDVI analysis, whereas only 2.3% showed vegetation decline. Field assessments revealed that 62.1% of monitoring plots were classified as high-recovery sites and only 6.9% as low-recovery sites. A significant positive relationship was observed between average plant height and growth percentage ( r = 0.423, R ² = 0.179, p = 0.022), indicating that vegetation structural development was associated with restoration performance. These findings demonstrate that the AFoCO-supported reforestation programme has effectively accelerated vegetation establishment and improved ecosystem condition within a degraded tropical dryland landscape. The integration of Sentinel-2-derived NDVI indicators with field measurements provides a practical, cost-effective, and scalable framework for monitoring FLR outcomes in data-limited regions and offers valuable evidence to support restoration planning and evaluation in Timor-Leste and comparable tropical dryland environments.

Why it matches plant phenotyping methodsSentinel-2 NDVIと圃場測定を統合し、植生回復・成長という植物状態を定量評価する監視フレームワークを中心的に適用しているため。

abstractusing multi-temporal Sentinel-2 imagery and field-based ecological measurements

Interpretable machine learning and causal inference for maize LAI estimation from UAV multimodal imagery ↗

Accurate retrieval of leaf area index (LAI) is vital for crop monitoring and genetic breeding. Although multi-modal unmanned aerial vehicle (UAV) remote sensing has advanced LAI estimation, conventional empirical models often overfit on small breeding populations and cannot disentangle the true causal effects of genetic backgrounds from confounding factors within a statistically rigorous framework. This study presents a robust framework for plot-scale maize LAI estimation across 800 breeding plots from four genetic subgroups: doubled haploid (DH), mixed, temperate (TEM), and tropical/subtropical (TST). From UAV RGB and multispectral imagery acquired at three phenological stages, we extracted 76 multi-modal features comprising point-cloud structural metrics, spectral vegetation indices, and texture features. Following a dual-criterion mutual information and multicollinearity filter, four algorithms, including traditional tree-ensembles and the Tabular Prior-data Fitted Network (TabPFN), were evaluated using nested validation. TabPFN achieved superior generalization performance, yielding a mean test R2 of 0.778 ± 0.031, an RMSE of 0.264 ± 0.024, and an MAE of 0.203 ± 0.022, significantly outperforming tree-ensemble models (p < 0.01). Across growth stages, retrieval accuracy peaked at the expanded bell-mouth stage (R2 = 0.802) and successfully captured the unimodal trajectory of canopy development. SHAP-based attribution showed that spectral indices contributed most to the predictions (55.9%), followed by canopy texture (26.3%), with the spatial heterogeneity metric Tex_Entropy being the most influential single feature (22.8%). When embedded as the nuisance estimator within a Double Machine Learning framework for causal inference, TabPFN confirmed that, relative to the TEM subgroup, only the DH genetic background exerted a consistent and significant negative causal effect on LAI (ATE = −0.070, p = 0.030). These results establish TabPFN as a reliable and extensible tool for non-invasive, high-throughput phenotyping in precision breeding.

Why it matches plant phenotyping methodsUAVマルチモーダル画像からトウモロコシLAIを推定する計算・画像解析ワークフローを開発・比較検証し、高スループット表現型解析への応用を示しているため、方法が中心的である。

abstractThis study presents a robust framework for plot-scale maize LAI estimation across 800 breeding plots from four genetic subgroups

From Phenotyping to Supervised Agentic Decision Support: A Review of Sensing and Artificial Intelligence for Greenhouse Strawberry Cultivation ↗

Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support.

Why it matches plant phenotyping methods温室イチゴのフェノタイピング、マルチモーダルセンシング、AIによる作物状態解釈を中心に整理する方法論レビューであり、植物状態の取得・推定手法が主題。

abstractThis review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation.

Simultaneously prediction of multiple wheat leaf phenotypes using hyperspectral imaging with multi-task machine and deep learning. ↗

Chlorophyll content (represented by the Soil and Plant Analyzer Development (SPAD) value) and leaf moisture content (LMC) are two key physiological phenotypic traits during wheat growth, and their variations among different wheat varieties reflect crop growth, stress response, and breeding evaluation. Simultaneous identification of wheat varieties and prediction of SPAD and LMC are therefore important for precision crop monitoring. In this study, hyperspectral imaging was employed to acquire leaf spectral information from four wheat varieties. After spectral preprocessing and outlier screening, 684 valid samples were retained for model development and evaluation. Single-task and multi-task models were constructed for wheat variety classification, SPAD prediction, and LMC prediction using support vector machine (SVM), partial least squares (PLS), convolutional neural network (CNN), and multi-task CNN (MLT-CNN) algorithms. In the MLT-CNN, a shared one-dimensional spectral feature extraction module and three task-specific branches were designed, and equal weight strategy (EWS), adjustable regularization weighted strategy (ARWS), and uncertainty-based weighted strategy (UWS) were compared. The best single-task models achieved a test-set classification accuracy of 0.75, with correlation coefficients (r) of 0.83 for SPAD and 0.84 for LMC. The MLT-CNN with EWS achieved balanced test-set performance across the three tasks, with a classification accuracy of 0.69, an r value of 0.84 for SPAD, and an r value of 0.81 for LMC. Shapley additive explanations (SHAP)-based visualization was further performed for both single-task CNNs and MLT-CNN task branches to identify important wavelengths and interpret shared and task-specific spectral contributions. These results indicate that hyperspectral imaging combined with multi-task learning provides a feasible and interpretable spectroscopic strategy for integrated wheat leaf phenotyping.

Why it matches plant phenotyping methodsハイパースペクトル画像からSPAD値と葉含水量という植物生理形質を推定し、複数の機械学習モデルとマルチタスク構成を開発・評価した研究であり、フェノタイピング手法が中心である。

abstractIn this study, hyperspectral imaging was employed to acquire leaf spectral information from four wheat varieties.

Integrating UAV-based dynamic phenotyping and GWAS to decipher the genetic basis of cotton defoliation ↗

Upland cotton (Gossypium hirsutum L.) is a critical economic crop, yet the efficiency of mechanized harvesting is heavily contingent upon effective pre-harvest defoliation. Traditional manual assessment of defoliation is labor-intensive and subjective, posing a significant bottleneck for large-scale genetic dissection of this dynamic trait. In this study, we established an integrated “high-throughput phenotyping-to-gene discovery” framework by utilizing UAV-based multispectral imaging to monitor 306 cotton cultivars across 4 environments. A Partial Least Squares Regression (PLSR) model was optimized to accurately estimate Leaf Area Index (LAI), and Gaussian curve fitting was employed to standardize LAI time series (ΔLAI) into a comparable dynamic phenotypic dataset. Genome-wide association studies (GWAS) based on these dynamic phenotypes identified 472 significant SNPs and 39 candidate genes. By integrating GWAS signals with transcriptome profiling of the petiole abscission zone and haplotype analysis, we identified 3 core regulatory genes: Ghi_A01G08401 (GhPIN3a), Ghi_D08G10716, and Ghi_D11G03091. Functional validation via virus-induced gene silencing (VIGS) and qRT-PCR demonstrated that Ghi_D08G10716 (encoding oxalyl-CoA synthetase) and Ghi_D11G03091 (encoding a VQ motif-containing protein) act as negative regulators in the defoliation process. These results provide a scalable technical paradigm and critical genetic resources for the precision breeding of cotton cultivars optimized for mechanized harvesting.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からLAIを推定し、時系列を動的表現型データへ変換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、GWASへの実質的適用も行っている。

abstractwe established an integrated “high-throughput phenotyping-to-gene discovery” framework by utilizing UAV-based multispectral imaging to monitor 306 cotton cultivars across 4 environments.

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.

Hyperspectral Imaging for Crop Disease Detection: A Systematic Literature Review and Research Gap Analysis ↗

Crop diseases cause 20-40% of food losses every year and cause economic damage of more than USD 220 billion per year on a global scale. The basic problems in precision agriculture remain the same as early and accurate disease detection. Hyperspectral imaging (HSI) is a type of imaging technique that captures hundreds of contiguous wavelengths of the electromagnetic spectrum spanning from 400 to 2500 nm that has been found to be a useful non-destructive diagnostic tool that can detect the subtle biochemical differences that occur in plant tissue before symptoms are visible. The paper critically summarizes and reviews the literature from 2000 to 2024, especially focusing on the application of AI and machine learning (ML) for HSI-based crop disease detection. A total of 48 primary studies are reviewed and grouped into five thematic categories: (1) spectral vegetation index methods, (2) classic machine learning classifiers, (3) deep learning architectures, (4) attention and transformer mechanisms and (5) disease severity quantification. Based on this review, four gaps in the literature are identified: (1) lack of comparison of classical and deep learning models on the same splits of the same datasets, (2) underutilisation of the SWIR-2 spectral range (>2000 nm) for the discrimination of diseases, (3) lack of integrated spatial mapping of the disease severity from spectral index fusion, and (4) lack of lightweight deep learning spectral-only architectures for field deployment in resource-constrained environments. These gaps together form a promising research program based on this AI approach to automated crop disease detection, and the experimental research work reported in our companion paper is fueled by these gaps.

Why it matches plant phenotyping methods植物病害の症状・重症度をハイパースペクトル画像とAIで推定する手法を対象とした体系的レビューであり、植物フェノタイピング手法のレビューが中心。

abstractHyperspectral imaging (HSI) is a type of imaging technique that captures hundreds of contiguous wavelengths of the electromagnetic spectrum spanning from 400 to 2500 nm that has been found to be a useful non-destructive diagnostic tool that can detect the subtle biochemical differences that occur in plant tissue before symptoms are visible.

Weakly Supervised Fine-Grained Discrimination of Wheat Mold Using Local RGB-HSI Fusion. ↗

Wheat is a major staple crop, and storage mold growth poses a severe threat to grain safety and quality stability. Natural mold development in stored wheat exhibits subtle, localized, and highly heterogeneous characteristics. Existing unimodal methods and global fusion approaches generally suffer from insufficient local feature sensitivity, hindering fine-grained mold severity grading. To address this limitation, we propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB-HSI fusion. This framework employs a dynamic parallel A/B experimental design to construct time-matched proxy labels via weakly supervised learning. A standardized preprocessing pipeline including single-kernel extraction, foreground segmentation, and cross-modal registration is established to resolve RGB-HSI spatial misalignment, ensuring physical-level spatial consistency of multimodal features. The model incorporates a Foreground-Aware Spectral Recalibration (FASR) module to suppress background noise, a Mask-Guided Dilated Cross-modal Local Attention (MDCLA) mechanism to establish fine-grained local mappings between RGB visual phenotypes and hyperspectral responses, and a sample-level adaptive fusion strategy to dynamically weight features by modal reliability, enhancing representation of complex samples across all mold stages. Experiments show that the Mask-Guided Fine-Grained Fusion Network achieves 0.9689 classification accuracy, 0.9698 Macro-F1 score, and 0.0593 Mean Absolute Error (MAE), significantly outperforming state-of-the-art unimodal deep models and global attention fusion baselines. This work provides a proof-of-principle framework for fine-grained non-destructive mold risk assessment in stored wheat.

Why it matches plant phenotyping methodsRGB-HSI融合と弱教師あり学習により、保存小麦粒のカビ状態・重症度を推定する手法が研究の中心であり、植物器官の病害状態を直接評価している。

abstractwe propose a Mask-Guided Fine-Grained Fusion Network, a weakly supervised framework based on local RGB-HSI fusion.

In-Field Assessment of Olive Fruit Quality Using a Low-Cost Multispectral Sensor and ANN Models ↗

Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R2p = 0.86), OCDM (R2p = 0.86), and M (R2p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R2p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring.

Why it matches plant phenotyping methods低コスト multispectral センサーとANNによるオリーブ果実の品質形質推定システムを開発・外部検証しており、植物形質取得法が中心的である。

abstractwe propose a methodology using a custom-made, low-cost multispectral device.

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

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

Improving Nitrogen Use Efficiency in Wheat: Integrating Agronomic, Genomics, and Remote Sensing for Sustainable Production. ↗

Improving nitrogen use efficiency (NUE) in wheat is critical for addressing the dual challenges of global food security and environmental sustainability. Globally, only 42%-47% of applied nitrogen (N) fertilisers taken up by crops, with remainder lost to the environment, driving soil and water pollution, greenhouse gas emissions, and ecological imbalances. This review provides a comprehensive synthesis and integrative framework- integrating agronomic practices, advanced remote sensing and genomic approaches to enhance wheat NUE. We first examine the physiological basis of NUE, emphasising the synergy between photosynthetic carbon assimilation and N metabolism, the critical role of Rubisco in carbon-nitrogen coupling, and the temporal dynamics of N uptake, transport, and remobilisation throughout the wheat growth cycle. The temporal mismatch between source-sink N partitioning during grain filling emerges as a major physiological constraint limiting NUE in modern high-yielding varieties. We then explore transformative advances in remote sensing technologies, highlighting the paradigm shift from traditional vegetation indices to physiological sensing approaches. Through integration of multispectral imaging, LiDAR, thermal infra-red sensing, and solar-induced chlorophyll fluorescence, coupled with three-dimensional radiative transfer models and machine learning algorithms, these technologies enable non-destructive, real-time monitoring of crop N status while overcoming spectral-structural ambiguity and saturation limitations. From a genomic perspective, we synthesise recent progress in quantitative trait loci mapping and genome-wide association studies (GWAS), identifying key genetic loci controlling root architecture, N uptake transporters (NRT/AMT families), and grain filling efficiency. Multi-omics integration-spanning genomics, transcriptomics, and metabolomics-reveals temporal genetic networks distinguishing short-term nitrogen signalling responses from long-term adaptive remodelling, with genes such as TaNAC2-5A, TaNPF6.2, and QMrl-7B emerging as promising targets for molecular breeding. High-throughput phenotyping platforms enable time-series GWAS analysis, capturing developmental dynamics and genotype × environment interactions that traditional approaches miss. Finally, we discuss sustainable N management strategies, including enhanced efficiency fertilisers, precision application technologies, and soil health optimisation. By integrating these multidisciplinary approaches within a Genotype × Environment × Management framework, this review provides a roadmap for developing climate-smart, N-efficient wheat varieties and precision N management systems that simultaneously enhance productivity, reduce environmental footprints, and ensure sustainable agricultural intensification.

Why it matches plant phenotyping methods小麦の窒素状態を非破壊・時系列に測定するリモートセンシングと高スループット表現型解析を、技術的課題や統合手法とともにレビューしており、表現型取得法が実質的に扱われている。

abstractWe then explore transformative advances in remote sensing technologies, highlighting the paradigm shift from traditional vegetation indices to physiological sensing approaches.

Beyond the Visual Spectrum: From RGB-Based Learning to Hyperspectral Intelligence for Plant Disease Detection—Challenges and Opportunities ↗

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security.

Why it matches plant phenotyping methods植物病害の症状・状態をハイパースペクトル画像と深層学習で推定する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。

abstractThis paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026.

Field Diagnosis of Potato Nitrogen Nutrition Using a Bayesian Critical Nitrogen Dilution Curve and Canopy Spectral Sensing ↗

Accurate diagnosis of potato nitrogen status is critical for optimized fertilizer management and sustaining productivity. We used data from nine field experiments (2010-2018) across major potato-producing regions in northern China to develop a regional critical nitrogen dilution curve via a Bayesian hierarchical model. The curve, Nc = 4.179 × DW -0.417 (DW = whole-plant dry matter), provided the basis for calculating the nitrogen nutrition index (NNI), which was related to canopy spectral indices from a GreenSeeker sensor. Relationships between spectral indices and NNI were strongly growth-stage dependent. The tuber initiation-bulking period, approximately 29-70 days after emergence (DAE), represented the effective phenological window, with 29-55 DAE as the primary operational window for quantitative spectral diagnosis. Stage-specific ratio vegetation index (RVI) showed the most consistent association with NNI, whereas pooled whole-season models had low predictive power. The Bayesian framework quantified uncertainty, emphasizing that near-threshold NNI values require cautious interpretation. The resulting regional-average reference supports rapid field diagnosis of potato N status while accounting for cultivar, year, and site variability. These findings provide practical guidance for stage-specific N management and demonstrate the importance of growth-stage-aware spectral assessment in operational decision-making.

Why it matches plant phenotyping methodsジャガイモの窒素栄養状態を対象に、Bayesian窒素希釈曲線とキャノピー分光センシングを開発・評価し、成長段階別の診断性能と不確実性を検証しているため、植物表現型取得法が中心である。

abstractWe used data from nine field experiments (2010-2018) across major potato-producing regions in northern China to develop a regional critical nitrogen dilution curve via a Bayesian hierarchical model.

VNIR Hyperspectral Signatures and Machine Learning for Early Detection and Classification of Barley Diseases. ↗

This study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections. We examined the causal agents of widespread crop diseases, including: loose smut, head blight, fusarium head blight (FHB), stem rust, net blotch, spot blotch, common root rot. Analysing disease-specific spectral characteristics with machine learning (ML) algorithms revealed the most informative spectral ranges: the green region (~520-560 nm), the red chlorophyll absorption zone (~650-680 nm), and the red-edge region (~700 nm). These ranges accurately reflect alterations in the plant's cellular structure and pigment complexes. Spectral data were processed using five ML algorithms. Random Forest (RF) proved to be the most effective for identifying and differentiating barley diseases, achieving an accuracy of up to 90.13% (MCC = 0.86). This superior performance stems from the ensemble method's robustness to noise and its ability to extract critical features from high-dimensional hyperspectral data, particularly when distinguishing diseases with overlapping spectral signatures. Furthermore, this study highlights the potential of integrating UAV-based remote sensing to delineate reference zones, proximal hyperspectral imaging (HSI), and ML for robust plant health monitoring. This combined approach shows significant promise for early disease diagnostics, enabling site-specific treatments, curbing disease progression, and reducing pesticide application. Ultimately, these findings offer practical value for the agro-industrial sector in major grain-producing countries, especially in Central Asia, where agricultural advancement is a strategic priority for sustainable development and food security.

Why it matches plant phenotyping methodsVNIRハイパースペクトル計測と機械学習により、オオムギの病害状態を植物体のスペクトル特徴から検出・分類する方法が研究の中心であり、植物フェノタイピング手法に該当する。

abstractThis study focuses on identifying barley diseases at various stages using the unique spectral signatures of phytopathogen infections.

Evaluation of a hyperspectral imaging data processing pipeline for early rust disease diagnosis in grain crops applied to wheat, rye, and barley phenotyping ↗

Hyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale. Hyperspectral images of healthy plants, obtained under laboratory conditions using a Cubert Ultris 20 camera (450–874 nm range, 106 channels), were utilized. The effectiveness of various preprocessing schemes was compared: full (including normalization, smoothing, calculation of derivatives, and identification of extreme features), reduced, and minimal. Machine learning models were exploited for classification: logistic regression, support vector machine, and gradient boosting, trained on averaged spectra. It is shown that the use of a full pipeline optimized for phytopathological diagnostics leads to reduced classification accuracy in phenotyping tasks. The best results (F1 = 0.97 ± 0.025) were achieved using the original averaged spectral curves without additional transformations. It is concluded that for healthy wheat, barley, and rye phenotyping, absolute reflectance levels are informative, whereas for disease diagnostics, changes in the shape of the spectral curve are more important. The obtained results clarify the applicability limits of pipelines developed for phytosanitary purposes and can inform the development of remote monitoring and phenotyping systems for cereal crops.

Why it matches plant phenotyping methods穀類の健全植物フェノタイピングに対するハイパースペクトル画像処理パイプラインの適用性を比較評価しており、前処理と分類性能の検証が研究の中心である。

abstractHyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale.

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.

Plant stress early detection through a low-cost multispectral device: Toward safer and more sustainable agricultural practices ↗

While multispectral sensors offer a cost-effective and robust solution for monitoring plant responses to environmental stress, their limited spectral resolution, largely dependent on vegetation indices, can hinder accurate classification of stress severity using machine learning. This paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection that is 1) affordable for a wide range of end-users, 2) robust to environmental factors, 3) capable of automatically finding the most meaningful features that maximize the stress detection accuracy, and 4) capable of discriminating different plant stress severity. The device integrates a broadband LED and a VIS-NIR multispectral sensor to early predict plant stress through machine learning algorithms (i.e., SelectKBest, kNN, SVM, and LDA). It was trained on spectral measurements acquired from tobacco plants under salinity stress. The results demonstrated its high capability to discriminate with high accuracy different stress severity (average accuracy of 91.0 ± 3.1%).

Why it matches plant phenotyping methods植物ストレスの重症度を推定する低コスト multispectral デバイスと機械学習手法を開発・評価しており、植物状態の取得・判別が研究の中心である。

abstractThis paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection

Stage-Specific Estimation of Maize Flavonoids Using UAV Multispectral Imagery and Spectral, Texture, and Phenological Features ↗

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R2) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-XII-CNN model, with R2 = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と特徴量・機械学習を用いて、トウモロコシ葉フラボノイド含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中心である。

abstractThis study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages

Deciphering "False Maturity" in Mountain Coffee: A Multimodal Hyperspectral Framework for Non-Destructive Sugar Content Assessment. ↗

In complex mountainous environments, the asynchronous development between external color turning and internal sugar accumulation (often termed "false maturity") in coffee cherries poses a severe challenge to post-harvest quality sorting and the consistency of final coffee products. To overcome the limitations of single-phenotype detection in raw material screening, this study proposed a multimodal quality discrimination framework integrating fruit hyperspectral imaging, micro-topography, and plant physiological characteristics. Taking typical mountain-grown fresh coffee cherries as the research object, and after comparing various spectral preprocessing and feature dimensionality reduction algorithms, the multimodal fusion efficacy of nine machine learning classifiers was systematically evaluated. The results demonstrated that: (1) Full-spectrum difference analysis quantitatively confirmed the limitations of visual harvesting; spectral reflectance differences between high- and low-sugar fruits were highly concentrated in the red and red-edge regions, with the maximum difference precisely located at 676 nm. (2) Compared to the single-spectrum model (mean accuracy of 75.93%), the fully fused Multilayer Perceptron (MLP) network effectively mitigated background noise induced by heterogeneous environments, improving the mean classification accuracy to 77.22% with a mean Area Under the Curve (AUC) of 0.827. (3) Correlation analysis clarified the quantitative association between topography and quality; micro-topographic slope (r = 0.346) was identified as the key environmental driver of spatial differentiation in fruit sugar content, while plant chlorophyll A content (r = 0.183) exhibited a corresponding physiological response trend. This study not only explains the root cause of visual assessment failure from a physical optics perspective but also reveals the spatial variation laws of quality driven by micro-topography, providing preliminary data support for the intelligent sorting of raw materials and ensuring post-harvest quality consistency of mountainous crops.

Why it matches plant phenotyping methodsコーヒー果実の糖含量という植物器官形質を、ハイパースペクトル画像・微地形・生理情報の融合で非破壊推定する方法が研究の中心であり、前処理、特徴削減、複数分類器の性能比較も行っている。

abstracta multimodal quality discrimination framework integrating fruit hyperspectral imaging, micro-topography, and plant physiological characteristics

From light supplementation to spectral analysis: machine learning and hyperspectral reveals plant health status ↗

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

Why it matches plant phenotyping methods植物の健康状態をハイパースペクトルと機械学習で推定する手法が題名の中心であり、植物状態の表現型推定に該当する。

titleFrom light supplementation to spectral analysis: machine learning and hyperspectral reveals plant health status

Research on dynamic monitoring of nitrogen-driven quality changes throughout the entire growth period of cucumbers based on deep learning. ↗

This study proposes a multivariable quality prediction framework for cucumbers based on hyperspectral imaging, addressing the limitations of single-indicator approaches through chemometric analysis. Experiments were conducted under varying nitrogen levels and growth stages, with principal component analysis identifying nitrate, soluble sugar, and soluble solids as core indicators significantly correlated with nitrogen content. Spectral data underwent preprocessing via SG smoothing, MSC, SNV, and their paired combinations. Feature wavelengths were selected using CARS, UVE, and SPA algorithms, followed by comparative modeling with PLSR, SVR, and CNN approaches. Results demonstrated optimal performance for the CNN model utilizing full-spectrum input, achieving calibration set R 2 values exceeding 0.913 for all three indicators. This model enabled visualization of spatial distribution patterns, revealing spatial heterogeneity in cucumber quality under different nitrogen treatments. The method offers systematic rigor and high accuracy, providing a technical foundation for precision nitrogen management and vegetable quality enhancement.

Why it matches plant phenotyping methodsキュウリの品質形質をハイパースペクトル画像から推定・可視化する手法が研究の中心であり、前処理、波長選択、機械学習モデル比較まで技術的に評価している。

abstractThis study proposes a multivariable quality prediction framework for cucumbers based on hyperspectral imaging

Multi-scale analysis of seed dormancy in Lonicera maackii and functional identification of LmABI5 in promoting dormancy. ↗

Lonicera maackii is a valuable medicinal shrub whose propagation is hindered by deep seed dormancy. Research on L. maackii seeds has been limited to dormancy classification and release methods, with little attention given to biochemical indices, systematic omics, or molecular mechanisms. In this study, we showed that seed dormancy in L. maackii can be effectively released through cold stratification treatment. Furthermore, using hyperspectral imaging technology, we established a non-destructive method for identifying the dormancy status of L. maackii seeds. Ultrastructural observations revealed that dormancy release involved lipid droplet degradation and nucleolar enlargement, indicative of activated metabolism. Biochemical indices showed that dormancy-released seeds exhibit enhanced metabolic activity. In addition, target hormones contents indicated a decline in abscisic acid (ABA) and a rise in gibberellic acid (GA) upon dormancy termination in this species. Moreover, transcriptomic analyses demonstrated that differentially expressed genes (DEGs) were primarily enriched in plant hormone signal transduction pathways, among which we identified LmABI5 as a gene markedly induced during dormancy compared to its expression upon dormancy release. Subsequently, subcellular localization analysis revealed that LmABI5 is localized in the nucleus. To further investigate its biological function, we generated and selected LmABI5-overexpressing (LmABI5-OE) transgenic Arabidopsis lines. Germination assays revealed that the seeds of LmABI5-OE plants exhibited significantly stronger dormancy than those of the wild-type (WT). This study deepens our understanding of regulatory network and provides a theoretical foundation for molecular breeding strategies in L. maackii.

Why it matches plant phenotyping methods種子の休眠状態という植物状態を、ハイパースペクトル画像から非破壊的に識別する手法を確立しており、表現型取得法が明示的な技術的貢献である。

abstractusing hyperspectral imaging technology, we established a non-destructive method for identifying the dormancy status of L. maackii seeds.

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.

Unlocking almond breeding for nutritional composition with hyperspectral imaging. ↗

High-throughput phenotyping is boosting plant breeding by generating large-scale phenotypic data for traits that were previously expensive and/or time-consuming to measure. A high-throughput phenotyping platform integrating hyperspectral imaging with a Python workflow has been developed to phenotype nutritional components in almond breeding populations, addressing the current phenotyping bottleneck of conventional methods. Kernel and powder samples from a reference set of 112 almond genotypes were scanned using a hyperspectral camera in the SWIR range (900–1700 nm) and subsequently analysed for nutritional components, including fats, protein, fiber, sucrose, fatty acids, and phytosterols. Partial Least Squares (PLS) models were developed to predict nutritional components in almond kernels, achieving cross-validation RMSE (RMSE CV ) values of 0.73, 1.28, 7.90, and 1.96, and corresponding R 2 CV values of 0.82, 0.86, 0.66, and 0.57 for protein, fats, β-sitosterol, and oleic acid (C18:1), respectively. Selected PLS models were implemented to predict the nutritional components of 528 genotypes from a germplasm collection and six F 1 populations. Narrow-sense heritability for these predicted traits was estimated using an advanced linear mixed model incorporating pedigree and genomic data using the 60K Almond SNP array, revealing relevant additive effects for predicted traits ( ℎ 2 >0.5). The approach employed here represents a major advance in nutritional almond breeding, enabling the phenotyping of six times more individuals than previous studies and generating the largest phenotypic dataset of nutritional components in almonds and other tree nuts.

Why it matches plant phenotyping methodsアーモンド育種集団の栄養成分を、ハイパースペクトル画像とPython/PLS解析で高スループット推定する測定プラットフォームを開発・適用しており、表現型取得・抽出法が中心である。

abstractA high-throughput phenotyping platform integrating hyperspectral imaging with a Python workflow has been developed to phenotype nutritional components in almond breeding populations

Explainable Deep Learning for Intelligent Plant Disease Detection ↗

The world suffers from 10–40% loss in crop yields each year because of plant disease. This threat is serious and growing; it threatens food security, rural livelihoods, and agricultural economies. Advances being made through deep learning, computer vision, and mobile technology have presented a unique opportunity to use leaf images to automatically recognize plant disease. Published classification accuracies on benchmark datasets now exceed 97%, which is an important achievement but achieving high accuracy on a benchmark alone does not indicate that traditional methods will work when deployed in the real world: all four stakeholders (i.e., farmers, agronomists, regulatory authorities, and extension agents) must therefore have the ability to understand, and interpret the output of automatically recognized plant diseases in a way that enhances human expertise rather than replacing it. In this chapter, we provide a compendium of technical deep learning architectures and methods related to Explainable Artificial Intelligence (XAI) for plant disease detection, including convolutional networks, residual architectures, dense architectures, transformer networks, and hybrid models. We also systematically evaluate the explainability methods used in both post-hoc and intrinsic explanation and evaluate the applicability of these methods across a variety of imaging modalities used in agriculture, including RGB, multispectral, and hyperspectral. This chapter characterizes major benchmark datasets; discusses major challenges to their deployment, including class imbalance, domain shift, model size reduction, and human–AI trust calibration; then ends with potential new directions for research in areas such as foundation models (FM), causal interpretable models (Explanations), federated learning, and continual learning to build resilience for each evolving pathogen landscape.

Why it matches plant phenotyping methods植物病害を葉画像から自動認識する画像ベースの表現型推定手法と、その説明可能性・データセット・評価課題を体系的に扱うレビューであり、方法論が中心である。

abstractIn this chapter, we provide a compendium of technical deep learning architectures and methods related to Explainable Artificial Intelligence (XAI) for plant disease detection

A compact multimodal and imaging system for presymptomatic plant stress detection in NASA-controlled space agriculture ↗

A hybrid AI framework combining a spatial–spectral–temporal Transformer and unsupervised clustering was applied to five microgreen species—Pak Choi Cabbage, Tatsoi Mustard, Red Mizuna, Chinese Cabbage, and Arugula—grown for 3–4 weeks under water, nutrient, and combined stresses. Across five datasets collected within six months, the system achieved a Macro-F1 of 0.91 and a pre-symptomatic F1 of 0.88, enabling early detection before visible symptoms. Applications include NASA’s APH, Mars and Moon habitats, and terrestrial precision agriculture.

Why it matches plant phenotyping methods植物の水・養分ストレス状態をマルチモーダル画像から早期推定するAI手法が研究の中心であり、性能評価も示されている。

abstractA hybrid AI framework combining a spatial–spectral–temporal Transformer and unsupervised clustering was applied

Improved Estimation of Leaf Nitrogen Content in Ginkgo Saplings and Trees Using Deep Gaussian Processes Models with Feature Selection Strategies ↗

Leaf nitrogen concentration (LNC) is an important indicator of Ginkgo nutritional status, but its hyperspectral estimation remains challenging because leaf spectra are high dimensional, strongly collinear, and affected by overlapping structural and biochemical signals. This study examined how spectral preprocessing, wavelength selection sequence, and regression model choice influence leaf scale Ginkgo LNC estimation, while separating simulation-assisted model development from measured sample-based prediction assessment. We assembled 717 field measured Ginkgo leaf spectra with corresponding laboratory measured LNC values and used PROSPECT-PRO simulated spectra only for wavelength screening or calibration augmentation, not as independent validation data. Three evaluation schemes were compared: measured-only analysis, simulated spectra-assisted wavelength selection followed by measured data calibration and testing, and simulated spectra-assisted wavelength selection and calibration followed by measured-only testing. The third scheme was used as the main inference framework because it retained an independent measured sample test boundary. Within this framework, multiple preprocessing methods, two wavelength selection sequences, and four regression models (PLSR, GPR, 1D-CNN, and DGP) were evaluated. MSC showed comparatively low error in the preprocessing comparison, and CARS-SPA identified a compact set of informative wavelengths concentrated mainly in the shortwave infrared region. Under the simulation-assisted calibration framework, the combination of MSC preprocessing, CARS-SPA wavelength selection, and DGP regression produced the lowest test error on the measured sample set (R2 = 0.82; RMSE = 2.07 mg g−1). These results indicate that Ginkgo LNC estimation depends on the combined choice of preprocessing method, wavelength selection strategy, and regression model, and provide a methodological reference for simulation-assisted hyperspectral modeling.

Why it matches plant phenotyping methodsイチョウ葉の窒素含量という植物形質を対象に、ハイパースペクトル推定の前処理、波長選択、回帰モデルを比較・検証し、シミュレーション支援ワークフローを評価しているため、フェノタイピング手法が中心である。

abstractThis study examined how spectral preprocessing, wavelength selection sequence, and regression model choice influence leaf scale Ginkgo LNC estimation

Integrating hyperspectral reflectance and machine learning for rapid diagnosis of nutrient deficiencies in greenhouse chrysanthemum leaves. ↗

Under greenhouse production conditions, variability in fertilization management, substrate properties, and microenvironmental factors can disrupt balanced nutrient uptake, often resulting in localized or transient multi-element nutrient imbalances. Hyperspectral sensing provides continuous and high-resolution spectral information for plant nutrient assessment. However, most existing studies focus on single-element deficiencies or simplified scenarios, which limits their applicability to complex nutritional environments encountered in practice. To address this limitation, we designed a series of single- and dual-element deficiency treatments in four cultivars of chrysanthemum (Chrysanthemum morifolium Ramat.), an important cut-flower crop whose ornamental quality is highly influenced by nutrient supply. Sampling was conducted at five key growth stages across three independent experiments, yielding a total of 615 data points. Each treatment included replicates and was confirmed based on characteristic deficiency symptoms. A hyperspectral-based qualitative classification framework was developed to assess nutrient imbalances under controlled greenhouse conditions. Results indicate that although some nutrient deficiencies exhibit similar visual or phenotypic symptoms, their hyperspectral responses are distinguishable, suggesting that hyperspectral data can capture subtle differences associated with distinct nutrient imbalance conditions. To mitigate class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, and multiple classification models were evaluated using cross-validation. The Gradient Boosting Decision Tree (GBDT) classifier combined with SMOTE showed the most consistent performance across nutrient-recognition tasks, achieving cross-validation accuracies from 0.9191 ± 0.0401 to 0.8556 ± 0.0516, balanced accuracies from 0.9595 to 0.8447, F1 from 0.9591 to 0.8496 and testing accuracies from 0.9200 to 0.8269, balanced accuracies from 0.9167 to 0.8269, F1 from 0.9140 to 0.8244. Overall, this study presents a non-destructive hyperspectral framework for classifying multi-element nutrient imbalances and demonstrates its effectiveness under greenhouse conditions, supporting hyperspectral-based nutritional assessment in ornamental crops. Further validation across diverse genotypes, seasons, and environmental conditions is needed to confirm broader applicability and model generalizability.

Why it matches plant phenotyping methodsキク葉の栄養状態という植物状態を、ハイパースペクトル計測と機械学習で非破壊的に分類する枠組みを開発・検証しており、表現型取得・抽出法が研究の中心である。

abstractA hyperspectral-based qualitative classification framework was developed to assess nutrient imbalances under controlled greenhouse conditions.

Comparing Unsupervised and Supervised Classifiers on Multispectral UAV Data to Detect Crop Water-Nitrogen Co-Limitation ↗

The high spatio-temporal resolution of UAV sensors requires robust analytical tools to classify subtle agroecosystem variations. This study compared unsupervised, supervised machine learning (ML), and deep learning (U‑Net) classifiers to identify nitrogen (N) and water (I) status, and their interaction (N×I) in potato crops using UAV multispectral imagery. The U‑Net model outperformed all other methods, achieving accuracies of 85% (N), 93% (I), and 70% (N×I). Supervised ML classifiers also performed well and Support Vector Machine achieved 71, 62, and 40% respectively, whereas Random Forest achieved 67, 61, and 40%. The unsupervised K‑means classifier yielded the lowest accuracies (41, 36, and 23%), demonstrating the necessity of substantial supervision to delineate crop N and water properties. These results were confirmed by repeated analysis on UAV imagery acquired later in the season. Deep learning classifiers should be adopted more widely in precision agriculture, as they offer new potential for optimizing N and irrigation co-management under field conditions with subtle spatial variation that is otherwise difficult to capture. Future research should test alternative deep learning algorithms and sensor data fusion to further improve classification accuracies.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からジャガイモの窒素・水分状態を推定し、複数の分類器を比較・反復検証しており、植物状態の取得・推定手法が研究の中心である。

abstractThis study compared unsupervised, supervised machine learning (ML), and deep learning (U‑Net) classifiers to identify nitrogen (N) and water (I) status, and their interaction (N×I) in potato crops using UAV multispectral imagery.

Application of hyperspectral reflectance for early detection of dry root rot and fusarium wilt in chickpea (Cicer arietinum L.) ↗

Abstract Early detection of soil-borne fungal diseases is essential for sustaining chickpea ( Cicer arietinum L.) productivity. This study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes. Spectral data were collected at regular intervals from 1 to 76 days after sowing (DAS) and used to derive vegetation indices including NDVI, NDWI, PRI, and DSWI. Visual symptoms appeared at 46 DAS (DRR), 42 DAS (wilt), and 43 DAS (combined stress), whereas spectral indices indicated stress-related changes earlier, typically between 36 and 40 DAS. NDVI reflected early reductions in canopy vigor, PRI captured changes in photosynthetic activity, and NDWI and DSWI indicated alterations in plant water status, with DSWI showing comparatively consistent early sensitivity. Resistant genotypes maintained relatively stable NIR reflectance and water-sensitive spectral responses, while susceptible genotypes exhibited reduced NIR reflectance and increased SWIR absorption. Significant differences (p

Why it matches plant phenotyping methodsハイパースペクトル反射測定とスペクトル指標を用いて、植物体の病害ストレスを症状発現前に推定する方法を評価しており、表現型取得・抽出が研究の中心である。

abstractThis study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes.

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

Characteristic wavelength selection for rice blast based on hyperspectral remote sensing and deep convolutional neural networks ↗

Background Hyperspectral remote sensing technology is one of the key technical methods for detecting rice blast in the field, but existing hyperspectral dimensionality reduction methods still suffer from information redundancy and insufficient feature interpretability. This study aimed to develop a feature wavelength selection method integrating deep learning and model attribution analysis to extract key spectral features across different disease severity levels. Results A residual network model Dilated Convolution and Deformable Convolution-Residual Network (DCR-ResNet) combining dilated convolution and deformable convolution was constructed to deeply mine spectral features across varying disease severities. Meanwhile, the Integrated Gradient (IG) and Gradient-weighted Class Activation Mapping (Grad-CAM) methods were combined to enable the selection of spectral wavelengths. The effectiveness of the proposed method was validated using statistical analysis (transformed divergence, within-class scatter) and modeling analysis. Findings reveal that the spectral feature wavelengths identified by DCR-ResNet in conjunction with the IG-GradCAM approach exhibit excellent inter-class separability and intra-class compactness. Furthermore, when benchmarked against conventional dimensionality reduction techniques such as Successive Projections Algorithm, Random Frog, and Competitive Adaptive Reweighted Sampling, the Support Vector Machine, Extreme Learning Machine, and Random Forest models developed using IG-GradCAM-selected feature wavelengths demonstrate superior classification performance. The overall accuracy reaches 85.9%, 85.5% and 86.2%, with kappa values of 81.3%, 80.6% and 81.6%, respectively. Conclusion The feature wavelength selection method combining DCR-ResNet with IG-GradCAM not only improves the accuracy of hyperspectral feature extraction but also provides an efficient and feasible approach for the precise identification of rice blast. © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methodsイネいもち病という植物の病害状態を対象に、ハイパースペクトル特徴波長の選択手法を開発し、複数手法との比較検証を行っており、フェノタイピング手法が研究の中心である。

abstractThis study aimed to develop a feature wavelength selection method integrating deep learning and model attribution analysis to extract key spectral features across different disease severity levels.

Individual plant-level (IPL) soybean biomass estimation and spatial mapping through UAV multisource feature-driven machine learning framework ↗

Abstract Rapid and accurate quantification of crop biomass using multisource UAV imagery–derived features, such as plant height, vegetation indices, and texture indices demonstrates strong potential for soybean high-throughput phenotyping. The indeterminate growth habit of soybean, alongside extensive nodulation and intense inter-plant competition, necessitates individual plant level (IPL) monitoring to quantify plant-specific nitrogen fixation and competitive vigor. However, most studies aggregate measurements at multi-plants at the plot level, thereby masking these soybean-specific traits. This study aims to develop and evaluate a UAV imagery-based framework for estimating soybean biomass at the IPL, with the objective of characterizing high-resolution spatial variability and supporting high-throughput phenotyping. Regions of interest (ROIs) for IPL data acquisition were defined as rectangular plots based on planting density and were generated from early-stage imagery before canopy overlap occurred. Using these ROIs, structural information (SIs) including plant height (PH) and vegetation fraction (VF)), vegetation indices (VIs), and texture indices (TIs) were derived for each individual plant from RGB and multispectral imagery and organized into sensor-specific feature groups. Recursive feature elimination was applied to select optimal features, which were then used as inputs for machine learning architectures including support vector regression (SVR) with a linear kernel, Random Forest (RF), and XGBoost (XGB). Among them, the SVR model using fused multisource features (SIs + VIs + Tis) achieved the best performance, with R² = 0.88, RMSE = 55.34 g, and rRMSE = 8.50% on an independent test dataset. The results show that: (1) tree-based models, including XGB and RF may suffer from overfitting due to limited sample size and feature redundancy, whereas linear SVR showed better generalization; (2) fusing RGB and multispectral features consistently improved biomass estimation accuracy. Inparticular, near-infrared and red-edge-based indices such as RECI and NDRE, along with VF and PH, were identified as important predictors, while texture indices were not selected as significant features; and (3) the proposed framework enabled spatially explicit IPL biomass mapping and time-series analysis, revealing variability in growth conditions and distinct growth trajectories. The framework provides a reliable solution for IPL soybean biomass estimation with practical potential for UAV-based agricultural decision-making.

Why it matches plant phenotyping methodsUAV画像由来の特徴量と機械学習により個体レベルのダイズ biomass を推定・評価する枠組みが研究の中心であり、植物表現型の取得・抽出手法に該当する。

abstractThis study aims to develop and evaluate a UAV imagery-based framework for estimating soybean biomass at the IPL

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.

Accurate detection of full-surface ear rot in maize using hyperspectral imaging and deep learning. ↗

Maize ear rot severely restricts maize yield and quality, making the breeding of disease-resistant varieties the core strategy for disease prevention and control. Due to the highly uneven spatial distribution of lesions on maize ears, precise full-surface detection is essential for objectively quantifying disease severity. However, traditional manual disease grading is highly subjective, and conventional RGB-based detection methods struggle to precisely identify lesion regions associated with maize ear rot. These limitations hinder the precise identification and quantitative analysis of maize ear rot infection regions, thereby limiting the reliability of phenotypic data used for resistance evaluation and subsequent genome-wide association studies (GWAS). To address these challenges, this study developed an integrated full-surface hyperspectral imaging system featuring line-scan imaging and synchronous rotation control. Non-redundant full-surface ear images were then generated using the oriented FAST and rotated BRIEF (ORB) algorithm combined with random sample consensus (RANSAC), hereafter referred to as ORB-RANSAC. Furthermore, after Savitzky-Golay (SG) preprocessing and feature selection using a genetic algorithm (GA), three machine learning models and three deep learning models were established, and their classification performance was compared. The results showed that the convolutional neural network-bidirectional long short-term memory network (CNN-Bi-LSTM) model achieved the best average performance, with an average overall accuracy (OA) of 95.61 ± 0.36%. It also achieved higher overall accuracy than traditional machine learning models such as random forest (RF), indicating that CNN-Bi-LSTM can achieve high-precision pixel-level detection of lesion regions showing Fusarium-associated maize ear rot symptoms. Additionally, this model was deployed in locally developed automatic analysis software, enabling an integrated analysis workflow from raw hyperspectral data input to the quantification of disease-related phenotypic parameters. This study not only fills the technical gap in the non-destructive full-surface detection of maize ear rot but also provides an efficient and reliable automated tool for high-throughput phenomics research, which holds great significance for accelerating the discovery of maize resistance genes and ensuring food security.

Why it matches plant phenotyping methodsトウモロコシ穂の病斑をハイパースペクトル画像と深層学習で定量し、全表面撮像システム、解析モデル、ソフトウェアを開発したため、植物表現型取得法が中心である。

abstractthis study developed an integrated full-surface hyperspectral imaging system featuring line-scan imaging and synchronous rotation control.

Supplemental figures for "Integrating Machine Learning and Remote Sensing to Determine Crop Nitrogen Content of Maize" ↗

This study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery. Several models were evaluated, with Random Forest demonstrating the best performance. The study emphasizes that combining vegetation indices enhances prediction accuracy more than using individual spectral bands. Results show reliable CNC estimation across growth stages, although predictions at early stages are less precise due to low canopy cover and soil interference. The approach allows for real-time, field-to-regional-scale nitrogen monitoring, supporting improved fertilizer management and precision agriculture decision-making.

Why it matches plant phenotyping methodsUAV・衛星画像からトウモロコシの窒素含量を推定する機械学習・リモートセンシング手法の開発とモデル比較が中心であり、植物形質の取得方法に該当する。

abstractThis study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery.

Efficient hyperspectral band selection via occlusion-based neural network ranking for detecting fruit-bruise severity ↗

Hyperspectral imaging (HSI) provides rich spectral information across hundreds of narrow bands, making it a powerful tool for material classification. However, processing all available bands is computationally expensive and often impractical for near real-time applications. In this work, an occlusion-based band-selection method— developed earlier by the authors—is applied to a multiclass classification task to identify the most informative spectral bands for a target task while substantially reducing data dimensionality. For a given application, realistic spectral variations are first simulated through data augmentation under changing intensity and noise conditions. The augmented spectra are then used to train an artificial neural network (ANN) with the full spectral input. Band importance is subsequently evaluated by systematically occluding individual spectral bands and measuring the resulting degradation in classification performance, thereby forming a reduced candidate pool. A computationally manageable exhaustive search is then performed within this pool to identify a smaller subset of bands. As a case study, the method is applied to Honeycrisp apple bruise-severity classification using spectra in the 900–1700 nm range with 336 bands. The full-band ANN achieves 97.7% classification accuracy, while the occlusion-based 16-band and 5-band subsets achieve 89.8% and 80.7%, respectively. Under the same subset sizes, PCA-based selection achieves 84.1% and 74.4%. These results indicate that the proposed method preserves task-relevant spectral information more effectively than the PCA-based baseline, while substantial band reduction can shorten acquisition time, lower computational cost, and support on-device or edge deployment in resource-constrained platforms such as smart cameras.

Why it matches plant phenotyping methodsハイパースペクトル画像からリンゴ果実の bruise severity を推定するためのバンド選択法を中心的に適用・評価しており、植物器官の状態を定量化するフェノタイピング手法に該当する。

abstractan occlusion-based band-selection method— developed earlier by the authors—is applied to a multiclass classification task to identify the most informative spectral bands for a target task while substantially reducing data dimensionality.

Supplemental figures for "Integrating Machine Learning and Remote Sensing to Determine Crop Nitrogen Content of Maize" ↗

This study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery. Several models were evaluated, with Random Forest demonstrating the best performance. The study emphasizes that combining vegetation indices enhances prediction accuracy more than using individual spectral bands. Results show reliable CNC estimation across growth stages, although predictions at early stages are less precise due to low canopy cover and soil interference. The approach allows for real-time, field-to-regional-scale nitrogen monitoring, supporting improved fertilizer management and precision agriculture decision-making.

Why it matches plant phenotyping methodsUAV・衛星画像と機械学習により、トウモロコシの作物窒素含量という植物形質を推定し、モデル性能を比較・評価しているため、リモートセンシング型フェノタイピング手法の適用が中心です。

abstractThis study developed machine learning models to predict maize crop nitrogen content (CNC) using vegetation indices derived from UAV and satellite imagery.

Using UAV Multispectral Imagery to Predict Leaf SPAD Dynamics During Maize Growth Under Different Plant Densities ↗

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD method, though easy to operate, is inefficient, destructive, and unsuitable for high throughput field monitoring. Unmanned Aerial Vehicle (UAV) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and 8 texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the full growth period. The correlations between SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms i.e. Random Forest (RF), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R²) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during entire maize growth period based on UAV data.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトウモロコシ葉のSPAD／クロロフィル含量を推定する指標体系と機械学習モデルを構築・比較しており、表現型取得手法が研究の中心である。

abstractThis study established a non-destructive monitoring framework for chlorophyll content during entire maize growth period based on UAV data.

Variational autoencoder enables unsupervised leaf diagnosis via hyperspectral imaging ↗

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

Why it matches plant phenotyping methods葉のハイパースペクトル画像から診断を行う手法開発がタイトルで明示されており、植物状態の画像ベース推定が中心と判断できる。

titleVariational autoencoder enables unsupervised leaf diagnosis via hyperspectral imaging

Interpretable CNN-Transformer Multimodal Hierarchical Fusion Network in Multivariate Calibration. ↗

This study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer. The approach enhanced model performance by fusing spectral features with some auxiliary factors of the samples, such as the locality of growth (region), type of produce (cultivar), and sample temperature (temp). Spectral data were extracted using one-dimensional CNN to capture local spectral features, while auxiliary factors underwent sine-cosine or label encoding before being embedded into the same feature space as spectral data via a fully connected network. Ultimately, a transformer was employed to achieve global interaction and fusion between spectral features and auxiliary factors rather than merely concatenating different feature types. The fusion strategy was validated using the ultraviolet (UV)-visible (vis)-near-infrared (NIR) spectra of mango and tobacco data sets. Compared to single-modal models using spectra only, the multimodal model using spectra coupled with the auxiliary factors achieved improved prediction performance on both validation and test sets for the mango dry matter content (DMC). The RMSE decreased from 0.984 and 1.03 to 0.577 and 0.613, respectively. These results outperformed those of the other 11 machine learning models. SHAP analysis revealed that the CNN-transformer framework successfully captured the underlying relationships between auxiliary factors (region, temp, and cultivar) and spectral features near 960 nm (due to the O-H absorption signal) with DMC, with the former contributing more significantly to the model than the latter. Similar observations were obtained in the tobacco data set. The results demonstrated the advantages of the CNN-transformer multimodal model in overcoming the limitations of single-modal information, providing novel technical support for quantitative analysis.

Why it matches plant phenotyping methodsCNN-Transformerによるスペクトルと補助情報の融合モデルを開発・検証し、マンゴーの乾物含量という植物器官の形質を定量推定しているため、方法が中心的である。

abstractThis study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer.

Latent Salinity Stress Detection in Opuntia ficus-indica Using Hyperspectral Imaging and a 3D-CNN Framework ↗

Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m−1. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m−1). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments.

Why it matches plant phenotyping methods若いウチワサボテンの塩ストレスを対象に、ハイパースペクトル画像と3D-CNNによる非破壊フェノタイピング手法を開発・検証しており、表現型取得・推定が研究の中心です。

abstractThe objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes.

Advances in Artificial Intelligence-Enabled Crop Pest and Disease Detection: A Systematic Review ↗

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection.

Why it matches plant phenotyping methods作物病害の画像・スペクトル観測による植物の病徴・病害状態推定を中心に、検出技術と計算手法を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。

abstractThis paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases.

Non-destructive Spatial Reconstruction of Plant Leaf Starch Using Reduced-Band SWIR Spectroscopy and Chemometric Modeling ↗

1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.

Why it matches plant phenotyping methods植物葉のデンプン状態を非破壊推定・空間再構成するSWIR画像計測とケモメトリック解析ワークフローを開発・検証しており、フェノタイピング手法が中心です。

abstractHere, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves.

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.

Physiology-informed LSTM framework integrating crop model and Sentinel-2 time series for rice nitrogen status estimation. ↗

Accurate assessment of plant dry matter (PDM) and plant N accumulation (PNA) provides essential indicators for precision nitrogen (N) management in rice production. However, purely data-driven models struggle to generalize due to the spatial scarcity of ground-truth physiological data. To address this, a physiology-informed long short-term memory (PI-LSTM) framework was developed for robust regional N diagnosis and variable-rate fertilization. First, the model was pretrained to internalize crop growth dynamics using a DSSAT-based simulation library, which spanned 2000 representative fields and 700 management scenarios to provide physiologically consistent pseudo-labels. Subsequently, the framework was fine-tuned using multi-year field observations (2020, 2023, 2024), Sentinel-2 time-series data, and meteorological inputs. The proposed LSTM framework outperformed conventional machine learning approaches in estimating PDM and PNA, achieving five-fold cross-validation R 2 values of 0.87 and 0.83, respectively. Based on these biophysical estimations, the N nutrition index (NNI) diagnosis achieved a 67.3% overall classification accuracy. Furthermore, by integrating the critical N dilution curve, the critical PNA and accumulated N deficiency (AND) were quantified, which served as the basis for developing the AND-based N recommendation algorithm (ANDA). Finally, variable-rate topdressing field experiments conducted across seven sites in 2024 and 2025 demonstrated that the ANDA reduced N input by 13.4% compared with farmers' practices, while maintaining or increasing yield and improving N partial factor productivity by 18.6%. This study provides a reliable, physically consistent decision-support framework for regional-scale precision N management.

Why it matches plant phenotyping methodsSentinel-2時系列とLSTMにより、イネの乾物量および窒素蓄積量という植物形質を推定する方法の開発・検証が研究の中心であり、施肥管理への応用も技術評価として記述されている。

abstracta physiology-informed long short-term memory (PI-LSTM) framework was developed for robust regional N diagnosis and variable-rate fertilization.

Research on potatoes defect classification based on hyperspectral imaging and convolutional neural networks ↗

Abstract Potato quality detection is a critical step that determines their market value. However, manual sorting suffers from low efficiency, high cost, and a high misjudgment rate. Therefore, the rapid and accurate classification of defective potatoes is of great economic significance for reducing industrial losses. In this study, a lightweight convolutional neural network (WavebandCNN) was constructed combined with hyperspectral imaging (HSI) technology to achieve rapid and accurate classification of four categories of potatoes: healthy, greening, skin damage, and dry rot. First, hyperspectral images of 400 potato samples were collected and calibrated, and regions of interest (ROI) were extracted to construct a spectral dataset. The performance of the lightweight convolutional neural network was evaluated using raw spectra and five preprocessed spectra, respectively, and compared with three traditional machine learning models: Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Meanwhile, the successive projections algorithm (SPA) was employed to select characteristic wavelengths for data dimensionality reduction. The results show that WavebandCNN achieved the highest classification accuracy of 93.11% with raw spectra, significantly outperforming all comparative models. After screening 20 characteristic wavelengths via SPA, the classification accuracy was improved to 95.98%, while the training time and data redundancy were greatly reduced. This study confirms that the combination of hyperspectral imaging technology and the WavebandCNN model enables accurate identification of potato defects, providing a new approach for the online detection and practical application of potato defects.

Why it matches plant phenotyping methodsジャガイモの欠損・病変状態をハイパースペクトル画像とCNNで直接分類する手法を構築・比較・評価しており、植物状態の取得・推定が研究の中心である。

abstracta lightweight convolutional neural network (WavebandCNN) was constructed combined with hyperspectral imaging (HSI) technology to achieve rapid and accurate classification of four categories of potatoes: healthy, greening, skin damage, and dry rot.

Recent trends in crop water stress monitoring using remote sensing technologies: A review ↗

Unmanned aerial vehicle (UAV) based remote sensing has emerged as a disruptive technology for detecting crop water stress (CWS) in real time, precisely and at low cost offering significant advancements over conventional approaches. The study examined the red green blue (RGB), multispectral (MSP), hyperspectral (HSP), thermal image sensors integrated with UAVs, which offers a high-spatial and temporal resolution of physiological indicators such as chlorophyll content and canopy cover, canopy temperature, stomatal conductance. The study highlights that in spring maize, random forest (RF) models using UAV-derived MSP and thermal indices with leaf area index (LAI) performed well (R² > 0.575, root mean square error (RMSE)

Why it matches plant phenotyping methodsUAV搭載センサーによる作物の水ストレスや生理形質のモニタリング技術をレビューしており、表現型取得法が中心である。

titleRecent trends in crop water stress monitoring using remote sensing technologies: A review

Developing a sinusoidal-polynomial fitting model for deriving the structural and biochemical circadian rhythms for different parts of Birch from hyperspectral LiDAR data. ↗

A deeper understanding of circadian rhythms in plants, especially trees, is crucial for uncovering how structural and physiological processes align with daily environmental cycles. However, most studies analyze biochemical changes and positional variations separately, with limited exploration of their coordination within the whole-plant system. Hyperspectral light detection and ranging (HSL) integrates three-dimensional (3D) structural mapping with hyperspectral reflectance, enabling non-destructive assessment of plant biochemistry. Previous work showed that HSL can detect nocturnal vertical canopy displacements with centimeter accuracy, but organ-level rhythmic patterns (e.g., branches vs. leaves) remain poorly studied. Here, we developed a hyperspectral point cloud classification method combining spectral and spatial data to separate branches from leaves and analyze their sleep movements independently. A novel sinusoidal-polynomial fitting model was then proposed to characterize circadian rhythms in both sleep movements and reflectance variations. We applied HSL data from a single birch tree (Betula pendula) collected at 30 distinct HSL measurement times over 24 h to develop a 3D canopy partitioning approach that divides the canopy into nine grids (3 × 3) and ten vertical layers per grid. Results revealed near-24-hour rhythmic patterns (max R² = 0.5841, P < 0.05) and stratified sleep movements: branches exhibited larger amplitudes than the corresponding canopy layers, with the overall maximum movement amplitude occurring shortly before sunrise (04:00-06:30) and recovering after sunrise. The model also effectively characterized diurnal reflectance variations (max R² = 0.5812, P < 0.05). In addition, chlorophyll-related spectral indices exhibited a sinusoidal variation, reaching a minimum around 03:00. These findings highlight the potential of HSL for the joint analysis of structural and biochemical circadian rhythms, providing a non-destructive approach to investigate plant rhythmicity in both structural and physiological domains.

Why it matches plant phenotyping methods hyperspectral LiDARによる植物の構造・生理形質の取得と、葉・枝の分類および概日リズム推定モデルの開発が研究の中心である。

abstractHere, we developed a hyperspectral point cloud classification method combining spectral and spatial data to separate branches from leaves and analyze their sleep movements independently.

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).

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

High-Precision Instance Segmentation of Tree Saplings by Multimodal Mask R-CNN Integrating RGB and Multispectral Image-Derived Indices Through a Field Phenotyping Platform ↗

The high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture. However, sapling segmentation remains challenging because of blurred boundaries, object adhesion, missed detections, and inaccurate mask delineation in field environments. To improve sapling segmentation performance and address these challenges, this study proposes a multimodal Mask R-CNN framework in which RGB imagery was paired with one multispectral-derived vegetation index at a time to construct separate RGB-VI input combinations, taking ginkgo saplings as a representative case. A dataset of 400 saplings was constructed using a high-throughput field phenotyping platform. The backbone network was extended with an independent vegetation index branch, and three fusion strategies (early, multi-step, and late fusion) were designed within a feature pyramid network to enable multi-scale multimodal feature integration. The results showed that all multimodal models outperformed unimodal baselines in terms of segmentation accuracy and recall. Among them, the multi-step fusion strategy achieved the best performance, while the RGB-EVI multi-step fusion model achieved the highest strict-matching precision (AP@75 = 87.7%) and recall (71.3%), with superior performance in dense sapling delineation and background suppression. These findings indicate that multimodal feature fusion can effectively improve sapling instance segmentation and provide methodological support for high-throughput plant phenotyping.

Why it matches plant phenotyping methodsマルチモーダル画像による樹木苗個体のインスタンスセグメンテーション手法を開発・比較し、高スループット表現型解析を支援することが中心である。

abstractThe high-precision instance segmentation of tree saplings is a fundamental prerequisite for the high-throughput phenotypic analysis of individual seedlings in intelligent tree breeding and precision silviculture.

Data Fusion of Sentinel-2 Spectral and Meteorological Data for Field-Scale Sugarcane Biomass Prediction in Humid Tropical Mexico Using Machine Learning ↗

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R2 values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce.

Why it matches plant phenotyping methodsSentinel-2と気象データの融合および機械学習により、サトウキビの茎・葉・総バイオマス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中心である。

abstractThis study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions.

Multi-source data combination for plant functional diversity estimation: an ecological and remote sensing integrative approach ↗

In this work, we estimate plant functional diversity of grass plots using high spatial resolution hyperspectral imagery. Results yielded negative correlations, independently of the image filters applied to remove non-vegetative elements and the selection of functional traits. Simulations performed with the Biodiversity Observing System Simulation Experiment (BOSSE) suggest that these results arise from the combination of different methodologies to estimate functional diversity: moving windows for the imagery and species’ averages for the plant functional traits measured in the field. We show that simulations are a valuable tool to understand experimental results in the field of remote sensing of plant functional diversity.

Why it matches plant phenotyping methods高空間分解能ハイパースペクトル画像を用いて草地プロットの植物機能多様性を推定し、画像処理条件とシミュレーションによる推定手法の評価を行っているため、植物形質取得・推定が中心です。

abstractwe estimate plant functional diversity of grass plots using high spatial resolution hyperspectral imagery.

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

Optimized modular transfer learning framework integrating PROSAIL and UAV-based hyperspectral reconstruction for cotton canopy water and nitrogen content retrieval ↗

Optimizing water and fertilizer management is crucial for improving cotton yield and quality. However, reliable and generalizable models for quickly and accurately estimating cotton canopy leaves water and nutritional status at a low cost throughout the entire growth stage are scarce. Therefore, this study aims to construct the generalization and adaptation retrieval model of cotton canopy leaf nitrogen content (LNC) and equivalent water thickness (EWT) based on PROSAIL, hyperspectral reconstruction with UAV multispectral imagery and module transfer learning. In the hyperspectral reconstruction module, the new hyperspectral reconstruction model (swinT-HSCNN) based on multispectral showing superior performance in reducing pixel-scale systematic errors and effectively captured spectral variations than HSCNN+ and MST++ model. In the PROSAIL module, the proposed Original-E2DCOS method demonstrated greater sensitivity to spectral response characteristics, especially for parameters and bands with low correlation values, and three bands (702 nm, 762 nm, and 938 nm) were selected as the sensitive bands corresponding to chlorophyll content (Cab) and equivalent water thickness (Cw) of cotton. The improved PROSAIL with hyperparameter optimization based on full spectrum and multispectral band shown better fitting performance than the model based on sensitive bands, and achieved high accuracy on simulated data, with R 2 values exceeding 0.98 for both Cab and Cw. Moreover, the new developed modular transfer learning retrieval model of cotton canopy water and nitrogen content through PROSAIL model and hyperspectral reconstruction with UAV multispectral imagery achieved good inversion accuracy with R 2 of 0.83, 0.85, RMSE of 0.0048, 0.0052, for LNC and EWT, respectively after verifying with actual experiment data. In summary, the proposed modular transfer learning retrieval model of cotton canopy water and nitrogen content integrates physical constraints into retrieval models, which enhancing their accuracy and generalization capability, and providing valuable technical support for precision agriculture in cotton production across different regions. • A modular transfer learning model integrates PROSAIL and hyperspectral reconstruction. • Achieves high accuracy for LNC and EWT estimation across diverse environments. • Combines UAV multispectral data with physical constraints for crop monitoring. • Reduces reliance on expensive hyperspectral sensors, ensuring cost-effectiveness. • Validated on multi-regional datasets, demonstrating scalability and generalizability.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像とPROSAIL、ハイパースペクトル再構成、転移学習を統合し、ワタの窒素・水分状態という植物形質を推定する手法を開発・実データで検証しており、フェノタイピング手法が中心である。

abstractTherefore, this study aims to construct the generalization and adaptation retrieval model of cotton canopy leaf nitrogen content (LNC) and equivalent water thickness (EWT) based on PROSAIL, hyperspectral reconstruction with UAV multispectral imagery and module transfer learning.

VE-MLM: A variable endmember-based multilinear mixing framework for crop FAPAR estimation using UAV multispectral imagery ↗

The fraction of absorbed photosynthetically active radiation (FAPAR) is critical for characterizing crop photosynthetic capacity and growth status. Remote sensing technology based on unmanned aerial vehicles (UAVs) enables efficient estimation of FAPAR, but multiple scattering and transmission in the complex and dynamically changing crop canopy and background limit the accuracy of vegetation index (VI)-based methods. This study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios, comprising three modules: (1) Variable Endmember Extraction , building a spectral library of foreground (crop) and background endmembers, by extracting pure pixels on the R-NIR feature space and reducing redundancy using k-means and iterative endmember selection algorithm; (2) Iterative Unmixing , iterating over foreground-background endmember combinations as input of the multilinear mixing model (MLM) pixel by pixel; (3) Optimal Selection , selecting the optimal combination according to RMSE and outputting corresponding canopy abundance A f . Taking sorghum and rice as study objects, this study collected UAV multispectral images and field-measured FAPAR at multiple periods to validate the advantages of VE-MLM. The results demonstrated that compared to fixed-endmembers and linear/bilinear mixing models, VE-MLM always achieved excellent unmixing performance, effectively quantifying canopy contributions. The derived A f mitigated the saturation and background interference that commonly existed in VI-based regression models and exhibited a higher correlation with FAPAR (sorghum: R 2 = 0.900, rRMSE = 7.753%; rice: R 2 = 0.807, rRMSE = 2.200%). In conclusion, VE-MLM has a great potential to address spectral variability, dynamic changes, and scene complexity in crop growth scenarios, providing a more accurate and generalizable approach for sorghum and rice FAPAR estimation in precision agriculture.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物キャノピーのFAPARを推定するスペクトルアンミキシング手法を開発し、ソルガムとイネで実測値により検証しており、植物表現型取得が中心である。

abstractThis study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios

Trait-Dependent Effects of Band Selection on Predicting Soybean Biomass, Leaf Area Index, and Canopy Cover from Hyperspectral Reflectance ↗

Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans across multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach), and model performance was assessed using independent validation datasets. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS provided the highest accuracy for AGB, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and provide a basis for efficient band design in crop phenotyping.

Why it matches plant phenotyping methodsハイパースペクトル反射を用いた作物形質推定について、バンド選択アルゴリズムと回帰モデルを比較・独立検証しており、フェノタイピング手法の技術評価が中心である。

abstractPredicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency.

Hyperspectral imaging meets 3D Gaussian Splatting: A novel approach beyond 3D plant morphology ↗

Accurate acquisition of plant phenotypes is crucial for elucidating plant growth and development, underlying genetic mechanisms, and responses to environmental stimuli. Traditional three-dimensional (3D) phenotyping mainly captures geometric traits such as height, leaf area, and canopy volume, while overlooking physiological and biochemical information. Here, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology. High-quality plant point clouds were first reconstructed using PlantGaussian, and hyperspectral images(HSI) were mapped onto them to produce hyperspectral point clouds. In potted soybean experiments, we built predictive models linking hyperspectral reflectance to SPAD (chlorophyll content) and EWT (equivalent water thickness), and visualized their 3D distributions. The hyperspectral point clouds achieved strong predictive performance for SPAD ( R 2 = 0.78, RMSE = 2.05) and EWT ( R 2 = 0.80, RMSE = 1.07), thereby validating the approach. It further revealed clear vertical stratification within the canopy, highlighting significant spatial heterogeneity of SPAD and EWT in individual plants. Temporal monitoring from August 6 to 21, 2025, captured a sharp increase in EWT after heavy rainfall on August 11. Overall, our results demonstrate that hyperspectral point clouds enable accurate, non-destructive trait estimation and provide a powerful tool for exploring plant function, monitoring stress responses, and advancing precision agriculture.

Why it matches plant phenotyping methods植物の3D形態とハイパースペクトル情報を統合してSPAD・EWTを推定する手法を開発し、予測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。

abstractHere, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology.

Synthesizing crop modelling and deep learning for remote estimation of wheat biomass dynamics from multispectral and weather observations ↗

Improving crop productivity while maintaining low environmental impact is essential for sustainable food production under increasing population pressure and irreversible climate changes. Dynamic biomass prediction is critical for effective crop growth monitoring and management, yet existing approaches struggle to provide consistent and reasonable predictions across diverse environments in a rapid, economic, and practical manner. Here, we propose SpecWeaNet, a biophysics-informed neural network framework that integrates explicit biophysical principles governing biomass accumulation with implicit mechanisms learned from representative training data. The framework enables dynamic prediction of wheat biomass from sowing to harvest using daily weather data and limited in-season spectral observations, without requiring model recalibration. From a systematic perspective, SpecWeaNet is designed as a flexible framework, from which we further developed three ready-to-use pre-trained variants with different input configurations tailored to commonly used sensors. Our comprehensive evaluation demonstrates the robustness and generalizability of pre-trained models for seasonal prediction of biomass dynamics from non-daily spectral observations (with random interval between two consecutive observations) and daily weather data, with coefficient of determination (R 2 ) higher than 0.99, relative mean absolute error (RMAE) within 26% and relative root mean square error (RRMSE) within 35% on more than 250,000 in-silico simulation scenarios across diverse environmental conditions, including different years, geographical locations, and crop varieties. Furthermore, validation on multiple field experiments showcases the capability of pre-trained models to provide reliable predictions at both trial (R 2 = 0.77–0.88, RMAE = 20–26%, RRMSE = 29–40%) and plot (R 2 = 0.83–0.93, RMAE = 12–19%, RRMSE = 15–26%) scales, utilizing daily weather observations and available satellite or drone-based imagery. This work demonstrates how integrating crop modelling with artificial intelligence can enable scalable estimation of crop biomass dynamics, advancing remote sensing–based crop phenotyping and monitoring for sustainable agricultural systems.

Why it matches plant phenotyping methodsSpecWeaNetは気象・スペクトル観測からコムギのバイオマス動態を推定する計算フェノタイピング手法であり、モデル開発、シミュレーション評価、複数圃場での検証が中心である。

abstractwe propose SpecWeaNet, a biophysics-informed neural network framework that integrates explicit biophysical principles governing biomass accumulation with implicit mechanisms learned from representative training data.

An early detection model of wheat stripe rust utilizing two-dimensional correlation spectroscopy for eliminate growth-related interference. ↗

Wheat stripe rust, caused by Puccinia striiformis f. sp. Tritici (Pst), represents a significant threat to global wheat production. Early detection, particularly during the asymptomatic phase, is critical for effective disease management. Hyperspectral sensing can detect subtle physiological alterations associated with initial infection; However, its effectiveness is frequently limited by substantial background interference from normal plant growth. In this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR). To mitigate background the interference, generalized two-dimensional correlation spectroscopy (2D-COS) was employed, utilizing infection time as the perturbation variable. This approach surpasses conventional dimensionality reduction techniques such as principal component analysis (PCA) and the chemometric feature selection algorithm known as competitive adaptive reweighted sampling (CARS). Through this methodology, six feature bands exhibiting distinct absorption changes were identified. Analysis of synchronous and asynchronous 2D-COS correlation features from 1 to 6 days post-inoculation (dpi), enabled effective discrimination between spectral variations attributable to growth and those specific to disease responses. The biological significance of these spectral dynamics was empirically validated using steady-state chlorophyll fluorescence imaging and destructive biomass measurements. This combined evidence confirmed that Pst-induced chloroplast functional impairment strictly precedes macroscopic tissue structural collapse. This process effectively suppressed background noise while preserving critical infection-related signals. Subsequently, three classifiers-support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)-were evaluated using the extracted 2D-COS features. Asynchronous features generally produced superior classification performance, with XGBoost achieving the highest accuracy (86.79%) and area under the receiver operating characteristic curve (AUC) (94.12%). Compared to conventional methods like PCA and CARS, 2D-COS more effectively attenuated growth-related interference and accentuated early disease signatures. These results demonstrate that the integrated framework of "multiplicative scatter correction (MSC) + Asynchronous Correlation Features + XGBoost" offers substantial potential for accurate, non-destructive, and early diagnosis of wheat stripe rust.

Why it matches plant phenotyping methods小麦赤さび病の無症状期を対象に、ハイパースペクトル計測と2D-COS・機械学習による植物病害状態の抽出手法を開発・評価しており、表現型取得が中心である。

abstractIn this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR).

Hyperspectral phenotyping reveals the genetic basis of grain quality in rice. ↗

Rice ( Oryza sativa ) grain quality is an important breeding target, yet its genetic basis remains incompletely understood. In this study, we integrated hyperspectral phenotyping with genome-wide association study (GWAS) to investigate apparent amylose content (AAC) and protein content (PC) in 241 modern rice varieties. Using a visible-shortwave infrared hyperspectral system combined with optimized preprocessing and machine-learning pipelines, we achieved accurate predictions for AAC ( R 2 = 0.97) and PC ( R 2 = 0.92). Hyperspectral-based GWAS identified both known loci and previously unreported genetic associations. For AAC, qAAC (780.791nm) -1-3 was mapped to the Green Revolution gene SD1 , showing that the sd1 allele increases AAC while conferring high yields. For PC, we identified qPC (1998.98nm) -5-1 and confirmed GW5 as the causal gene, linking the high-yielding gw5 allele with high grain PC. Hyperspectral features outperformed traditional measurements, enhancing the detection of genetic signals. This study provides an efficient strategy for elucidating the genomic architecture of complex grain-quality traits.

Why it matches plant phenotyping methodsイネ穀粒の品質形質を対象に、ハイパースペクトル計測、前処理、機械学習による形質推定を開発・評価しており、フェノタイピング手法が研究の中心である。

abstractUsing a visible-shortwave infrared hyperspectral system combined with optimized preprocessing and machine-learning pipelines, we achieved accurate predictions for AAC ( R 2 = 0.97) and PC ( R 2 = 0.92).

Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning. ↗

To overcome the limitations of single remote-sensing features in estimating maize canopy leaf area index (LAI), this study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning. Field experiments were conducted in Dehong, Yunnan Province, China, during 2023-2024, and UAV multispectral images and DSM products were acquired for maize grown under three planting-density treatments. Five vegetation indices and three DSM-derived texture/structural features were retained according to their correlation with measured LAI, statistical significance, and complementary spectral or structural information. The VI-based random forest (VI-RF) model achieved an R 2 of 0.835 and an NRMSE of 9.5%, whereas the DSM-based model showed lower performance (R 2 = 0.641; NRMSE = 14.2%). Under the same random-forest modeling framework, fusing VIs with DSM features improved the overall model performance to R 2 = 0.892 and NRMSE = 7.6%, indicating that DSM-derived structural information mainly enhanced the feature representation of maize LAI. Using the same VI-DSM feature set, the stacking model with support vector machine (SVM) as the meta-learner further improved the overall performance to R 2 = 0.930 and NRMSE = 6.3%. The additional gain from stacking was moderate but consistent, whereas feature fusion contributed the dominant improvement. The combined VI-DSM-Stacking workflow improved prediction stability across planting densities, especially under low- and high-density canopy conditions where soil background interference and spectral saturation were more evident. These results demonstrate that integrating spectral and DSM-derived structural information with stacking ensemble learning can improve the accuracy and robustness of UAV-based maize LAI estimation.

Why it matches plant phenotyping methodsUAV画像・DSM・アンサンブル学習を統合し、トウモロコシのLAI推定法を開発・比較検証しており、表現型取得・推定手法が研究の中心である。

abstractthis study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning

MA-UQNet: A multi-modal uncertainty quantification neural network for remote sensing-based wheat aboveground biomass estimation ↗

Accurate aboveground biomass estimation with quantified uncertainty is essential for precision agriculture, enabling risk-aware decision-making and strategic model improvement. Existing approaches predominantly provide point estimates without uncertainty quantification, limiting their operational utility for trustworthy Artificial Intelligence (AI) deployment. This study presents a Multi-modal Attention-based Uncertainty Quantification Network (MA-UQNet), which achieves superior prediction accuracy (R 2 = 0.856) with well-calibrated uncertainty (97.18% coverage) for wheat aboveground biomass estimation through integrated multi-modal attention, growth stage-specific processing, and epistemic–aleatoric uncertainty decomposition. The framework integrates hyperspectral remote sensing with environmental variables via joint attention mechanisms that adapt to phenological variations. Model development employed a decade-spanning dataset (2012–2022, 1272 samples) collected under factorial combinations of nitrogen rates (0–270 kg/ha), irrigation levels (0–384 mm), and wheat cultivars across four growth stages. Temporal extrapolation validation using chronological partitioning (2012–2019 for training and 2020–2021 for testing) demonstrated robust generalization, substantially outperforming Random Forest (R 2 = 0.751, coverage = 76.61%) and nine representative baselines, including Bayesian Neural Networks (R 2 = 0.805, coverage = 38.31%). Uncertainty decomposition revealed epistemic uncertainty to be moderately dominant (53%) relative to aleatoric uncertainty (47%), indicating that strategic data collection offers greater potential for uncertainty reduction than improving measurement precision alone. These findings provide validated tools for uncertainty-aware biomass estimation in precision agriculture.

Why it matches plant phenotyping methods小麦の地上部バイオマスという植物形質を、ハイパースペクトルリモートセンシングと不確実性推定ネットワークで抽出する手法を開発し、時系列分割と既存手法との比較で検証しているため、植物フェノタイピング手法が中心である。

abstractThis study presents a Multi-modal Attention-based Uncertainty Quantification Network (MA-UQNet), which achieves superior prediction accuracy (R 2 = 0.856) with well-calibrated uncertainty (97.18% coverage) for wheat aboveground biomass estimation

Estimation of relative chlorophyll content in winter wheat employing hyperspectral reflectance: A comprehensive analysis of data-driven ensemble learning methods ↗

Ensemble learning is increasingly used for remote sensing-based plant phenotyping data. A thorough evaluation of its effectiveness in estimating relative chlorophyll content is essential to optimize model selection and enhance prediction accuracy. This study aimed to assess the performance of parallel, sequential, and hybrid ensemble learning approaches, as well as individual machine learning models, for cross-environment estimation of SPAD-based chlorophyll content using hyperspectral reflectance data. Canopy hyperspectral reflectance and SPAD measurement were collected from winter wheat during the late growth stages across two distinct environments. Parallel and sequential ensemble learning strategies were represented by random forests (RF) and eXtreme gradient boosting (XGBoost), respectively. The hybrid ensemble integrated the performance of K-nearest neighbors, support vector machine, partial least squares regression, generalized linear model (GLM), deep neural network, and Gaussian process. Gray relational analysis (GRA) was employed to evaluate band features, and the positive association between feature quality and prediction accuracy validated its effectiveness. During modeling, XGBoost (R 2 = 0.657–0.658) underperformed compared to RF (R 2 = 0.687–0.691). GLM consistently excelled across most hybrid ensemble members in most feature intervals (R 2 = 0.137–0.692, 0.512–0.723), achieving superior prediction accuracy across several feature intervals and also outperforming RF (R 2 = 0.156–0.687, 0.535–0.691). Among the six forecast combination strategies used in the hybrid ensemble, inverse rank (R 2 = 0.640–0.726) demonstrated robust performance across different feature intervals but provided only marginal improvement over the best individual ensemble member. Moreover, incorporating RF and XGBoost in the hybrid ensemble did not result in significant accuracy gains. To balance computational efficiency and prediction accuracy, GRA-based optimal features combined with GLM modeling are recommended for similar applications, rather than relying on complex ensemble learning methods. These findings offer valuable insights for estimating relative chlorophyll content and hold potential for large-scale estimation of crop traits.

Why it matches plant phenotyping methodsハイパースペクトル反射データから冬コムギの相対クロロフィル含量を推定する機械学習手法を比較・評価しており、植物形質の取得・推定方法が研究の中心である。

abstractA thorough evaluation of its effectiveness in estimating relative chlorophyll content is essential to optimize model selection and enhance prediction accuracy.

Genomic selection for nitrogen use efficiency in perennial ryegrass ( Lolium perenne L.) under field conditions ↗

Abstract Background Improving the nitrogen use efficiency (NUE) of pastures has the benefit of reducing costs of production and reducing nitrogen loss to the environment. Genetic variation has been shown to exist for NUE, and hence NUE is a trait for breeding programs. Methods In this study, we develop genomic selection methods for NUE in perennial ryegrass through developing high‐throughput sensor‐based phenotyping and genotyping by sequencing (GBS) technologies. NUE of an advanced perennial ryegrass breeding population was screened in a spaced plant field trial which contained 644 genotypes, 3 nitrogen treatment levels (0, 20, and 40 kg ha −1 per application), and 3 replicates. The trial was conducted for 2 years with a total of 6 nitrogen applications. An unmanned aerial system (UAS) equipped with multispectral sensors was deployed weekly over the trial. Approximately 4–5 weeks after nitrogen fertilizer application, 75–675 selected samples were cut for ground truthing. Prediction models for biomass were developed based on spectral and ground truth data and biomass for each plant was computed. Plants were genotyped by GBS transcriptomics. Results NUE, defined as biomass production per unit of N application, varied significantly with N application level, season and among genotypes. Moderate broad‐sense heritability (0.61–0.72) for NUE was observed. Genomic prediction accuracies were in the range of 0.3–0.5. Conclusions Our results demonstrated that genomic selection for NUE was possible. The genomic prediction developed in these advanced breeding lines may be tested in other genetic backgrounds. The technologies are ready to be extended into other perennial pasture grass species.

Why it matches plant phenotyping methodsUASマルチスペクトルセンシングと予測モデルによるバイオマス推定を開発し、NUE表現型の高スループット取得に用いており、フェノタイピング手法が中心的である。

abstractwe develop genomic selection methods for NUE in perennial ryegrass through developing high‐throughput sensor‐based phenotyping

ARTIFICIAL INTELLIGENCE FOR PLANT DISEASE DETECTION, MONITORING, AND FORECASTING: ADVANCES, CHALLENGES, AND FUTURE GAPS ↗

Plant diseases are one of the main limiting factors in global agricultural productivity, causing significant losses and compromising food security. The increasing complexity of production systems and the limitations of traditional diagnostic methods, based mainly on visual assessment and laboratory analyses, have driven the incorporation of artificial intelligence (AI) in plant pathology. In this context, the present study aimed to synthesize the advances, challenges, and gaps related to the application of AI in the detection, monitoring, and forecasting of plant diseases. This is an integrative literature review, conducted through systematic searches in national and international scientific databases, encompassing studies that addressed machine learning techniques, deep learning, and hybrid models applied to plant pathology. Approaches based on RGB images, multispectral and hyperspectral data, integration with unmanned aerial vehicles (UAVs), and forecasting models based on climatic variables were analyzed. The results show that convolutional neural networks and temporal architectures, such as LSTM, have substantially increased the diagnostic accuracy and forecasting potential of the systems, especially when integrated with environmental data. However, challenges persist related to the generalization of the models, scarcity of representative databases, field variability, and high computational cost. It is concluded that AI represents a strategic tool for the transition from a from a reactive phytopathology to a predictive and decision-support approach. However, its consolidation under real cultivation conditions depends on robust agronomic validation, methodological standardization, and multidisciplinary integration, aiming at more precise, sustainable systems applicable to precision agriculture.

Why it matches plant phenotyping methods植物病害の画像・マルチスペクトル・ハイパースペクトル観測とAIによる検出・予測を主題とするレビューであり、植物状態（病害）の取得・推定手法が中心です。

abstractthe present study aimed to synthesize the advances, challenges, and gaps related to the application of AI in the detection, monitoring, and forecasting of plant diseases.

UAV multispectral to hyperspectral reconstruction based on deep learning and radiative transfer models for crop nitrogen monitoring ↗

• Proposed a multispectral to hyperspectral reconstruction framework M2H–SWIR. • The M2H–SWIR framework integrates the PROSAIL and deep learning. • M2H–SWIR reconstructs VNIR multispectral to full-range hyperspectral (400–2500 nm) • Reconstructed SWIR bands enhance UAV-based canopy nitrogen mapping accuracy. • M2H–SWIR outperforms traditional PROSAIL inversion for canopy nitrogen estimation.

Why it matches plant phenotyping methodsUAVマルチスペクトルからハイパースペクトルを再構成し、作物キャノピー窒素を推定する手法が研究の中心であり、植物形質の取得・推定方法を開発している。

abstractProposed a multispectral to hyperspectral reconstruction framework M2H–SWIR.

Hyperspectral Imaging for Early Detection and Severity Grading of Potato Bacterial Wilt. ↗

Potato ( Solanum tuberosum ) is a vital global non-cereal food crop severely threatened by bacterial wilt, caused by Ralstonia solanacearum ( R . solanacearum ). Conventional diagnostics like PCR and ELISA, though effective, are destructive and time-consuming, limiting large-scale field applications. This study investigates hyperspectral imaging (HSI) as a non-invasive, rapid, and accurate alternative for early detection and severity grading of potato bacterial wilt. Using a portable HSI system (400-1000 nm), spectral data were collected from inoculated potato plants ('Longshu No. 7') at 0, 24, 48, and 72 h post-inoculation, alongside disease severity assessment (grades 0-4). After comprehensive spectral preprocessing and feature band extraction via Competitivse Adaptive Reweighted Sampling (CARS), we developed two distinct sets of models: one for early detection (temporal classification) using Partial Least Squares-Discriminant Analysis (PLS-DA) and Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), and another for severity grading. The SNV + SG + MC + PLS-DA model achieved exceptional accuracy, exceeding 97% for early detection, while the MSC + SG + MC + CARS + PLS-DA model yielded >97% accuracy for severity grading. These results were supported by low misclassification rates in confusion matrices. This work establishes a robust HSI-based framework for high-throughput screening of resistant potato germplasm and advances precision agriculture strategies for bacterial wilt management.

Why it matches plant phenotyping methodsHSIによるジャガイモ植物体の細菌性萎凋病の早期検出と重症度推定が研究の中心であり、画像取得、特徴抽出、分類・重症度モデルを開発している。

abstractThis study investigates hyperspectral imaging (HSI) as a non-invasive, rapid, and accurate alternative for early detection and severity grading of potato bacterial wilt.

Fusion of IoT Sensor Data and Image Processing for Comprehensive Crop Health Assessment ↗

Effective crop health monitoring requires the integration of heterogeneous data sources that capture both environmental conditions and crop-level responses. Conventional single-modality approaches, relying either on in-ground sensor measurements or aerial imagery in isolation, fail to exploit the complementary strengths of each technique, resulting in limited diagnostic accuracy and delayed intervention. This study proposes an integrated approach that fuses data acquired by IoT-connected sensor networks with multispectral images captured by unmanned aerial vehicles (UAVs), enabling comprehensive crop health assessment across an entire field. Consider a system that deploys a network of sensors throughout a 2-hectare area to continuously watch the moisture and temperature, as well as other critical parameters, such as electrical conductivity, atmospheric humidity as well as the nutrient level in the soil. Simultaneously, a special UAV, a drone known as the DJI Phantom 4 Multispectral, take pictures of the field at five different spectral frequencies. However, the most interesting thing is the following: to do all this, the system relies on a special type of artificial intelligence known as a hybrid deep learning architecture. It resembles a twostep procedure, where the one section analyses the trends in the sensor data across time and is called one dimensional convolutional neural network, whereas the other section uses a pre-trained version of ResNet-50 and is responsible of making significant features of the images captured by the drone. Then, it is all unified with an eight-head multi-head attention mechanism, taking all the various kinds of data and forming a bigger picture. This will enable the system to memorize and establish relationships among the various kinds of data, which forms a potent source of knowledge and management of the field. The synchronized dataset comprised 2,520 data points collected over 120 days (April–August 2024), with 103 days of active data capture. The proposed hybrid deep learning architecture achieved a crop health classification accuracy of 94.5%, compared to 80% for conventional single-modality methods — a statistically significant improvement of 14.5 percentage points. The system classifies crop status into five categories: healthy, water stress, nutrient deficiency, disease, and pest infestation. The results demonstrate that cross-modal IoT–image fusion delivers earlier, more reliable diagnosis of crop stress conditions, enabling data-driven farm management decisions that support precision agriculture at scale.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像とIoTセンサーデータを融合し、作物の健康状態・ストレス状態を分類する深層学習手法が研究の中心であり、植物状態の取得・推定方法として適格。

abstractThis study proposes an integrated approach that fuses data acquired by IoT-connected sensor networks with multispectral images captured by unmanned aerial vehicles (UAVs), enabling comprehensive crop health assessment across an entire field.

Review and perspective of key generic technologies in crop phenomics ↗

传感技术、人工智能算法及高性能计算能力的飞速发展正推动作物表型检测从单一性状描述向多尺度、智能化感知体系的转变。在此背景下，作物表型智能检测应运而生，其核心原理在于解析电磁波谱与植物组织相互作用的物理机制，即不同波段的光子与植物组织发生电子跃迁、分子振动及热辐射等能量交换，从而携带形态结构、生理生化及环境互作等多维信息。基于此，本文系统阐述了可见光成像、三维成像、光谱成像、叶绿素荧光成像、热红外成像等传感器的技术特性与适用场景，并梳理了从细胞/组织到器官、单株及群体水平的表型检测平台，比较了不同平台的应用场景及其优势与局限。针对当前多源数据异构性高、标准化体系缺失及模型泛化能力不足等核心瓶颈，本文提出发展作物表型智能检测的关键路径：建立面向多平台的数据标准化采集与校准体系，从源头保证数据质量；发展鲁棒性强、可迁移的智能解析算法，推动表型解析从传统特征工程向视觉基础模型及多模态大模型的演进；构建符合FAIR原则的共享数据库，打破“数据孤岛”现象；明确模型在不同物种、环境及生育阶段的适用边界，提升其在真实农业场景下的可靠性与泛化能力。利用智能检测技术实现作物表型的高通量、精准化解析，是未来作物表型组学发展的必然趋势，也为智慧育种与精准农业提供核心技术与理论支撑。

Why it matches plant phenotyping methods作物表型检测技术综述，系统评述多类成像传感器、表型检测平台及智能解析算法，方法学内容是核心。

abstract本文系统阐述了可见光成像、三维成像、光谱成像、叶绿素荧光成像、热红外成像等传感器的技术特性与适用场景，并梳理了从细胞/组织到器官、单株及群体水平的表型检测平台

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.

Classification of Pestalotiopsis sp. Leaf Fall Disease Severity in Rubber Plants using UAV Multispectral Vegetation Indices and 1-D Convolutional Neural Networks ↗

Leaf-fall disease caused by Pestalotiopsis sp. is a major threat to rubber (Hevea brasiliensis) plantations because it suppresses photosynthetic activity, accelerates defoliation, and reduces latex productivity. In operational practice, severity assessment is still dominated by visual field inspection, which is subjective, time-consuming, costly, and difficult to standardize across large plantation areas. This study develops a disease severity classification model for Pestalotiopsis sp. using a Convolutional Neural Network (CNN) based on vegetation-index features derived from UAV multispectral imagery. The model classifies disease severity into four levels: L1 (Light Infection), L2 (Moderate Infection), L3 (Severe Infection), and L4 (Very Severe Infection). To represent temporal and biological variability in disease expression, multispectral data were collected from multiple rubber clones over two observation periods. Feature construction focused on NDRE, LCI, CI, NDVI_NDRE_Interaction, and GCI_Ratio, which capture chlorophyll-related and canopy condition responses to infection. Because severity classes were imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) was applied before model training. A one-dimensional CNN was then trained to learn nonlinear patterns among index-based predictors for multilevel severity classification. Hyperparameter tuning improved overall accuracy from 85.30% to 90.00%. Class-wise F1-scores changed from 0.91 to 0.94 (L1), 0.83 to 0.84 (L2), 0.75 to 0.88 (L3), and 0.97 to 0.84 (L4), with the largest improvement in L3 recall (0.67 to 0.94). These results indicate that the selected vegetation indices and interaction terms are informative predictors for objective and scalable disease severity classification under heterogeneous plantation conditions.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からゴム樹の感染症重症度という植物状態を推定するCNN手法を開発・評価しており、表現型取得・抽出が研究の中心である。

abstractThis study develops a disease severity classification model for Pestalotiopsis sp. using a Convolutional Neural Network (CNN) based on vegetation-index features derived from UAV multispectral imagery.

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.

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

Early Apple Bruise Detection via Discrete Hyperspectral Signatures with SHAP-Guided Feature Selection and a CNN-Transformer Model. ↗

Accurate detection of early invisible apple bruises is important for post-harvest quality assessment. Although hyperspectral imaging (HSI) provides rich spectral information, its high dimensionality introduces substantial redundancy and weak-signal interference. This study proposes an integrated framework combining waveband optimization and discrete spectral modeling for efficient bruise detection. A Selection-Refined Improved Grey Wolf Optimization (SR-IGWO) algorithm was developed to select 18 bruise-sensitive wavebands from 273 channels (996-2501 nm), achieving a 93.4% reduction in spectral dimensionality. SHAP analysis was further used to interpret the selected bands in relation to biochemical responses associated with bruising. To address the mismatch between conventional CNNs and sparse discrete spectral inputs, a CNN-Transformer hybrid model (DSFormer) was designed using pointwise convolution for band embedding and a Transformer encoder to capture global dependencies. Experimental results across ten independent runs achieved a classification accuracy of 99.11% ± 0.08%, a recall of 96.04% ± 1.08%, and an F1-score of 95.95% ± 0.39% under the tested conditions. Ablation studies suggest that the proposed architecture supports effective detection under sparse spectral conditions. Although validation was limited to a single cultivar and controlled sampling, the proposed framework provides a promising preliminary exploration of reduced hyperspectral data for non-destructive fruit bruise detection.

Why it matches plant phenotyping methodsリンゴ果実の打撲状態を対象に、ハイパースペクトル波長選択とCNN-Transformerによる症状検出手法を開発・検証しており、植物器官の状態取得が研究の中心である。

abstractThis study proposes an integrated framework combining waveband optimization and discrete spectral modeling for efficient bruise detection.

Using shortwave infrared spectral indices to monitor short-term water stress dynamics in peach orchards ↗

Abstract Purpose Capturing rapid changes in water status is key to optimizing deficit irrigation in Mediterranean orchards, but thermal remote sensing is constrained by the availability of high-spatial-resolution data. This study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards. Methods An experiment was conducted in two commercial orchards in south-eastern Spain, where mild water stress was induced by withholding irrigation for four days. High-resolution hyperspectral and thermal imagery were acquired concurrently with stem water potential measurements (ψ stem ). Structural, pigment-related, and water-sensitive indices were evaluated at high (20–50 cm) and medium (30 m) spatial resolutions to analyze the effects of pixel size on stress detection. The Crop Water Stress Index (CWSI), derived from thermal imagery, served as a reference indicator. Results Those optical indices based on SWIR reflectance at 1240 nm, the Normalized Difference Water Index (NDWI₁₂₄₀) and the Simple Ratio Water Index (SRWI), showed the strongest sensitivity to ψ stem variability (R² = 0.63, p

Why it matches plant phenotyping methods桃樹の水ストレス状態を高解像度ハイパースペクトル・熱画像とスペクトル指標で推定し、茎水ポテンシャルを用いて検証しており、植物表現型取得手法が中心である。

abstractThis study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards.

Validation and application of high-throughput 3D multispectral phenotyping platform for evaluating seasonal adaptation in Chinese cabbage ↗

Climate change poses increasing challenges to Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production through unpredictable weather patterns that induce premature bolting and physiological disorders. Traditional breeding programs rely on labor-intensive visual assessment that cannot capture continuous developmental dynamics or precisely quantify stress responses across variable environments. This study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes across contrasting autumn (favorable) and spring (stressful) seasons in Taiwan. The system, based on a FieldScan gantry platform equipped with multispectral 3D scanners, operated autonomously 2-3 times daily, continuously monitoring morphological parameters (3D leaf area, digital biomass, plant height) and spectral indices (NDVI, PSRI) throughout the growth cycle. The system’s automated components -continuous data acquisition and real-time parameter extraction – generated approximately 100,000 data points from 63 morphological, spectral, and structural parameters during 6-week pre-harvest period. Subsequent quality and statistical analysis enabled objective genotype classification and breeding decisions. Automated measurements showed season-dependent associations with visual assessment scores (R² = 0.37-0.56 in autumn; R² = 0.73-0.80 in spring), with spring models substantially outperforming autumn models due to enhanced physiological differentiation under stress. Spring cultivation induced severe stress responses, evidenced by 71% increase in PSRI (0.12 vs. 0.07) and 26% increase in plant height, with bolting resistance emerging as the critical determinant of adaptation. A quantile-based multi-dimensional classification framework integrating seasonal composite scores and Euclidean distances stratified germplasm into actionable breeding categories: stable genotypes (3.7%), spring-specific types (0.7%), poor performers (13.4%), and intermediate materials (82.1%). Continuous temporal monitoring enabled early stress detection, with binned PSRI measurements predicting subsequent morphological development one week in advance (R² = 0.62). This integrated phenotyping framework provides efficient tools for accelerating climate-resilient breeding through objective genotype classification, early stress detection, and data-driven decision support, with potential adaptation to other vegetable crops and integration with IoT-based collaborative breeding network.

Why it matches plant phenotyping methods高スループット3D・マルチスペクトル表現型計測プラットフォームの検証と応用が研究の中心であり、形態・スペクトル形質の自動取得、抽出、予測性能、遺伝子型分類を評価している。

abstractThis study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes

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.

Curvelet Decomposition-Based Tri-Branch Coupling Network for Hyperspectral Unsound Maize Seeds Identification. ↗

The rapid and nondestructive classification of maize kernels is of great significance for seed screening and quality evaluation. Existing hyperspectral image classification methods based on the Mamba architecture can effectively represent spectral and spatial features; however, they still face limitations in time-frequency analysis and multimodal feature fusion. In addition, traditional approaches often rely heavily on spectral preprocessing, which may introduce additional errors and compromise the model's robustness and generalization ability. To address these challenges, this paper proposes a novel cross-modal classification framework named CD-TriMamba, which jointly leverages hyperspectral data and visible-light images for comprehensive feature extraction and deep fusion. Specifically, an innovative feature extraction module is designed, consisting of a Spectral Curvelet Convolution (SCC) module for hyperspectral data and a Curvelet-Decomposed Convolution (CDC) module for spatial modeling. A feature rearrangement mechanism is further introduced to mine critical information from both spectral and spatial modalities. Finally, a ConvNeXt-guided tri-branch cross-fusion structure (TriMamba) is constructed to achieve deep collaboration and efficient integration between spectral and spatial features. Experimental results demonstrate that the proposed model achieves outstanding performance in seed classification, with an accuracy (Acc) of 99.2% and a Kappa value of 99.1%. These results strongly confirm the effectiveness and broad application potential of cross-modal feature fusion in maize kernel classification.

Why it matches plant phenotyping methodsマルチモーダル画像からトウモロコシ種子の健全性を推定する新規分類フレームワークを開発しており、種子状態の取得・抽出手法が研究の中心である。

abstractthis paper proposes a novel cross-modal classification framework named CD-TriMamba, which jointly leverages hyperspectral data and visible-light images for comprehensive feature extraction and deep fusion.

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

Explainable machine learning to predict root biomass of field crops using UAV multispectral data ↗

Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。

abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.

Interpretable hyperspectral analysis of soluble solids content in apples: spectral attribution and mechanistic insights from linear and deep learning models. ↗

Soluble solids content (SSC) is a key determinant of apple sweetness and market quality, and its rapid, nondestructive assessment is essential for postharvest grading. Hyperspectral imaging (HSI) provides rich spectral information for SSC prediction; however, conventional wavelength selection strategies are largely data-driven and lack interpretability, while the underlying mechanisms of spectral information utilization across different modeling approaches remain insufficiently understood. In this study, an interpretable hyperspectral analysis framework was developed to investigate both effective wavelength selection and model-dependent spectral response mechanisms. Average spectra extracted from the pulp region of interest (ROI) were used for analysis. A linear model (PLSR) combined with SHAP (SHapley additive exPlanations) was employed to quantify global feature contributions, while a one-dimensional convolutional neural network with dual-attention mechanisms (BrixCNN) was constructed and interpreted using integrated gradients (IG) to capture nonlinear spectral dependencies. The consistency and divergence between the two attribution strategies were further quantitatively analyzed. Results showed that both SHAP-PLSR and IG-BrixCNN identified informative wavelength subsets that significantly reduced spectral dimensionality while maintaining comparable predictive performance (R 2 ≈ 0.83-0.84, RPD > 2.4). Despite similar predictive accuracy, the two models exhibited distinct spectral utilization patterns: The linear model primarily relied on dominant, high-variance spectral variations, whereas the deep learning model captured weaker, more distributed, and nonlinear spectral patterns. Meanwhile, partial overlap in the 1100-1300 nm region suggested that both models may utilize correlated spectral variations within similar wavelength domains for SSC prediction. These findings indicate that comparable predictive performance can arise from distinct yet complementary spectral utilization patterns, reflecting model-dependent information extraction mechanisms rather than direct chemical specificity. This study provides new insights into wavelength selection strategies and enhances the interpretability and reliability of hyperspectral analysis for fruit quality assessment.

Why it matches plant phenotyping methodsリンゴ果実のSSCという植物器官形質を対象に、ハイパースペクトル画像、波長選択、SHAP/IG解釈を統合した予測・解析フレームワークを開発しており、形質取得手法が研究の中心である。

abstractIn this study, an interpretable hyperspectral analysis framework was developed to investigate both effective wavelength selection and model-dependent spectral response mechanisms.

UAS-Based Analysis of a Black Locust Clone Trial ↗

Black locust (Robinia pseudoacacia L.) is a key tree species globally and in Hungary, valued for its economic benefits, adaptability, and ecosystem services. Despite its invasiveness and susceptibility to frost damage, its high-quality timber and significant nectar production make it economically important. This research, conducted as a collaboration between the Hungarian Forest Research Institute and the University of Debrecen, aimed to evaluate the applicability of remote sensing technologies in supporting black locust (Robinia pseudoacacia L.) research and monitoring efforts. A clonal trial established in 2020 in eastern Hungary aimed to assess the performance of newly bred black locust clones. Tree height was measured using both conventional ground-based methods and photogrammetric analysis of unmanned aerial system (UAS) data, enabling comparison between the two approaches. Tree vitality was evaluated through UAS-based multispectral analysis using vegetation indices, including NDVI, GNDVI, NDRE, and LCI. Our findings revealed no significant differences (p>0.05) between UAS-based and traditional height measurements, confirming UAS as a reliable tool. Clones »NK2« and »PL251« showed superior growth (height of 7.6 m and 7.4 m) and health, while »Üllői« cultivar performed the weakest (5.3 m). Strong correlations were found between some vegetation indices (NDRE and LCI) and tree heights (r=0.593 and r=0.587), emphasizing the potential of remote sensing in efficient forest management. This study highlights the value of integrating UAS technology in forestry, offering cost-effective, accurate and comprehensive data for improving black locust cultivation practices.

Why it matches plant phenotyping methodsUASの写真測量・マルチスペクトル解析により樹高と樹体活力を推定し、地上測定との比較検証を行っており、植物表現型取得手法が中心的です。

abstractTree height was measured using both conventional ground-based methods and photogrammetric analysis of unmanned aerial system (UAS) data, enabling comparison between the two approaches.

Multisource Grapevine Phenology Dataset for Smart Farming and AI Modeling ↗

Abstract Artificial Intelligence and Machine Learning rely on large, high-quality datasets for accurate and robust models, yet data scarcity remains a major challenge, especially in smart farming. Phenology modeling, a key application, studies how plant biological events relate to climate and seasons. Accurate phenology models improve crop quality, support climate adaptation, and guide decisions such as pesticide use and harvesting, enhancing environmental and economic sustainability. However, agricultural data are highly diverse and heterogeneous, complicating model development. This study presents a proposed georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Aragón, Spain. Developed by a multidisciplinary team, the dataset combines 9 datasets from 8 sources (including meteorological time series, field phenology observations, and Copernicus Sentinel-2 multispectral imagery) covering the period 2016–2022. It supports both physical and Machine Learning-based phenology modeling and facilitates knowledge extraction in agronomy and plant biology. Its relevance lies in its comprehensive scope, the inclusion of 9 phenological stages, and a rigorous methodology ensuring reproducibility. This framework enables the creation of similar datasets for other regions or crops, advancing smart farming through scalable, data-driven solutions. The open publication of the code further supports this objective. We further anticipate its potential contribution to developing foundation models as well as to the creation of new knowledge in biology and agronomy.

Why it matches plant phenotyping methodsブドウの9段階のフェノロジーを対象に、圃場観測と衛星画像などを統合した再利用可能な地理参照データセットを構築しており、植物形質取得・モデル化の方法論が中心である。

abstractThis study presents a proposed georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Aragón, Spain.

Hyperspectral-Informed Sentinel-2-Based Monitoring of Paddy Residue Burning through Crop-State Discrimination ↗