Accurate and dynamic monitoring of wheat phenotypes is essential for breeding decision-making and crop management. However, RGB image-based phenotyping still suffers from expensive pixel-level annotation, unstable organ-level segmentation across growth stages, and limited multi-trait extraction under complex field conditions. To address these issues, a high-throughput phenotyping framework (WheatScoper) was proposed, enabling organ-level segmentation and plot-level multi-trait extraction. To reduce annotation cost, a structure-aware geometry-assisted annotation (SAGA) algorithm was developed, yielding an approximately 7.6-fold improvement in annotation efficiency over fully manual annotation. To enable efficient organ-level segmentation, a lightweight semantic segmentation network (WheatScopeNet) was developed by integrating parallel hybrid spatial modeling with cross-scale feature fusion. On the held-out test set from the same site and growing season, WheatScopeNet achieved an mIoU of 0.869 and an mDice of 0.930. Leveraging the segmentation results, an automated system was established to extract 41 multi-view image-derived traits (I-traits) across four core phenotypic dimensions. The extracted I-traits supported the estimation of eight manually measured agronomic traits, with R 2 values ranging from 0.477 to 0.697. Notably, the correlations between stay-green-related-traits and yield varied distinctly with viewing-position. Only upper side-view indicators remained significantly correlated with yield, with Side-up final GPAR showing the strongest association, whereas top-view GPAR-derived indicators showed weak associations. Finally, a web-based platform integrating cascaded inference, segmentation visualization, and automatic I-trait extraction was developed. The platform provides an end-to-end solution for field wheat phenotyping and supports breeding decision-making and crop management.
Why it matches plant phenotyping methodsRGB画像から小器官を分割し、多数の植物形質を自動抽出・推定する手法とプラットフォームが研究の中心であるため、植物フェノタイピング手法として明確に採用。
abstracta high-throughput phenotyping framework (WheatScoper) was proposed, enabling organ-level segmentation and plot-level multi-trait extraction.
Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
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.
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)
Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. We construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW, which is captured under controlled indoor scenarios, by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD- \(L_1\) and CD- \(L_2\) values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for 3D wheat phenotyping under occlusion and provides a systematic reference for applying 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下の単一画像から小麦器官を3D再構成し、形質推定精度を改善する手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractTo address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat.
Grain yield is a central target in wheat breeding, yet accurately predicting it remains challenging because it depends on many genes and responds strongly to environmental variation. Genomic selection (GS) has improved breeding efficiency by enabling genome-based prediction of genetic merit, but predictability (PA) for grain yield is often limited under stress environments. At the same time, advances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance and may complement genomic information. In this study, we evaluated genomic and phenomic models for predicting grain yield in elite bread wheat lines across irrigated, drought, and heat-stress environments. Using a sparse phenotyping framework, we compared parametric and non-parametric models. PA was evaluated within environments and under cross-environment sparse phenotyping scenarios. Genomic models provided a stable baseline and enabled effective information sharing across environments when phenotypic data were incomplete. Phenomics-only models captured environment-specific plant responses but were more sensitive to environmental context. Multiomics models that integrated genomic and phenomic information consistently achieved the highest PA, with the largest gains observed under stress conditions. Overall, our results demonstrate that integrating genomics and UAV-based phenomics within sparse phenotyping designs offers a practical and scalable approach to improve grain yield prediction in wheat.
Why it matches plant phenotyping methodsUAV由来のフェノミクスを用いた疎な表現型取得と予測モデルを中心に、環境横断で評価しており、収量という植物形質の推定手法が主要な貢献である。
abstractadvances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance
Reproduction assets foundThe paper's grain yield BLUEs, spectral wavelength BLUEs, and genotypic data are publicly deposited in the CIMMYT data repository (https://doi.org/10.71682/10549399), directly reproducing this paper's phenotyping measurements. No author analysis code with a public URL is stated; other URLs are generic tools/services.Dataset · publicok.com.
Paolo Vitale, Email: p.vitale@cgiar.org.
DATA AVAILABILITY STATEMENT
The datasets generated and analyzed during this study, including best linear unbiased estimates (BLUEs) for grain yield and spectral wavelengths, as well as the corresponding genotypic information, are publicly available in the CIMMYT data repository ( https://doi.org/10.71682/10549399 ).
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Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.
Why it matches plant phenotyping methodsFWRRを用いて穀類の収量安定性を推定・予測する統計的手法を検討し、環境数やデータ構成による予測性能を評価しているため、収量形質の計算的フェノタイピング手法が中心です。
abstractMethods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability.
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.
Plant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks. Detection accuracy, precision, recall, F1-score, and area under the curve remain indispensable, but they are insufficient for judging whether a warning can support timely and proportionate phytoprotection under field variability. This Mini Review argues that intelligent plant health warning systems should be evaluated not only as prediction models, but also as safety-relevant decision-support systems embedded in biological, agronomic, and operational contexts. We first relate AI-based detection to established plant disease forecasting and decision-support traditions, including weather-based models, epidemiological forecasting, and integrated disease management. We then adapt selected safety-assurance concepts, including risk assessment, failure mode and effects analysis, Bow-tie reasoning, warning-threshold governance, reliability analysis, resilience thinking, and response closure, to host-pathogen-environment warning chains. The proposed framework links AI or sensor outputs with pathogen biology, host susceptibility, environmental conduciveness, inoculum pressure, uncertainty assessment, risk classification, threshold decisions, human or automated verification, intervention, and feedback learning. Illustrative crop-pathogen scenarios, including wheat rust, rice blast, potato late blight, grapevine downy mildew, and citrus greening, show how safety assurance can complement existing forecasting and decision-support systems rather than replace them. The framework remains conceptual, and whether these added assurance functions improve existing warning systems requires comparative evaluation under field conditions. Future systems should be evaluated through detection performance and response-oriented indicators such as lead time, calibration, false-alert burden, missed-warning rate, response completion, disease suppression, economic value, and learning after field action.
Why it matches plant phenotyping methods植物病害・ストレスの検出を含む知的警戒システムについて、AI・リモートセンシング・デジタルフェノタイピング・センサーネットワークの評価枠組みを体系的に論じる方法論レビューであり、方法論が中心です。
abstractPlant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks.
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
Sun-induced chlorophyll fluorescence (SIF) is an effective proxy for vegetation photosynthesis, but tower-based retrieval suffers from atmospheric path interference under humid and variable conditions. We present a DOAS-based SIF retrieval algorithm that operates in Fraunhofer lines (680–686 nm, 745–758 nm) and water vapour-sensitive bands (717–727 nm). It constructs an adaptive reference spectrum from SCOPE simulations and PCA and incorporates H2O absorption cross-sections into the fitting process for active atmospheric correction. The algorithm is implemented in a dedicated tower-based system integrating a 1° scanning gimbal with a high-resolution spectrometer. Validation with simulated and field data demonstrates the following: (1) the algorithm retrieves SIF with high fidelity (correlation coefficients >0.9 across all windows); (2) it exhibits lower water-vapour sensitivity and greater cloudy-sky stability than FLD, 3FLD, and SFM, achieving the lowest coefficient of variation (CV = 0.356); (3) over a complete wheat–rice rotation, the retrieved SIF tracks crop growth and phenological stages. This work provides a reliable solution for automated, high-precision tower-based SIF observation under complex atmospheric conditions.
Why it matches plant phenotyping methods植物の光合成状態を示すSIFを取得するタワー型分光観測システムとDOAS補正アルゴリズムを開発し、シミュレーションおよび圃場データで検証しているため、植物フェノタイピング手法が中心である。
abstractWe present a DOAS-based SIF retrieval algorithm
Wheat grain number integrates fate of individual florets, strongly affected by environmental stress during sensitive stages like meiosis. Because development is asynchronous across tillers, spikelets, and florets, it is hard to distinguish stress tolerance from stress escape. Based on 2400 destructive measurements of spike and anther length together with non-destructive morphological measurements from 158 plants grown in four experiments in controlled-environment, we developed a framework to track individual floret developmental stages at plant level. We applied it in two case studies for connecting within-plant developmental asynchrony to reproductive success under favorable or heat conditions. All florets showed a common relative growth rate, producing additive delays across tillers (1-7 d), spikelets (1-5 d), and floret positions (1-6 d). This generated a developmental map for every floret based on external traits. Under control conditions, grain set probability at floret level combined both positional and developmental effects within a spike. Under heat stress, grain loss occurred only in florets at meiosis during the stress, allowing to quantify a true "stress response", while later florets escaped damage. This framework allows understanding and predicting floret development and linking it to grain set, clearly distinguishing timing effects from positional influences and separating tolerance from stress escape.
Why it matches plant phenotyping methods外部形態測定から個々の小花の発育段階を追跡する方法・発育マップを開発し、複数実験で適用しているため、表現型取得・推定が研究の中心である。
abstractwe developed a framework to track individual floret developmental stages at plant level
Reliable and objective phenotyping is essential for plant breeding programs to characterize genetic variation and accelerate crop improvement. Conventional disease assessment relies on expert visual scoring, which is labor-intensive, subjective, and prone to inter- and intra-rater variability. Although image-based phenotyping methods have been proposed, many require manual intervention, specialized imaging setups, or single time-point measurements, limiting their ability to capture disease progression over time. Here, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images. The pipeline performs semi-automated leaf and automated pustule segmentation from images acquired in situ , enabling objective disease severity estimation with minimal user intervention and without requiring solid backgrounds or manual leaf manipulation or detachment. By extracting temporal traits, including disease severity trajectories and standardized area under the disease progress curve, the method provides a comprehensive characterization of disease development throughout infection. Association between automated and expert assessments was moderate for stripe rust ( R 2 = 0.58) and strong for leaf rust ( R 2 = 0.85), while expert inter-rater reliability was moderate for both diseases (ICC = 0.675 and 0.800, respectively). The proposed approach establishes a scalable and reproducible framework for longitudinal disease phenotyping in controlled environments, with broad applications in disease resistance screening and crop breeding.
Why it matches plant phenotyping methods画像時系列から植物病害の進展と重症度を抽出する半自動・自動解析パイプラインを開発し、専門家評価との比較で検証しており、表現型取得手法が研究の中心です。
abstractHere, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images.
Reproduction assets foundThe paper's Code and Data Availability section explicitly states that software and datasets (the phenotyping pipeline and imaging datasets) are publicly available at the authors' GitHub repository and project website, both of which are in the allowed URL list.Code · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗USask-BINFO/greenskeye_analysislines:195-225Dataset · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗lines:195-225Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically composites spectral observations over fixed calendar windows, implicitly assuming phenological synchrony across fields. We present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction, for a quality trait whose physiology, senescence-linked nitrogen remobilization, contrasts with the season-integrating behavior of yield. Integrating Sentinel-2 imagery (31 vegetation indices, 10 spectral bands), ERA5-Land reanalysis, gSSURGO soil properties, and USGS 3DEP topography across 228 commercial winter wheat fields in western Kansas (2024–2025), we compared six temporal strategies (peakrelative vs. calendar × monthly, biweekly, growth-stage) using three ensemble tree models under nested cross-validation with Boruta feature selection. A single 30-day post-peak window (peak + [16,45] days) was the top-performing and most consistently selected window, chosen in 4 of 5 outer folds, reproducing prior accuracy under random cross-validation (R2 ≈ 0.28); though its advantage over the best calendar window was not statistically significant (paired bootstrap p = 0.08). Under leave-county spatial cross-validation, however, this skill did not transfer across counties (Sentinel-2–only R2 ≈ 0.01; per-county median R 2 = −0.23), indicating the satellite signal supports within-region interpolation but not spatial extrapolation to unseen counties; ablation shows that neither the spectral nor the static features transfer across counties on their own, and the residual crosscounty skill emerges only from their combination. A near-real-time application at ∼3 weeks before harvest retains most within-region skill at a modest accuracy cost. The results delineate where spectral-peak-relative alignment helps, concentrating a senescence-linked signal within region, and where it does not, providing an honest operational baseline for satellite-based grain-quality monitoring.
Why it matches plant phenotyping methods小麦の穀粒タンパク質濃度という植物形質を対象に、衛星時系列のスペクトルピーク相対アラインメントを開発・比較評価し、交差検証で性能と空間移 transfer 性を検証しているため、方法が中心的である。
abstractWe present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction
Reproduction assets foundThe preprint explicitly releases the authors' analysis code (data-acquisition pipeline, feature engineering, cross-validation/modeling, figure scripts) at a public GitHub repository, and a de-identified field-level GPC dataset released alongside the code repository. Both are paper-specific, public, and actionable. The Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗Ciampitti-Labpdf-page:48 lines:1-55Dataset · publica de-identified version of the dataset is released alongside the code repositoryOpen asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Accurate classification of unsound wheat kernels is important for automated grain quality assessment, but improved recognition performance often comes at the cost of increased model complexity. Methods This study presents ECA-ModNet, a parameter-efficient convolutional network derived from EfficientNetV2-S. The architecture replaces two early-stage Fused-MBConv blocks with Mod-FusedMBConv blocks to introduce input-dependent local contextual modulation and replaces the squeeze-and-excitation modules in later stages with efficient channel attention to model local cross-channel interactions using fewer attention-related parameters. Experiments were conducted on the seven-class G600 wheat subset of the GrainSpace dataset. Results Across three independent runs, ECA-ModNet achieved an accuracy of 90.17 ± 0.21% and a macro-F1 score of 90.23 ± 0.21%, improving upon EfficientNetV2-S by 3.80 and 3.84 percentage points, respectively. The parameter count decreased from 20.19M to 16.49M, while FLOPs increased marginally from 2.90G to 2.95G. ECA-ModNet achieved accuracy statistically comparable to that of ConvNeXt-Tiny and InceptionNeXt-T while using substantially fewer parameters, and obtained 3.03-4.55 percentage points higher mean accuracy than six lightweight baselines. Discussion Ablation experiments identified two Stage 1 Mod-FusedMBConv blocks with a 3×3 context kernel as the configuration with the highest mean accuracy among those evaluated. These results indicate that ECA-ModNet offers a favorable accuracy-parameter trade-off for image-based classification of unsound wheat kernels.
Why it matches plant phenotyping methods小麦粒の状態（unsound kernel）を画像から分類するためのCNNを開発・比較・アブレーション評価しており、植物器官の状態推定手法が研究の中心である。
abstractThis study presents ECA-ModNet, a parameter-efficient convolutional network derived from EfficientNetV2-S.
Reproduction assets foundThe paper analyzes the public GrainSpace dataset (G600 seven-class unsound wheat kernel subset) and provides an explicit data availability statement with a public GitHub URL. No author analysis code or trained model deposit is stated.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hellodfan/GrainSpace .Open asset ↗hellodfan/GrainSpacelines:1035-1076Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Published18 Aug 2026Journal of King Saud University - Computer and Information SciencesCited by 0 · OpenAlex ↗
Plant phenotyping is essential for modern crop breeding, yet traditional static image analysis fails to capture the nonlinear dynamics of plant growth. Existing time-series forecasting models exhibit notable limitations when processing multimodal data: global pooling operations may compress local 2D spatial topology of plants, and shallow feature concatenation may be insufficient for effective cross-modal semantic alignment. Moreover, current methods typically regress absolute morphological states, which may contribute to temporal lag during nonlinear growth spurts. In this paper, we propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth. The network removes the final global pooling and classification layers to preserve spatial topology and incorporates a scalar-guided cross-modal attention module based on the standard query-key-value formulation. This module utilizes 1D morphological features as queries to dynamically weight local visual regions, promoting multimodal feature alignment. Concurrently, a residual incremental forecasting strategy is introduced to predict short-term growth increments rather than absolute states, aiming to improve tracking sensitivity to sudden growth events. Evaluations on the UNL-CPPD maize dataset and supplementary validation on the FIP1 wheat field dataset show that the proposed model achieves competitive single-step forecasting accuracy and favorable temporal trajectory alignment compared with adapted spatiotemporal attention, graph-based, and physics-informed baselines under the evaluated settings. In particular, the FIP1 results suggest that ST-CrossGro-Former can maintain favorable height trajectory alignment under a field-acquired wheat setting, indicating its potential for helping mitigate temporal misalignment in dynamic growth forecasting.
Why it matches plant phenotyping methods植物の動的形態成長を予測する新規クロスモーダル手法を開発し、トウモロコシ・コムギデータセットで評価しており、表現型の抽出・予測手法が中心である。
abstractwe propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth.
Reproduction assets foundThe paper evaluates its ST-CrossGro-Former model on two public plant phenotyping datasets: the UNL-CPPD maize dataset (explicitly stated as publicly available with a repository URL) and the FIP1 wheat field dataset (public dataset from ETH Zürich, with its GigaScience dataset publication DOI). No author analysis code, Dataset · publicThe UNL-CPPD dataset used in this research was acquired from the UNL Plant Phenotyping Datasets repository, accessible at https://plantvision.unl.edu/datasets.Open asset ↗UNL Plant Phenotyping Datasets · UNL-CPPDlines:266-273Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.
Why it matches plant phenotyping methods植物組織の細胞セグメンテーションとトランスクリプト割当てを改善する実用ワークフローを開発し、複数種・組織でベンチマークしている。植物形態そのものの測定ではないが、細胞レベルの空間状態を抽出する解析手法が中心である。
abstractHere, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics.
This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.
Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育（フェノロジー）段階を自動推定する手法が研究の中心であり、植物状態の取得・分類に直接関わる。
abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.
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
The inflorescence is a key yield-determining organ, yet its complex morphology makes manual pixel-level annotation time-consuming, leading to a scarcity of high-quality segmentation datasets. To address this bottleneck, we propose ZCAT (Zero-shot Cross-crop Annotation Transfer), a novel paradigm for zero-annotation cross-crop pseudo-mask screening. ZCAT completely eliminates pixel-level manual annotation of the target crop, requiring only holistic quality assessment of model-generated pseudo-masks (5-10 s per image). Specifically, we train a SegFormer model on public rice panicle datasets (CVRP and RiceSEG) and transfer it across crops to the wheat spike segmentation task. The key innovation is the introduction of a human-defined quality function Q, which circumvents the fundamental challenge in self-learning algorithms: the inability of computers to autonomously distinguish good masks from bad ones. Through iterative human-in-the-loop pseudo-label screening with a curriculum learning strategy, each round adds only a few high-quality pseudo-masks to the training set, continuously improving model performance. After four iterations, ZCAT produces pseudo-masks with an average Spike IoU of 0.7003, evaluated against the GWFSS manual annotations as ground truth. Moreover, the pseudo-mask dataset exhibited higher benchmark performance than the GWFSS manual annotations (Spike IoU 0.7612 vs. 0.7027; mIoU 0.8627 vs. 0.8247), suggesting stronger self-consistency. A generalization test on a strictly held-out set of 100 manually annotated wheat spike images showed that the model trained on ZCAT-generated pseudo-masks achieved marginally better performance than that trained on the GWFSS manual annotations (Spike IoU: 0.5112 vs. 0.4927; mIoU: 0.5627 vs. 0.5247). The time budget of the ZCAT pipeline was substantially lower than that of manual annotation. ZCAT opens a new pathway for rapid annotation of plant reproductive structures or other organs and significantly reduces data preparation costs in plant phenomics. The generated wheat spike pseudo-mask dataset and the mask quality screening tool (Mask Quality Screener) are open-sourced.
Why it matches plant phenotyping methods植物器官セグメンテーションのためのゼロショット転移、擬似マスク品質評価、反復学習パイプラインを開発・検証しており、表現型取得基盤が中心である。
abstractThe key innovation is the introduction of a human-defined quality function Q
Reproduction assets foundThe paper explicitly open-sources two paper-specific assets: the ZCAT-generated wheat spike pseudo-mask dataset and the Mask Quality Screener tool, both with public GitHub URLs in the Data availability statement.Dataset · publicThe wheat spike pseudo-mask dataset and Mask Quality Screener are available at https://github.com/zyxyes1/MaskQualityScreener and https://github.com/zyxyes1/Wheat-Spike-Semantic-Segmentation , respectively.Open asset ↗Wheat-Spike-Semantic-Segmentationlines:415-440Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
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)
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
Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.
Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。
abstractThis project develops a workable solution.
Reproduction assets foundThe paper builds its multimodal wheat phenotyping analysis on several explicitly cited public data assets: the Kaggle Wheat Plant Diseases image dataset (used for disease classification, Tables 2 and 9), the Global Wheat Head Detection dataset (used for head detection, Tables 1 and 6), FAOSTAT and India Open GovernmentDataset · publicAvailable online at: https://www.fao.org/faostat/ . FAOSTAT statistical database.Open asset ↗lines:1110-1162Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.
Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育・フェノロジー段階を自動推定する方法が研究の中心であり、植物状態の抽出性能も評価している。
abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.
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.
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
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.
Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response. Modern 3D reconstruction methods perform strongly on generic benchmarks, but rendered appearance may not translate into metrically and agronomically useful geometry in crop fields. We introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys. It contains 88,830 RGB images at $5280 \times 3956$ pixels, with a ground sampling distance of 3.6-5.8 mm, from 91 scenes spanning corn, soybean, wheat, and oat. Track A evaluates seven scene-optimized methods -- Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) variants -- on held-out views, photogrammetry-referenced depth, and canopy-height recovery. Track B tests four pretrained feed-forward models on zero-shot camera-pose and geometry estimation. The scene-optimized methods rank differently across the three targets: Splatfacto-big leads appearance, whereas Scaffold-GS leads depth and is statistically tied with Splatfacto for canopy height. Among feed-forward models, MapAnything leads on seven of the eight metrics, while the remaining models vary more across crops and fail severely on absolute scale in a way that alignment conceals. Repeated acquisitions reveal further sensitivities that differ by output type and by model, associated with position within the acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/
Why it matches plant phenotyping methods植物キャノピー高さという明示的な形質を対象に、UAV 3D再構成手法をベンチマークし、公開データセットとして提供しているため、フェノタイピング手法が中心である。
abstractWe introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys.
Reproduction assets foundThe paper introduces UAV3DCrop, a public benchmark of repeated multi-angle UAV crop surveys (88,830 RGB images, 91 scenes, four crops) with refined poses, photogrammetric depth references, and linked canopy-height and effective-LAI field measurements. The dataset is explicitly stated to be publicly available under CC BDataset · publiche acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/ .
Keywords:
UAV imagery; agricultural datasets; crop-field reconstruction; neural radiance fields;
Gaussian splatting; feed-forward geometry.
1 IntroductionOpen asset ↗UAV3DCroplines:1-90Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Spike count and spatial arrangement are among the strongest determinants of grain yield in wheat, yet reliable spike-level phenotyping under field conditions remains challenging. Field-acquired images are often affected by occlusion, heterogeneous illumination, and dense canopy overlap. In addition to these visual complexities, the effort required to generate large-scale instance-level annotations makes it difficult to build the extensive labeled datasets typically needed for robust segmentation models. This paper addresses both problems. The core methodological contribution is a semi-automated iterative annotation workflow: a YOLOv11x-seg model trained on a small manually annotated set is applied to unlabeled field images, and its predictions, after post-processing to remove duplicated, fragmented, and merged detections, are incorporated back into training. To prevent augmentation from distorting the training distribution, we introduce a distribution-aware augmentation strategy guided by Fréchet Inception Distance (FID), which retains only those augmented samples that remain within an acceptable distance from the original data distribution. Together, these components allowed us to build an effective training set from 3,899 high-resolution RGB images (4000 × 3000 pixels) of durum wheat collected at the CREA Research Centre for Cereal and Industrial Crops, with substantially reduced manual annotation effort. On the independent test set(242 images), the final YOLOv11x-seg model (M5 model) achieved a mask-level precision of 86.73%, recall of 83.02%, F1-score of 84.83%, mAP@50 of 89.42%, and mAP@50:95 of 60.51%. Spike masks were used to derive image-based traits including spike count, spike density, canopy coverage, spike area, spatial distribution, and vegetation indices. Their relationships with measured grain yield were explored through statistical analysis and machine-learning-based yield estimation.Both statistical and machine-learning analyses demonstrated that image-derived spike traits provided meaningful information for grain yield estimation, with canopy coverage showing the strongest positive association with yield. Using repeated nested cross-validation with out-of-fold (OOF) predictions, XGBoost achieved the highest yield estimation performance ( R OOF 2 = 0.312 , RMSE = 106.61 g/plot), supporting the potential of near-image phenotyping for late-stage yield estimation in wheat. These results show that semi-automated iterative annotation can enable practical wheat spike segmentation and image-based phenotyping under realistic open-field conditions. While grain yield estimation should be interpreted within the context of the experimental setting, the proposed framework highlights the value of image-derived spike traits for late-stage phenotyping and yield assessment in breeding experiments rather than for early-season yield forecasting.
Why it matches plant phenotyping methods半自動アノテーション、YOLOv11x-segによる小麦穂の画像セグメンテーション、分布認識型データ拡張、形質抽出と検証が中心的な方法論的貢献である。
abstractThe core methodological contribution is a semi-automated iterative annotation workflow
To address the challenges of insufficient sensor stability and poor consistency among multi-source data during crop phenotyping in unstructured field environments, this study develops a hardware-software co-optimization framework for wheat canopy sensing based on a four-wheel-drive, high-clearance phenotyping platform. A multi-sensor stabilization device integrated with an ESO-LQR control strategy suppresses pitch disturbances during motion. A ROS-based hierarchical framework coordinates LiDAR, cameras, and inertial sensors, while spatial calibration and timestamp-based software synchronization ensure spatiotemporal consistency. A tightly coupled LiDAR-IMU SLAM algorithm enables centimeter-level 3D reconstruction of farmland. To mitigate terrain effects, a two-stage ground point extraction method integrating verticality and spatial distribution features improves canopy height estimation. Field experiments demonstrate stable platform operation at 1.5 m s⁻¹ while maintain high efficiency and data quality, with a coverage efficiency of 95.2%, retained-point ratio of 85.3%, and an MTF of 0.35 for image clarity. Phenotypic evaluation shows strong agreement between predicted wheat plant height and manual measurements, with R² values of 0.898 and 0.729 and RMSE values of 1.30 cm and 1.64 cm at the jointing and grain-filling stages, respectively. Moreover, at the jointing stage, both the 2D green area index (GAI) and the 3D point-cloud-based canopy coverage exhibit strong consistency with ImageJ-derived results (R² = 0.873 and 0.910). These findings demonstrate that the proposed approach enables stable and efficient acquisition of crop phenotypic information in complex field environments, providing a solid technical foundation for digital field monitoring, data-driven crop management, and intelligent agricultural systems.
Why it matches plant phenotyping methods小麦キャノピーの表現型取得を目的としたマルチセンサープラットフォーム、データ同期・3D再構成・キャノピー高さ推定を開発し、手動測定やImageJとの一致性を検証しているため、方法が研究の中心である。
abstractthis study develops a hardware-software co-optimization framework for wheat canopy sensing based on a four-wheel-drive, high-clearance phenotyping platform.
The extent of canopy coverage (CC) prior to the booting stage is a useful indicator of environmental adaptation and may help anticipate key developmental events such as heading and flowering. We used UAV-based high-throughput phenotyping to monitor CC in a 262-line F 8 recombinant inbred line population grown under four irrigation-year environments across two seasons. To quantify CC dynamics, we fitted regression models using either days after sowing (DAS) or accumulated active temperature (AT). These models showed a high goodness-of-fit (overall coefficient of determination ( R 2 ) > 0.90 across environments; per-timepoint prediction R 2 = 0.60‒0.99 with root mean squared error (RMSE) = 0.00‒0.02) and were used to derive 31 CC-related traits. Principal component analysis (PCA) showed that the first two components explained 80.00% of total variance in CC traits, with PCA1 accounting for 48.88%‒62.51% of the variance for DAS-based traits and 50.26%‒54.93% for AT-based traits. PCA1 reflected early canopy vigor and rapid coverage increase, while PCA2 reflected canopy maintenance after jointing. Genotypes in the top 15% for PCA1 differed significantly in flowering time, heading time, and plant height from those in the bottom 15%. Cross-environment comparisons showed moderate to high consistency of CC traits (average correlation ( r ) = 0.41‒0.65 for DAS-standardized CC of 20‒160 DAS and 0.48‒0.67 for AT-standardized CC of 100‒1000 AT). Using CC features derived from both DAS and AT, we trained a random forest model to predict flowering time, highlighting the contribution of both temporal and thermal information to predictive accuracy. This model achieved an independent test set R 2 of 0.81 with an RMSE of 1.25 days within the current dataset. All 31 CC-derived traits were also used for QTL mapping. Inclusive composite interval mapping identified 156 QTL detection events across 15 chromosomes (0.80%‒27.70% phenotypic variance explained), which were consolidated into 26 QTL regions, including loci such as QCC.caas.7A (671.47‒680.09 Mb on 7A) and QCC.caas.5D2 (426.67‒459.42 Mb on 5D). Several of these regions co-localized with previously reported genes associated with tillering, flowering time, winter hardiness and plant height. These findings indicate that CC, characterized by DAS- and AT-based traits, is genetically tractable and predictive of flowering within the current dataset, supporting its potential as a phenology-related trait for wheat improvement. Further validation across broader environments, years, and genetic backgrounds will be needed before broader application.
Why it matches plant phenotyping methodsUAV高スループット表現型解析を用いて小麦のキャノピー被覆動態を定量化し、31形質を抽出・検証しているため、表現型取得と解析が研究の中心である。
abstractWe used UAV-based high-throughput phenotyping to monitor CC in a 262-line F 8 recombinant inbred line population grown under four irrigation-year environments across two seasons.
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.
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.
Multi-temporal data from unoccupied aerial systems (UAS) offer insights into growth parameters for winter wheat breeding decisions. Weekly UAS data were collected during the 2019 and 2020 growing seasons from dryland and irrigated nurseries at Bushland, Texas, within the Texas A&M Uniform Variety Trials. Canopy cover (CC) was extracted from orthomosaic images and modeled using a double-sigmoid function with a second-order derivative that captured genotypic variation in canopy growth and senescence with high coefficients of determination (R² > 0.99) and low root mean square error (RMSE) values ranging from 1.73 to 4.06. Analysis of variance (ANOVA) revealed highly significant genotypic effects (p<0.001) for yield, heading, and Green Leaf Area Duration (LAD) in all environments except 2020 dryland, where no significant differences among genotypes were detected. Extracted parameters showed positive correlations with agronomic traits, particularly under rainfed and stress-prone conditions. The end decrease stage (EDS) was correlated with grain yield (r=0.56 in 2019 dryland, and r=0.53 in 2020 irrigated, p<0.001), and the start decrease stage (SDS) was highly correlated with yield (r=0.53 in 2020 irrigated, p<0.001). The maximum decrease rate date (MDRD) was positively correlated with yield in 2019 dryland (r=0.55, p<0.001), while LAD had a correlation of r=0.56 in 2019 dryland and r=0.58 in 2020 irrigated (p<0.001). These findings demonstrate that double-sigmoid model provides a powerful, non-invasive framework for quantifying canopy development, senescence timing, and stress responses. By distinguishing genetics from environmental influences on canopy dynamics, this approach enhances selection accuracy and accelerates the development of stress-resilient winter wheat cultivars.
Why it matches plant phenotyping methodsUAS画像からキャノピー被覆を抽出し、二重シグモイドモデルで生育・老化動態を定量化する手法が研究の中心であり、精度評価と遺伝型・農業形質との検証も行っている。
titleA double-sigmoid approach for high-throughput phenotyping of winter wheat growth dynamics
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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.
Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。
abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.
The 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
In India, to increase the wheat yield production and support sustainable agricultural practices, timely and correct identification of wheat crop diseases such as yellow rust, brown rust, etc., is very much essential. Further, the majority of deep learning algorithms show high efficiency in plant disease detection and recognition, but these studies rely on handcrafted datasets captured under controlled laboratory conditions and accordingly will not perform well in the real world domain. Furthermore, there is a lack of real field wheat crop disease datasets collected in India, particularly from Haryana state, being the largest producer of wheat crops. Therefore, to address the issue of lack of region specific crop diseases dataset, which are region-specific, the wheat crop disease data set is curated, consisting of 2672 images of healthy and diseased leaves of wheat crops collected from the real field of CCS Haryana Agricultural University, Hisar. The dataset includes typically three classes of leaves of wheat crop, i.e., Yellow Rust (910), Brown Rust (922) and Healthy leaves (840) collected using both a DSLR camera and different smartphone cameras under natural lighting and field conditions. All images were validated and labeled by taking the expertise of wheat pathologists to ensure reliability on the dataset. The study also provides a comprehensive, systematic workflow for transforming raw data into a high-quality benchmark dataset for training using image preprocessing and augmentation techniques. Besides this, a comparative benchmarking analysis is performed under identical experimental conditions using three widely adopted deep learning architectures, which includes ResNet50, MobileNetV2 and EfficientNet-B0. The experimental results show that ResNet50 and EfficientNet-B0 achieve similar performance i.e. approximately 91% classification accuracy on real-field data. However, MobileNetV2 offers a lightweight alternative suitable for mobile and edge deployment. Furthermore, Grad-CAM based explainability analysis was performed to validate model predictions and highlight disease specific regions in wheat leaves. Therefore, this study contributes a practical region specific WheatDisease-HRY dataset and baseline benchmarking framework for developing robust AI based wheat disease diagnosis tools for real world agricultural applications in India.
Why it matches plant phenotyping methodsコムギ葉の病徴を画像から判定するデータセットを構築し、前処理・拡張、深層学習ベンチマーク、説明可能性解析までを中心的に扱うため、植物病害状態の画像ベース表現型手法として採用。
abstractThe study also provides a comprehensive, systematic workflow for transforming raw data into a high-quality benchmark dataset for training using image preprocessing and augmentation techniques.
Seedling establishment represents a critical phase in early crop growth and development, directly influencing biomass accumulation and yield potential. To characterise early growth dynamics under field conditions, both growth rate and uniformity of emergence need to be assessed continuously; however, manual quantification of these dynamic traits in large-scale trials remains impractical. Here, we present LeafTip-RN, an open-source and deep learning (DL)-powered pipeline for dynamically measuring wheat (Triticum aestivum L.) early establishment in the field. To enable flexible and scalable data collection, ultralow-altitude drone phenotyping was employed, followed by the development of an optimised DL model to automate leaf-tip-related feature extraction from complex backgrounds. Notably, to address data sparsity arising from eight phenotyping timepoints, we integrated an image-to-video generative AI (GenAI) module into the pipeline to interpolate keyframes between early and late seedling stages (i.e. 18-40 days after sowing), resulting in a training library comprising 353,019 labelled leaf tips. Using the pipeline, we successfully quantified multiple agronomically important establishment-related traits (e.g. plot-level leaf tips and seedling spatial uniformity), followed by deriving their growth curves for 51 wheat varieties across two growing seasons (2024-2026). After validating these LeafTip-RN-derived traits, we further computed varietal relative growth rates and uniformity indices, based on which the 51 varieties were classified into high-, medium-, and low-performance groups, revealing discrepancies between LeafTip-RN-derived classification (18-40 DAS) and manual assessment at 40 DAS when dynamic early performance was considered. Finally, to facilitate broad adoption by the plant research community, we developed an openly accessible graphical user interface (GUI) for non-expert users to visualise and analyse rapid seedling developmental changes. Taken together, our study provides a scalable GenAI-powered solution for evaluating seedling establishment in wheat, offering valuable tools for breeders and researchers to identify varieties with enhanced early growth vigour and emergence dynamics that are extensible to other cereal crops.
Why it matches plant phenotyping methodsLeafTip-RNは、ドローン画像と深層学習・生成AIによって小麦の葉先や出芽均一性などの形質を自動抽出・連続推定する手法およびGUIを開発し、導出形質を検証しているため、植物フェノタイピング手法が研究の中心である。
abstractHere, we present LeafTip-RN, an open-source and deep learning (DL)-powered pipeline for dynamically measuring wheat (Triticum aestivum L.) early establishment in the field.
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
Wheat head detection is a critical component in high-throughput phenotyping, holding significant application value for wheat yield estimation and breeding analysis. With the continuous advancement of general object detection models, state-of-the-art detectors achieve high accuracy in same-distribution wheat head detection scenarios. However, when applied to cross-distribution environments, their performance often degrades significantly. To investigate this issue, this paper adopts the official division of the GWHD2021 public dataset as the cross-distribution evaluation setting. Using the recently state-of-the-art object detection model YOLOv13 as the baseline, we systematically explore effective approaches to enhance cross-distribution generalization performance in wheat head detection. Specifically, we propose two lightweight modifications to YOLOv13’s Full-PAD architecture and HyperACE’s core modules, analyzing their potential mechanisms: (1) Replacing scalar gating in Full-PAD with channel-level gating enables finer-grained branch injection control. Concurrently, channel-level gating introduces equivalent stronger weight penalties during training, generating additional regularization effects. Through exploratory controlled experiments aligning weight-penalty strengths, we find that this gain depends on both channel decoupling and the accompanying implicit regularization rather than on decoupling alone. (2) Removing Batch Normalization from HyperACE’s core C3AH modules and adopting normalization strategies independent of batch statistics—such as Identity, Group Normalization, or Instance Normalization. Preliminary experiments show that while this results in a small, directionally positive change, the change remains within run-to-run variance; we therefore do not claim BN removal as a reliable standalone improvement. Furthermore, combining channel gating with BN removal does not yield further additive improvements compared to channel gating alone, indicating an interaction effect. To address this, we analyze relevant statistics between channel gating and HyperACE branches, providing an exploratory explanation for this non-additive phenomenon. In summary, this paper delivers empirical evidence and preliminary insights for enhancing generalization performance in the cross-distribution wheat head detection task of the advanced general-purpose detection model YOLOv13.
Why it matches plant phenotyping methods小麦穂の画像検出を対象に、YOLOv13の改良と異分布ロバスト性をGWHD2021で系統的に評価しており、植物表現型取得・解析手法が中心である。
abstractWheat head detection is a critical component in high-throughput phenotyping
With climate change and global population growth, accelerating the breeding of superior crop varieties is essential for food security. Genomic prediction, which uses genome-wide genetic markers to predict crop traits, plays an important role in intelligent crop breeding. However, existing methods often lack stable and accurate performance across crops and traits. Here, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm. Compared with the best-performing single base learners, GEG2P improves prediction accuracy by 4.02% on average across maize, wheat, rice, chickpea, and soybean. We use SHAP to quantify the contribution of SNPs to phenotype prediction and find that SNPs with large effects captured by different base learners are functionally complementary. This study provides a robust and accurate genomic prediction method for crop breeding.
Why it matches plant phenotyping methods作物形質の遺伝子型から表現型を予測するアンサンブル計算法を開発し、複数作物で精度比較・検証しており、表現型推定手法が研究の中心である。
abstractHere, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm.
Reproduction assets foundThe paper provides public author code (GitHub GEG2P repository and Docker Hub image), a Zenodo deposit of significant SNP interaction pairs generated in this study, and a Figshare link with the wheat genotypic and phenotypic data used in the analyses. These are paper-specific, publicly available, and actionable.Code · publicScripts used in this study are available at GitHub [ https://github.com/Deep-Breeding/GEG2P ] 89 .Open asset ↗GitHub · Deep-Breeding/GEG2Plines:236-266Dataset · publicThe genotypic and phenotypic data of wheat are available at Figshare [ https://figshare.com/s/287c2c7f1623008487a5 ] 68 .Open asset ↗Figsharelines:236-266Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published20 Jul 2026International Journal of Agriculture Forestry and Life SciencesCited by 0 · OpenAlex ↗
Selecting ideal drought-tolerant wheat varieties requires a holistic synthesis of digital phenotypes, molecular markers, and agronomic indices. This study evaluated 16 wheat genotypes for drought tolerance by integrating digital phenotyping (UAV-based thermal imaging), molecular data (DREB gene expression profiles), and 12 agronomic indices. While vegetative DREB accumulation remained mostly homogeneous, the generative stage triggered pronounced transcriptional shifts, and late-stage thermal screening revealed highly significant genotypic differences during grain filling. A multivariate PCA biplot identified early canopy temperature differences during tillering (ΔCT_TL) as the most informative non-destructive selection indicator. ΔCT_TL showed a strong positive association with terminal yield stability metrics (YSI and RSI) and a marked negative relationship with the drought sensitivity index (SDI). This early canopy temperature regulation contributed to the maintenance of yield stability in the modern hexaploid variety MFTBY-T and advanced tetraploid lines OR2-T and OR4-S. In contrast, poorly adapted ancient varieties (P5-S and S3-S) exhibited high drought sensitivity accompanied by pronounced late-stage induction of DREB1 and DREB2, suggesting a delayed stress-response mechanism activated under severe tissue dehydration. Conversely, the modern tetraploid variety KZLTN-T and hexaploid landraces appeared to rely on an early vegetative molecular priming strategy. These findings suggest that breeding programs should prioritize the incorporation of vegetative transcriptional traits associated with effective canopy temperature homeostasis into elite genetic backgrounds.
Why it matches plant phenotyping methodsUAV熱画像によるキャノピー温度の非破壊測定をデジタル表現型として用い、乾燥耐性選抜指標として評価しており、表現型取得・解析が研究の主要な構成要素である。
titleINTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES
Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.
Why it matches plant phenotyping methodsUAV multispectral HTPによる小麦黄さび病重症度の推定と、複数の予測モデルの比較・検証が研究の中心であり、植物病害状態を直接推定する実質的なフェノタイピング手法研究である。
abstractHTP data were processed to extract spectral wavelengths and vegetation indices (VIs)
Reproduction assets foundThe article's Data Availability statement deposits the datasets generated and analyzed in this study (yellow rust phenotyping with UAV spectral data and genomic markers in bread and durum wheat) in the CIMMYT repository under DOI 10.71682/10549375, which is an allowed URL. No author analysis code or trained model is avDataset · publicand scalable strategy for YR assessment in wheat breeding.
Funding
The authors gratefully acknowledge financial support from the Government of Mexico through
the “MasAgro – Cultivos para México” initiative.
Data Availability
The datasets generated and/or analyzed during the current study are available in the CIMMYT
repository: https://doi.org/10.71682/10549375.Acknowledgements
We are deeply grateful to Julio Huerta-Espino for his guidance and support throughout all stages
of this manuscript. We also thank Hedilberto Velásquez Miranda for his valuable assistance with
rust visual score phenotyping, and Neftalí Cruz Pérez for his dedicated support in trial sowing and
field management.
Conflict Open asset ↗10.71682/10549375pdf-raw-page:30 lines:1-37Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published17 Jul 2026Zenodo (CERN European Organization for Nuclear Research)Cited by 0 · OpenAlex ↗
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerを用いた根系画像解析が研究の中心で、根長・径・体積・表面積・分枝などの植物形質をデジタル抽出しているため、実質的な植物フェノタイピング応用研究である。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerによるデジタル根系表現型計測が研究の主要な方法として明示され、根長・径・体積・表面積・分枝などの植物形質を抽出しているため。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
Early crop establishment strongly influences plant performance and yield, making seedling emergence an important trait in crop phenotyping, breeding, and stress physiology studies. However, emergence monitoring is still commonly performed manually and typically records only the final emergence percentage, limiting the analysis to other dynamic observations. Automated image-based approaches are promising but remain challenging due to the small size of plant structures, heterogeneous soil backgrounds, and variability across imaging systems. Here, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction. The system integrates instance segmentation, object-detection–based data reduction, and temporal deep learning to estimate the emergence time of individual seedlings from RGB image sequences. The pipeline then automatically reconstructs emergence curves and extracts associated traits, including final emergence percentage, EC50, and emergence synchronicity. SPROUT was developed and evaluated using barley and wheat datasets acquired with different RGB cameras under controlled growth-chamber conditions. In the development and retraining settings, the best-performing TCN model achieved 90.0% per-well accuracy with a ± 2h tolerance, supporting accurate emergence curve reconstruction. In an independent inference-only dataset, the model still captured approximate emergence dynamics, although accuracy decreased to 59.3%, indicating that SPROUT is best used as a modular pipeline that can be retrained or fine-tuned for new crop, camera, or experimental domains. A cadmium-stress case study in two contrasting wheat genotypes showed that SPROUT-derived traits captured genotype-specific establishment strategies associated with growth and metabolic responses.
Why it matches plant phenotyping methodsRGB時系列画像から出芽動態を推定し、出芽率・EC50・同時性などの形質を抽出するパイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction.
Reproduction assets foundThe paper's SPROUT emergence-prediction pipeline code is publicly available on GitHub with explicit availability language, and raw images plus morphology/metabolic data are deposited on Zenodo (10.5281/zenodo.18889863). The GitHub URL is in allowed_urls; the Zenodo DOI is not, so only the code asset is listed as an ad-Code · publiccan be found online at https://doi.org/10.1016/j.compag.2026.112184.Data availability
The raw images and raw data for the morphology and metabolic
profiling on the case study are available in ZENODO (10.5281/zen
odo.18889863), and the code for the machine learning pipeline and
emergence curve analysis are available on GitHub (https://github.com/kit-pef-czu-cz/sprout-emergence-prediction).References
Albarenque, S., Basso, B., Davidson, O., Maestrini, B., Melchiori, R., 2023. Plant
emergence and maize (Zea mays L.) yield across multiple farmers’ fields. Field Crops
Res. 302. https://doi.org/10.1016/j.fcr.2023.109090.Arsovski, A.A., Galstyan, A., Guseman, J.M., Nemhauser, J.L., 2012.
PhotomorpOpen asset ↗kit-pef-czu-cz/sprout-emergence-predictionpdf-raw-page:13 lines:78-112Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Whole plant leaf expansion (shoot expansion) under drought drives the trade-off between water saving for later grain production and canopy photosynthesis. Fine-tuning shoot expansion could therefore become a target of genetic progress for drought-prone environments. However, its components (axis production, i.e. tillering, leaf production on each axis, and individual leaf elongation) may have their own genotypic variability and plasticity under drought, making hard to calibrate crop simulation models and specify breeding targets. In this study, we focused on the genetic diversity of bread wheat and durum wheat to determine the links and trade-offs between the underlying processes of shoot expansion under drought and how it translates at the whole plant and canopy level. For that, we used non-destructive imaging both in the field and controlled condition platforms to determine their dynamics and analyze their relative contribution to the genotypic variability of whole-plant shoot expansion under drought. Results show that shoot expansion measured at plant level in controlled environment was associated with that measured at canopy level in the field, indicating that controlled phenotyping platforms can capture the genotypic variability of growth in the field. Both whole-plant and canopy expansion were associated with tillering rate. In addition, the sensitivity of shoot growth and tillering to soil water deficit were correlated, indicating that both tillering ability and sensitivity to water deficit drive the genotypic variability of shoot expansion. Overall, dissecting shoot- expansion dynamics allowed determining the links between shoot expansion traits under drought, and provides key targets in phenotyping, modelling and breeding for drought environments.
Why it matches plant phenotyping methods非破壊画像と管理環境・圃場のフェノタイピングプラットフォームを用いて、シュート伸長・分げつの動態を測定し、環境間での性能を比較しているため、表現型取得法の応用が研究の中心的要素です。
abstractFor that, we used non-destructive imaging both in the field and controlled condition platforms to determine their dynamics and analyze their relative contribution to the genotypic variability of whole-plant shoot expansion under drought.
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.
Accurate yield prediction for major grain and oilseed crops, including soybean, corn, wheat, and rice, is essential for food-security assessment and precision field management. This study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis. Seventy peer-reviewed studies published between 2018 and 2025 were synthesized within a "Data-Ground Truth-Model-Decision" framework. Beyond summarizing UAV platforms, sensor configurations, feature-engineering strategies, and model architectures, the review explicitly distinguishes among microplot, field, and regional prediction scales, and evaluates the characteristics and limitations of yield-label acquisition methods, including manual harvest, plot-combine harvest, and combine yield-monitor data. Existing evidence indicates that the reliability of UAV-based yield prediction depends not only on optimal image acquisition windows, multi-source feature fusion, and model architecture, but also on scale-consistent yield labels, spatially aware validation strategies, and clearly defined model outputs, such as plot-level scalar yield, field-scale yield maps, and regional yield estimates. Major bottlenecks include scale mismatch between UAV imagery and yield labels, error propagation during yield-map generation, limited cross-year and cross-region transferability, weak causal interpretability, and difficulties in deploying models under complex operational field conditions. Future research should emphasize scale-explicit benchmark datasets, quality-controlled ground-truth yield acquisition, UAV-satellite-ground data fusion, spatiotemporal deep learning, and edge-cloud collaborative systems that can translate prediction outputs into agronomic decisions. This review provides a practical pathway for developing robust, interpretable, and deployable UAV-based yield prediction systems for major grain and oilseed crops.
Why it matches plant phenotyping methodsUAV画像から作物の収量という植物形質を推定する手法を中心に、プラットフォーム、特徴量、モデル、検証尺度、グラウンドトゥルースを体系的にレビューしているため、方法レビューとして収載。
abstractThis study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis.
Phenotyping stomatal traits and their developmental plasticity is time-consuming but holds potential to improve water use efficiency and photosynthesis for designing stress-tolerant crops under climate change. Here, we develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning. We (1) analyze over 25,000 images from 60 wheat cultivars grown in growth chamber, greenhouse, and field conditions; (2) investigate the impact of light, temperature, and reduced water and nitrogen supply on stomatal traits and their developmental plasticity across adaxial and abaxial surfaces; and (3) evaluate genetic diversity and breeding progress of stomatal traits. Stomatal traits were highly broad-sense heritable, were largely plastic in response to environmental conditions, and showed genotype-specific responses. Stomatal traits of third leaves under controlled environments with stable light and temperature conditions reliably captured the genetic variance of flag leaves under field conditions. Our data suggests that the upper leaf surface contributed more to transpiration and cooling through consistently higher stomatal density, area, and maximum conductance, while the lower surface facilitated CO₂ diffusion via systematic proper patterning and spacing. Breeding maintains the genetic diversity of stomatal traits, and our pipeline facilitates breeders to target them to enhance water use efficiency in high-yielding modern cultivars.
Why it matches plant phenotyping methods高スループットで14種類の気孔形質を抽出するパイプラインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning.
Reproduction assets foundThe paper's Data and code availability section states that all data are publicly available in a Zenodo repository and that the stomatal identification and trait quantification code is in the authors' public GitLab repository. Both URLs appear verbatim in the supplied blocks and match allowed_urls. The Zenodo DOI in theCode · publicThe code for all the programs in this paper, including the stomatal identification and trait quantification, can be found in our GitLab repository, https://scm.cms.hu-berlin.de/intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotyping .Open asset ↗intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotypinglines:197-215Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Context Precision agriculture can benefit from within-field boundary line analysis (BLA) to identify the most limiting factors impacting crop yield. In this study, the BLA was applied at the within-field scale using yield monitor data and key environmental factors, including evapotranspiration (ET), elevation, and the apparent soil electrical conductivity (ECa). Aims We aimed to develop and evaluate a novel BLA method to estimate Yp and quantify Yg at the within-field scale, and to assess the diagnostic potential of freely available proxy variables for identifying spatially variable yield-limiting factors in dryland wheat production. Methods We combined Mahalanobis distance-based filtering for data denoising with 2Dl kernel density estimation (KDE) and percentile thresholding to select high-density, high-yield points that define the upper yield envelope. A generalised additive model (GAM) was then used to produce the boundary line through these selected points to represent the Yp. Key results Results from the two case studies showed that this approach was robust and less sensitive to noise and outliers in fine-scale datasets. Freely available ET and elevation could be proxies to highlight the impact of some limiting factors, such as frost events or waterlogging. The ECa could identify areas where some potential soil-related factors (e.g. lower clay content reducing plant available water capacity) could be the limiting factors. Conclusions While the proxy variables effectively indicated potential limiting factors, ground-truth validation is required to confirm the underlying causal mechanisms. Implications Growers could benefit from the BLA approach to identify local yield constraints, estimate site-specific Yp and Yg, and fine-tune their inputs, leading to more efficient resource use and improved profitability.
Why it matches plant phenotyping methods作物収量という植物形質を推定する境界線分析法を開発・評価し、ノイズ除去、KDE、GAMによるワークフローを中心的に提示しているため。
abstractWe aimed to develop and evaluate a novel BLA method to estimate Yp and quantify Yg at the within-field scale
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.
This study presents a large-scale framework for the digitalization of wheat mold odor based on controlled volatile release and standardized gas acquisition. Seven wheat-varieties harvested from 2022 to 2025 in three provinces of China were cultivated into four spoilage levels (normal, mild, moderate, and severe mildew), and a graphene-based sensor array was developed for multidimensional odor detection. A dual pre-treatment strategy integrating temperature-regulated volatilization and cooling-assisted dehumidification was implemented to ensure stable and comparable signal acquisition. Under optimized conditions, 1491 odor response curves from the first three batches were used for machine learning modeling, while an independent fourth batch (n = 503) was used for external validation. For binary classification (normal vs. moldy wheat), the optimized Light Gradient Boosting Machine achieved 95.8% accuracy, 95.5% sensitivity, 96.8% specificity, and an AUC of 0.984. This proposed approach enables rapid, non-destructive mold assessment and supports standardized grain quality monitoring.
Why it matches plant phenotyping methods小麦穀粒のカビ状態を対象に、グラフェンセンサーアレイと揮発成分取得前処理を開発し、機械学習分類と独立バッチ検証まで行っており、植物状態の取得・判定法が研究の中心である。
abstracta graphene-based sensor array was developed for multidimensional odor detection
Introduction Wheat is one of the most widely planted staple crops worldwide and underpins global food security. Fungal diseases severely threaten wheat growth and trigger massive yield losses during cultivation. Traditional manual diagnosis is time-consuming and highly subjective, while existing deep learning models often struggle to achieve high accuracy and robustness in complex field environments. Accurate identification of these fungal diseases is therefore vital to secure grain production. Methods This paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification. First, the original basic blocks of ShuffleNetV2 are substituted with SS-Conv-SSM modules to strengthen the extraction of fine-grained lesion features amid visually analogous fungal disease samples; half convolution is embedded to cut down model computational overhead. Second, a Mixed Local Channel Attention (MLCA) unit is attached to the convolution branch of each SS-Conv-SSM module, which adaptively highlights discriminative disease features and filters irrelevant background noise. Standard training configurations and five-fold cross-validation are adopted for fair model evaluation. Results Comparative experiments reveal that the presented network reaches a classification accuracy of 91.35%, which surpasses the original ShuffleNetV2 baseline by 1.16 percentage points. Controlled ablation tests verify the independent performance gain of each core component: the SS-Conv-SSM module raises overall accuracy by 0.89%, and the MLCA mechanism brings an extra 0.27% accuracy increment. Discussion The proposed ShuffleNetV2_SSM_MLCA architecture strikes a favorable trade-off between model lightweight property and classification performance. It delivers a low-computation, high-precision recognition scheme for wheat fungal diseases and lays a solid technical foundation for real-time disease monitoring in intelligent agricultural scenarios.
Why it matches plant phenotyping methods小麦葉片の病斑特徴を画像から抽出し、植物の真菌病状態を分類する軽量深層学習手法を開発・検証しており、病害表現型の取得・推定が研究の中心である。
abstractThis paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification.
Reproduction assets foundThe paper's plant-image measurements are based entirely on publicly available wheat disease image datasets: a primary Kaggle dataset (Wheat Plant Diseases by Kushagra Agarwal) used for model development, and two additional public datasets (Alibaba Cloud Developer Community and CSDN Modelers) used for generalization andDataset · publicThe dataset is publicly available at https://www.kaggle.com/datasets/kushagra3204/wheat-plant-diseases and was accessed on September 5, 2025.Open asset ↗Kaggle · kushagra3204/wheat-plant-diseaseslines:322-374Dataset · publicThe second dataset was contributed by blogger DL data set and released on December 25, 2025 via the CSDN Modelers platform ( https://modelers.csdn.net/69a67f4c7bbde9200b9c3240.html )Open asset ↗CSDN Modelerslines:644-669Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Crop targets in UAV aerial images are typically characterized by small scale, dense distribution, severe mutual occlusion, and complex backgrounds, which often lead to low detection accuracy and large counting errors for existing deep learning models. To address these issues, this study proposes an improved YOLOv12-based crop detection and counting model, named YOLO-DC. By introducing an attention mechanism (LGCB-AM) and a multi-scale detection head (MS-DH), the proposed model effectively enhances local texture extraction, global modeling, foreground–background contrast, and boundary perception for dense small objects. Subsequently, a series of comparative experiments, ablation studies, and transfer experiments were conducted on the wheat and rice datasets. The results show that YOLO-DC achieves a favorable balance among detection accuracy, counting error, and model efficiency and overall outperforms the other comparison models. Ablation studies further verify the effectiveness of the proposed design, showing that LGCB-AM is the key contributor to the performance improvement, while the boundary branch and repulsion branch play critical roles in dense-target discrimination. In addition, an appropriate module insertion strategy can effectively balance high-level semantic enhancement and feature fusion stability. Transfer experiments demonstrate that pretraining on the wheat dataset and fine-tuning on the rice dataset significantly outperform training from scratch, indicating strong cross-crop transfer potential. Overall, the proposed YOLO-DC provides an effective solution for high-precision crop detection and counting in agricultural scenarios.
Why it matches plant phenotyping methodsUAV画像から作物個体を検出・計数する手法を中心に、モデル開発、比較、アブレーション、転移検証を行っており、植物個体数という観測可能な形態・集団特性を抽出するため、植物フェノタイピング手法として適格です。
abstractthis study proposes an improved YOLOv12-based crop detection and counting model, named YOLO-DC.
Abstract. 3D instance segmentation for laser scanning (LiDAR) point clouds remains a challenge in many remote sensing-related domains. Successful solutions typically rely on supervised deep learning and manual annotations, and consequently focus on objects that can be well delineated through visual inspection and manual labeling of point clouds. However, for tasks with more complex and cluttered scenes, such as in-field plant phenotyping in agriculture, such approaches are often infeasible. In this study, we tackle the task of in-field wheat head instance segmentation directly from terrestrial laser scanning (TLS) point clouds. To address the problem and circumvent the need for manual annotations, we propose a novel two-stage pipeline. To obtain the initial 3D instance proposals, the first stage uses 3D-to-2D multi-view projections, the Grounded SAM pipeline for zero-shot 2D object-centric segmentation, and multi-view label fusion. The second stage uses these initial proposals as noisy pseudo-labels to train a supervised 3D panoptic-style segmentation neural network. Our results demonstrate the feasibility of the proposed approach and show performance improvements relative to Wheat3DGS, a recent alternative solution for in-field wheat head instance segmentation without manual 3D annotations based on multi-view RGB images and 3D Gaussian Splatting, showcasing TLS as a competitive sensing alternative. Moreover, the results show that both stages of the proposed pipeline can deliver usable 3D instance segmentation without manual annotations, indicating promising, low-effort transferability to other comparable TLS-based point cloud segmentation tasks.
Why it matches plant phenotyping methods圃場小麦穂の3D個体セグメンテーションを対象に、TLS点群とゼロショット・マルチビュー融合・擬似ラベル学習を組み合わせた表現型取得手法を開発・評価しており、方法が研究の中心である。
abstractsuch as in-field plant phenotyping in agriculture, such approaches are often infeasible.
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.
High-throughput plant phenotyping generates valuable data that often remains trapped in unstructured text and isolated RGB images. To bridge this semantic gap, we propose a framework for constructing a multimodal granular Knowledge Graph (KG) to monitor genotype-phenotype interactions across time and experiments. In this work, we focus on wheat Triticum aestivum as a representative target crop to validate our methodology across complex canopy environments. Our pipeline first distills noisy field notes to extract entities and relations, dynamically constructing the KG by converting unique instances into hierarchical class entities via RDF-typing. These graph nodes are then aligned with standardized ontologies (PO, RO, WTO) using PlantDeBERTa. To visually ground the constructed graph, a Vision-Language Model paired with a wheat-segmentation ViT generates attention-based softmaps, linking specific KG entities directly to image pixels. We introduce a central observation node Plant_Obs_Id to connect these multimodal subgraphs temporally. Evaluated on 500 curated WisWheat samples using Pointing Game accuracy, Visual Word Sense Disambiguation (VWSD), and rank-based metrics, our neuro-symbolic approach successfully maps complex field observations to a structured graph. This enables automated field note auditing, temporal stress monitoring, and precise spatial trait localization for wheat breeders.
Why it matches plant phenotyping methods植物のマルチモーダル表現型データを知識グラフと画像に統合し、画像画素への形質局在化を行う中核的な計算フレームワークを提案・評価しているため。
abstractwe propose a framework for constructing a multimodal granular Knowledge Graph (KG) to monitor genotype-phenotype interactions across time and experiments
Near-infrared (NIR) spectroscopy combined with machine learning algorithms has been widely adopted for rapid assessment of grain quality attributes. However, conventional calibration models often suffer from overfitting and instability when applied to high-dimensional spectral data with limited sample sizes. In this study, we developed a novel bagging partial least squares (BA-PLS) algorithm for accurate and stable prediction of wheat protein content. A total of 394 wheat samples were collected and their NIR spectra from 950 to 1650 nm were acquired. The BA-PLS algorithm generates multiple bootstrap subsamples, trains PLS models on each subsample, and aggregates their predictions through averaging, effectively reducing prediction variance while preserving the low-bias properties of PLS. The performance of BA-PLS was comprehensively compared with that of standard PLS, support vector regression (SVR), and extreme gradient boosting (XGBoost). The results demonstrated that BA-PLS achieved superior predictive performance with a coefficient of determination ( R P 2 ) of 0.9600 and a root mean square error (RMSE P ) of 0.3058%. Notably, while SVR and XGBoost exhibited severe overfitting with training to test R 2 gaps exceeding 0.4045, BA-PLS exhibited excellent generalization with a minimal R 2 gap of 0.0261. Furthermore, BA-PLS provided reliable prediction uncertainty estimates through the standard deviation of ensemble predictions. The proposed BA-PLS algorithm offers a practical and stable solution for rapid wheat protein quantification, with potential applicability to other cereal quality assessment tasks.
Why it matches plant phenotyping methods小麦種子のタンパク質含量という植物器官の形質をNIR分光で推定する新規BA-PLS法を開発し、既存手法との比較検証を行っており、表現型取得・推定手法が研究の中心である。
abstractwe developed a novel bagging partial least squares (BA-PLS) algorithm for accurate and stable prediction of wheat protein content.
The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping
Why it matches plant phenotyping methods植物器官の3D再構成と形態情報抽出を目的とする計算手法を開発し、既存法と形態忠実度・器官構造整合性・処理速度で比較評価しており、フェノタイピング手法が中心である。
abstractThe objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
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.
Understanding elemental distributions in plants is critical for agricultural productivity, nutritional quality, and limiting the transfer of toxic elements into the food chain. Conventional elemental mapping techniques typically require thin sectioning or complex tomographic reconstructions, making three-dimensional analysis labor-intensive and destructive. Here, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1]. This method defines a localized 3D detection volume within the sample, enabling the generation of elemental virtual cross-sections without physical sectioning while preserving native spatial relationships. We demonstrate the capability of this technique by mapping Fe distributions in carrot (Daucus carota) roots and shoots and Cd distributions in root tips of near-isogenic wheat (Triticum aestivum) lines. Integration of multiple virtual sections enabled three-dimensional reconstructions that reveal distinct Cd translocation pathways between accumulating and non-accumulating wheat lines, tracing elemental movement from the epidermis through cortical layers into vascular tissues. The method is applicable to diverse plant morphologies, including cylindrical roots and irregular leaf and stem tissues. This approach enables high-sensitivity, non-destructive 3D elemental imaging, providing a powerful tool for studying elemental transport in plants with direct relevance to crop breeding, food safety, and agricultural sustainability [2].
Why it matches plant phenotyping methods植物組織内の元素分布という生理状態を、非破壊3D XRFで取得・再構成する手法の開発と植物試料での実証が中心である。
abstractHere, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1].
ABSTRACT Plant disease is a physiological or structural problem caused by pathogens such as fungi, bacteria, viruses, or environmental factors, which disrupts plant development, yield, and overall health. Furthermore, the formation of new and more aggressive diseases complicates disease control, making it harder for farmers to preserve their crops while ensuring consistent food production. In this manuscript, to advance Progressive Graph Convolutional Networks enable early detection and continuous monitoring of plant infections in smart agriculture (PGCN‐EDM‐PID) is proposed. Initially, input images of food grains such as rice, wheat, and maize are collected from internet sources. To implement this, the input image is preprocessed using the Adaptive Two‐Stage Unscented Kalman Filter (ATSUKF), which performs resizing, sharpening, cropping, contrast enhancement, brightness adjustment, and Gaussian blurring on the images from the dataset. Then the preprocessed images are augmented based on horizontal flip, width shift, height shift, vertical flip, rotation range, shear, zoom and brightness. Additionally, Make Sense AI is proposed to annotate the images in the dataset under each class. Then the preprocessed and augmented images are fed to Progressive Graph Convolutional Networks (PGCN) to detect and classify the plant diseases. Generally, PGCN does not show adapting optimization approaches to find ideal factors to assure accurate plant disease detection. Therefore, the Augmented Red Panda Optimizer (ARPO) was proposed to optimize the weight parameter of PGCN, which accurately detects the plant disease. Then the proposed PGCN‐EDM‐PID is executed in Python and the performance metrics such as Accuracy, Precision, False Positive Rate (FPR), True Positive Rate (TPR), Specificity, Recall, F1‐score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are analyzed. Performance of the PGCN‐EDM‐PID approach attains high accuracy, high Precision, high Recall when analyzed through existing techniques like Real‐time plant disease dataset improvement and detection of plant disease utilizing DL (PDD‐DPD‐CNN), Detection of plant leaf diseasesusing deep convolutional neural network methods (DPLD‐DCNN), New DL algorithm for cross‐crop detection of plant disease: A generalized model for detecting unhealthy leaves (CPDD‐SVM) methods respectively.
Why it matches plant phenotyping methods植物画像から病害を検出・分類する画像解析手法の開発と性能評価が研究の中心であり、感染状態という植物表現型を直接推定している。
abstractProgressive Graph Convolutional Networks enable early detection and continuous monitoring of plant infections in smart agriculture (PGCN‐EDM‐PID) is proposed.
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.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsコムギうどんこ病の早期検出に向け、クロロフィル蛍光画像法を圃場で検証し、QYmaxという植物生理形質を指標化する研究であり、フェノタイピング手法が中心です。
titleField Validation of Chlorophyll Fluorescence Imaging for Early Detection of Wheat Powdery Mildew: Identifying QYmax as a Key Physiological Indicator
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.
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.
To address the challenge of simultaneously optimizing model accuracy and computational efficiency in the automated inspection of wheat grain appearance quality, this study proposes a lightweight deep learning model called MobileNet-WDD. Based on the MobileNetV4-small architecture, this model incorporates the SimAM attention mechanism to enhance feature discrimination capabilities and employs Ghost convolutions and the Mish activation function to optimize the network structure, thereby significantly reducing model complexity while maintaining high recognition accuracy. Using six typical categories of wheat grains-diseased spots, insect damage, mold, sprouting, damage, and intact grains-as the research subjects, experimental results show that compared to the baseline model, MobileNet-WDD achieves a 6.1% increase in accuracy, reaching 94.2%; a 30.3% reduction in the number of parameters; computational cost by 27.6%; and inference speed from 155 FPS to 183 FPS, representing a 1.18-fold acceleration. Quantitative analysis confirms that this model achieves high-precision recognition while offering significant advantages in terms of lightweight design and efficient computational performance, providing an efficient and feasible technical solution for real-time non-destructive inspection of wheat grains.
Why it matches plant phenotyping methods小麦粒の病斑・虫害・カビ・発芽などの可視状態を画像から分類する軽量深層学習手法を開発しており、表現型状態の取得・抽出が研究の中心である。
abstractthis study proposes a lightweight deep learning model called MobileNet-WDD
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.
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Crop diseases pose a serious threat to agricultural yield and global food security. Accurate detection using Unmanned Aerial Vehicle (UAV) remote sensing imagery is of great significance for precision agriculture. However, this task remains challenging due to complex field backgrounds, diverse spectral-spatial characteristics of diseased leaf regions, irregular lesion boundaries, and variable texture patterns. To address these issues, this paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery. The model integrates two task-driven attention modules: (1) Disease-Aware Dynamic Attention (DADA), which enhances the representation of diseased regions through disease feature enhancement, multi-scale dynamic channel attention, and texture-guided spatial attention; and (2) Channel-Spatial Visual State Space (CSVSS), which enables efficient long-range dependency modeling and local-global feature fusion while maintaining linear computational complexity. A hybrid loss strategy combining binary cross-entropy (BCE) loss, Dice loss, and cross-entropy (CE) loss with optimized coefficients is employed to address class imbalance and boundary delineation challenges. Extensive experiments are conducted on a self-constructed UAV crop disease unified mix dataset, comprising soybean disease images from Maharashtra, India, and rust disease images from wheat, corn, and other crops in Yangling, China, totaling 6,680 raw collected images, which after deduplication yields 5,000 images for experimentation. The results demonstrate that TDAVM-UNet achieves 26.87M parameters and 31.45 GFLOPs for 256×256 inputs, maintaining O(N) linear complexity (80% lower than TransUNet’s 156.78 GFLOPs), with 82.22% mIoU. This work provides a high-accuracy, robust, and computationally efficient method for UAV-based crop disease detection, offering significant technical support for precision agriculture applications.
Why it matches plant phenotyping methodsUAV画像から作物病害の病変領域・病害状態を推定する深層学習モデルを開発し、データセット上で性能評価しており、植物表現型取得・抽出手法が研究の中心である。
abstractthis paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery.
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.
The paper discusses the preparation of experimental data used to measure the biopotentials of Novosibirskaya 41 wheat seedlings exposed to elevated and lowered temperatures, in order to conduct phenotyping of these plants using cluster analysis. It is noted that such a preparation is necessary for long-term experimental studies that take several calendar days (up to 10 or more), during which metabolic changes in seedling samples occur, affecting their biopotential values. The paper is based on experimental data obtained in 2020 and 2022 and their regression analysis, as reported in [13]. The results of changes in seedling biopotentials depending on their age are briefly described, and an algorithm for calculating corrective biopotential values for each magnification level of the objects is provided. Statistical regressions of changes in biopotential values depending on the need to preserve seedlings of these wheat varieties were obtained. This allowed the development of an algorithm for correcting the initial average biopotentials for these conditions without preliminary regression analysis of the data. Two data sets were generated for assessing the phenotype of the objects: the original data set, obtained through primary processing of changes in these seedling biopotentials under exposure to elevated and lowered temperatures, and the corrected data set, in the partial parameter (smax.c.) of the maximum filtered centered value (cf) of the wheat seedling biopotentials under these conditions. Plant phenotyping was performed based on the data sets using the original Eclaster program, which implements this methodical spectral clustering from the sklearn.cluster library in the Python programming environment. The clustering results presented in the form of a scatterplot demonstrate improved cluster separation for the corrected data.
Why it matches plant phenotyping methods小麦幼苗のバイオポテンシャルを用いた表現型評価のため、データ補正アルゴリズムとクラスタリング解析プログラムを開発・適用しており、表現型取得・抽出手法が研究の中心である。
abstractThe paper discusses the preparation of experimental data used to measure the biopotentials of Novosibirskaya 41 wheat seedlings exposed to elevated and lowered temperatures, in order to conduct phenotyping of these plants using cluster analysis.
PHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases, with two AI-powered applications now reaching proof-of-concept stage. The first, FUSASEYD, addresses Fusarium Head Blight (FHB), a major fungal disease in winter wheat. Using RGB field images and a deep learning instance segmentation model (YOLOv11), the application detects and quantifies FHB symptoms on wheat ears, offering an automated alternative to time-consuming expert visual scoring. GEVES has developed both a PC interface and a smartphone application to visualise model predictions in the field. Validation in French registration trials is planned for the 2026 campaign. A companion article by V. Cadot et al. is currently under review in the Journal of Experimental Botany special issue on Plant Phenomics & Enviromics Across Scales. The second, COYL (Counting Orange and Yellow Larvae), tackles a practical challenge faced by breeders, and rapidly counting wheat blossom midge larvae, both Sitodiplosis mosellana and Contarinia tritici, to characterise variety susceptibility. Using smartphone RGB images and YOLOv-based object detection, the best-performing model achieved high accuracy and successfully distinguished between the two visually similar species. An online counting application has been developed, currently accessible to Walloon Agricultural Research Centre members. The labelled COYL-1 dataset is publicly available at https://doi.org/10.5281/zenodo.19402333 for community use. A companion article by Antoine Deryck et al. is under submission at Plant Phenomics Journal.
Why it matches plant phenotyping methodsRGB画像と深層学習によるコムギ穂の病徴検出・定量化を開発し、専門家評点の自動化とセンサー／画像法の検証を目的とするため、植物フェノタイピング手法が中心である。
abstractPHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases
PHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases, with two AI-powered applications now reaching proof-of-concept stage. The first, FUSASEYD, addresses Fusarium Head Blight (FHB), a major fungal disease in winter wheat. Using RGB field images and a deep learning instance segmentation model (YOLOv11), the application detects and quantifies FHB symptoms on wheat ears, offering an automated alternative to time-consuming expert visual scoring. GEVES has developed both a PC interface and a smartphone application to visualise model predictions in the field. Validation in French registration trials is planned for the 2026 campaign. A companion article by V. Cadot et al. is currently under review in the Journal of Experimental Botany special issue on Plant Phenomics & Enviromics Across Scales. The second, COYL (Counting Orange and Yellow Larvae), tackles a practical challenge faced by breeders, and rapidly counting wheat blossom midge larvae, both Sitodiplosis mosellana and Contarinia tritici, to characterise variety susceptibility. Using smartphone RGB images and YOLOv-based object detection, the best-performing model achieved high accuracy and successfully distinguished between the two visually similar species. An online counting application has been developed, currently accessible to Walloon Agricultural Research Centre members. The labelled COYL-1 dataset is publicly available at https://doi.org/10.5281/zenodo.19402333 for community use. A companion article by Antoine Deryck et al. is under submission at Plant Phenomics Journal.
Why it matches plant phenotyping methodsRGB画像と深層学習によりコムギ穂の病徴を検出・定量する手法の開発とセンサー／画像手法の検証が中心であり、植物病害表現型の取得に該当する。
abstractPHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically relies on spectral observations composited over fixed calendar windows, implicitly assuming phenological synchrony across fields. This study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction. We integrated Sentinel-2 multispectral imagery (32 vegetation indices, 10 spectral bands), ERA5-Land meteorological reanalysis, gSSURGO soil properties, and USGS 3DEP topographic data, and systematically compared six temporal strategies, the factorial combination of two normalization approaches (peak-relative vs.\calendar) and three resolutions (monthly, biweekly, growth stages), across 228 commercial winter wheat fields in western Kansas (2024--2025). Three ensemble tree models (Random Forest, XGBoost, LightGBM) were trained under nested cross-validation with Boruta feature selection. Peak-relative monthly normalization achieved the highest accuracy (\((R^2 = 0.304 \pm 0.051)\), RMSE \((= 1.11)\)%), explaining an additional 5.1% of variance compared with the best calendar strategy (\((R^2 = 0.253)\)). A single 30-day post-peak window (M\((+)\)1, \((\sim)\)15--45 days after maximum canopy greenness) carried more predictive information than any broader aggregation. SHAP analysis identified topsoil organic matter, SWIR-based senescence indices (NBR2, MIRBI), and grain-filling temperature as the most influential predictors. Three-class quality classification reached 47--49% accuracy (versus 33.3% by chance), indicating practical utility for early grain segregation. While demonstrated for wheat GPC, the framework is transferable to other crop traits with temporally concentrated satellite signals, particularly those tied to specific developmental stages. The results highlight phenological alignment as a generalizable strategy for trait prediction from Earth observation data.
Why it matches plant phenotyping methods衛星リモートセンシング時系列を用いた小麦粒タンパク質濃度予測のため、フェノロジー整列と複数の時間集約戦略を体系的に比較・検証しており、植物形質推定手法が研究の中心である。
abstractThis study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction.
Reproduction assets foundThe paper's data availability statement releases a de-identified field-level GPC dataset alongside a public authors' code repository (Ciampitti-Lab WheatGPCPipeline) implementing the data-acquisition, feature-engineering, and modeling pipeline. Both are paper-specific, public, and actionable.Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗pdf-page:48 lines:1-55Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。
abstractThis review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding
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.
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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
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。
Wheat (Triticum aestivum L.) is a staple crop of paramount importance to global food security; however, its productivity is significantly compromised by foliar diseases. Conventional diagnostic approaches, relying on manual observation or laboratory analyses, are often labor-intensive and susceptible to inaccuracies. While recent advancements in deep learning present promising avenues for automated disease detection, persistent challenges such as limited annotated datasets, environmental heterogeneity, and model generalization continue to hinder optimal performance. This study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases. While ConvNeXt ensures local feature extraction and ViT provides global contextual understanding, CBAM dynamically highlights the most discriminative features. Additionally, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is employed to enhance the visibility of disease symptoms in low-contrast leaf images. Unlike conventional hybrid CNN-Transformer approaches that rely on static feature concatenation, the proposed model employs an adaptive gated fusion mechanism to dynamically balance local and global feature representations. The fused features are further refined using a lightweight CBAM module to enhance discriminative capability. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to improve feature visibility under varying illumination conditions. Evaluated on three different datasets obtained under both controlled and field conditions, HybridViT achieved 100% accuracy on balanced datasets and 99.10% accuracy on complex images captured in real-world conditions, surpassing existing methods. Furthermore, a 5-fold cross-validation strategy yielded an average accuracy of 99.04% ± 0.22, demonstrating the model's robustness and stability across different data splits. The results demonstrate the model's robustness against environmental noise, lighting variations, and class imbalance. This approach, which enables early and accurate disease diagnosis, supports sustainable agricultural practices, reduces pesticide use, and contributes to global food security.
Why it matches plant phenotyping methods小麦葉の病徴を画像から分類する深層学習手法を開発・検証しており、植物病害状態の取得・推定が研究の中心です。
abstractThis study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases.
Reproduction assets foundThe paper evaluates HybridViT on three public wheat leaf disease image datasets from Kaggle, cited in the reference list with explicit URLs. These are the paper-specific image inputs used for its disease-classification measurements. No author analysis code, trained model checkpoints, or supplementary code deposit is披露dDataset · publicAvailable: https://www.kaggle.com/datasets/olyadgetch/wheat-leaf-datasetOpen asset ↗Kaggle · olyadgetch/wheat-leaf-datasetpdf-page:51 lines:1-64Dataset · public[78] J. Jayaprakash, “Wheat Leaf Disease,” Kaggle. Accessed: May 1, 2026.
[Online]. Available:
https://www.kaggle.com/datasets/jayaprakashpondy/wheat-leaf-diseaseOpen asset ↗Kaggle · jayaprakashpondy/wheat-leaf-diseasepdf-page:51 lines:1-64Dataset · public[79] S. Kumar, “Multiple Plant Diseases Dataset,” Kaggle. Accessed: May 1, 2026.
[Online]. Available:
https://www.kaggle.com/datasets/samareshkumar/multipleplantdiseasesOpen asset ↗Kaggle · samareshkumar/multipleplantdiseasespdf-page:51 lines:1-64Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The global food security is severely threatened by various bacterial and fungal diseases that significantly degrade the quality, yield and productivity of wheat crop. This increases the need for an accurate and efficient system to improve wheat yield and mitigate these losses by enabling early intervention. The dataset used in this research comprises of 10,000 images from brown rust, yellow rust, powdery mildew, loose smut diseases and healthy wheat plants. The existing neural networks, ensembling and transformer-based models used for classifying wheat diseases are limited by high computational resource requirements that leads to inefficient feature extraction. These challenges are addressed by proposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer, a lightweight and optimized transformer model that enhances feature extraction while significantly reducing computational overhead. In particular, Swin-SHARP results in 82.5% reduction (48.9M to 8.5M parameters), making it an attractive solution for resource-constrained environments. The extracted features are further optimized by integrating the Swin-SHARP transformer with boosted Model-Agnostic Meta-Learning (MAML) and a weighted ensembling strategy to enhance generalization and classification accuracy. Our proposed model achieves a remarkable 98.1% accuracy, significantly outperforming existing CNN-based solutions, ensemble approaches, transformer, and deep learning models. We also cross-validated our proposed model on an unseen wheat plant diseases dataset, achieving 95.57% accuracy. Our proposed model is also compared against prominent models such as Inception-v3, ResNet-18, and VGG-16, which outperforms them by 1.6%, 1.7%, and 2.6%, respectively. The comparison with existing state-of-the-art models, including Sequential CNN, SGDR-S, Inception-v3, Cereal Conv, Darknet-53 CNN, EfficientNet B3, GLNet, CNN & SVM, Customized CNN, CaiT-YOLOv9 and MSFNet revealed that our method outperforms them by 0.6%, 5.8%, 5.3%, 0.75%, 2.8%, 2.68%, 1.42%, 1.3%, 3.31%, 3.29%, and 2.4% respectively. These results demonstrate the effectiveness and practicality of the Swin-SHARP transformer for wheat disease classification, particularly for real-time agricultural applications on mobile and embedded systems aimed at early disease detection and crop management.
Why it matches plant phenotyping methods小麦の病徴画像から植物の病害状態を推定する深層学習手法を開発し、別データセットで交差検証しており、植物表現型取得・判定が研究の中心である。
abstractproposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the authors' wheat disease dataset and analysis code are publicly available on GitHub (https://github.com/SWIN-SHARP/), which is a paper-specific, actionable asset. The paper also uses third-party public datasets (Zindi ICLR Workshop, Mundi, Watershed/GrabcCode · publicThe dataset and code used in this research have made publicly available on https://github.com/SWIN-SHARP/
SWIN-SHARP for reproducibility purposes.Open asset ↗SWIN-SHARPpdf-page:25 lines:1-104Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management. Methods We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening. Results The weighted optimization increased overall F1-scores by 10.0%-18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6-7. Across the interpretability analyses, the 480-550 nm blue-green region emerged as the main source of information for pre-symptomatic detection, whereas the 750-870 nm near-infrared region contributed more general information on disease presence. Discussion These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.
Why it matches plant phenotyping methods小麦の病害状態をハイパースペクトル画像と深層学習で検出する方法が研究の中心であり、時系列評価・モデル比較・性能改善・解釈性分析を含むため、植物フェノタイピング手法として含める。
abstractWe evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe data can be accessed at: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:787-847Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
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.
An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and cross-sectional area profile. New shape descriptors based on cross-sectional area profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態を3D画像取得と計算解析で定量化するパイプラインを開発し、形状記述子の抽出と遺伝子型間での検証を行う、植物フェノタイピング手法の中心的研究である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe paper's Data Availability and Code Availability sections point to the authors' public GitHub repository containing sample 3D spike data and the analysis code for the wheat spike morphology pipeline.Code · publicCode Availability
The codes are available at the following link: https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtractionOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionlines:316-410Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially advanced automatic wheat spike detection and counting. However, precise spikelet-level phenotyping remains largely underexplored. This study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations. For spike detection, the pre-trained YOLOv11 achieved superior accuracy (mAP@0.5 = 95.8%, Pearson r = 0.993) with shorter training and inference times compared to YOLOv12. For spikelet detection, the non-pretrained YOLOv11 demonstrated higher accuracy (mAP@0.5 = 99.0%), while counting performance was comparable across models. These results establish OBB-based YOLO detection as a robust and scalable approach for AI-driven wheat phenotyping.
Why it matches plant phenotyping methods小麦の穂・小穂という収量関連形質の画像ベース検出・計数手法を比較評価し、大規模ベンチマークデータセットも構築しているため、フェノタイピング手法が中心である。
abstractThis study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations.
Reproduction assets foundThe paper openly states its supporting data (spike/spikelet imagery with OBB annotations) is available on Zenodo, and the underlying models are deployed on the authors' public WheatAI cloud platform.Dataset · publicData availability
The data supporting the findings of this study are openly available at: https://doi.org/10.5281/zenodo.20215489 .Open asset ↗zenodo · 10.5281/zenodo.20215489lines:219-266Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
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.
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.
Abstract Continuous, high‐frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season‐long trait assessment. This study introduces AGIcam, an open‐source Internet of Things (IoT) camera system for automated and continuous in‐field plant phenotyping and yield prediction. The platform integrates solar‐powered Raspberry Pi units with a modular software stack, comprising Node‐RED, InfluxDB, Grafana, and Microsoft Azure, for automated data acquisition, transfer, and visualization. In the 2022 growing season, 18 AGIcam systems were deployed in spring and winter wheat ( Triticum aestivum ) breeding trials, maintaining an uptime of over 85% while capturing frequent red‐green‐blue and no‐infrared imagery. Time‐series vegetation indices derived from these images were used to predict yield using random forest and long short‐term memory (LSTM) models. The LSTM approach achieved the highest accuracy approximately one week after heading, with mean prediction errors of 3.41% for spring wheat and 1.62% for winter wheat. These results highlight the potential of IoT‐based platforms such as AGIcam to enable real‐time, scalable, and effective phenotyping solutions for data‐driven crop improvement. The presented work provides open‐source resources for the development and time‐series analysis of IoT data for phenotyping and precision agricultural applications.
Why it matches plant phenotyping methods植物フェノタイピング用のIoTカメラ基盤を開発・実証し、画像由来の時系列形質から収量を予測する方法が研究の中心である。
abstractThis study introduces AGIcam, an open‐source Internet of Things (IoT) camera system for automated and continuous in‐field plant phenotyping and yield prediction.
Accurate wheat spike detection is essential for crop phenotyping and yield estimation, but real-world field conditions—such as dense spike overlap, environmental domain shifts, and degradation-induced failures like motion blur—pose significant challenges. Achieving robust perception under these circumstances while maintaining a strict accuracy-efficiency trade-off for edge devices remains a pressing research problem. To overcome these limitations, we propose GG-YOLO, a unified lightweight detection framework specifically tailored for complex agricultural environments. Rather than a simple recombination of existing lightweight modules, GG-YOLO integrates three original structural adaptations: First, a Dual-path Attentive Ghost Mechanism (DAGM) introduces gradient-guided attention modulation to enhance feature discrimination and explicitly resolve feature confusion in dense, overlapping regions. Second, a C3Ghost module combines multi-branch aggregation with linear feature generation, mitigating parameter redundancy in the prediction head by approximately 31% compared to the standard YOLOv8s without sacrificing semantic capacity. Third, DSample, a dynamic upsampling operator featuring an original dual-mode adaptive mechanism, robustly recovers fine-grained spatial details during multi-scale feature pyramid fusion. Extensive cross-dataset experiments on the GlobalWheat2020 and HNKJXYwheat datasets validate the model’s exceptional resilience to domain shifts and varying growth stages. GG-YOLO achieves a precision of 94.35%, a recall of 91.93%, and a state-of-the-art mAP@50 of 96.47%. Furthermore, the model contains only 7.89 M parameters and requires 20.4 GFLOPs, reaching an inference speed of 165 FPS on a desktop GPU and a validated real-time speed of 64 FPS on an NVIDIA Jetson edge computing platform. These results demonstrate that GG-YOLO establishes a superior accuracy-efficiency frontier, making it highly reliable for real-time field deployment in precision agriculture.
Why it matches plant phenotyping methodsコムギ穂の検出を作物フェノタイピングおよび収量推定に用いる画像解析手法を開発し、複数データセットで性能検証しているため、フェノタイピング手法が中心である。
abstractAccurate wheat spike detection is essential for crop phenotyping and yield estimation
Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.
Why it matches plant phenotyping methods植物CT画像から3D形状を抽出する、アノテーション不要・学習不要の分割手法を開発し、複数作物データセットで性能評価しており、表現型取得手法が研究の中心である。
abstractwe introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT.
Reproduction assets foundThe paper open-sources the DiffPlantCT implementation code on GitHub and benchmarks on two public plant CT datasets (walnut fruit via figshare; barley spike via Plant Methods), all with explicit availability statements and matching allowed URLs.Code · publicThe datasets and implementation code of the DiffPlantCT framework are open-sourced on GitHub at https://github.com/WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentation .Open asset ↗WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentationlines:220-287Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Published11 Jun 2026Proceedings of the National Academy of Sciences of the United States of AmericaCited by 0 · OpenAlex ↗
The spatial organization of essential, nonessential, and toxic metal(loid) elements (MEs) within plant cells underpins physiological function. Yet, comprehensive subcellular imaging of the full ME spectrum remains challenging due to trade-offs among spatial resolution, elemental coverage, and structural correlation. Here, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution. Applying this high-fidelity workflow to Arabidopsis , soybean, and wheat, we constructed single-cell metallome maps revealing an evolutionarily conserved subcellular architecture: chloroplasts enrich essential MEs (e.g., magnesium, iron, copper), whereas vacuoles compartmentalize nonessential [e.g., lanthanum (La)] and toxic MEs [e.g., cadmium (Cd), lead, arsenic]. We demonstrate that while this architecture remains stable under homeostasis, it undergoes dynamic, stimulus-specific, and dose-dependent remodeling under stress. Low-dose La(III) enhances pairwise and higher-order colocalizations of essential MEs within chloroplasts, correlating with improved photosynthetic efficiency and growth. High-dose La(III) induces nonphysiological La-ME associations and, critically, drives aberrant Cd(II) accumulation in chloroplasts-revealing a cross-toxicity mechanism wherein La(III) disrupts native sequestration barriers. In contrast, although high-dose Cd(II) is largely excluded from chloroplasts, it triggers a widespread redistribution of essential MEs, progressively eroding spatial organization. Thus, while both ions inhibit growth, they perturb metallomic networks via distinct mechanisms: La(III)-mediated disruption of sequestration vs. Cd(II)-induced systemic compartmental collapse. Our findings establish that subcellular ME networks are dynamically regulated and orchestrate physiological outcomes.
Why it matches plant phenotyping methods植物細胞内の金属元素分布と超微細構造を取得する統合イメージング基盤とワークフローの開発が中心であり、植物の生理状態・ストレス応答に結び付けて実証している。
abstractHere, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution.
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
Abstract This study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding. This research, conducted using 782 wheat genotypes in Rafsanjan, Iran, compares MLR and ANN methodologies. MLR, using seven traits selected via stepwise regression, achieved an R² of 0.90, the root of mean square of error (RMSE) of 14, Average absolute percentage error (MAPE) of 13.3, and Average deviation of prediction from the actual value (MAE) of 10. Key traits identified were biological weight (weight of total plant per line, WPP) and harvest index (HI). Conversely, the GA-ANN model, employing six selected traits, demonstrated superior performance with R² values of 0.94, 0.96, and 0.94 for training, testing, and combined datasets respectively. Validation metrics for the ANN model were MSE of 144.3, RMSE of 12, and MAE of 5.7. GA-ANN selected height, peduncle length, days to flowering, spike length, biological weight, and harvest index as significant predictors. The results underscore that ANN models, particularly when combined with genetic algorithms for feature selection and optimization, can improve prediction accuracy by modeling complex, non-linear relationships in agricultural data, therefore providing more precise yield prediction tools for plant breeders. This study emphasizes the need for advanced predictive techniques in achieving more accurate assessments of crop yield for sustainable agriculture.
Why it matches plant phenotyping methods小麦遺伝子型レベルの収量という植物形質を対象に、MLRとGA-ANNの予測精度を比較・検証しており、計算による形質推定手法が研究の中心です。
abstractThis study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Root anatomical traits and spatial architecture play a critical role in crop water acquisition and utilization, directly impacting drought tolerance. However, comprehensive studies examining the synergistic effects of deep root configuration and cortical tissue organization under drought stress during the seedling stage remain scarce. Additionally, the underlying physiological mechanisms are not yet well understood. Methods: In this study, we utilized a high-throughput, paper-based phenotyping platform to simulate drought stress using 10% PEG. An efficient, multi-trait evaluation framework was employed to classify the 28 tested genotypes into five drought tolerance categories. Results: This approach enabled the identification of drought-tolerant cultivars "Ruichun 1," "Ningchun 11," and "Ningchun 57," as well as the drought-sensitive cultivar "Dingxi 48." Root traits, including maximum depth, convex hull area, and plant height, demonstrated strong explanatory power and could serve as valuable phenotypic indicators for seedling stage screening. Our findings suggest that drought adaptation in spring wheat involves a strategic coupling in which specific cortical configurations facilitate the development of deep root architecture. While previous studies have often focused on individual parameters, we show that drought-tolerant genotypes optimize root growth in deeper segments of the growth medium by adjusting cortical tissue proportions, potentially minimizing metabolic costs. Discussion: This integrated perspective offers a detailed physiological framework for understanding drought resilience and moves toward a mechanism-based interpretation of resource reallocation. However, it is important to note that these results were obtained using a paper-based phenotyping platform under PEG-induced osmotic stress, reflecting the genotypic potential at the seedling stage rather than actual field drought tolerance. In conclusion, combining the paper-based high-throughput phenotyping platform with a multi-trait evaluation framework allows for the accurate classification of drought tolerance types and the efficient identification of representative spring wheat cultivars. The findings emphasize the importance of deep root configuration and optimized cortical allocation as fundamental components of the root structural basis for drought adaptation in spring wheat. These results provide clear phenotypic targets for early-stage screening, which should be further validated at later developmental stages and under field conditions before being applied in breeding programs.
Why it matches plant phenotyping methods紙ベースのハイスループット表現型解析プラットフォームと多形質評価フレームワークが、根形態を用いた耐乾性分類の中心的手法として明示されているため。
abstractwe utilized a high-throughput, paper-based phenotyping platform
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Wheat spike detection is essential for yield estimation in precision agriculture, yet it remains challenging due to the small size of targets, dense distribution, and complex field environments. In this study, we propose LiteMS-YOLO, a lightweight object detection framework based on YOLO26n. The model integrates a Feature Complementary Mapping (FCM) module to enhance spatial-semantic feature interaction and a Multi-Kernel Perception (MKP) unit to improve multi-scale feature representation. In addition, targeted redundancy reduction strategies are introduced to significantly lower model complexity. Experiments are conducted on a combined dataset comprising the public Global Wheat Head Detection (GWHD) dataset and 100 field images collected by the Tangshan Academy of Agricultural Sciences, with a total of 6,378 high-resolution images and over 44,000 annotated wheat spikes. LiteMS-YOLO achieves a mAP50 of 92.28% and a mAP50–95 of 52.56%, while using only 0.627 million parameters. Compared with YOLO26n and YOLOv8n, the proposed method reduces parameters by approximately 75% and 79%, respectively, while maintaining competitive accuracy. These results demonstrate that LiteMS-YOLO strikes an excellent balance between detection accuracy and efficiency, making it well-suited for real-time deployment in resource-constrained agricultural scenarios.
Why it matches plant phenotyping methods小麦穂の検出による収量推定を目的に、画像ベースの検出モデルを開発し、複数データセットで性能検証している。植物器官の検出・計数に基づく表現型取得が研究の中心である。
abstractWheat spike detection is essential for yield estimation in precision agriculture
Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield ( r = 0.79-0.82, p r = 0.72 in hard spring, r = 0.53 in soft spring). These conditions also improved discrimination between varieties. Although heritability patterns were not clearly differentiated by stress clusters, environments with higher genetic control of yield also tended to show stronger NDVI heritability. Overall, NDVI reliably captured wheat grain yield, which is governed by the genotype × environment driven variability, with its predictive value strongest in stress-prone conditions. These findings underline NDVI's usability as a practical digital trait for improving variety testing and guiding breeding decisions in challenging environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを抽出し、複数環境・品種で収量との関係、予測性、遺伝率を評価しており、デジタル植物形質の測定・検証が中心である。
abstractThis study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicTrial data, including grain yield, variety, and market class information, were obtained from the Washington State University Extension Cereal Variety Selection and Testing Program ( https://smallgrains.wsu.edu/variety/ ).Open asset ↗lines:38-48Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafGrowth / time-series analysisGrowth / development / phenology
Disentangling genotype × environment (G×E) controls of flowering time requires phenotypes that link molecular regulation, developmental physiology and environment. Here, we integrated time-resolved measurements of apical development, final leaf number (FLN), and expression of the flowering-time genes VRN1, VRN2 and VRN3 across contrasting temperature and photoperiod regimes in six wheat genotypes spanning a wide range of developmental sensitivities. By combining controlled-environment phenotyping with concurrent gene-expression profiling, we show that environmentally driven variation in FLN is coherently explained by shifts in the timing of key apical transitions and associated VRN gene-expression dynamics. These integrated datasets were used to parameterise and interrogate the Cereal Anthesis Molecular Phenology (CAMP) model, enabling direct comparison between observed foliar gene-expression time courses and modelled gene activity. While overall developmental responses were well captured by the model, systematic differences between observed and modelled gene-expression patterns highlight the importance of distinguishing foliar expression from apical regulatory activity, as well as differences in temporal scaling. Building on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Why it matches plant phenotyping methodsFLN応答に基づくフェノタイピングプロトコルを提示し、温度・光周期処理下で遺伝的に解釈可能な発育表現型を取得する方法が中心的に扱われている。
abstractBuilding on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Reproduction assets foundThe paper's CAMP model code and the analysis scripts producing its figures are explicitly stated as publicly available on the authors' GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicwere also validated and the best-performing sets selected. A
348 description of each of the primers used in this study is given in the supplementary material
349 (Table SA1).
350 2.9 Verification of CAMP predictions
351 2.9.1 Model set-up and operation.
352 The CAMP model was coded into a Python script which is available at
353 https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
354 description of the code and parameterisation scheme is given in the supplementary material.
355 The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
356 derive the Vrn expression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicpression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
358 Vrn gene expression could be compared with those observed. The script running the CAMP
359 code and producing the graphs displayed in this paper can be viewed at
360 https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.
14
UNOFFICIALOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicnd testing of the model in
690 broader contexts. EW contributed substantially to the improvement of model concepts and the
691 manuscript and all authors provided final checking.
692 8. Data Availability
693 All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
694 publicly available at https://github.com/HamishBrownPFR/CAMP/
695 9. References
696 Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
697 response of wheat vernalization to environmental variables indicates that vernalization is not
698 a response to cold temperature. Journal of Experimental Botany 63: 847–857.
699 Baumont M, Parent B, Manceau L, Brown HE,Open asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:31 lines:1-60Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Hybrid wheat breeding offers a promising route to enhance grain yield and yield stability through heterosis, yet hybrid grain production remains constrained by limited cross-pollination efficiency due to high rates of autogamy. To achieve cross-pollination in an autogamous species like wheat, pollen must shed outside the floret. This is typically assessed by scoring visual anther extrusion (VAEX), a key floral trait that sets the foundation for cross-pollination. However, VAEX explains only part of the variation in hybrid grain set. To address this, we analyzed floral structures and reproductive processes underlying cross-pollination efficiency in wheat. From 24 elite winter wheat genotypes, we developed traits describing anther extrusion kinetics, pollen release, and floral bract architecture. These traits showed substantial genotypic variation and high heritability. While VAEX alone explained approximately 49% of the variation in hybrid grain set, combined trait analyses explained up to 77%, demonstrating that hybrid grain production is governed by coordinated floral and reproductive trait interactions. Together, our analyses define a hierarchical trait architecture linking floral bract mechanics, anther extrusion dynamics, and pollen shedding to cross-fertilization success. This establishes a systems-level phenotyping framework for improving male parent selection in hybrid wheat breeding. Highlight High cross-pollination efficiency in wheat is a multi-factorial process that requires lighter floral bract architecture combined with adequate anther extrusion and pollen release for improving hybrid grain production.
Why it matches plant phenotyping methods交雑受粉効率を評価するための葯突出動態、花粉放出、苞葉構造の形質を開発し、統合的なフェノタイピング枠組みとして解析しており、表現型取得・評価法が研究の中心である。
abstractFrom 24 elite winter wheat genotypes, we developed traits describing anther extrusion kinetics, pollen release, and floral bract architecture.
Background Genomic selection (GS) has revolutionized modern breeding by utilizing genome-wide single nucleotide polymorphisms (SNPs). While traditional models such as GBLUP and Bayesian approaches remain prevalent, several deep learning approaches have recently been introduced for plant GS, demonstrating superior predictive performance. Here, we introduce Gsformer, a novel deep learning framework designed to predict phenotypes by modeling complex genetic architectures. It features two distinct architectures: CSA, which combines convolutional neural networks (CNNs) with self-attention to capture local and long-range genomic dependencies, and NSA, which employs a native sparse attention mechanism to enhance computational efficiency by focusing on the most informative features. We evaluated Gsformer on six datasets spanning animal and plant species-pig, cattle, chicken, mouse, wheat, and maize-and compared its phenotypic prediction performance against five established GS methods: DNNGP, MLP, LightGBM, SVR, and GBLUP. Results Gsformer generally ranked among the top two models across six diverse animal and plant genomic prediction datasets. Specifically, Gsformer-CSA yielded notable improvements in predicting cattle fat percentage, while Gsformer-NSA was more accurate in predicting chicken first egg weight, pig age at 100 kg body weight, and mouse anxiety. With the topN hyperparameter set to 20%, Gsformer-NSA matched or marginally exceeded Gsformer-CSA for most traits-though it showed lower accuracy for a subset of traits. Adjusting the topN value further enhanced Gsformer-NSA's performance, allowing it to match that of Gsformer-CSA. Ablation studies confirmed the complementary roles of CNN and self-attention modules in the CSA architecture. To enhance interpretability, we applied SHAP (SHapley Additive exPlanations) to identify influential SNPs and annotate candidate genes associated with growth and body size traits in pigs. Functional enrichment analysis revealed biologically relevant pathways involved in nervous system development, glycolytic process regulation, and digestive tract morphogenesis. Conclusions In summary, Gsformer establishes a flexible and powerful framework for genomic prediction, demonstrating broad applicability across both animal and plant breeding. Owing to its lower computational cost, Gsformer-NSA is recommended over Gsformer-CSA in scenarios where the minor sacrifice in prediction accuracy is acceptable.
Why it matches plant phenotyping methods植物の表現型を予測する深層学習フレームワーク自体を開発し、コムギ・トウモロコシを含むデータセットで既存手法と比較検証しており、表現型推定法が中心である。
abstractHere, we introduce Gsformer, a novel deep learning framework designed to predict phenotypes by modeling complex genetic architectures.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe Gsformer software is available on GitHub at (https://github.com/hajudien/GSformer/tree/master).Open asset ↗https://github.com/hajudien/GSformer/tree/masterhtml-lines:256-299Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Interest in light detection and ranging (LiDAR) for the precise monitoring of vegetative growth of grain crops has increased. The study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM). A commercial LiDAR system was used for data collection in the middle and late growth stages using static and dynamic scanning. A small number (ten) of data frames, consisting of a region of interest (ROI) of 1 m × 0.9 m for each frame, were selected as data samples. The data processing workflow consisted of data conversion, targeted data frame selection, visualization, region of interest (ROI) segmentation, outlier and untargeted point removal, downsampling, denoising, voxelization, preparation of the convex hull, and 3D PCD density map. To estimate the plant size and distance of wheat, the results obtained using CHM and VGM were compared with measured data results, and both methods were applied for the middle and late growth stages of wheat. The relative accuracy of LiDAR-estimated plant height, canopy volume, plant spacing, and row distances with respect to the measured results were 94%, 87%, 94%, and 87%, respectively, using CHM, and 76%, 72%, 62%, and 71% by VGM for static data scanning; for dynamic scanning, the estimated relative accuracy percentages were 87%, 91%, 94%, and 93%, respectively, using CHM, and 77%, 74%, 75%, and 74%, respectively, using VGM. The same methods were applied to the late growth stage data sets. Between the two methods, CHM provided higher accuracy for static and dynamic data-scanning approaches in the middle and late growth stages because the complex geometry of plants, thin and sparse leaf area, and structure complicated voxelization. Despite several challenges in PCD collection and processing, this study supports size and distance estimation for wheat and similar grains as non-destructive methods.
Why it matches plant phenotyping methodsLiDARと3D点群処理によりコムギの草丈、キャノピー体積、株間・畝間距離を推定し、凸包法とボクセル法を実測値と比較検証している。表現型取得・抽出手法が研究の中心である。
abstractThe study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM).
Abiotic stresses such as drought and salinity significantly constrain the productivity of in vitro-grown wheat (Triticum aestivum L.) by disrupting its biochemical and physiological homeostasis. Rapid, non-destructive, and data-driven diagnostic approaches are therefore essential for the early detection of stress conditions and for supporting sustainable crop management. In this study, Raman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments. Distinct Raman spectral features associated with pigments, proteins, carbohydrates, and lipids were analyzed alongside biochemical indicators, including proline, chlorophyll, and malondialdehyde levels. Overall, the integration of RS with machine learning provides a rapid, robust, and non-invasive framework for the early detection of drought and salinity stress in wheat. Notably, Raman intensity variations observed at 737, 996, 1051, 1064, and 1518 [Formula: see text] exhibited consistent spectral trends that closely mirrored changes in conventional biochemical stress markers, confirming that these spectral shifts directly reflect underlying physiological stress responses. To classify stress levels and to identify key Raman-derived biomarkers associated with each stress type, a machine learning approach was implemented, achieving a classification accuracy exceeding 85% in discriminating control, drought-stressed, and salinity-stressed plants. Furthermore, characteristic Raman bands, particularly those associated with C-H and amide vibrational modes, showed strong correlations with established biochemical indicators, underscoring their potential as reliable, non-invasive stress biomarkers. Collectively, these findings provide mechanistic insight into stress-induced structural and biochemical alterations and support the application of RS-machine learning integration for precision agriculture and resilient crop management under changing environmental conditions.
Why it matches plant phenotyping methodsラマン分光と機械学習により、コムギの乾燥・塩ストレス状態を非破壊的に検出・分類する方法を開発・検証しており、植物状態の取得が研究の中心である。
abstractRaman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments.
This study suggests a novel extraction pipeline based on terrestrial laser scanning across multiple growth stages to address the current deficiency of three-dimensional (3D) phenotypic traits for wheat populations derived from 3D point clouds. This study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network that incorporates an SA-CrossAttention module to address the difficulties presented by complex structures, background noise, non-uniform point distributions, and scale variations in plot-level wheat point cloud data. Plot height, canopy area, and volume are examples of common phenotypic parameters that are successfully extracted using this technique. Additionally, two new phenotypic parameters: plot extension distance and lodging angle are suggested by fusing the centroid and slice-skeletonization algorithms. A software platform called 3D Trait Analysis was created to facilitate multi-sensor 3D data processing and trait extraction. A genome-wide association study (GWAS) was then conducted using the extracted population-level traits to find potential genes linked to these new phenotypes. While the segmentation accuracies of 3D WP-seg Net achieved 93.1%, 88.3%, and 92.5% under various sensor systems, the results showed a strong correlation between the predicted and measured plot heights (R 2 = 0.954). Furthermore, four candidate genes linked to extension distance were found on chromosomes 1A, 2A, and 4A, and five putative genes controlling plot lodging angle were found on chromosomes 2D, 3A, and 7A. The multi-stage 3D phenotyping and analysis framework for wheat populations established by this study improves the accuracy of point cloud segmentation and trait quantification while offering a new and efficient method for the genetic analysis of important population-level traits.
Why it matches plant phenotyping methodsLiDAR点群の分割、3D形質抽出、検証、ソフトウェア基盤の開発が研究の中心であり、コムギの形態・倒伏関連形質を定量化しているため。
abstractThis study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' source code (3D WP-seg Net segmentation pipeline and 3D Trait Analysis software), testing data, and supporting datasets in a public GitHub repository, directly supporting this paper's wheat 3D phenotyping and segmentation analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/AI-PhenoLab/3D-WP-seg-Net .Open asset ↗AI-PhenoLab/3D-WP-seg-Netlines:511-575Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Ear density ( ) and ear surface area in cereals are important traits for adaptation to low inputs and climate change. Here we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot. First, the YOLOv5 ear detection algorithm is applied to nadir RGB images to estimate . Second, an ear segmentation algorithm is applied to nadir and 45° RGB images to compute the ear gap fraction at different viewing angles. The Beer-Lambert law is then inverted to compute the ear area index (EAI) from the observed ear gap fraction. is finally derived as the ratio between EAI and . We applied the methodology to a panel of 10 commercial bread wheat varieties how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m -2 ) for and 18% (1.3 cm 2 ) for . For awned varieties, ground-truth observations of were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm 2 ). was strongly correlated with grain dry mass per ear at harvest ( r 2 = 0.80 across genotypes and environments, r 2 per genotype ranged between 0.80 and 0.95) and was strongly correlated with grain yield ( r 2 = 0.83). These results indicate that both EAI and can be interesting non-destructive proxies for yield and grain dry mass per ear.
Why it matches plant phenotyping methodsRGB画像と地上ロボット、物体検出・セグメンテーション・Beer–Lambert法を組み合わせ、コムギ穂の密度と表面積を推定・検証する手法が研究の中心であるため。
abstractHere we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot.
Reproduction assets foundThe authors publicly release their ear surface area estimation algorithm with an example dataset on an INRAE forge repository, and the Phenomobile-derived ear density/ear surface area estimations used in the multi-environment analysis are included as supplemental material with the open-access article. The YOLOv5 GWC_SoDataset · publicThe algorithm developed to estimate the EAI and the average ear surface using binary images from ear segmentation are publicly available in the repository https://forge.inrae.fr/raul.lopez-lozano/wheat-ear-surface , jointly with an example dataset from the Mauguio 2023 trial (4 treatments, 1 replicate). The Phenomobile estimations of ear surface area and ear density used in the multi-environmental mixed model presented in Section 2.5 are included as supplemental material.Open asset ↗lines:614-652Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
: This study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale. Therefore, the study investigated multi-orbit, multi-feature time series derived from Sentinel-1 (S1) VV/VH polarizations. This multi-feature approach encompasses backscatter intensity, interferometric coherence and alpha/entropy decomposition features. Crop phenology tracking is crucial for assessing agricultural resilience under climate change, yet existing approaches face challenges due to uncertainties and variability in SAR signal interpretation as well as in situ data. Building on previous landscape-level analyses, this work introduces the concept of trackability, defined as the temporal range during which SAR-derived time-series metrics (TSM), such as breakpoints in backscatter intensity or interferometric coherence, align with key phenological stages (e.g., stem elongation in winter wheat). A growing degree day (GDD)-based normalization contextualizes field-specific deviations relative to landscape averages, enabling quantification of uncertainties inherent in both SAR signals and ground observations. The framework captures the spatio-temporally variable nature of crop development by estimating the first and last phenologically relevant TSM occurrence within a defined uncertainty window, thus providing relational and relative indicators of phenological tracking. This approach reduces dependencies of extensive in situ data and enhances comparability across studies with differing SAR processing methods and their acquisition geometries. Results reproduce known feature-stage relationships (e.g., tracking for stem elongation by interferometric coherence) and reveal inter-seasonal variability influenced by weather conditions and acquisition parameters. On average relevant TSM occurrences were found at approximately 90% of GDD progression of in situ reported phenological stages, while systematic differences of around 5% by relative orbit were discovered. The study highlights the potential of integrating multiple S1 features and orbits without optimization-induced information loss, producing quality masks that identify optimal tracking performance at the field level. This framework advances SAR-based phenology monitoring by offering scalable, transferable insights for precision agriculture, while practical implementation still requires detailed field boundaries and early-season crop management information.
Why it matches plant phenotyping methodsSAR時系列から作物フェノロジーを追跡・定量化する不確実性評価フレームワークが研究の中心であり、圃場レベルの植物状態測定法として開発・検証されている。
abstractThis study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale.
Accurate prediction of nitrogen utilization efficiency (NUtE) is critical for breeding nitrogen-efficient crop cultivars and optimizing field nitrogen management. Traditional prediction methods are time-consuming and limited to resolving plant-level nitrogen dynamics, hindering effective phenotype acquisition at the field scale and understanding of nitrogen uptake and transport in crops. This study aims to couple proximal remote sensing (PRS) and a crop growth model (CGM, i.e., WheatGrow model) via high-throughput phenotyping (HTP) techniques to establish a non-destructive prediction framework for plot-level NUtE at the field scale. Firstly, the organ-level nitrogen submodule was developed and integrated into the WheatGrow model, improving the simulation accuracy of plant nitrogen accumulation dynamics. Second, proximal RGB data combined with a deep-shallow machine learning approach enabled high-precision estimation of organ-specific critical nitrogen concentrations (leaf: R 2 = 0.94, RMSE = 0.21%; spike: R 2 = 0.95, RMSE = 0.10%). Fitted parameters of critical nitrogen dilution curves (CNDCs) demonstrated variations between cultivar and management in both organs, with spike nitrogen dilution rates exhibiting greater sensitivity to management practices than leaves. Finally, coupling PRS-derived organ-specific CNDCs with the enhanced WheatGrow model through the ensemble Kalman filter (EnKF) algorithm, yielded precise NUtE predictions at a small spatial scale (RMSE = 4.54 kg kg −1 , Bias = 0.05). Validation across multi-year, multi-cultivar trials demonstrated robust performance, reducing NUtE prediction errors below 10% (RRMSE = 9.9% ± 0.8%). This framework bridges HTP techniques with crop modeling, may catalyze a paradigm shift in CGMs from empirical parameterization to real-time sensing, and advance scalable nitrogen use efficiency phenotyping in sustainable crop improvement and smart agriculture.
Why it matches plant phenotyping methods高スループット表現型計測、近接リモートセンシング、RGB画像、機械学習、作物モデルを統合し、器官別窒素形質と圃場スケールのNUtEを推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractThis study aims to couple proximal remote sensing (PRS) and a crop growth model (CGM, i.e., WheatGrow model) via high-throughput phenotyping (HTP) techniques to establish a non-destructive prediction framework for plot-level NUtE at the field scale.
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.
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).
Abstract Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. To support model training and evaluation, we construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD-L 1 and CD-L 2 values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for robust 3D wheat phenotyping under occlusion and provides a systematic reference for applying general-purpose 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下小麦の単視点3D再構成とアモーダル補完を開発し、データセット構築、複数手法の系統評価、器官形質推定誤差の検証まで行っており、植物フェノタイピング手法が中心である。
abstractthis study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat
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
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.
RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.
Why it matches plant phenotyping methods衛星データと作物モデルを統合し、LAI・地上部乾物量・収量という植物形質を推定するソフトウェア手法を開発・検証しており、形質取得・推定法が研究の中心である。
abstractRSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization.
Understanding how crop trait variability shapes genotype × environment × management (G × E × M) interactions remains a key uncertainty in predicting agricultural performance under a changing climate. Continental-scale crop models commonly rely on spatially uniform parameters, limiting their ability to represent adaptive variation in phenology, allocation, and yield formation. Here we integrate the mechanistic agroecosystem model Ecosys with a deep learning-enabled model-data fusion inversion to infer spatially explicit physiological controls (trait proxies) of U.S. winter wheat directly from observations. By constraining simulations with satellite-derived photosynthesis and county-level yield records from 2008 to 2022 across ~1000 winter wheat-producing counties, the inversion recovers coherent patterns of maturity group, reproductive capacity, harvest index, and root-shoot allocation. The optimized simulations reproduce observed carbon uptake and yield variability (gross primary productivity r = 0.76-0.88; phenology bias 90% of yields within ±20% of reports) and reveal distinct physiological profiles that align with the geographic distributions of major winter wheat market classes. The inferred controls explain class- and region-specific climate sensitivities: warmer winters reduce vernalization success in late-maturing cultivars, while elevated vapor pressure deficit causes strong yield losses in rainfed Hard Red Winter wheat. The results demonstrate that observation-constrained trait inversion within model-data fusion framework reveals biologically meaningful crop-class variation, thereby providing a scalable, physiologically grounded framework for diagnosing adaptive diversity and climate vulnerability across agroecosystems.
Why it matches plant phenotyping methods衛星由来の光合成観測と収量記録を用い、深層学習によるモデル・データ融合で冬コムギの生理・形態形質を空間的に推定する手法が研究の中心であるため。
abstractHere we integrate the mechanistic agroecosystem model Ecosys with a deep learning-enabled model-data fusion inversion to infer spatially explicit physiological controls (trait proxies) of U.S. winter wheat directly from observations.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods冬小麦の葉・植物体窒素量をリモートセンシングと植生指数クラスタリングで推定する手法開発が題名の中心であり、植物形質の取得・推定方法に該当する。
titleRemote sensing estimation for winter wheat plant nitrogen based on nitrogen distribution model building and leaf nitrogen quantitatively inversion using vegetation index clustering method
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.
Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping. In this study, we applied a deep transfer learning (DTL) approach to enhance GY prediction using UAV‐derived spectral and textural traits across multiple environments and developmental stages of winter wheat ( Triticum aestivum L.). The model's transferability and generalizability were evaluated across years, locations, nurseries, and stage‐specific scenarios. We employed fine‐tuning, which involves retraining a pretrained one‐dimensional convolutional neural network (1D‐CNN) model on scenario‐specific target data to enhance generalizability. Fine‐tuning was tested with 20%, 40%, 60%, and 80% of the target data to identify an optimal balance between model accuracy and adaptation, and compared with 1D‐CNN without DTL (baseline model). In cross‐year predictions, the baseline model (from 2022) performed poorly ( R 2 = −2.9 in 2023), while DTL improved prediction to an R 2 of 0.83 with 20% fine‐tuning, demonstrating strong temporal adaptability. Similarly, in cross‐location scenarios, baseline model performance was poor ( R 2 ranging from −15.3 to −0.4) but improved to 0.29–0.69 with 40% fine‐tuning. Stage‐specific predictions benefited most at Feekes stages 10.5 and 11, where DTL achieved an R 2 of 0.78 and 0.82, respectively, compared to baseline model R 2 values near 0. These results demonstrate that DTL improves model transferability and generalizability, increasing prediction accuracy and offering a resource‐efficient tool to accelerate selection for complex and labor‐intensive traits in modern breeding programs.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から抽出した植物形質を用い、深層転移学習による穀粒収量予測の汎化性・転移性を検証しており、表現型取得・推定ワークフローが中心である。
abstractRemote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.
To address the poor regression performance caused by strong spatiotemporal heterogeneity, inconsistent information scales and complex feature relationships in field-based multimodal data, this study proposes a hierarchical fusion framework embedded within an encoder – decoder network. The framework integrates multi-scale, interpretable, and cross-modal representations through coordinated modules that bridge feature discrepancy understanding and information flow regulation. A multi-scale parallel pathway structure is designed to enhance joint perception of local and global information by leveraging feature mappings with different receptive fields. An interpretable feature importance allocation strategy is further introduced to improve the backbone network’s ability to provide dynamic guidance on feature contributions. This enables the model to perform adaptive weighting and feature selection during multimodal fusion. In addition, a cross-modal dense interaction and gated fusion mechanism is constructed to regulate information flow and capture fine-grained associations across modalities. The improved feature-guided model is applied to yield regression using multimodal data collected over three consecutive years and multiple wheat varieties. Results show that, in the first year, the highest prediction accuracy reaches an R2 of 0.8112 with an rRMSE of 16.85%. Validation using data from the same planting region in the second and third years yields a highest R2 of 0.8107 and 0.7986, with corresponding rRMSE values of 17.92% and 17.27%, respectively. Compared with other deep learning models within the same year, the proposed approach improves R2 by up to 30.52%, 33.96% and 32.79% across the three years, while reducing rRMSE by up to 41.59%, 45.01% and 46.43%. The results demonstrate that the coordinated interaction among modules establishes an integrated optimization pathway that spans from feature discrepancy understanding to information flow regulation, while maintaining interpretability in the decision process. Under complex field conditions with multiple sources of uncertainty, the proposed framework achieves stable module contributions ranging from 5% to 10% based on cross-validation and t-test analyses. This effectively alleviates the difficulty of efficient multimodal feature fusion for robust yield prediction under stress conditions. The study provides a new methodological perspective for multimodal agricultural sensing and crop phenotyping. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings.Develops a weight-guided method to enhance interpretability of multimodal features.Designs a cross-modal dense interaction and gated fusion mechanism.Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings. Develops a weight-guided method to enhance interpretability of multimodal features. Designs a cross-modal dense interaction and gated fusion mechanism. Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features.
Why it matches plant phenotyping methods小麦の収量という植物形質を対象に、マルチモーダルデータ融合と収量回帰のための新規エンコーダ・デコーダ手法を開発し、複数年・品種で検証している。フェノタイピング手法が中心である。
abstractthis study proposes a hierarchical fusion framework embedded within an encoder – decoder network
To address the poor regression performance caused by strong spatiotemporal heterogeneity, inconsistent information scales and complex feature relationships in field-based multimodal data, this study proposes a hierarchical fusion framework embedded within an encoder – decoder network. The framework integrates multi-scale, interpretable, and cross-modal representations through coordinated modules that bridge feature discrepancy understanding and information flow regulation. A multi-scale parallel pathway structure is designed to enhance joint perception of local and global information by leveraging feature mappings with different receptive fields. An interpretable feature importance allocation strategy is further introduced to improve the backbone network’s ability to provide dynamic guidance on feature contributions. This enables the model to perform adaptive weighting and feature selection during multimodal fusion. In addition, a cross-modal dense interaction and gated fusion mechanism is constructed to regulate information flow and capture fine-grained associations across modalities. The improved feature-guided model is applied to yield regression using multimodal data collected over three consecutive years and multiple wheat varieties. Results show that, in the first year, the highest prediction accuracy reaches an R2 of 0.8112 with an rRMSE of 16.85%. Validation using data from the same planting region in the second and third years yields a highest R2 of 0.8107 and 0.7986, with corresponding rRMSE values of 17.92% and 17.27%, respectively. Compared with other deep learning models within the same year, the proposed approach improves R2 by up to 30.52%, 33.96% and 32.79% across the three years, while reducing rRMSE by up to 41.59%, 45.01% and 46.43%. The results demonstrate that the coordinated interaction among modules establishes an integrated optimization pathway that spans from feature discrepancy understanding to information flow regulation, while maintaining interpretability in the decision process. Under complex field conditions with multiple sources of uncertainty, the proposed framework achieves stable module contributions ranging from 5% to 10% based on cross-validation and t-test analyses. This effectively alleviates the difficulty of efficient multimodal feature fusion for robust yield prediction under stress conditions. The study provides a new methodological perspective for multimodal agricultural sensing and crop phenotyping. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings.Develops a weight-guided method to enhance interpretability of multimodal features.Designs a cross-modal dense interaction and gated fusion mechanism.Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features. Proposes a multi-scale parallel-path architecture for joint perception of multimodal feature mappings. Develops a weight-guided method to enhance interpretability of multimodal features. Designs a cross-modal dense interaction and gated fusion mechanism. Establishes an encoder–decoder-based framework for coordination and fusion of heterogeneous multimodal features.
Why it matches plant phenotyping methodsマルチモーダル作物センシングから小麦収量を推定する encoder–decoder 手法を開発し、複数年・品種データで検証しており、表現型推定手法が研究の中心である。
abstractthis study proposes a hierarchical fusion framework embedded within an encoder – decoder network.
To address the poor regression performance caused by strong spatiotemporal heterogeneity, inconsistent information scales and complex feature relationships in field-based multimodal data, this study proposes a hierarchical fusion framework embedded within an encoder – decoder network. The framework integrates multi-scale, interpretable, and cross-modal representations through coordinated modules that bridge feature discrepancy understanding and information flow regulation. A multi-scale parallel pathway structure is designed to enhance joint perception of local and global information by leveraging feature mappings with different receptive fields. An interpretable feature importance allocation strategy is further introduced to improve the backbone network’s ability to provide dynamic guidance on feature contributions. This enables the model to perform adaptive weighting and feature selection during multimodal fusion. In addition, a cross-modal dense interaction and gated fusion mechanism is constructed to regulate information flow and capture fine-grained associations across modalities. The improved feature-guided model is applied to yield regression using multimodal data collected over three consecutive years and multiple wheat varieties. Results show that, in the first year, the highest prediction accuracy reaches an R2 of 0.8112 with an rRMSE of 16.85%. Validation using data from the same planting region in the second and third years yields a highest R2 of 0.8107 and 0.7986, with corresponding rRMSE values of 17.92% and 17.27%, respectively. Compared with other deep learning models within the same year, the proposed approach improves R2 by up to 30.52%, 33.96% and 32.79% across the three years, while reducing rRMSE by up to 41.59%, 45.01% and 46.43%. The results demonstrate that the coordinated interaction among modules establishes an integrated optimization pathway that spans from feature discrepancy understanding to information flow regulation, while maintaining interpretability in the decision process. Under complex field conditions with multiple sources of uncertainty, the proposed framework achieves stable module contributions ranging from 5% to 10% based on cross-validation and t-test analyses. This effectively alleviates the difficulty of efficient multimodal feature fusion for robust yield prediction under stress conditions. The study provides a new methodological perspective for multimodal agricultural sensing and crop phenotyping.
Why it matches plant phenotyping methodsマルチモーダル農業センシングから小麦収量という植物形質を推定するエンコーダ・デコーダ手法を開発し、複数年・品種データで検証しているため、フェノタイピング手法が中心的である。
abstractthis study proposes a hierarchical fusion framework embedded within an encoder – decoder network.
Fractional Vegetation Cover of Crops (CropFVC) is a critical canopy parameter for monitoring crop growth, yet the behavior of widely used global FVC products (GLASS, GEOV1, GEOV2, and GEOV3) over croplands remains insufficiently understood due to fragmented validation references and limited crop-specific assessments. This study compiled a multi-source global CropFVC reference dataset (2000–2024) by integrating five international validation networks, the literature-derived samples, and newly acquired UAV and Jilin-1 satellite-derived CropFVC samples from China in 2024. The references were organized into three complementary validation contexts (V1~V3) to examine product behavior under different temporal coverage, crop purity, and reference conditions, together with spatio-temporal observations at the KONZ site. Results show that (1) across validation contexts, the evaluated products showed consistent behavior patterns, including shared overestimation under dense canopy conditions and reduced differences at low FVC levels; (2) spatio-temporal analysis at the KONZ site confirmed that peak-season deviations reflect shared response behavior rather than site-specific reference uncertainties; (3) historical mixed references (V1~V2) showed similar bias structures, whereas crop-specific validation (V3) preliminary revealed clearer crop-dependent responses, with predictive difficulty following winter wheat > maize > rice > soybean and improved stability after integrating 2024 observations. The integration of recent high-resolution crop observations expands existing global CropFVC references and enables behavior-oriented interpretation of global FVC products beyond simple accuracy ranking, providing an updated validation perspective for future development and application of global CropFVC products in agricultural monitoring.
Why it matches plant phenotyping methods作物キャノピーのFVCという植物形質を対象に、複数の全球FVC推定プロダクトを多様な参照データで体系的に検証し、UAV・衛星観測を含むCropFVC参照データセットを構築している。形質取得・検証が研究の中心である。
titleMulti-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References
A comprehensive description of plant phenotypes of certain taxa is an important task when describing genera and species, as well as when setting their natural taxonomies. The development of modern technologies of effective phenotyping makes it possible to obtain a large amount of data with a quantitative and/or qualitative description of various traits in plants, mainly based on the analysis of their digital images. The study compared the results of the F2 hybrids assessment - visually and using machine learning methods - of two endemic tetraploid (2n = 4x = 28) wheat species which are Ethiopian wheat (Triticum aethiopicum Jakubz.) and Kartalian or Dika wheat (T. carthlicum Nevski). In the latter case, it is proposed to use the method of a mixture of Gaussian (normal) distributions in plant morphometry in order to identify groups that differ in character values. Most taxonomically important (species-specific) traits are controlled oligogenically and have a clear phenotypic manifestation, so hybridological analysis was an indispensable and basic type of analysis for subsequent detailed phenotyping of wheat spikes using machine-learning methods. According to a number of criteria, the estimates of patterns of inheritance obtained by different methods coincide. Based on the conducted research, we can state that the trait "tetraaristatum" (the presence of awns on both flower and spike glumes) is species-specific (taxonomically important) for T. carthlicum and it can be effectively used for taxonomic purposes both in carrying out hybridological analysis and in experiments using machine learning. Such a species-specific character is the "character (type) of awnedness" for T. aethiopicum. Our study demonstrates that a combination of automatic phenotyping methods and a model of a mixture of Gaussian distributions can, in principle, lead to an automatic analysis of the allocation of classes in F2 hybrids. It allows, in turn, to detect the presence of genes associated with species-specific traits of wheat plants. Further, the improvement of the applied artificial intelligence (AI) algorithms is required.
Why it matches plant phenotyping methodsコムギ穂の形態形質を対象に、画像に基づく機械学習フェノタイピングとガウス混合モデルを提案・適用しており、表現型の自動抽出・分類が研究の中心である。
abstractThe study compared the results of the F2 hybrids assessment - visually and using machine learning methods
Reproduction assets foundThe paper's supplementary materials (Supplementary Tables S1–S3 and Figure S1) contain the paper-specific phenotyping data: species-specific trait descriptions, the 19 spike morphometric characters per projection, and the Gaussian mixture model splitting results (means, variances, group sizes, χ² values). The full textSupplement · publicof these traits
are controlled by oligogenes and have a clear phenotypic
manifestation, the hybridological method was an indispensable
and primary type of analysis for subsequent detailed
phenotyping spikes of wheat species using machine learning
methods.
Supplementary Materials are available in the online version of the paper:
https://vavilov.elpub.ru/jour/manager/files/Suppl_Kruch_Engl_30_3.pdf
Plant material. The object of study was interspecific
hybrids obtained by crossing two endemic tetraploid
wheat species ♀T. aethiopicum Jakubz. (k-19301/2) with
♂T. carthlicum
Nevski (k-32496). The experiment was produced
in spring sowing in the greenhouses of the Breeding
and Genetics Complex (BGC)Open asset ↗lines:111-200Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 13 Sept 2026
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
Abstract Plant breeding is essential for crop improvement, yet progress is often hindered by slow, laborious, and subjective field phenotyping methods. High‐throughput phenotyping (HTP), particularly image‐based methodologies powered by machine learning, offers a pathway to overcome these limitations. However, achieving robustness and generalization when analyzing diverse genotypes within a crop and across reproductive stages remains challenging and can affect model performance and the accurate extraction of phenotypic features. This study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages, using wheat ( Triticum aestivum L.), sorghum ( Sorghum bicolor L.), and corn ( Zea mays L.) as case studies. The primary objectives were to analyze (i) the overall prediction performance on the aggregated dataset for each crop, (ii) the stratified performance by genotype and collection date, and (iii) the temporal and genotypic transferability across growth stages and unseen genotypes. Four distinct smartphone cameras were used to collect images of the reproductive structure across crop growth stages (different collection dates) from 160 corn, 80 sorghum, and 40 wheat genotypes. The total number of images per crop was 2000 for wheat, 4000 for sorghum, and 3840 for corn. Five semantic segmentation models were tested in this study—DeepLabv3+, MaskFormer, SegFormer, SegNet, and U‐Net—using the images and respective binary masks for training and testing. The SegFormer model achieved the highest intersection over union (IoU) values for corn (0.90) and sorghum (0.92), while the U‐Net model performed best for wheat (0.89). A minor performance decline, with IoU differences up to 0.1, was observed when testing the same model across different genotypes. However, the temporal transferability drops up to 0.5 IoU when training and inferring on different crop growth stages. The main reason for those changes may lie in the natural color and organ architecture temporal changes between the trained and tested datasets when transferring the models across growth stages. These results highlight the urgent need to prioritize robustness and transferability when developing reliable in‐field HTP methodologies.
Why it matches plant phenotyping methods植物の生殖器官画像からの表現型抽出に用いるセマンティックセグメンテーション手法を、作物・遺伝子型・生育段階間で性能と転移性の観点から比較検証しており、方法論が研究の中心である。
abstractThis study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages
The rapid and accurate quantification of plant phosphorus (P) content is essential for the real-time assessment of crop P status and improvement of P fertilizer use efficiency. However, non-destructive and rapid approaches for P monitoring are limited. In this study, the feasibility of monitoring plant phosphorus content (PPC) in winter wheat was evaluated through multi-source feature fusion of unmanned aerial vehicle (UAV) imagery based on a long-term field experiment with five P treatments. Multiple spectral features, including color indices (CIs), fractional vegetation cover (FVC), vegetation indices (VIs), texture features (TFs) and texture indices (TIs), were extracted from UAV RGB and multispectral images. Sensitive spectral features were systematically screened using Pearson correlation analysis, random forest (RF) importance ranking, and the Relief algorithm. Selected features were then fed into three machine learning models, RF, support vector machine (SVM), and k-nearest neighbor (KNN) to predict PPC. The results showed that GRI, VARI, MGRVI, TGI, NDRE, and CIred edge were highly correlated with PPC at the maturity stage (r = 0.96). Both TFs and TIs demonstrated stronger correlations with PPC at the 750 and 840 nm bands, with most TIs outperforming TFs, confirming the feasibility of spectral-based PPC estimation. Based on the selected input variables including DTI (450-Ent, 750-Mea), 840-Mea, and RVI, the SVM model achieved the best performance (R 2 c=0.94, RMSEc=0.29, RPDc=4.03; R 2 v=0.92, RMSEv=0.36, RPDv=3.48). These results highlight the potential of combining VIs, TFs, and TIs features for training machine learning models for PPC prediction, while the organ-level physiological explanations warrantee further investigations under controlled P gradients. This study provides data-driven insights for UAV-based monitoring of plant P nutritional status under local experimental conditions.
Why it matches plant phenotyping methodsUAV画像から特徴量を抽出し、機械学習で冬コムギの植物リン含量という生理形質を推定する手法の開発・評価が中心であり、方法論的検証も実施している。
abstractMultiple spectral features, including color indices (CIs), fractional vegetation cover (FVC), vegetation indices (VIs), texture features (TFs) and texture indices (TIs), were extracted from UAV RGB and multispectral images.
Ensuring global food security under rapid climate change demands accelerated genetic gain and breeding strategies that address complex Genotype-by-Environment (G×E) interactions. Traditional genomic selection models often fail to account for novel or extreme climates.Furthermore, integrating mechanistic crop growth models (CGMs) using traditional Bayesian frameworks to solve this issue presents severe computational bottlenecks. Here, we introduce DeepBioGS, a novel hybrid framework that integrates genomic selection with biophysical growth modelling via a fully differentiable deep learning architecture. DeepBioGS utilises a parameter-prediction multi-layer perceptron to map high-dimensional genomic markers to latent, highly heritable physiological traits (Genotype-Specific Parameters; GSP). These parameters mechanistically predict crop phenology across diverse environments. Using two multi-environment wheat datasets comprising over 6,000 genotypes, DeepBioGS extracted latent traits with near-perfect SNP-based heritability values (0.95-1.00). Crucially, the framework demonstrated superior or comparable predictive accuracy (up to r 2 = 0.77) against standard genomic best linear unbiased prediction (GBLUP) and traditional Bayesian CGM-WGP models. Its architecture drastically improved computational scalability by enabling standard backpropagation, effectively bypassing the stochastic sampling limitations of approximate Bayesian methods. Most importantly for climate adaptation, DeepBioGS allowed accurate forecasting of genotype performance in entirely unobserved environmental conditions. By merging the representational power of deep learning with the structural constraints of biophysics, DeepBioGS provides a highly scalable, interpretable tool to navigate G×E interactions, enabling the assessment of cultivars under future climate scenarios, thus optimising crop breeding for a changing global environment.
Why it matches plant phenotyping methodsゲノム情報から生理形質・作物フェノロジーを推定し、環境別の性能を予測する新規計算フレームワークが研究の中心であり、植物形質推定法の開発に該当する。
abstractHere, we introduce DeepBioGS, a novel hybrid framework that integrates genomic selection with biophysical growth modelling via a fully differentiable deep learning architecture.
Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remains challenging. Active 3D sensing methods such as Light Detection and Ranging (LiDAR) or time-of-flight (ToF) are sensitive to plant motion or poorly suited to outdoor conditions, while 3D reconstructions are computationally expensive. Direct 2D image processing would offer computational advantages, but image-based models lack explicit geometric information. We therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference. First, we train a rigid-invariant point cloud network using distance-based histogram features to obtain pose-robust geometric representations. We then combine the 3D model with a proposed multi-view image-based regulated Transformer (RT) in an ensemble architecture. Finally, we distill the ensemble knowledge into a purely image-based student model using either feature-based or label-based distillation. The two distilled RTs reduce the mean absolute error (MAE) from 654.31 mm$^3$ of the non-distilled RT to 639.93 mm$^3$ and 644.62 mm$^3$, and increase correlation from 0.76 to 0.77 and 0.82, respectively. At the same time, inference time is reduced from 160 ms to 1.4 ms per spike. Distillation further mitigates volume-dependent bias and reshapes the latent representation of the image model toward a geometry-aware shape. Our results demonstrate that 3D-informed training of a 2D Transformer allows for scalable and efficient spike volume estimation for high-throughput field phenotyping.
Why it matches plant phenotyping methods小麦穂の体積を画像・3D再構成・知識蒸留で推定する手法の開発と性能評価が中心であり、高スループット植物フェノタイピングへの応用も明示されている。
abstractWe therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference.
Reproduction assets foundThe paper explicitly states that links to its wheat spike dataset (multi-view images and 3D scans) and its analysis code are available via the authors' project webpage, which is an allowed URL. Other URLs (pyrender, CORDIS projects) are generic libraries or unrelated funding projects, not paper-specific assets.Dataset · publictance of around 2.5 m with a ground sampling distance of 0.3 mm (Fig. S1 a). The tagged and imaged spikes (Fig. S1 b) were sampled and ground truth volumes were acquired with a 3D light scanner (Shining 3D Einscan-SE V2, SHINING3D, Hangzhou, China) following the protocol of [ 76 ] . Links to the dataset and code can be found at https://oliviazum.github.io/3DKD-wheat/ . Detailed information about the dataset can be found in Sec. A .
3.3 Data Pre-Processing
Field images contained approximately 300-500 spikes per genotype within a plot of about 1.5 m 2 m^{2} . To reduce background inference, spike detection was first performed, and all subsequent processing was restricted to the detected regioOpen asset ↗lines:91-104Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Wheat kernel hardness, vitreousness, and creaseness are key determinants of milling performance, yet they reflect different physical scales of grain structure and are not necessarily coupled. Methods: We developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat. Pixel-level spectral unmixing resolved glassy, intermediate, and mealy endosperm components within individual kernels, enabling vitreousness to be expressed as a continuous spatial index. Results: The hyperspectral-derived vitreousness index showed moderate associations with kernel protein content and the protein-to-starch ratio, consistent with variation in endosperm packing density, but weak relationships with kernel hardness and crease geometry. Kernel hardness, primarily determined by puroindoline genotype, showed limited association with bulk protein and starch composition. Crease geometry, quantified using composite indices from RGB images, captured macroscopic grain features largely independent of both hardness and vitreousness. Discussion: These results demonstrate that hardness, vitreousness, and creaseness represent complementary but largely independent dimensions of grain quality, corresponding to molecular-scale adhesion, mesoscale packing, and macroscopic geometry, respectively. The proposed framework provides a scalable, non-destructive approach for resolving intra-kernel heterogeneity, enabling improved digital phenotyping for wheat breeding and quality assessment.
Why it matches plant phenotyping methodsハイパースペクトル画像とスペクトルアンミキシングを用いて小麦粒の硝子質を定量するデジタル表現型解析フレームワークを開発しており、形質取得手法が中心的である。
abstractWe developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat.
Reproduction assets foundThe paper's data availability statement deposits full hyperspectral image cubes and RGB image datasets on Figshare, and the supplementary material includes Python analysis scripts (Supplementary Code S1–S2) and processed feature tables (Supplementary Table S3) directly reproducing the paper's phenotyping measurements.Dataset · publicfull hyperspectral image cubes and associated RGB imagedatasets are available via Research Datas 1 – 3 at Figshare: https://doi.org/10.6084/m9.figshare.31259530Open asset ↗Figshare · 10.6084/m9.figshare.31259530lines:151-201Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.
Why it matches plant phenotyping methods葉の病害症状を画像解析で定量化するワークフローを開発し、色空間比較と手動アノテーションによる検証を行っており、植物表現型取得法が中心である。
abstractwe present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously
Reproduction assets foundThe paper's authors explicitly state that all code implementing the leaf disease quantification workflow (SegLeaf, including the graphical interface and documentation) is hosted in a public GitHub repository. No separate public phenotype dataset or trained model checkpoint is described in the supplied blocks.Code · publicted by the Agence Nationale de
la Recherche (ANR) (project SCOOP, grant no. ANR-19-CE32-0011; and project MOBIDIV,
grant no. ANR-20-PCPA-0006).
Code and Data Availability
The method and associated scripts developed in this work are freely available to the re-
search community. All code is hosted in a public GitHub repository at https://github.com/titouanlegourrierec/SegLeaf, which includes the full implementation of the method includ-
ing the graphical interface and documentation to guide users through the analysis pipeline.
15
.
CC-BY 4.0 International license
made available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display tOpen asset ↗titouanlegourrierec/SegLeafpdf-raw-page:15 lines:1-39Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチモーダルデータと機械学習により、作物水分状態（PMC）、LAI、草高、蒸発散量を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractCrop Height was extracted from oblique photogrammetry point cloud data.
Given the global importance of wheat cultivation in the agricultural landscape, the practicality of the tetrazolium test in post-harvest management of seed lots, and the significant increase in the use of technologies in agriculture, this work aimed to evaluate predictive models for high-efficiency phenotyping and classification of the viability of different commercial wheat cultivars using the tetrazolium test. The predictive models proved accurate in estimating the viability of the 50 seed lots, reaching over 90% viable seeds, depending on the wheat cultivar. The recommended tetrazolium salt solution concentrations were 0.125% for the BRS 264 cultivar, 0.1% for MGS Brilhante and BRS 404, and 0.075% for TBIO DUQUE and BRS 394, with a pre-conditioning period of 9 h, regardless of the cultivar. Correlations between the percentage of viable seeds obtained by the tetrazolium test and physiological vigor variables were statistically significant, serving to validate each chosen predictive model, as well as to rank the seed lots by cultivar. Through computational phenotyping of over 10,000 seed images, and subsequently, individual digital analyses of the respective embryonic tissues, the cultivars BRS 264, TBIO DUQUE, and BRS 404 were classified into four viability classes, and MGS Brilhante and BRS 394 into three classes. Therefore, predictive models, specific to each cultivar, associated with the tetrazolium test and image analysis resources, represent significant advances for decision-making regarding the implementation or post-harvest management of wheat crops, especially cultivars planted under different climates and regions.
Why it matches plant phenotyping methods小麦種子画像とテトラゾリウム試験を用いて、生存性を推定・分類する予測モデルを開発し、相関で検証しており、植物表現型取得・抽出法が研究の中心である。
abstractthis work aimed to evaluate predictive models for high-efficiency phenotyping and classification of the viability of different commercial wheat cultivars using the tetrazolium test.
Wheat is one of the most widely cultivated and essential staple crops, playing a crucialrole in global food security. However, wheat production is significantly affected by various diseasessuch as Yellow Rust, Brown Rust, and Septoria, which lead to reduced yield and economic lossesfor farmers. Early and accurate detection of these diseases remains a major challenge due to relianceon manual inspection and limited access to agricultural expertise [1], [2]. This paper presents animage-based disease classification system for wheat crops using deep learning techniques. Theproposed system utilizes Convolutional Neural Networks (CNNs) to automatically analyze wheatleaf images and classify them into healthy or diseased categories. The model is trained on a labeleddataset of wheat leaf images and is capable of identifying multiple disease types with high accuracy.The system enables efficient and rapid disease detection, reducing dependency on manual methodsand supporting timely decision-making for crop management. Additionally, the integration ofcomputer vision and artificial intelligence improves scalability and can be extended to real-time andmobile-based applications. By leveraging modern deep learning approaches, the proposed solutioncontributes to precision agriculture, enhances productivity, and helps reduce economic losses in thefarming sector
Why it matches plant phenotyping methods小麦葉画像から健全・罹病状態および病害種を分類する画像ベース手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractThis paper presents animage-based disease classification system for wheat crops using deep learning techniques.
Common beanRiceWheatLeafClassificationDisease symptoms / severity
In Bangladesh, crop leaf diseases create a serious risk to food security and production from agriculture. Timely identification of leaf diseases in rice, wheat, and bean crops is considered crucial for the implementation of effective disease detection and classification strategies. To address this challenge, a MobilenetV2-based disease identification and classification system is proposed in this research. Previous studies focus on classifying diseases of a single species, leaving the need to train models separately for each species. This research focuses on forming a single standard model to perform leaf disease classification for multiple crop species including rice, wheat, and beans. The approach makes use of transfer learning with the MobilenetV2 model, which is fine-tuned using a dataset of annotated crop leaf images specific to Bangladesh. Following a comprehensive evaluation, an overall accuracy of 97.87% was achieved in the classification of crop leaf diseases, which surpasses the accuracy of a number of previous studies focusing on leaf disease detection of a single crop. The system demonstrates the capability to rapidly diagnose diseases in real time by enabling the users to prompt intervention to mitigate potential crop losses, ultimately leading to amplified crop yield and food security. Overall, the research highlights the promise of AI-powered solutions in tackling crop leaf disease detection, which in turn encourages greater research and technology adoption to support sustainable farming methods especially in the crop disease classification domain in Bangladesh and throughout the world. Received: 24 May 2025 | Revised: 9 March 2026 | Accepted: 14 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Bangladeshi Crops Disease Dataset at https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset. Author Contribution Statement Md. Mahmudul Hasan: Conceptualization, Methodology, Visualization, Supervision. Md. Omar Faruq: Software, Validation, Writing – original draft. Mahadi Hasan Musa: Formal analysis, Investigation. Mohammad Mamunur Rashid: Resources, Data curation, Writing – review & editing. Khandaker Mohammad Mohi Uddin: Writing – review & editing, Project administration, Supervision.
Why it matches plant phenotyping methods葉画像から作物の病害状態を推定する深層学習手法を開発・評価しており、植物病害フェノタイピングが中心的な技術貢献である。
abstracta MobilenetV2-based disease identification and classification system is proposed in this research.
Reproduction assets foundThe paper's Data Availability Statement openly provides the Bean Disease Dataset on Kaggle, which is one of the two public image datasets used to train the multi-crop leaf disease classification model. The Bangladeshi Crops Disease Dataset URL is not among the allowed URLs, so only the bean dataset is reported. No codeDataset · publict
The authors declare that they have no conflicts of interest to
this work.
Data Availability Statement
The data that support the findings of this study are openly
available in the Bangladeshi Crops Disease Dataset at https://
www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-
dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset.Author Contribution Statement
Md. Mahmudul Hasan: Conceptualization, Methodology,
Visualization, Supervision. Md. Omar Faruq: Software, Valida-
tion, Writing – original draft. Mahadi Hasan Musa: Formal
analysis, Investigation. Mohammad Mamunur Rashid: Resources,
Data curation, Writing – review & editing.Open asset ↗Kaggle · therealoise/bean-disease-datasetpdf-raw-page:11 lines:1-83Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Wheat is one of the most widely cultivated crops worldwide and provides essential nutrition for millions of people. Accurate and timely wheat yield mapping is critical for strategic planning and decision making to ensure global food security, particularly through early prediction at the field scale to support precision agriculture. Recently, hyperspectral imaging (HSI) integrated with machine learning (ML) techniques has emerged as a robust and reliable approach for assessing crop characteristics and predicting yield. This study combines Visible Near Infrared (VNIR) HSI data (400-1000 nm) with explainable ML models to enhance early and accurate wheat yield prediction and enable comprehensive image based agricultural analysis. The performance of Partial Least Squares Regression (PLSR), Random Forest (RF), and Convolutional Neural Networks (CNN) was compared. Hyperparameters of the CNN model were optimized using Bayesian optimization, resulting in superior performance with R 2 of 0.76, RMSE of 1022.24 gm/plot, and RPD of 2.03 compared with optimized PLSR and RF models. Three distinct explainable artificial intelligence (XAI) methods (Kernel-SHAP, Tree-SHAP and DeepExplainer) were further employed to analyze the predictions of the PLSR, RF, and CNN models and to determine the relative importance of key wavelengths. The CNN model developed using important wavelengths was further employed to visualize the spatial distribution of leaf regions most influential for yield prediction. These findings demonstrate the effectiveness of integrating HSI with explainable ML for advanced agricultural analysis and reliable early yield prediction.
Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習による小麦収量の早期推定を中心に、複数モデルの比較・性能評価・重要波長解析を行っており、植物形質取得手法が主要な貢献です。
abstractThis study combines Visible Near Infrared (VNIR) HSI data (400-1000 nm) with explainable ML models to enhance early and accurate wheat yield prediction
The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment. Empirical validation across major crops (rice, wheat, maize, tomato) in diverse eco-regions of China (2023--2025) demonstrates significant improvements: the MDA-HI model achieved average RMSE reductions of 42.7% for yield prediction and 38.1% for key phenological stage prediction relative to best-in-class standalone models. A large-scale case study on rice-wheat rotation systems showed that data-driven prescriptions reduced nitrogen fertilizer use by 22.5% and irrigation water by 18.3% while increasing yield by 5.1%. The study further establishes a five-dimensional evaluation system covering accuracy, robustness, interpretability, scalability, and economic benefit. Remaining challenges include edge computing for real-time inference, federated learning for privacy-preserving collaboration, and explainability of complex ``black-box'' models. This research concludes that agricultural big data constitutes a foundational catalyst for predictive, precise, and proactive cognitive agriculture, with profound implications for global food system resilience.
Why it matches plant phenotyping methods農業ビッグデータを用いて生育・収量・フェノロジーを推定するMDA-HI手法を提案し、複数作物・地域で性能検証しており、植物形質推定手法が研究の中心である。
abstractWe propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Wheat (Triticum aestivum L.) is a globally essential cereal crop whose productivity and processing efficiency are critically influenced by the morphological traits of the grain. While biotic and abiotic stresses reduce field yields, post-harvest milling losses further diminish flour output, underscoring the importance of optimizing grain morphology for processing efficiency. This study investigates the relationships between the wheat grain shape and size parameters and their impact on milling performance outcomes to identify optimal morphological characteristics that minimize yield losses. Using a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness. Multivariate analyses through k-means clustering and principal component analysis showed two distinct morphological groups and highlighted the kernel width and uniformity as potential indicators. Strong positive correlations between size traits and negative correlations between shape descriptors emphasize the trade-offs influencing milling quality. Optimal wheat grains for enhanced the milling yield exhibited large, plump, regular kernels with high circularity and low skewness. These findings provide quantitative criteria to guide wheat breeding programs with the goal of genetically optimizing the grain morphology to improve the milling yield and processing quality, thereby contributing to global food security.
Why it matches plant phenotyping methods画像解析による小麦粒の形態形質の定量が研究の中心であり、大規模コレクションに適用して形質抽出・解析を行っているため、植物表現型手法の実質的な応用に該当する。
abstractUsing a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness.
Abstract This study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences under well‐watered and reduced watering conditions in genetically uniform wheat ( Triticum aestivum L.) populations. It aims to support the design of breeding populations by identifying parents with complementary coping mechanisms that can be combined in crosses to produce superior progeny. We used RGB imaging to monitor side‐projected area (SPA) in BC 2 F 6 wheat progenies under well‐watered, pre‐anthesis, and post‐anthesis reduced watering conditions. SPA was modeled with logistic growth curves per genotype to extract dynamic canopy traits, which, together with the area under the SPA‐based growth curve, were then correlated with yield, straw biomass, harvest index, and spike traits measured at maturity. Despite genetic similarity, RGB‐based imaging revealed distinct phenotypes under normal conditions and stress response strategies among wheat lines, highlighting the value of dynamic, non‐destructive phenotyping for identifying complementary response patterns. Under well‐watered conditions ( n = 36), area under the curve was strongly associated with grain weight ( R 2 = 0.76, 95% confidence interval [CI]: 0.59–0.87), but relationships weakened under reduced watering, especially post‐anthesis, indicating a reduced association of canopy size with reproductive output. The data revealed contrasting response patterns among breeding lines based on characteristics of the logistic growth curve under normal conditions, their recovery slope after pre‐flowering reduced watering, or conversion of their straw biomass into harvestable grains. RGB imaging enables real‐time, non‐destructive detection of reduced watering responses in genetically similar wheat lines and provides complementary in‐season data to design next‐generation breeding populations for climate‐resilient cultivars.
Why it matches plant phenotyping methodsRGB画像で動的なキャノピー形質を抽出し、育種利用に向けた非破壊・リアルタイム表現型解析を実質的に評価しているため。
abstractThis study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences
Abstract Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum . This disease can cause significant economic loss due to decreasing yield, reducing seed quality, and the production of deoxynivalenol (DON); therefore, resistance to the disease is a primary concern for breeders. Phenotyping methods largely depend on the resistance mechanism being evaluated, but traditional approaches are often time‐consuming, subjective, and largely inaccurate. This review explores and contrasts traditional and modern phenotypic methods for various FHB resistance components. Digital image‐based phenotyping spans low‐cost RGB (red, green, blue) (i.e., Bayer array) sensors, multispectral sensors, and hyperspectral sensors. Across these sensors, approaches using spectral indices or deep learning have shown strong promise for detecting and classifying infection in both wheat spikes and kernels. Hyperspectral imaging has been largely explored and can be used to accurately estimate infection in spikes and kernels, as well as estimate DON content in the grain, using spectral indices or models input with specific wavebands. However, waveband‐specific approaches do not generalize well to new data, and hyperspectral imaging is significantly more resource‐intensive than RGB or multispectral cameras, limiting its practicality for most breeding programs. Phenotypic approaches using spectral indices and/or deep learning on digital images show the most potential for use in wheat breeding, due to their scalability and low cost. However, the widespread adoption of these techniques will depend on standardized imaging protocols, robust generalization across diverse genotypes, and effective integration into breeding pipelines.
Why it matches plant phenotyping methodsコムギ赤かび病抵抗性の表現型取得手法を、従来法からRGB・マルチスペクトル・ハイパースペクトル画像解析まで比較・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 explores and contrasts traditional and modern phenotypic methods for various FHB resistance components.
Abiotic stresses such as heat waves significantly reduce wheat productivity by altering leaf anatomy and physiology, leading to reduced photosynthetic carbon assimilation and crop yield. Despite the advancement in various imaging technologies at the field, canopy, plant, tissue, cellular, and subcellular levels, phenotyping of imaging-based leaf structural traits (e.g. vein density, stomatal density, and stomatal aperture) for abiotic stresses is still time-consuming and expensive without the aid of artificial intelligence (AI) and machine learning (ML). This review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits. Recent high-resolution, non-destructive imaging technologies, including confocal laser scanning microscopy, X-ray computed tomography, and optical coherence tomography, have enabled in vivo visualization of plants. Integrating these imaging techniques with AI/ML facilitates high-throughput phenotyping and the modelling of stress responses. We emphasize the potential for future research to leverage these technological advancements in imaging and AI, combining imaging data with physiological and multi-omics studies to deepen the understanding of plant heat tolerance mechanisms. Such multidisciplinary integration in leaf structure phenotyping will accelerate the development of resilient wheat varieties, offering critical insights for crop improvement in the face of climate change.
Why it matches plant phenotyping methods植物の葉構造・機能形質を対象とする画像計測技術とAI/MLによる表現型解析を中心に整理したレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits.
High-throughput phenotyping is a core prerequisite for breeding climate-resilient crops. To complete related breeding work, breeders must evaluate the performance of large-scale crop populations under seven types of field abiotic stresses including drought and high temperature, and the combined technology of unmanned aerial vehicle (UAV) imaging and artificial intelligence can provide core support to meet this demand. This review centers on three core sets of content: first, the integration of various UAV platforms, five types of imaging technologies, and machine learning and deep learning models to support phenotyping selection of abiotic stress-related traits; second, sorting out the biological significance of 12 categories of image-derived traits; third, breaking down the seven full workflow nodes ranging from flight planning to breeding decision support. Existing prior research on six crop types including wheat and rice has confirmed that this technology can improve the speed, scale and repeatability of field screening, and delivers outstanding effects when combined with multi-environment testing, genomic tools, and breeders’ expertise. This paper also sorts out six core limitations currently restricting the real-world deployment of this technology, and puts forward six future development directions to support its large-scale application.
Why it matches plant phenotyping methodsUAV画像とAIによる作物のストレス関連形質の取得・選抜ワークフローを中心に整理したレビューであり、植物フェノタイピング手法が中核です。
abstractThis review centers on three core sets of content: first, the integration of various UAV platforms, five types of imaging technologies, and machine learning and deep learning models to support phenotyping selection of abiotic stress-related traits;
Improving transpiration efficiency (TE) offers a mechanistic pathway to enhance yield under drought by modulating the balance between carbon assimilation and water loss. This study quantified genotypic variation in TE and dissected the physiological processes underlying this variation across a diverse panel of wheat genotypes. Following an initial experiment with six cultivars, 105 genetically diverse lines were evaluated under well-watered conditions across naturally fluctuating vapour pressure deficit (VPD). Transpiration was measured at 10-minute intervals using a high-throughput lysimeter platform and normalised daily at low VPD to minimise confounding effects arising from genotypic differences in canopy size. Variation in TE was strongly associated with reduced normalised transpiration rate at high VPD (TR norm-highVPD ). No relationship was detected with maximum photosynthetic capacity, indicating that TE differences were driven primarily by regulation of water loss rather than carbon gain. High-TE genotypes achieved either greater biomass for a given water use or equivalent biomass with reduced water use, consistent with conservative stomatal regulation under high evaporative demand. A complementary experiment conducted under low VPD revealed limited genotypic variation in intrinsic TE, suggesting that genetic control of TE is predominantly expressed under high atmospheric demand. The consistency of TE–TR norm-highVPD relationships across experiments highlights TR norm-highVPD as a robust and physiologically meaningful phenotyping target. Several high-TE genotypes outperformed modern cultivars, offering novel sources of allelic variation for transpiration regulation. Collectively, these results define a mechanistically grounded, scalable phenotyping framework to target VPD-responsive water-use traits and support breeding strategies aimed at improving drought resilience and water productivity.
Why it matches plant phenotyping methods高スループットライシメータで蒸散を定量し、VPD応答性の水利用形質を検証・評価するスケーラブルな表現型解析枠組みが研究の中心である。
abstractTranspiration was measured at 10-minute intervals using a high-throughput lysimeter platform
Motivation The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data. Results We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data. Availability and implementation The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi: 10.5281/zenodo.19436445). Supplementary information A Jupyter Notebook file for the validation example using a marker-trait relationship found in the literature.
Why it matches plant phenotyping methods植物の表現型データを含む分散データを統合・提供するBrAPIプロキシの技術実装が中心であり、表現型データ基盤・再利用可能なソフトウェアとして対象に含める。
abstractWe discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI.
Reproduction assets foundThe paper's authors publicly released the BrAPI proxy software used to merge the AGENT project's phenotypic/genotypic BrAPI endpoints, available on PyPI and archived in Zenodo. The supplementary Jupyter Notebook for the marker-trait validation example is mentioned but no public URL is provided, so it is not listed as aCode · publicThe BrAPI proxy implementation and documentation is available at the Python Package Index ( https://pypi.org/project/brapi-proxy ) and archived in Zenodo (doi: 10.5281/zenodo.19436445).Open asset ↗brapi-proxylines:1-44Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k ‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in wheat ( Triticum aestivum L.), using 2414 lines across six environments. Three multispectral vegetation indices (VIs) collected over time from aerial imagery were used as predictors for imputation of simulated missing data ranging from 10% to 70%. Statistical analyses examined the accuracy of imputed GY (IGY) as well as its reliability, heritability, and genetic correlation with GY. Imputation accuracies were highest for MLR, but accuracy differences between methods were small. Accuracies only decreased slightly as percent missing data increased. Genetic correlations between IGYs and observed GY within the environment ranged from −0.01 to 0.51, consistently greater than the corresponding genetic correlations between VIs and GY. Respectively, the reliabilities and heritabilities of IGY were 24% and 45% lower than those of GY, and like those of the VIs. Altogether, this study found that HTP data can be used to impute GY phenotypes suitable for use in further analyses; however, imputed and observed GY should be modeled as separate traits. Further research is needed to improve the heritability of IGY and to evaluate the utility of IGY in GS models.
Why it matches plant phenotyping methodsHTP由来の航空画像データを用いて小麦収量表現型を機械学習で補完し、複数手法の精度・信頼性・遺伝率を評価しており、表現型推定手法が研究の中心である。
abstractThree machine learning models: multiple linear regression (MLR), missForest, and k ‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in wheat
Accurate yield estimation is vital for precision wheat management and breeding. Traditional methods based on single growth stages or single-source data cannot capture cumulative growth effects, limiting prediction accuracy. UAV remote sensing provides high-resolution, multi-source, and multi-temporal data, enabling improved non-destructive yield estimation. In this study, UAV-based multispectral and RGB imagery were collected at six key growth stages, and vegetation indices, texture, and color features were extracted to develop yield prediction models using RF, XGBoost, and KNN under single- and multi-temporal scenarios. The results showed that red-edge-based vegetation indices were highly sensitive to wheat yield and outperformed texture- and color-based features. Multi-feature fusion further improved prediction accuracy at key growth stages, particularly during booting and flowering (R 2 = 0.53-0.67). Compared with single-temporal models, multi-temporal data fusion significantly enhanced yield estimation accuracy, achieving a maximum R 2 of 0.72 by integrating data from the late-jointing, booting and flowering stages. Among the algorithms, XGBoost and KNN exhibited superior accuracy and stability across most growth stages. Overall, these results demonstrate that integrating UAV-based multi-source and multi-temporal remote sensing data effectively improves the accuracy and robustness of wheat yield estimation, providing valuable technical support for precision agriculture and phenotyping-assisted breeding.
Why it matches plant phenotyping methodsUAV画像から特徴量を抽出し、機械学習でコムギ収量を推定する手法が研究の中心であり、マルチソース・マルチテンポラル統合の技術評価も行っている。
BarleyRyeWheatField / plotLeafRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Summary Cereal architecture is underpinned by the coordinated development of modular phytomer units. While above-ground phenology is well characterized by metrics such as the phyllochron, an equivalent framework for root system development is lacking. Because each phytomer node initiates both leaves and adventitious roots, root and shoot development are inherently linked. Here, we quantified this coordination in wheat, barley, and rye across contrasting temperature regimes and validated the results under field conditions. We introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes. Root development followed a highly conserved thermal sequence synchronized with shoot phenology. Across species and environments, the rhizochron averaged 146.1°C d, closely matching the phyllochron (126.6°C d). We also identified a consistent thermal offset, with nodal roots emerging approximately 185.3°C d after the corresponding leaf on the same phytomer node. The root appearance interval averaged 45.3°C d, reflecting continuous root deployment across active nodes. By integrating root phenology into a node-based framework, the rhizochron provides a predictive tool for crop modeling, trait-based breeding, and more target phenotyping aimed at improving resource acquisition and climate resilience.
Why it matches plant phenotyping methods根系と地上部の発達を定量化する新しい熱時間指標（rhizochron等）を導入し、複数種・環境および圃場条件で検証しており、表現型測定法が研究の中心である。
abstractWe introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes.
Grain diseases lead to losses of 20-40% of the harvests each year, representing a threat to the food security of the world. Accurate and automated diagnosis of disease from remote picture taking would be key to prompt and directed interventions. Most current deep-learning approaches, however, are based on controlled lab images, on a single crop and ignore the assessment of disease severity. CropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation. CropDisease-12 is a benchmark dataset of 43200 images belonging to 12 classes representing four major crops (tomato, wheat, rice, and cotton) from PlantVillage and its own disease dataset collected in Yavatmal, Maharashtra, India. When evaluated on the CropDisease-12 test split, CropHybrid-Net outperforms all baselines tested such as standalone Swin-T (94.5%), ViT-B/16 (93.9%) and EfficientNet-B4 (93.7%), with the highest accuracy of 97.8%, and macro f1 score of 97.3%. The average value of AUC for the 12 classes is 0.995. In addition, a comprehensive literature review has been conducted, comprising of 62 papers (2015-2024), and grouped into five research streams: conventional machine learning, CNN-based methods, transfer learning, transformer-based methods, and multi-task severity approaches. The Grad-CAM visualizations are in line with the locations of biologically meaningful lesions. The framework proposed is deployed on common precision agriculture-edge of-use devices and achieves the goal of 39.3 ms per image, being relevant to smart precision agriculture applications.
Why it matches plant phenotyping methods植物病害の画像から病害状態と重症度を推定するCNN・Transformer手法を開発し、ベンチマークデータセットで評価しているため、植物フェノタイピング手法が中心である。
abstractCropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation.
WheatField / plotRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Root hydraulic properties affect water uptake in wheat (Triticum aestivum L.) and are strongly influenced by root anatomy, yet how they vary along root axes and differ among cultivars remains underexplored. We investigated crown roots of six German winter wheat cultivars spanning one century of release, sampled from a field experiment. Roots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to the GRANAR-MECHA model to estimate radial and axial hydraulic conductance. Longitudinal anatomical gradients were pronounced: tissue dimensions, metaxylem number, and apoplastic barriers decreased from the base onwards, resulting in radial conductance increasing and axial conductance decreasing with distance from the base. Cultivar differences were also apparent: modern cultivars had smaller tissues and fewer metaxylem vessels, reducing both axial and radial conductance and lowering whole-root water uptake capacity (∼20-30%). By integrating field sampling with high-throughput image analysis and modeling, this study establishes an integrated phenotyping approach linking root anatomy to hydraulic function and uncovering anatomical traits relevant for water uptake. The results show that longitudinal gradients and cultivar-associated anatomical differences contribute to variation in hydraulic properties and persist along fully mature root segments.
Why it matches plant phenotyping methods根の高スループット画像解析とモデル推定を統合した表現型解析手法が研究の中心であり、解剖学的形質と水理機能を定量化している。
abstractRoots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to the GRANAR-MECHA model to estimate radial and axial hydraulic conductance.
WheatSeed / grainPhysiological trait estimationGrowth / development / phenology
Multi-layer perceptron (MLP) neural networks can be used to develop accurate models for quantifying plant responses to environmental factors. This study aimed to quantify wheat seed germination in response to temperature, water potential, and salinity using an ANN. Results indicated that the MLP model could predict total germination percentage with high model accuracy, including R 2 (0.99), MSE (0.342), RMSE (0.585), and MAE (2.166) for the test data. Time to 50% germination (T50) was also accurately estimated using the MLP model (R 2 = 0.97, MSE = 26.2, RMSE = 5.11, MAE = 5.70). Water potential was identified as the most significant variable affecting total seed germination and T50, followed by salinity and temperature. Seed germination was maximum at 20.5 °C and decreased at higher and lower temperatures. The optimal temperature for T50 was 25.3 °C. Higher salinity and more negative water potential led to lower total seed germination. The results of this study can be used to develop process-based models of crop growth and development and predict total seed germination and germination time under different conditions of temperature, water potential, and salinity.
Why it matches plant phenotyping methodsANNを用いて温度・水ポテンシャル・塩分条件から発芽率とT50という植物状態を定量予測するモデルを開発・評価しており、計算的な表現型推定が研究の中心である。
abstractThis study aimed to quantify wheat seed germination in response to temperature, water potential, and salinity using an ANN.
Accurate and efficient grain quality assessment is critical for making informed decisions throughout the grain value chain. Early detection of disease enables actions to mitigate spread and further damage, and optimal batch mixing to fulfill specified quality requirements allows for maximizing value and minimizing scrapping. Vision based machine learning and deep learning approaches are gaining attention in the agricultural sector and are useful for the development of automated grain quality assessment. These techniques can reduce the current manual inspection load and are key for objective and precise analysis. Yet, the majority of prior studies are constrained to small or controlled and curated datasets. Practical challenges associated with real-world deployment and reliability are rarely addressed. That is the focus of this work. We present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification. The objective is to determine a CNN model that ensures high and robust classification accuracy, while elucidating and explaining how different image dataset characteristics and training parameters affect performance and reliability. We use a commercial mirror-based imaging system that captures over 90% of each kernel's surface and contrast and compare model architectures, robustness, the effect on pre-processing and image resolution. Our results show similar and high overall performance for ResNet50V2 and EfficientNetV2B0 ([Formula: see text]% accuracy), but per-class analysis indicate that the smaller classes suffer from lack of representative examples, and that most classes benefit from pre-processing including downsampling whereas others benefit from higher resolution. Interactive visualizations reveal that another contributing factor is dubious annotation and multi-class belongingness. Thus, our step-by-step analysis of CNN performance underscores the need for representative data, proper pre-processing, and class-aware evaluation to ensure trustworthy deployment in wheat grain quality assessment.
Why it matches plant phenotyping methods小麦粒画像から品質・病害クラスを推定するCNN画像解析手法の性能、頑健性、前処理、解像度、データ特性を体系的に評価しており、フェノタイピング手法が中心的である。
abstractWe present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification.
Reproduction assets foundThe paper's wheat grain image dataset has a publicly available subset deposited on Zenodo (DOI 10.5281/zenodo.17397123), explicitly stated in the Data Availability statement. The full dataset is proprietary; code is only available upon request, so no qualifying code asset.Dataset · publicA publicly available subset of the segmented wheat grain images used in this study has been deposited in Zenodo to support transparency and reproducibility. The dataset includes representative samples per class collected from instrument and can be accessed at https://doi.org/10.5281/zenodo.17397123.Open asset ↗Zenodo · 10.5281/zenodo.17397123html-lines:337-368Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published12 Apr 2026International Journal of Emerging Technologies and Advanced ApplicationsCited by 0 · OpenAlex ↗
This paper studies the intelligent identification and location method of crop diseases based on multispectral images of unmanned aerial vehicles. With the development of precision agriculture, traditional crop disease monitoring methods have become difficult to meet the demands of large-scale, high-efficiency and early warning. The article first constructs a multispectral image dataset including visible light, near-infrared and red-edge bands, covering common types of crop diseases. Subsequently, an improved deep learning network architecture was proposed. The attention mechanism was adopted to enhance the model's ability to extract disease features, and a multi-scale feature fusion strategy was introduced to handle disease spots of different sizes. The research designed a data augmentation method based on spectral-spatial joint optimization, which effectively solved the problem of unbalanced samples of crop diseases. To improve positioning accuracy, this paper proposes a disease area positioning algorithm combined with geographic information system, achieving centimeter-level positioning accuracy. The experimental results show that the proposed method improves the accuracy of disease identification by 15.3% compared with the traditional methods, reduces the positioning error to an average of 3.2 centimeters, and can maintain high stability in complex field environments. In addition, this paper has established a complete technical system covering data collection, disease identification and information visualization, and has conducted application verification on crops such as wheat and rice. It has been confirmed that this method can effectively support precise pesticide application decisions in agricultural production and has significant economic and ecological benefits
Why it matches plant phenotyping methodsマルチスペクトル画像から作物病害の特徴・病斑領域を抽出する認識および位置推定手法の開発、検証、実地適用が研究の中心であり、植物の病害状態を直接評価している。
abstractThis paper studies the intelligent identification and location method of crop diseases based on multispectral images of unmanned aerial vehicles.
Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of in-dividual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterra-nean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield varia-bility (up to R² ≈ 0.82) under controlled analytical conditions. In contrast, cotton yield var-iability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.76. Recurrence analysis confirmed the stability of these indicator families across analytical stages. Overall, the results indicate that parsimonious, physiologically interpretable indi-cator combinations can account for a substantial proportion of yield variability without reliance on black-box modelling, supporting crop-aware indicator selection for precision agriculture applications.
Why it matches plant phenotyping methodsSentinel-2のスペクトル・熱指標から作物収量を推定する統一ワークフローを構築・比較し、指標の安定性と予測性能を検証しており、収量フェノタイピング手法が中心です。
abstractThis study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework
Abstract In sustainable agriculture, detecting pests and diseases early is critical. Recent technological advances in deep learning (DL) and multimodal imaging like multispectral and thermal data crop health monitoring is promising. Despite the progress, obtaining high accuracy across various crops with real-time performance is still a challenge. The hybrid convolutional neural network (CNN)-attention model integrating multispectral and thermal data for pest and disease detection has been introduced. A total of 1760 samples were collected from six crops (maize, rice, wheat, tomato and cassava), across different growth stages, labelled fungal, bacterial, viral and pest infections. The data was divided into 70% training, 15% validation, and 15% test sets. 3,500 samples were used for training. 750 samples were used for validation and test set. The hybrid CNN-attention model was contrasted with certain baseline models (SVM, Random Forest, CNN-RGB, CNN-Multispectral) and certain fusion methods (early, late, and hybrid fusion) based on accuracy, precision, recall, F1-score, and early detection sensitivity. The highest accuracy of 91.0% for rice at the vegetative stage was achieved by the hybrid model. It beats baseline and fusion models. The F1-score of the classification was reasonably high. Rice's sensitivity is 88.1%, and maize is 87.3%. The model fared well for all classes, getting 92.0 % for the healthy plant and 88.2 % for pest infestation. Future work can enhance the dataset with more crops and diseases and environmental factors and optimize detection time and early sensitivity for real-time deployment in agricultural decision support systems.
Why it matches plant phenotyping methodsマルチスペクトル・熱画像から植物の病害および害虫状態を推定するCNNモデルを開発し、複数モデルとの比較検証を行っており、表現型取得・判定手法が中心である。
titleUsing Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
It has been repeatedly shown that spike productivity is the main component of wheat yield. The main spike parameters related to productivity are size, the number of grains and spikelets per spike, and the presence or absence of awns. In modern genetic research, morphometric analysis of hundreds and thousands of spikes is required to determine the loci that control spike productivity traits. On the other hand, thousands of accessions in modern collections of wheat genetic resources need detailed description. These considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods. These methods allow for automated acquisition of trait values that can serve as the basis for digital plant collections. Here we propose an extended set of spike characteristics obtained both manually and through digital image analysis and present plant characterization. These data form the basis of the updated version of the SpikeDroidDB database (http://spikedroid.biores.cytogen.ru/). The digital description of the spike consists of two blocks. The block of uploaded data includes a description of the plant and contains five tables: collection; variety sample (year of cultivation (vegetation), sowing identifier, taxonomic information, etc.), planting site, and characteristics of the spike determined manually (length, width of frontal and lateral views, type and color of the spike, etc.) The block of extracted features includes spike characteristics obtained by digital phenotyping and contains six tables: characteristics of the spike outline in the image; characteristics of the quadrangle model, values of the color components of the spike, dominant colors of the spike, and texture characteristics of the spike in the image. The most illustrative and significant features of the spike have been identified, allowing for the formation of the spike digital certificate, which includes size, shape, and color features derived from the digital images. The features forming the digital certificate have been compared between two wheat species, T. aethiopicum and T. carthlicum. It is shown that the features of the digital certificate allow for a clear representation of the spike model and the identification of distinct parameters: colors of the spike and awns and roundness of the frontal view of the spike. The database interface has been supplemented with the ability to upload data on plant and spike characteristics, as well as their images, in the batch mode.
Why it matches plant phenotyping methods小麦穂の画像解析による形態形質の自動取得、デジタル表現、データベース基盤を中心に開発・提示しており、植物フェノタイピング手法が研究の中核である。
abstractThese considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods.
Accurate detection of wheat seedlings is crucial for monitoring early population establishment and evaluating sowing quality. However, detection in real field environments remains challenging due to diverse seedling morphology, varying planting densities, occlusion, and complex background interference. Although deep learning has promoted the development of agricultural vision systems, existing wheat seedling detection methods still suffer from two key limitations: (1) insufficient modeling of spatial contextual relationships, leading to degraded accuracy under dense planting and complex field conditions; and (2) difficulty in balancing detection performance and computational efficiency, restricting real-time deployment on resource-limited agricultural devices. To address these issues, this study proposes Transformer-Coordinate Attention-Efficient YOLO (TCE-YOLO), a detection framework designed with three key modules: (1) the Depthwise-Transformer-Vision (DTV) module integrates Depthwise Separable Convolutions (DSC), Vision Transformer, and multi-scale spatial pooling to efficiently represent local structures, spatial context, and global patterns of wheat seedlings; (2) the Feature Enhancement Module(FEM) incorporates coordinate attention to enhance seedling-related features while suppressing background interference; and (3) the Feature Coordination Module (FCM) performs multi-scale feature interaction with reduced computational cost. These components jointly improve robustness under dense planting and complex field conditions while maintaining lightweight deployment characteristics. Furthermore, we construct the Wheat Seedling Dataset (WSD), covering multiple planting densities, varieties, and field environments across two growing seasons. Experimental results show that TCE-YOLO outperforms mainstream detectors while maintaining high efficiency, providing a deployable solution for wheat seedling detection under real field conditions.
Why it matches plant phenotyping methods小麦苗の画像検出手法を開発し、複数条件・作期を含むデータセットを構築して性能比較しているため、植物フェノタイピング手法が研究の中心である。
abstractthis study proposes Transformer-Coordinate Attention-Efficient YOLO (TCE-YOLO), a detection framework designed with three key modules
Accurate estimation of leaf chlorophyll content is essential for monitoring crop growth and supporting precision agricultural management. The soil and plant analyzer development (SPAD) instrument readings represent the relative chlorophyll content (RCC) in leaves, a key indicator of photosynthetic capacity and physiological status in wheat. This study proposes a multimodal data fusion approach integrating unmanned aerial vehicle (UAV)-derived vegetation indices (VI) and texture features (TF) from multispectral imagery with short-term environmental time-series data collected from in-field meteorological and soil sensors to estimate winter wheat RCC. A self-attention deep neural network (SA-DNN) was developed to capture complex nonlinear relationships among multimodal inputs. Employing a multi-stage progressive feature selection strategy that combines Pearson and Spearman correlations, minimum redundancy maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO), eight optimal features were ultimately selected from VI and TF, together with indicators of short-term variability in environmental factors (EF). These features included OSAVI, NDRE, R-Mea, R-SEM, AH-std-7, DPT-std-7, SD-std-7, and pH-std-7. The SA-DNN model achieved the best estimation performance (R² = 0.913, RMSE = 3.945), significantly outperforming traditional machine learning models such as XGBoost, random forest (RF), support vector regression (SVR), Adaboost, and partial least squares regression (PLSR). SHapley Additive exPlanations (SHAP) analysis further quantified the contributions of individual features, revealing that short-term (7-day) fluctuations in EF, particularly sunshine duration and air humidity, played a dominant role in regulating variations in RCC. Overall, this study demonstrates that the synergistic integration of multimodal data within an attention-based deep learning framework significantly augments the precision of winter wheat RCC estimation, providing a powerful tool for real-time crop growth monitoring and the optimization of precision agricultural management.
Why it matches plant phenotyping methodsUAV画像・環境センサーデータから小麦葉のクロロフィル含量を推定する深層学習手法を開発し、複数モデルとの性能比較で検証しており、植物表現型取得が研究の中心である。
abstractThis study proposes a multimodal data fusion approach integrating unmanned aerial vehicle (UAV)-derived vegetation indices (VI) and texture features (TF) from multispectral imagery with short-term environmental time-series data collected from in-field meteorological and soil sensors to estimate winter wheat RCC.
MaizeRiceSoybeanWheatRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Root phenotyping is crucial for advancing our understanding of plant development and adaptation. However, existing platforms often face challenges in balancing high-throughput capacity with long-term, high-frequency monitoring. To overcome this limitation, we present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis. Its design features a circulating zone that accommodates 141 specialized root boxes for high-throughput operation synchronously. Root boxes follow a continuous S-shaped trajectory step by step, facilitating repetitive imaging for high-throughput, time-series data acquisition. To address challenges such as water vapor condensation and fine root entanglement, we developed a dedicated segmentation algorithm, achieving 89.56 % accuracy in root isolation. Combining morphological and skeleton-based feature extraction techniques, the platform ensures comprehensive and efficient phenotypic trait quantification. We validated HTPRootSlides by dynamically monitoring root development in four staple crops (soybean, maize, wheat, and rice) during early-stage germination (<14 d). The results demonstrate the capability of HTPRootSlides for high-frequency, high-precision and large-scale root phenotyping (< 1h with 141 root boxes per run), offering researchers a powerful tool to investigate root dynamics and optimize crop performance through trait selection.
Why it matches plant phenotyping methods根の動態を高スループットで撮像・分割・特徴抽出し、形態・骨格形質を定量するプラットフォームの開発と検証が中心である。
abstractwe present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis
PURPOSE: Above-ground biomass (AGB) is a critical indicator for assessing the growth status of winter wheat. Since the range of extracted color indices (CIs) tends to remain constant after flowering and UAV RGB images cannot capture the lower and middle structures of the canopy under dense planting conditions, the efficiency of AGB estimation models is limited. Therefore, this study aimed to improve the accuracy of winter wheat AGB estimation by incorporating canopy volume information with RGB-based CIs. METHODS: RGB images were acquired to generate Digital Orthophoto Maps (DOM) and Digital Surface Models (DSM) at Feekes 10, Feekes 10.5.2, Feekes 10.5.4, and Feekes 11.3 growth stages. Eight biomass-related CIs were extracted from the DOM, and canopy volume (V) was calculated from the DSM for corresponding regions. The RReliefF algorithm was applied to rank feature importance and select optimal features. Eight statistical and machine learning regression algorithms, including Gaussian process regression (GPR), were used to construct AGB estimation models with different feature combinations. RESULTS: The results showed that the GPR algorithm outperformed other regression methods, achieving the highest estimation accuracy with R² values of 0.775, 0.741, 0.702, and 0.568 at the four growth stages, respectively. Compared with models using CIs alone, integrating canopy volume with CIs improved AGB estimation accuracy from Feekes 10.5.2 to Feekes 11.3, with R² increases of 7.31%, 6.55%, and 22.98%, respectively. CONCLUSION: Overall, combining canopy volume features derived from UAV-based RGB imagery with CIs and applying effective machine learning algorithms enables rapid and accurate estimation of winter wheat AGB.
Why it matches plant phenotyping methodsUAV画像から色指数とキャノピー体積を抽出し、冬コムギの地上部バイオマスを推定する取得・解析手法が研究の中心であるため。
titleWheat biomass estimation by fusing color index and canopy volume based on UAV RGB images
Plant breeding has been used for over 10,000 years to adapt crops like wheat to different environments. Continuous breeding efforts have led to high-yielding and resilient wheat varieties, with Canada being the 6th largest producer in 2024-25. However, challenges like leaf rust, caused by Puccinia triticina Erikss., still impact global wheat production. Rapid pathogen evolution requires ongoing identification and deployment of novel resistance sources. Accurate phenotyping of key traits like disease resistance, winter survival, and plant height is a major bottleneck in wheat breeding. Advances in high-throughput (HTP) genotyping and phenotyping offer new opportunities to enhance genetic gain. This study integrated genetic mapping and UAV-based HTP approaches to evaluate leaf rust resistance and key agronomic traits in winter wheat. A doubled-haploid (DH) population (n = 130) developed from the cross W538/Emerson was evaluated for leaf rust resistance at the seedling and adult plant stages. Genotyping was performed using a 25K Infinium SNP array, and linkage and QTL analyses mapped resistance genes. Seedling-stage resistance was associated with a locus on chromosome 1B, while adult plant resistance was governed by multiple QTL, including QLr.umb-1B, QLr.umb-2A, QLr.umb-3B, and QLr.umb-4D. Lines carrying multiple resistance QTL exhibited enhanced leaf rust resistance, highlighting the importance of QTL stacking for durable resistance. UAV-based HTP methods were evaluated for assessing spring stand and plant height in winter wheat breeding nurseries. Manual ratings were compared with RGB and multispectral-based UAV metrics, including relative plant pixel area and vegetation indices like NDVI and EPVI. NDVI was the most robust method for spring stand assessment, with four times higher heritability than manual ratings. Manual measurements were more accurate than UAV-based methods for plant height, but SfM and LiDAR had comparable performance. This study highlights the complementary value of genetic mapping and UAV-based HTP in wheat breeding, emphasizing multi-QTL resistance for leaf rust and the potential of UAVs to improve phenotyping efficiency for key agronomic traits.
Why it matches plant phenotyping methodsUAV画像・マルチスペクトル・SfM・LiDARによる春季スタンドと草丈の測定を、手動評価と比較・検証しており、植物表現型取得法が研究の実質的な構成要素である。
abstractUAV-based HTP methods were evaluated for assessing spring stand and plant height in winter wheat breeding nurseries.
Early and accurate identification of crop diseases is essential for ensuring agricultural productivity, food security, and sustainable farming practices. This study presents an automated computer vision based framework for multi-crop disease classification using a lightweight deep learning architecture. The proposed system employs the ReXNet-1.5 convolutional neural network as the core feature extractor, integrating efficient hierarchical feature learning with low computational complexity. A publicly available multi-crop dataset comprising 13,324 images across 17 disease and healthy classes covering corn, rice, potato, wheat, and sugarcane is used for model training and evaluation. Experimental results demonstrate strong performance, achieving 97.45% accuracy and a macro-F1 score of 96.26%, indicating reliable class-balanced prediction under dataset imbalance. Grad-CAM based visual explainability is incorporated to provide interpretable disease localization, enhancing transparency and trust in model predictions. Additionally, the model exhibits high computational efficiency, enabling real-time inference suitable for deployment on resource constrained platforms. The proposed framework offers an accurate, interpretable, and deployable solution for real world crop disease diagnosis, supporting intelligent decision-making and scalable agricultural monitoring systems.
Why it matches plant phenotyping methods植物画像から病害状態を分類・局在化するコンピュータビジョン手法が研究の中心であり、単なる病害測定ではなく、モデル開発と性能評価を実施している。
abstractThis study presents an automated computer vision based framework for multi-crop disease classification using a lightweight deep learning architecture.
Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)‐based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above‐ground biomass, serving as a ground truth for the HTP‐derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi‐trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro‐environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV‐based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield.
Why it matches plant phenotyping methodsUAV植生指数によるバイオマス推定を中心に、実測値を用いて検証し、遺伝解析・収量予測への利用可能性を評価しているため、植物フェノタイピング手法の実質的な適用・検証に該当する。
abstractCumulative vegetation indices were found to be a reliable method to infer biomass accumulation.
Abstract Fusarium Head Blight (FHB) is one of the most destructive fungal diseases in global wheat production. Traditional methods for FHB detection face limitations such as high technical expertise requirements, limited coverage scope, and insufficient timeliness, making them inadequate for modern precision agriculture management demands. To address this challenge, this study proposes a lightweight MSA-YOLO detection model based on the YOLO11 deep learning framework. The proposed model achieves a favorable balance between performance and efficiency through three innovative design aspects: first, it replaces the original backbone network with the MobileOne network, establishing a foundation for model lightweight design; second, it substitutes the multi-head attention mechanism in the C2PSA module's PSABlock with a more computationally efficient SE module, further reducing model complexity while maintaining detection performance; finally, it introduces an Adaptive Threshold Focal Loss (ATFL) function to address class imbalance issues, enhancing the model's recognition capability for minority classes. The experimental data comprise 629 photographs of wheat spikelets covering various growth and development stages. Results demonstrate that the improved MSA-YOLO model reduces parameter count from 2.58M to 1.65M and computational complexity from 6.4 GFLOPs to 3.9 GFLOPs. Furthermore, comparative analysis with YOLOv10, YOLOv9, YOLOv8, and YOLOv5 models shows that MSA-YOLO exhibits an exceptional balance between speed and accuracy, making it well suited for practical applications in precision agriculture monitoring systems.
Why it matches plant phenotyping methods小麦穂のFHB症状を画像から検出する軽量深層学習モデルを開発・比較評価しており、植物病害状態の取得手法が研究の中心である。
abstractcomparative analysis with YOLOv10, YOLOv9, YOLOv8, and YOLOv5 models shows that MSA-YOLO exhibits an exceptional balance between speed and accuracy
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.
Why it matches plant phenotyping methods植物の3D形態形質を抽出するためのスケール復元法とスマートフォン型フェノタイピング・パイプラインを開発し、複数植物種・環境で検証しており、手法が研究の中心である。
abstractHere, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract High-throughput phenotyping using unmanned aerial vehicle (UAV) multispectral imagery offers a promising approach for predicting wheat yields under variable sowing conditions. This study evaluated the effectiveness of UAV-based vegetation indices compared to the GreenSeeker handheld sensor in estimating yield-related traits in 13 bread wheat genotypes. UAV-based multispectral indices— Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red-edge Normalized Difference Vegetation Index (RNDVI and simple ratio (SR) were captured using a MicaSense sensor at two growth stages [52 and 79 days after sowing (DAS) for timely sown; 16 and 43 DAS for late sown]. Simultaneously, NDVI was recorded using a GreenSeeker handheld sensor for direct comparison with UAV-derived NDVI. UAV-derived indices showed consistently stronger correlations with biological yield (BY), grain yield (GY), and thousand grain weight (TGW), particularly during the anthesis stage. GNDVI and SR emerged as the most predictive indices for BY and GY, while TGW showed stronger associations with early-stage indices. GreenSeeker NDVI correlations were weaker and less consistent across growth stages and sowing conditions. Genotypes such as Phule Samadhan, MACS 2496, and GS 4042 exhibited superior adaptability under late-sown heat stress, maintaining higher vegetation index values throughout. UAV-based multispectral imaging outperformed the handheld sensor in predicting key yield traits and detecting inter-genotypic variation under stress. Statistical and multivariate analyses (ANOVA, PCA, and heatmap visualization) revealed distinct inter-genotypic variability in vegetation indices, effectively distinguishing high-vigor and stress-susceptible wheat genotypes under varying sowing environments. These findings highlight UAV-based multispectral imaging as a robust, efficient, and scalable phenotyping tool for identifying stress-tolerant and high-yielding genotypes, underscoring the importance of phenological timing and optimal index selection in breeding and precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と地上センサーによる植生指数の取得・比較を中核とし、収量関連形質と遺伝子型差の推定に用いる実質的な植物フェノタイピング研究である。
abstractHigh-throughput phenotyping using unmanned aerial vehicle (UAV) multispectral imagery offers a promising approach for predicting wheat yields under variable sowing conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
WheatNeRF / 3D Gaussian SplattingLiDAR / point cloudWhole plant / canopy / plot / fieldPose / keypoint estimation2D/3D reconstruction
Precise reconstruction of plant phenotypes is crucial for smart agriculture. Conventional methods struggle with low efficiency and strong dependency on high-quality data, especially for low-texture and structurally complex crops like wheat. We propose a novel 3D reconstruction framework—Plant3R—that fuses deep feature learning with 3D Gaussian Splatting (3DGS). It innovatively uses the Matching and Stereo 3D Reconstruction (MASt3R) model for sparse point cloud reconstruction and camera pose estimation via its 3D feature matching capabilities, which substantially improve image matching rates and the quality of sparse point clouds. Subsequently, 3DGS is employed for rendering and optimization, enabling end-to-end, high-fidelity, and high-robust 3D reconstruction of wheat plants. Validated on potted wheat at multiple growth stages using handheld images, our experimental results demonstrate that Plant3R performs well in feature extraction and matching, and the reconstructed point cloud provides a good geometric prior for the subsequent rendering stage. In most scenes, its key rendering metrics—Peak Signal-to-Noise Ratio (PSNR) > 34, Structural Similarity Index Measure (SSIM) of 0.94, and Learned Perceptual Image Patch Similarity (LPIPS) 0.94), confirming its utility for accurate and quantitative phenotype analysis. Overall, Plant3R not only improves the rendering quality and geometric precision of 3D modeling, but also provides a reliable tool for accurate phenotypic parameter extraction and high-throughput crop phenotyping in precision agriculture.
Why it matches plant phenotyping methods小麦植物の3D再構成と表現型パラメータ抽出を目的とする画像解析手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe propose a novel 3D reconstruction framework—Plant3R—that fuses deep feature learning with 3D Gaussian Splatting (3DGS).
Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha−1. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions.
Why it matches plant phenotyping methodsハイパースペクトル反射と複数センサーを統合し、作物の水分状態・キャノピー構造・老化を推定して収量との関係を評価することが中心であり、植物表現型の実質的な方法適用に該当する。
abstractThis study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield
Accurate delineation of crop growth stages under real-world field conditions remains a long-standing challenge in computational phenotyping, particularly for wheat whose developmental phases are characterized by subtle, continuous morphological transitions and environmental noise. In this study, we propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery. Unlike conventional architectures that struggle with ambiguous inter-stage boundaries and rigid receptive structures, AMFR-Net leverages a ResNet-101 backbone augmented by a novel Adaptive Multi-Scale Attention Fusion (AMSAF) module-comprising cross-scale interaction blocks and confidence-weighted feature aggregation-to hierarchically recalibrate spatial-semantic representations. This design enables the network to adaptively amplify phenologically salient cues while suppressing irrelevant context, ensuring robust generalization under constrained annotation and deployment conditions. Evaluated on the expert-labeled CGIAR benchmark, AMFR-Net achieves state-of-the-art performance across all major metrics (Top-1 Accuracy: 89.10%; Macro-F1: 89.10%; AUC: 97.88%) and demonstrates superior discriminability in phenologically adjacent stages compared to lightweight and deep CNN baselines. Ablation studies validate the synergistic effect of multi-level attention and scale-aware refinement. The proposed framework offers a scalable, interpretable, and field-deployable solution for in-situ phenology monitoring, and sets a foundation for future integration of multimodal sensing, weak supervision, and cross-seasonal adaptation.
Why it matches plant phenotyping methods小麦の生育ステージを地上RGB画像から推定する新規深層学習手法を開発し、ベンチマーク、比較、アブレーションで検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery.
Reproduction assets foundThe paper's phenotyping analysis is built on the public CGIAR Wheat Growth Stage Challenge dataset (ground-level RGB wheat images with growth-stage labels), which the authors explicitly state is publicly available on Zindi with a direct link. No author analysis code, trained model checkpoints, or supplementary code/deeDataset · publicPublicly available datasets were analyzed in this study. This data can be found here: Dataset name: CGIAR Wheat Growth Stage Challenge Primary repository: Zindi (official competition page) Direct link: https://zindi.africa/competitions/cgiar-wheat-growth-stage-challengeAccession/Open asset ↗Zindilines:808-824Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Hyperspectral imaging has shown potential for estimation of wheat protein content, but it requires expensive equipment and generates high-dimensional data. This study identifies a minimal set of informative wavelengths to reduce computational complexity and facilitate the development of low-cost spectral imaging systems. We employed thirteen wavelength selection algorithms and their combinations on the raw and preprocessed spectral data with a 5 nm resolution to identify optimal wavelengths. The best results were obtained with 6 wavelengths (R 2 = 0.9790, RMSE = 0.2104) using a two-step hybrid strategy combining Random Forest and Genetic algorithm coupled with support vector regression. The accuracy remained comparable (R 2 = 0.9688, RMSE = 0.2564) when the resolution was reduced to 10 nm using spectral binning. This indicates that six wavelengths and 10 nm resolution can be used for accurate estimation of the wheat protein content. These findings highlighted the potential for developing an inexpensive multispectral imaging device.
Why it matches plant phenotyping methods小麦タンパク質含量という植物形質を推定するハイパースペクトル画像法について、波長選択・スペクトルビニングを開発し、低コスト装置化を検討しており、表現型取得・推定手法が中心である。
abstractThis study identifies a minimal set of informative wavelengths to reduce computational complexity and facilitate the development of low-cost spectral imaging systems.
Abstract Crop diseases remain a critical threat to global food security, contributing to substantial yield losses and reduced farmer incomes. Timely and accurate identification of these diseases is essential to mitigate their impact. Traditional diagnostic methods, dependent on expert visual inspection, are labour-intensive, time-consuming, and prone to judgment errors. Accurate and timely detection of crop diseases supports sustainable agricultural management and contributes to achieving global objectives under the United Nations Sustainable Development Goal 2 on Zero Hunger. This study proposes a three step framework that relies on pattern recognition and classification of visual disease symptoms to deliver reliable, field-applicable diagnostics. The approach combines image acquisition through smartphone camera with a structured processing pipeline that includes feature extraction, classification, and result delivery via a mobile application built on a three-tier architecture. Convolutional Neural Networks and an optimized VGG-16 model form the core classification engine, trained to recognize 19 leaf based diseases across wheat, rice, fodder, maize, and sugarcane. The models were trained and evaluated on a dataset comprising both field-collected and publicly available images using repeated stratified k-fold cross-validation. The framework achieves accuracies of 84.61% for wheat, 44.15% for rice, 85.71% for fodder, 95.23% for maize, and 64.28% for sugarcane (testing accuracy of the best-performing model per crop, where VGG-16 demonstrated superior generalization). The framework is able to support farmers, by integrating a technically robust backend with a simple and oriented interface, with diagnosis of multiple crops from a single platform, offering a scalable solution for precision agriculture and sustainable crop protection.
Why it matches plant phenotyping methods植物葉の病徴画像を対象に、画像取得・特徴抽出・分類・モバイルアプリ提供を一体化した診断手法を開発・評価しており、植物病害状態の表現型推定が中心です。
abstractThis study proposes a three step framework that relies on pattern recognition and classification of visual disease symptoms to deliver reliable, field-applicable diagnostics.
3D instance segmentation for laser scanning (LiDAR) point clouds remains a challenge in many remote sensing-related domains. Successful solutions typically rely on supervised deep learning and manual annotations, and consequently focus on objects that can be well delineated through visual inspection and manual labeling of point clouds. However, for tasks with more complex and cluttered scenes, such as in-field plant phenotyping in agriculture, such approaches are often infeasible. In this study, we tackle the task of in-field wheat head instance segmentation directly from terrestrial laser scanning (TLS) point clouds. To address the problem and circumvent the need for manual annotations, we propose a novel two-stage pipeline. To obtain the initial 3D instance proposals, the first stage uses 3D-to-2D multi-view projections, the Grounded SAM pipeline for zero-shot 2D object-centric segmentation, and multi-view label fusion. The second stage uses these initial proposals as noisy pseudo-labels to train a supervised 3D panoptic-style segmentation neural network. Our results demonstrate the feasibility of the proposed approach and show performance improvementsrelative to Wheat3DGS, a recent alternative solution for in-field wheat head instance segmentation without manual 3D annotations based on multi-view RGB images and 3D Gaussian Splatting, showcasing TLS as a competitive sensing alternative. Moreover, the results show that both stages of the proposed pipeline can deliver usable 3D instance segmentation without manual annotations, indicating promising, low-effort transferability to other comparable TLS-based point cloud segmentation tasks.
Why it matches plant phenotyping methodsTLS点群と深層学習によるコムギ穂の3D個体分割パイプラインを開発・評価しており、植物表現型取得手法が中心である。
abstractsuch as in-field plant phenotyping in agriculture
WheatLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementStress / disease detectionGrowth / time-series analysisRoot system architectureWater status / transpiration
Soil structure creates spatial heterogeneity that shapes ecosystem functions, including water retention and root colonization. Chernozems – soils characterized by exceptionally stable aggregation resulting from millennia of root-soil co-evolution – offer a unique model to investigate how aggregate-scale pore architecture controls plant responses to drought. Using soil microcosms (4 × 10 cm, ~80 g soil) with aggregates from Native Steppe and Arable Chernozems, we established six experimental treatments (3 aggregate sizes × 2 soil types) with three replicates each. Root-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution. Imaging was synchronized with plant developmental stages – germination, first leaf, and third leaf stage at permanent wilting point – yielding a total of 54 soil tomograms for analysis.Preliminary processing of the data reveals distinct pore network architectures across aggregate size classes. Small aggregates exhibited low CT-visible porosity (24%) with high solid phase connectivity (6.60 mm⁻³), while medium aggregates showed moderate porosity (39%) with lower connectivity (0.64 mm⁻³), and large aggregates had the highest porosity (49%) but the lowest connectivity (0.51 mm⁻³). This structural gradient directly controlled root colonization: solid phase connectivity showed a strong negative correlation with root volume growth (r = −0.76), suggesting that matrix mechanical cohesion, rather than pore characteristics alone, limits root expansion. Medium aggregates – which naturally dominate in undisturbed steppe soils – provided optimal conditions for root development, with 90% greater root surface expansion compared to small aggregates. Root sphericity decreased 3–4 times more in medium aggregates (−0.14) than in small aggregates (−0.04), indicating greater architectural plasticity critical for water acquisition. Importantly, our preliminary results also show that medium aggregates provided the greatest drought resistance: plants in these microcosms reached the permanent wilting point latest, suggesting that this aggregate fraction optimizes both root development and water availability over time.These findings demonstrate that native Chernozem aggregate structure represents an optimized spatial configuration balancing root accessibility with water retention. The strong coupling between aggregate-scale heterogeneity and root response suggests that tillage-induced disruption of natural aggregate distributions may compromise this evolutionary optimization. Our approach – combining high-resolution CT with growth stage-synchronized imaging – offers a framework for quantifying how spatial heterogeneity translates into ecosystem-relevant soil functions. Data processing is ongoing, and final results will include expanded replication and additional root morphometric parameters.
Why it matches plant phenotyping methods高解像度X線CTを用いて根の体積成長、表面拡大、球形度などの形態形質を反復取得・定量する手法が研究の中心であり、植物フェノタイピングへの実質的応用に該当する。
abstractRoot-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution.
Accurate and early prediction of crop yield at the sub-field scale is essential for precision-agriculture and food-system planning. This study evaluates a phenology-based machine learning framework for winter wheat yield prediction using Sentinel-2 satellite imagery, climate reanalysis data, and field-level yield data. Phenological metrics derived from the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and the normalised difference red-edge index (NDRE) were combined with accumulated seasonal rainfall and seasonal potential evapotranspiration, and multiple modelling strategies were assessed using a leave-one-field-out cross-validation (LOFO CV) scheme to ensure spatial generalisation. Among the evaluated models, the Random Forest (RF) algorithm achieved the highest overall performance, explaining up to 73% of the yield variability with a root mean square error (RMSE) of 0.88 t ha−1 at optimal prediction timing (day of year 160–175). Integrating phenological and climatic covariates consistently improved prediction accuracy compared to models based only on phenological variables, while the inclusion of soil properties provided limited additional benefit at the examined spatial scale. Phenological metrics based on red-edge data, particularly the maximum NDRE, were the most influential predictors, highlighting the added value of red-edge spectral information beyond traditional red–near-infrared indices. Uncertainty analysis revealed spatially heterogeneous prediction uncertainty, particularly near field boundaries and in areas of complex spatial patterns. Overall, the proposed framework enables robust, early, and interpretable yield prediction at the sub-field scale, supporting uncertainty-aware decision-making in precision agriculture and offering a scalable foundation for regional crop monitoring.
Why it matches plant phenotyping methods衛星画像から抽出した作物フェノロジー指標を用いて圃場内の収量を推定する機械学習フレームワークを構築・交差検証しており、植物形質の取得・推定手法が中心である。
abstractThis study evaluates a phenology-based machine learning framework for winter wheat yield prediction using Sentinel-2 satellite imagery
Plant density at the wheat emergence stage is a fundamental structural attribute of agroecosystems, exerting strong control on early competition, resource use efficiency, and yield formation. While UAV-based counting approaches have been widely explored for visually distinct crops such as maize and cotton, accurate and scalable estimation of wheat seedlings remains challenging due to their small size, high spatial density, and spectral similarity to soil and residue backgrounds. Moreover, existing RGB-based UAV and ground imaging approaches face an inherent trade-off between spatial resolution, spectral sensitivity, and operational efficiency.Here, we propose MS²‑Net (Multi-altitude, Multispectral Seedling Network), a high-throughput Earth-observation framework that integrates multi-altitude multispectral UAV observations with deep learning to enable robust estimation of wheat plant density at the emergence stage. Field experiments were conducted across three major wheat-growing regions in China (Henan, Hebei, and Shaanxi), covering approximately 1,500 plots spanning large variability in sowing density, genotype, and early growth conditions. Multispectral UAV imagery (blue, green, red, red-edge, and near-infrared) was acquired at four flight altitudes (12, 15, 20, and 40 m), enabling systematic evaluation of the trade-off between spatial detail and mapping efficiency. High-resolution smartphone images collected synchronously at plot level provided accurate reference plant counts for model training and validation.All UAV data were radiometrically calibrated to surface reflectance and used to derive conventional vegetation indices (NDVI, GNDVI, NDRE, OSAVI, and a red-edge chlorophyll index) for spectral interpretability. Wheat plant density was estimated using a deep regression framework built on an EfficientNet-B6 backbone and enhanced with spectral-aware adaptation, spatial attention, and scale-consistent feature learning, allowing MS²-Net to exploit both multispectral information and multi-scale spatial patterns. Across five-fold cross-validation over regions and flight altitudes, MS²-Net achieved robust density estimation (R² = 0.86, RMSE = 37.20 plants m⁻², averaged across sites and flight altitudes), with red-edge and near-infrared bands contributing substantially to model stability across observation scales.Results demonstrate that multi-altitude multispectral UAV observations provide a practical balance between spatial resolution, spectral sensitivity, and survey efficiency, outperforming both ground-based imaging and RGB-only UAV approaches for early wheat stand assessment. By enabling rapid, field-scale and spectrally informed plant density mapping, MS²-Net provides a scalable pathway for operational agroecosystem monitoring, high-throughput phenotyping, and precision crop management under real field conditions.
Why it matches plant phenotyping methodsマルチスペクトルUAV画像と深層学習によってコムギの出芽期植物密度を推定する手法を開発・検証しており、植物形質の取得・抽出が研究の中心である。
abstractwe propose MS²‑Net (Multi-altitude, Multispectral Seedling Network), a high-throughput Earth-observation framework that integrates multi-altitude multispectral UAV observations with deep learning to enable robust estimation of wheat plant density at the emergence stage.
Climate change is intensifying soil moisture variability, atmospheric evaporative demand, and salinity intrusion in agricultural landscapes, creating new challenges for sustainable food production. Understanding how soil hydrology and plant physiological stress interact under these conditions is essential for designing resilient irrigation strategies. This study presents a hydro-physiological assessment of wheat and maize grown under controlled combinations of soil salinity and deficit irrigation, and introduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support in semi-arid farming systems of northern India.Field experiments (2023–2025) were conducted to measure canopy temperature, air temperature, relative humidity, vapor pressure deficit (VPD), and soil moisture under varying salinity (EC levels) and irrigation regimes. These data were used to develop whole-season and stage-specific ANN models capable of capturing non-linear interactions between soil hydrology, crop physiology, and atmospheric demand. The ANN-based CWSI successfully distinguished mild-to-severe stress transitions and detected early-stage water stress acceleration during periods of high VPD, indicating a propensity toward flash drought development under combined salinity–moisture constraints.Results show that salinity amplifies crop water stress by reducing effective root-zone moisture availability, leading to higher canopy–air temperature gradients and elevated CWSI values even under moderate irrigation. Stage-specific ANN models achieved strong performance (R² = 0.87–0.94), particularly during flowering and grain filling, where hydrological stress most affects yield. The framework demonstrates how data-driven CWSI modeling can translate complex soil–plant–atmosphere interactions into actionable irrigation insights for farmers.This work highlights a scalable approach to precision irrigation scheduling, enabling reduced water use without compromising crop health in regions vulnerable to hydrological extremes and sociohydrological pressures. By linking soil hydrology, irrigation management, and physiologically informed stress indicators, the study contributes to sustainable food production strategies in a global climate change context.
Why it matches plant phenotyping methodsANNによる作物水ストレス指標（CWSI）の開発と性能評価が中心で、キャノピー温度などから植物の生理的ストレス状態を推定している。
abstractintroduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support
Abstract Early and accurate diagnosing of crops that contract diseases is critical in sustaining agricultural production and managing economic losses. Despite the massive success of the deep learning in the automated diagnosis of plant disease, new practices are largely only applicable to specific crops, and also need to be in controlled conditions and not in the field. In response to the aforementioned problems, a new Efficient Attention-based Hybrid Deep Learning (EA-HDL) has been suggested in this paper to perform the classification of multi-crop leaf diseases using real-field images. The architecture is based on an EfficientNetV2 backbone pretrained and has an attention-based pooling mechanism to encourage the use of discriminative features by the effective synthesis of information of the disease-relevant areas and the elimination of background noise. It is a tested, validated and benchmarked framework that was experimented on four of the most crucial crops: cotton, chickpea (chana), Black Gram and wheat in different field conditions. Strong and consistent results have been obtained in experiment work with a 100% record of classification accuracy in the cotton case, 98.64% in the chickpea case, 97.53% in the wheat case and competitive results in the Black Gram case in spite of difficult visual variability. It can be compared to the latest state-of-the-art deep learning models to prove that our approach is more accurate, as it generalizes and works with a variety of crops. The results are evidence that attention-based hybrid deep learning models have a tremendous potential of enhancing accuracy in disease classification in real-life agricultural 1 conditions. The EA-HDL is an effective and scalable platform to real-world crop disease surveillance and precision agriculture system.
Why it matches plant phenotyping methods葉画像から植物の病徴・病害を分類する深層学習手法を開発し、複数作物・圃場条件で検証・ベンチマークしており、植物表現型取得が中心である。
abstracta new Efficient Attention-based Hybrid Deep Learning (EA-HDL) has been suggested in this paper to perform the classification of multi-crop leaf diseases using real-field images.
Key message A new approach is proposed for the use of the genetic markers to manage DUS trials, targeted at individual phenotypic characteristics using genomic prediction, as well for supporting Distinctness decisions. High-performing crop varieties underpin food security. Due to the cost of developing varieties, systems have been established to provide breeders with legal protection for their varieties. In many countries, such protection is afforded by the International Union for the Protection of New Varieties of Plants (UPOV) system. New varieties must be phenotypically Distinct from existing varieties using a set of crop-specific characteristics, as well as Uniform and Stable (DUS). For many crops, DUS assessment is costly as candidates must be compared to many existing varieties in field trials, based on numerous DUS characteristics. The use of genetic markers has long been considered as a potential tool for managing costs of such trials, for example, by identifying existing varieties that need not be compared to candidate varieties. Under UPOV guidance, the use of genetic markers must be reflective of phenotypic differences in DUS characteristics. Within this framework, we propose a new approach for using markers based on the application of genomic prediction, which is used to predict variety differences in individual characteristics. The approach is evaluated with perennial ryegrass and wheat, yielding promising results. Additionally, we propose a novel approach in which genomic prediction is used to refine Distinctness decisions after DUS trials have been run by integrating genetic and trial information. Using perennial ryegrass as an example, we demonstrate that this approach, which respects the primacy of phenotype in DUS testing, could be used to support distinctness decisions, especially for cross-pollinated agricultural crops where Distinctness may be harder to achieve.
Why it matches plant phenotyping methodsゲノム予測を用いてDUS特性の品種差を予測し、DUS試験後のDistinctness判定を支援する手法を提案・評価しており、植物表現型の推定と判定支援が研究の中心である。
abstractWithin this framework, we propose a new approach for using markers based on the application of genomic prediction, which is used to predict variety differences in individual characteristics.
Optimizing nitrogen (N) fertilization is essential for sustaining durum wheat yield and grain quality while reducing the environmental impacts associated with N over-application. Hyperspectral sensing provides a rapid and non-destructive approach for monitoring crop N status. However, high-dimensional data, phenology-dependent spectral responses, and spatial autocorrelation in field measurements limit robust nitrogen classification and interpretation. This study evaluated hyperspectral-based nitrogen status classification in durum wheat under Mediterranean field conditions and identified key spectral regions using explainable artificial intelligence. A field experiment was conducted in Southern Italy using ten N fertilization rates (0-180 kg N ha -1 ). Canopy reflectance was acquired at the booting and heading stages from georeferenced sampling locations. Three nitrogen stratification strategies (binary Low-High, Extreme, and three-level) were evaluated using Random Forest, SVM-RBF, and XGBoost classifiers. Model performance was assessed using spatially independent Leave-One-Plot-Out cross-validation at both the sample and plot levels, with plot-level predictions derived through majority voting. Classification robustness was strongly influenced by the stratification strategy and phenological stage. The binary Low-High stratification achieved the highest sample-level accuracy, with a maximum of 0.78 at booting (SVM-RBF) and 0.75 at heading (SVM-RBF), whereas the Extreme stratification produced intermediate performance, with maximum accuracies of 0.73 at booting (SVM-RBF) and 0.63 at heading (XGBoost). Plot-level aggregation improved performance, reaching up to 0.90 at booting and 1.00 at heading. SHAP analysis highlighted red, red-edge, and near-infrared wavelengths as the dominant contributors, with increased reliance on longer wavelengths at the heading. Overall, explainable machine learning provides a robust framework for hyperspectral nitrogen monitoring in durum wheat.
Why it matches plant phenotyping methodsコムギの窒素状態をハイパースペクトル反射から分類する手法を中心に、複数モデル、空間独立交差検証、SHAPによる波長解釈を評価しており、植物状態の取得・推定が実質的な方法論的貢献である。
abstractHyperspectral sensing provides a rapid and non-destructive approach for monitoring crop N status.
Common beanCucumberMaizePeaPotatoTomatoWheatMultispectral / hyperspectralLeafPhysiological trait estimation
The objective of this study was to assess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data. The dataset encompassed leaves from multiple crops, including potatoes, beans, wheat, maize, peas, tomatoes, basil, and cucumbers, collected under varying growth conditions, cultivation systems, seasonal contexts, and developmental stages. As an initial benchmark, commonly used narrow-band spectral indices and their combinations were evaluated, but they exhibited limited predictive performance for dry matter content. Consequently, several full-spectrum machine learning models were trained and compared to assess their individual predictive ability. Given their complementary strengths, these models were integrated into a stacked ensemble framework to enhance overall accuracy. The resulting ensemble, combining the outputs of multiple base learners through a meta-learner, achieved a coefficient of determination of R2=0.896 on an independent test set, outperforming all individual models. The findings highlight the potential of a multi-model stacking approach to improve the accuracy and robustness of leaf biochemical property estimation from hyperspectral data.
Why it matches plant phenotyping methodsハイパースペクトル反射データから葉乾物含量を推定する機械学習手法を開発・比較・検証しており、植物形質の取得方法が研究の中心である。
abstractassess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
This study addresses the challenge of automated high-throughput phenotyping of wheat spike characteristics using modern computer vision and deep learning methods. Accurate estimation of spikelet number is a key indicator of plant productivity, yet traditional manual counting approaches are labor-intensive, slow, and difficult to scale to large breeding datasets. To overcome these limitations, we propose a spikelet detection strategy based on simplified point annotations, where an expert marks only the centers of spikelets rather than drawing detailed segmentation masks or bounding boxes. This significantly reduces annotation time and lowers the overall cost of preparing training datasets for machine learning models. To determine the most effective way of utilizing such simplified annotations, three computational methods were explored: segmentation of binary masks using a U-Net architecture, density regression based on two-dimensional Gaussian distributions optimized via Kullback-Leibler divergence, and detection of fixed-size bounding regions using the YOLOv8 object detection framework. The models were evaluated on dedicated test datasets using both quantitative metrics (MAE, MAPE) and spatial localization metrics (Precision, Recall, F1 score). The results demonstrate that U-Net-based approaches provide consistently high accuracy in spikelet localization and counting while maintaining robustness to annotation imperfections. In contrast, the YOLOv8-based method showed reduced performance, likely due to the geometric mismatch between fixed-size boxes and the natural elongated shape of spikelets. Overall, the proposed methodology highlights the effectiveness of combining minimalistic point-level annotation with advanced segmentation models for automating phenotyping workflows. This approach has the potential to accelerate breeding programs, enhance the efficiency of large-scale phenotypic data collection, and support further development of robust computer-vision tools for plant science applications.
Why it matches plant phenotyping methodsコムギの穂の小穂数をRGB画像から自動推定する画像解析・深層学習手法を開発し、複数モデルを定量評価しており、フェノタイピング手法が研究の中心である。
abstractThis study addresses the challenge of automated high-throughput phenotyping of wheat spike characteristics using modern computer vision and deep learning methods.
Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE
Why it matches plant phenotyping methods作物の葉面積被覆率という明示的な植物キャノピー形質を推定するハイブリッドモデルを開発し、多数のサンプルと既存プロダクトで検証しており、測定・推定手法が研究の中心である。
abstractHere, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model.
The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m -2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1 , TaTB1-4D , and TaBGC1-4D . Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.
Why it matches plant phenotyping methodsデュアルアングルRGB画像と熱時間を統合してGAIを推定する深層学習法を開発し、独立データで検証しているため、植物形質取得法が研究の中心です。
abstractwe constructed a comprehensive image dataset spanning eight field experiments across China and France
Reproduction assets foundThe paper publicly releases its pre-trained multimodal GAI-estimation model weights and inference code on Hugging Face, directly reproducing this paper's phenotyping analysis. The raw image and phenology datasets are not public and require contacting the authors.Code · publicThe pre-trained model weights, inference code, and usage instructions are publicly available in the Hugging Face repository at https://huggingface.co/PheniX-Lab/GAI-Estimation/tree/main .Open asset ↗PheniX-Lab/GAI-Estimationlines:259-277Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract Biofortification is a sustainable and cost‐effective strategy that uses plant breeding and agronomic approaches to improve the nutrient content of staple crops consumed by vulnerable populations. The approach requires high‐throughput phenotyping to effectively identify and develop nutrient‐rich genotypes. This study aimed to develop a multielement, nondestructive method to quantify calcium (Ca), manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn) in whole seed wheat ( Triticum aestivum L.) samples using a benchtop energy dispersive x‐ray fluorescence (EDXRF) spectrometer. Grain samples from 29 and 41 wheat genotypes were used for the EDXRF calibration and validation, respectively. A microwave plasma–atomic emission spectrometer (MP‐AES) provided the analyte reference values for each sample. The EDXRF calibration showed moderate to high correlation with MP‐AES values for Ca, Mn, Cu, and Zn, while Fe exhibited a weak correlation. The limits of quantification (mg kg −1 ) were 103.9 for Ca, 8.5 for Mn, 3.5 for Fe, 4.7 for Zn, and 1.0 for Cu—all below the observed analyte range in wheat grain. The method is suitable for use in early generation selection, as indicated by standard errors of prediction (mg kg −1 ) of 36.4 for Ca, 3.3 for Mn, 2.5 for Fe, 0.3 for Cu, and 1.5 for Zn. This study builds upon previous nondestructive EDXRF methods by introducing additional elements that can be reliably phenotyped in wheat, supporting broader use in biofortification programs.
Why it matches plant phenotyping methods小麦種子の無破壊多元素組成を定量するEDXRF法を開発し、独立試料と基準法で校正・検証しており、植物形質取得法が研究の中心です。
abstractThis study aimed to develop a multielement, nondestructive method to quantify calcium (Ca), manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn) in whole seed wheat ( Triticum aestivum L.) samples using a benchtop energy dispersive x‐ray fluorescence (EDXRF) spectrometer.
Developing climate-resilient wheat varieties requires combining high yield with stability across diverse environments, especially under increasingly variable precipitation and rising temperatures. This study evaluated 64 post-Green Revolution durum wheat cultivars under irrigated and rainfed conditions at two contrasting Mediterranean sites in Spain. A classification framework was developed to support genotype selection based on yield and yield stability, estimated using linear mixed models and yield slopes across environments. Genotypes were classified by interquartile thresholds, and those showing either low yield or low stability were considered undesirable for selection. High-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data. VIs and TIR at anthesis and grain filling, and their differences (senescence proxies), were used to train Random Forests for yield and stability estimation including sequential feature selection. Environmental covariates (water input, reference evapotranspiration) were integrated in yield models, with strong outcomes (R 2 > 0.74; MAPE <23.6%). Stability predictions were based on VI stability and, though moderate (R 2 up to 0.56; MAPE <17.75%), outperformed previous studies. Selected features were used to evaluate seasonal reflectance phenotypes: “keep” genotypes (intermediate/high yield or/and stability) exhibited early-vigor but lower green retention by the end of grain filling, while “discard” genotypes (low yield or/and stability) showed reduced early vigor and “stay-green” behavior. This study highlights early-vigor and earlier senescence over “stay-green” for wheat selection, offering a cost-effective approach shifting the breeding focus from yield maximization to joint yield-stability evaluation, promoting sustainability.
Why it matches plant phenotyping methods高スループットの地上・UAVセンサーによる表現型取得と、機械学習による収量・安定性推定が研究の中心であり、育種選抜に用いる手法を実質的に評価・適用している。
abstractHigh-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data.
Reproduction assets foundThe authors explicitly state that the datasets and analysis scripts for all analyses (yield/stability modeling, VI extraction, Random Forest workflows) are publicly available in their Zenodo repository (DOI 10.5281/zenodo.17435708), referenced both in the statistical analysis section and the Data Availability statementCode · publicThe datasets and scripts for all the analyses conducted are available in our repository ( https://doi.org/10.5281/zenodo.17435708 ).Open asset ↗zenodo · 10.5281/zenodo.17435708lines:222-237Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Accurate prediction of phenotypes across genotypes and environments is crucial for accelerating crop improvement. Process-based crop growth models (CGMs) can capture complex genotype-by-environment interactions, but their use is limited by labor-intensive genotypic parameter measurements. Here, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the SiriusQuality wheat model to efficiently estimate key genotypic parameters and predict genotype performance. Using time-series RGB imagery from a ground-based Phenomobile, we assimilated intercepted photosynthetically active radiation (fIPAR), heading date, and final grain yield to jointly assimilated to calibrate twelve genotypic parameters governing phenology, canopy development, light interception, biomass accumulation, and grain filling. Two data assimilation strategies—a Bayesian DREAM (zs) algorithm and a lookup table (LUT) inversion—were compared through both in silico experiment and eight years of multi-environment field trials of nine durum wheat cultivars. The LUT method demonstrated superior computational efficiency, with prediction accuracy comparable to Bayesian inference on real field data. Multi-year field trials showed that two environments (year / site) were sufficient to reliably characterize genotypic parameters and predict performance across environments. By combining time-series HTP data with ecophysiological modeling, our data assimilation pipeline offers breeders a powerful tool for genotype characterization. It streamlines the process of capturing environmental variance and phenotypic stability, reducing time and effort in crop improvement.
Why it matches plant phenotyping methodsHTP画像を作物成長モデルへ統合するデータ同化パイプラインを開発し、複数アルゴリズムと実圃場データで性能比較・検証しており、表現型取得・推定手法が研究の中心である。
abstractHere, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the SiriusQuality wheat model to efficiently estimate key genotypic parameters and predict genotype performance.
Precise monitoring of wheat phenology (BBCH scale) is essential for agricultural optimization, yet UAV-based single-phase monitoring encounters spectral ambiguities where multiple vegetation indices correspond to identical growth stages. A dual-mode framework integrating time-series reconstruction with hybrid deep learning was developed to resolve this limitation. UAV multispectral and digital imagery (333 plots, 2023–2024) enabled reconstruction of daily-resolved vegetation indices, color/texture features, and BBCH stages using Gaussian, PCHIP, and linear fitting to mitigate environmental noise. Synthetic datasets incorporating Gaussian noise (5–100 % relative intensity) simulated field variability. Feature selection was optimized through Competitive Adaptive Reweighted Sampling (CARS) and Variance Inflation Factor (VIF). Hybrid CNN-GRU and CNN-LSTM architectures surpassed standalone networks by resolving spectral ambiguities in single-phase data and leveraging temporal patterns during time-series analysis. Time-series models attained maximum accuracy under noise-free conditions (CNN-GRU: R 2 = 0.90–0.98, RMSE = 3.61–7.65 BBCH units), with accuracy decreasing proportionally to noise intensity. Conversely, single-phase models demonstrated peak performance at 20 % noise intensity (CNN-GRU: R 2 = 0.56–0.70, RMSE = 15.33–17.22 BBCH units), achieving optimal balance between robustness and practicality for real-time farm monitoring. Extreme noise (100 %) distorted feature distributions (7.25–8.73× expansion), validating controlled augmentation. A novel Rate of Phenological Development (RPDW) —quantified as the slope of BBCH progression—was derived to inform breeding programs, while the noise-optimized single-phase approach enables resource-efficient phenology tracking for family farms. This work bridges methodological innovation (adaptive noise strategies, hybrid architectures) with scalable solutions for precision agriculture, advancing UAV-based phenology monitoring in both academic and applied contexts.
Why it matches plant phenotyping methodsUAV画像・時系列再構成・深層学習を統合し、BBCH生育段階を推定するフェノタイピング手法の開発と性能評価が中心である。
abstractA dual-mode framework integrating time-series reconstruction with hybrid deep learning was developed to resolve this limitation.
Abstract Traditional disease classification is slow and lab‐dependent. Machine learning aids faster image‐based detection but faces challenges like lighting variations, complex leaf shapes, background noise, and limited labeled data. This research develops a robust image‐based method to automatically classify corn, rice, and wheat leaf diseases under diverse environmental and imaging conditions. A Hybrid Morlet Wavelet Interactive Attention Neural Network optimized by the red‐billed blue magpie optimizer (HMWIANN‐RBBMO) is proposed in this study for accurate classification corn ( Zea mays ), rice ( Oryza sativa ), and wheat ( Triticum aestivum ) leaf diseases. First, a modified square‐root SageHusa adaptive Kalman filter is used to remove noise and improve image quality by image preprocessing. The DeepLabV3+ is used to accurately segment disease‐prone areas, and then the Sharpbelly Fish Optimization is used to identify the most discriminative features in the images. The HMWIANN will combine Morlet wavelet transformation with interactive attention to exhaust the capabilities of the classifier to recognize Healthy (No pathogen), Common Rust ( Puccinia sorghi ), Blight ( Xanthomonas oryzae ), Gray Leaf Spot ( Cercospora zeae‐maydis ), BrownSpot ( Bipolaris oryzae ), Hispa ( Dicladispa armigera ), LeafBlast ( Magnaporthe oryzae ), Stripe rust ( Puccinia striiformis ), and septoria ( Zymoseptoria tritici ). Furthermore, the RBBMO will be used to improve convergence speed, generalization, and classification accuracy. A graph‐based hybrid recommendation system is also incorporated to assist disease management decisions. Experimental evaluation on corn, rice, and wheat leaf disease dataset demonstrates superior performance, achieving 99.70% accuracy, 99.80% precision, 99.50% recall, 99.40% F1‐score, and a low false positive rate of 0.8%, outperforming existing state‐of‐the‐art methods.
Why it matches plant phenotyping methods植物葉の病害症状を画像から分割・特徴抽出・分類する手法を開発し、病害状態という植物表現型を直接推定して性能評価しているため、方法が中心的である。
abstractThis research develops a robust image‐based method to automatically classify corn, rice, and wheat leaf diseases under diverse environmental and imaging conditions.
Automated phenotyping of wheat growth stages from 3D point clouds is still limited. The study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping. A novel 3D wheat plot detection network—integrating spatial–channel coordinated attention and area attention modules—improves depth-direction feature recognition, and a point-cloud-density-based row segmentation algorithm enables planting-row-scale plot delineation. A supporting software system facilitates 3D visualization and automated extraction of phenotypic parameters. We introduce a dynamic phenotypic index of five temporal metrics (growth stage, slow growth stage, height/area reduction stage, maximum height/area difference stage, and height/area change rate) for growth-stage classification and yield prediction using static and time-series models. Experiments show strong agreement between predicted and measured plot heights (R 2 = 0.937); the detection net achieved AP 3D = 94.15 % and AP BEV = 95.35 % in “easy” mode; and a Bi-LSTM incorporating dynamic traits reached 82.37 % prediction accuracy for leaf area and yield, a 6.14 % improvement over static-trait models. This workflow supports high-throughput 3D phenotyping and reliable yield estimation for precision agriculture. • Developed a novel 3D wheat plot detection net with spatial–channel coordinated attention and area-attention modules, reaching 94.15% AP 3D and 95.35% AP BEV in high-precision mode, outperforming traditional methods. The CFPT 3D module boosts depth-direction feature extraction for dense planting. • Introduced 5 temporal phenotypic metrics (e.g., growth stage transitions, height/area change rates) to capture dynamic growth patterns. • Bi-LSTM models using these traits predicted yield with 82.37% accuracy, 6.14% higher than static-trait models. • Released a PyQt5-based 3D phenotype extraction tool for automated parameter calculation (height, canopy area, LAI) and visualization. • Proposed a density-based row segmentation algorithm enabling accurate row-level phenotyping, validated in single- and multi-row systems.
Why it matches plant phenotyping methods3D画像・点群から小麦区画の形態形質を抽出する手法、検出・行分割アルゴリズム、動的形質指標、ソフトウェアを中心的に開発・検証しているため。
abstractThe study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping.
Improving yield is one of the core goals of crop breeding. By predicting the potential yield of different breeding materials, breeders can screen these materials at different growth stages to select the best-performing breeding materials. However, existing yield prediction methods struggle to balance accuracy, interpretability, and robustness, often facing trade-offs between model complexity, data requirements, and generalizability. To address the above challenges, this study proposed a new hybrid method integrating remote sensing data assimilation and deep learning. The leaf area index was assimilated into the calibrated and validated WOFOST crop model using a newly designed data assimilation algorithm. The dataset, including partial outputs from the WOFOST model, development day, and vegetation indexes (VIs), was used to train the Temporal Fusion Transformer model for wheat yield prediction. The results showed that the new hybrid method achieved the highest performance in wheat yield prediction for different breeding materials in different study areas (R² of 0.831 and RMSE of 372.8 kg/ha in Yuhang experiment; R² of 0.704 and RMSE of 605.3 kg/ha in Zijingang experiment), which was better than other process-based model-driven methods and data-driven methods. This showed that the hybrid method had superior applicability in accurate yield prediction. Other results showed that increasing the number of data collections during the growth stage could significantly improve the performance of yield prediction and reduce error. Data from the middle and late growth stages contributed more to prediction performance than data from the early stages. Physiological variables and some VIs were the most important factors for yield prediction, while morphological characteristics contributed less. The importance and impact of each feature varied at different growth stages, highlighting the complex nonlinear relationship between characteristics and yield. In addition, since existing methods are difficult to fully utilize multi-source heterogeneous data related to yield in the breeding process, and the usability and user-friendliness of yield prediction software are also insufficient, an interactive yield prediction website has been developed based on a new hybrid method, a large language model (Llama), and related technologies to assist breeding decisions. This study aims to improve the efficiency of breeding material screening, provide an accurate, user-friendly, and well-interpretable yield prediction tool for wheat breeding, and facilitate smart breeding and decision making.
Why it matches plant phenotyping methodsコムギの収量という植物形質を、リモートセンシング・データ同化・深層学習で予測する手法を開発・比較検証し、育種向けソフトウェアも開発しているため、フェノタイピング手法が中心的である。
abstractThe results showed that the new hybrid method achieved the highest performance in wheat yield prediction for different breeding materials in different study areas
Robust quantification of crop status in real-time is essential for agile decision-making. While use of unmanned aerial vehicle data (UAV) appears promising in this vein, the contribution and transferability of various features (e.g. vegetation indices, plant height and texture features) in crop above-ground biomass (AGB) prediction remain poorly understood. Here, our objectives were to (1) evaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features, (2) elicit the contribution of various UAV features, (3) assess the transferability of features across growth stages and sites. Four field experiments, incorporating several water and nitrogen treatments across two sites, were assembled for use in AGB prognostics. We invoked four ML algorithms—Random forest (RF), Lasso regression (LR), K-nearest neighbors (KNN) and a stacked ensemble integrating the three methods (SML)—to predict wheat AGB using multiple UAV data and phenological information. Additionally, interpretable ML techniques were employed to elucidate the influence of UAV features on AGB prediction across growth stages. Our results showed that all algorithms exhibited robust performance in predicting wheat biomass, with RMSE values of 1.64, 1.71, 1.71, and 1.57 Mg ha −1 for RF, LR, KNN, and SML, respectively. RF predominantly relied on plant height features, LR leveraged vegetation indices, and KNN prioritized texture features, while SML synthesized the advantages of multiple ML algorithms. Fusion of multiple datasets amplified model prognostic capacity and scalability, with R 2 and rRMSE of 0.92 and 22 % when using data from external sites. Features pertaining to vegetation indices and plant height during vegetative growth and around flowering had seminal contributions of model predictions. Texture features significantly reduced the saturation effect during the reproductive stage but diminished the model’s transferability during the vegetative stage. Complementarity among data types enhanced effectiveness of ensemble machine learning, which leverages strengths of diverse data to improve the accuracy and robustness of AGB predictions. Future studies could combine multiple sources of remote sensing, such as LiDAR and thermal infrared alongside system modeling, to improve ML accuracy and generalization capability.
Why it matches plant phenotyping methodsUAV由来の植物高・植生指数・テクスチャ等から小麦バイオマスを推定する機械学習手法を比較・検証し、異なる生育段階や圃場への転移性も評価しており、表現型推定法が中心である。
abstractevaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features
This study examines the ultrastructure of the outer layers of hexaploid wheat (Triticum aestivum L.) seeds using atomic force microscopy (AFM) in air tapping mode. The specimens were resin-embedded after hydration. A standardised protocol for preparing specimens specifically for AFM investigations is presented, focusing on revealing the ultrastructure while minimising artefacts and optimising the resolution of the structural morphology. AFM provides a comprehensive histological description of the hydrated mature wheat seed, encompassing each layer from the outer pericarp to the starchy endosperm. This study highlights the ultrastructural details of the tissues in their hydrated state, particularly with regard to morphology and size. Thus, AFM shows great potential for revealing intricate details of plant tissues structure and ultrastructure.
Why it matches plant phenotyping methods成熟コムギ種子の組織形態・超微細構造をAFMで取得するための標準化試料調製プロトコルを提示しており、植物形態の観察手法が中心である。
abstractA standardised protocol for preparing specimens specifically for AFM investigations is presented, focusing on revealing the ultrastructure while minimising artefacts and optimising the resolution of the structural morphology.
Lodging is a complex trait that limits wheat (Triticum aestivum L.) yield potential, and no single trait can fully capture lodging resistance. Identifying key traits and developing reliable, field-applicable indicators are crucial for breeding lodging-resistant cultivars. In this study, lodging resistance was systematically assessed in 274 wheat varieties across three consecutive growing seasons (2022–2024). Genotype, growing season, growth stage, and their interactions significantly affected lodging-associated traits, with a clear temporal alignment between meteorological conditions and lodging events. Comparative analysis between lodged and non-lodged plants revealed that lodging negatively influenced spike and kernel traits. Multivariate analyses indicated that height-related traits accounted for nearly 50 % of the phenotypic variance related to lodging resistance and showed negative correlations, while traits related to stem weight and fullness explained 24 % and 8 %, respectively. Among these, stem wall thickness (SWT), second basal internode fullness (SBF), single stem elasticity (SSE), and stem strength (SS) emerged as key positive contributors, whereas plant height (PH), center of gravity height (CGH), and basal internode lengths were negatively associated. Stepwise regression and path analyses further identified SWT and SBF as primary determinants of SS, while CGH was the key factor influencing SSE. Structural equation modeling demonstrated that height-related traits exerted significant negative effects on stem anatomical structure, mechanical traits, and lodging index. Furthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed. It exhibited a strong correlation with the comprehensive lodging score (D value) and high consistency with clustering results, providing a practical assessment tool. These findings provide valuable insights for assessing lodging resistance and guiding strong-stem breeding strategies in wheat.
Why it matches plant phenotyping methods小麦の倒伏抵抗性を複数形質から統合的に評価し、新規倒伏指数を提案して既存スコアやクラスタリング結果で検証しているため、形質評価手法の開発・検証が中心である。
abstractFurthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed.
This paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification. This framework relies on a mathematical model based on neural networks that classifies and detects the condition of agriculture, removing the reliance on manual tasks and subjective diagnosis. This paper focuses on three main aspects of our framework: data acquisition, training and prediction. Data is collected using sensors like drones, cameras, and satellite imagery and is pre-processed to filter out noise and improve quality. The training part uses CNN to learn features from the data and become more meaningful. The prediction part of the task classifies, and diagnoses crop health through the trained model using the features. The framework accuracy for crops such as maize, potato, and wheat has been tested and yielded over 90% accuracy. The novelty of this work resides in the development of a multi-modal deep learning architecture that fuses macro-scale satellite imagery with micro-scale drone and IoT sensor data to improve diagnostic reliability. The framework was validated on a multi-source agricultural dataset using a 70% training, 15% validation, and 15% testing protocol. Experimental results demonstrate an accuracy exceeding 90% for staple crops. Using this framework can increase the visibility and quality of information maintained for crop health and improve the decision-making routine of farmers in real time. Additionally, automation of this process can significantly reduce labor costs and increase productivity per crop. Implementing this framework can contribute to precision agriculture and sustainable management practices.
Why it matches plant phenotyping methods作物の健康状態を植物の表現型・状態として推定するマルチモーダル画像・センサ基盤と深層学習手法を開発し、複数作物・データセットで検証しているため、方法が中心である。
abstractThis paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification.
Reproduction assets foundThe article's Data availability section points to a public Kaggle dataset used for the crop classification/health diagnosis experiments, matching an allowed URL. No code or model checkpoints are disclosed.Dataset · publicript. The research work was guided by Dr. B.D.K.P. The Corresponding author Shshank Chaube collaborated for review and supervision. All authors reviewed the manuscript.
Funding
Open access funding provided by Symbiosis International (Deemed University). No funds, grants, or other support was received.
Data availability
Dataset: https://www.kaggle.com/datasets/bhagvendersingh/precision-agriculture-dataset .
Declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
References
1. Mohyuddin, G. et al. Evaluation of machine learning approaches for preciOpen asset ↗kaggle · bhagvendersingh/precision-agriculture-datasetlines:473-545Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Abstract Context The amount and ratio of data required for training spectral grain yield (GY) prediction models remain critical for sampling reference data, but have been rarely addressed so far. Given the high cost for collecting ground truth data, common data splitting ratios which use the majority of the data for training and the rest for testing the models, should be reduced, which in turn may affect prediction accuracy. Methods Therefore, this study evaluated 11 different train-test data splitting ratios (TSR), ranging from using 5% to 95% of the data for training, and the remaining portion as test set. Models with six different machine learning algorithms were compared in winter wheat breeding trials conducted with each several thousand plots in two locations in Germany over a period of four years. The input data consisted of the UAV-based NDRE index data from individual measurements dates as well as incremental date combinations. Results The results indicate that GY prediction was relatively stable when decreasing TSR to about 0.30. Conversely, multi-date models tended to profit more from higher TSR than single-date models. Support vector machine and random forest algorithms showed relative advantage for higher TSR and multi-date models, whereas partial least squares and ridge regression were the best algorithms for lowest TSR-values. Prediction results from repeated data splitting indicates minimum R²-variability at TSR-values of 0.30 but substantially increasing R²-variation for high TSR-values. Conclusions It is concluded that decreasing TSR while considering algorithm selection can reduce costs without compromising prediction accuracy, therefore making spectral phenotyping methods more accessible and ready-to-use.
Why it matches plant phenotyping methodsUAV multispectral画像と機械学習による収量推定について、データ分割比・アルゴリズム・予測安定性を体系的に評価しており、スペクトル表現型計測ワークフローの技術的検証が中心である。
abstractTherefore, this study evaluated 11 different train-test data splitting ratios (TSR), ranging from using 5% to 95% of the data for training, and the remaining portion as test set.
In wheat breeding, the number of spike grains is a key indicator for evaluating wheat yield, and timely and accurate detection of wheat spike grain is of great practical significance for yield estimation. However, in actual field production, the counting of spike grain still relies on manual counting after threshing, which poses problems such as complex measurement processes, time-consuming and laborious. At present, achieving automated and intelligent detection of wheat spike grain still faces significant challenge. Therefore, the focus of this study is to use the most advanced computer vision technology for fast and automatic detection of wheat spike grain. During the wheat filling stage, a total of 936 wheat spike grain images were collected, and these images were expanded through data augmentation to ultimately obtain 3700 wheat spike grain images. According to the partition ratio of the small scale dataset, 80% of the 3700 images are used for training, 10% for validation, and the remaining 10% for testing. This study selected six state-of-the-art deep learning models: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x, and Faster R-CNN. In all wheat spike grain test, YOLOv8n showed high precision, recall, mAP50, and mAP50-95, with values of 96.8%, 96.8%, 98.9%, and 58.4%, respectively. The precision of other models was 96.7% for YOLOv8m, 96.5% for YOLOv8s, 96.3% for YOLOv8l, 96.2% for YOLOv8x, and 95.7% for Faster R-CNN. YOLOv8n not only has a lower number of parameters, FLOPs, inference time, model size, and GPU memory usage, as well as higher detection precision in wheat spike grain counting tasks, fully meet the spike grain counting requirements of wheat breeding. The multi-scale feature fusion and lightweight computing of YOLOv8n help improve model performance, and its performance is better compared to other deep learning models. This study designed and implemented a WeChat mini program for wheat spike grain counting, so as to achieve automatic detection and counting of wheat spike grains, which provided valuable reference for grain detection, counting, and yield estimation of other crops.
Why it matches plant phenotyping methods小麦穂粒数という植物形態・収量関連形質を、画像と深層学習で自動取得・計数する手法が研究の中心であり、複数モデルの性能比較とアプリ実装も行っているため。
abstractthe focus of this study is to use the most advanced computer vision technology for fast and automatic detection of wheat spike grain
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Heat stress damage leads to yield penalties in many wheat-growing areas. Climate change models predict warmer scenarios and more frequent heat shocks. Consequently, wheat breeders need to develop more productive varieties for warm conditions, and therefore, the identification of heat-tolerance traits is needed. Albedo is an integrative trait of the optical properties of the canopy defined as the ratio of reflected light to total light received. High albedos in warm conditions may help reduce damaging radiation. Despite its potential relevance for heat avoidance, albedo has been little explored in wheat breeding. In this work, a selection of 30 wheat ( Triticum aestivum L.) genotypes of diverse origin were sown at two sowing dates in Australia (NSW) in 2018 and 2019. A high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed to explore its relationship with temperature and other heat tolerance-related traits. ASD albedo was validated via continuous albedometer measurements on a subset of genotypes. Data were captured at flowering (one of the most critical periods for heat-related damage). Genotypic differences for albedo were found in most environments. However, genotypic effects were most noticeable at noon in optimally sown materials (H 2 0.71–0.86). Albedo was directly related to canopy architecture and light interception (r = 0.74) and varied depending on genotype and genotype by environment interaction. Air temperature in the canopy profile and canopy temperature (CT) were also monitored continuously in a subset of genotypes to explore the relationship between albedo and canopy micrometeorology. Canopies with higher albedos had larger air temperature differences across the canopy profile at the flowering stage (r = 0.48). However, canopy temperature was not related to albedo, even though it was strongly correlated (r = 0.99) with air temperature around the spike. Overall, these results indicate that canopy architecture is the primary influence on albedo under warm conditions. Although higher albedo was not associated with lower canopy temperature, its influence on canopy micrometeorology suggests that albedo may contribute to heat avoidance and could therefore be considered an additive trait for phenotyping and breeding for environments under high temperatures.
Why it matches plant phenotyping methods小麦群落アルベドを分光放射計で測定するハイスループット表現型計測法を開発し、連続アルベドメーターで検証しているため、方法が研究の中心である。
abstractA high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed
Key message A genetic algorithm-optimized deep neural network was developed using proximal sensing data to accurately predict wheat yield at field scale, outperforming traditional machine learning models under diverse conditions. Hand held or vehicle-mounted active proximal sensing technologies offer a rapid, non-destructive method for real-time crop monitoring through spectral vegetation indices. This study integrates such proximal sensing data into a deep learning framework for field-scale wheat yield prediction. Specifically, wheat yield is predicted using normalized difference vegetation index (NDVI), canopy temperature (CT), and plant height (PH) through a deep neural network (DNN) optimized using a genetic algorithm (GA). The model is trained on data from 3,350 diverse wheat germplasm grown under irrigated and rainfed conditions at two locations during the 2020-2021 winter season. Comparative analysis demonstrates that the GA-optimized DNN outperforms traditional machine learning models such as Random Forest Regression (RFR), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Regression (SVR). Among individual feature groups, NDVI measured at five wheat growth stages showing strong predictive capability, with R 2 values ≥ 60% under irrigated and ≥ 50% under rainfed conditions. Additionally, RFR is employed to identify the most influential features for predicting grain yield. This pioneering study introduces the first-ever application of a GA-optimized deep neural network, leveraging handheld or vehicle-mounted proximal sensing data for predicting crop yield, in the context of Indian agriculture. The proposed approach offers a robust and scalable solution for pre-harvest yield estimation, supporting breeders and researchers in efficient genotype selection and contributing to the achievement of sustainable development goals.
Why it matches plant phenotyping methods近接センシングによるNDVI・群落温度・草丈からの収量推定と、遺伝的アルゴリズム最適化DNNの開発・比較検証が研究の中心であり、植物表現型（収量）を推定する方法論的研究である。
abstractA genetic algorithm-optimized deep neural network was developed using proximal sensing data to accurately predict wheat yield at field scale, outperforming traditional machine learning models under diverse conditions.
Precise and timely prediction of wheat yield is pivotal for ensuring global food security and optimizing agricultural management practices, particularly through advanced remote sensing and machine learning techniques. In this study, wheat yield was accurately estimated by leveraging remote sensing-derived soil and vegetation indices. Yield data from 189 study points were collected, and Sentinel-2 (10-meter resolution) imagery from the Tillering and Anthesis growth stages was used. The models incorporated 25 variables, including 16 optical indices (e.g., NDVI, SAVI, MSAVI2) and three topographic factors. The key novelty of this research is the rigorous comparison of the predictive value and synergistic contribution of Sentinel-1 Synthetic Aperture Radar (SAR) data when integrated with Sentinel-2 optical indices within machine learning frameworks. Three machine learning approaches (Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF)) were employed and evaluated using 70% training and 30% testing subsets. Results revealed that the RF model, leveraging data from the Anthesis phenological stage, exhibited superior performance in wheat yield estimation, achieving an R² of 0.92 and an RMSE of 0.14 ton ha -1 for the training set, and an R² of 0.90 and an RMSE of 0.29 ton ha -1 for the testing set. To enhance model accuracy, Sentinel-1 radar data were integrated into the RF framework. This addition reduced the training set RMSE to 0.13 ton ha -1 but increased the testing set RMSE to 0.33 ton ha -1 , with R² values remaining stable at 0.92 and 0.90 for the training and testing sets, respectively. Variable importance analysis indicated that optical soil and vegetation indices were the dominant predictors. Although the inclusion of Sentinel-1 SAR data offered additional insights, it did not outperform the predictive capacity of optical indices. These findings validate the efficacy of the combined Sentinel-2 remote sensing approach for generating reliable wheat yield forecasts approximately 50 days prior to harvest.
Why it matches plant phenotyping methodsSentinel-1/2リモートセンシングと機械学習による小麦収量という植物形質の推定が中心で、モデル比較・評価と予測精度検証を実施している。
abstractwheat yield was accurately estimated by leveraging remote sensing-derived soil and vegetation indices.
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / tolerance
The Multiple Synthetic Derivatives (MSD) population is a unique hexaploid wheat resource that captures extensive genetic diversity from Aegilops tauschii and exhibits wide variation in agronomic traits. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype. A two-dimensional cultivation platform enabling continuous imaging of seedling root growth under controlled conditions was established to quantify RSA traits and their responses to high temperatures. MSD417 was compared with its recurrent parent, Norin 61 (N61). Under controlled conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61, indicating enhanced early root vigor. This genotype also exhibited a wider seminal root angle, suggesting improved horizontal soil exploration while maintaining root depth. High-temperature treatment reduced overall root growth and minimized genotypic differences, indicating that temperature stress constrains RSA expression. Microscopic observations further revealed a lower height-to-width ratio of coleorhiza tissue of MSD417, suggesting restricted downward expansion. Collectively, this study establishes a practical framework for RSA phenotyping and demonstrates the potential of Aegilops tauschii-derived germplasm to enhance wheat root-related adaptive traits.
Why it matches plant phenotyping methods根系構造を連続画像化して定量する2次元表現型解析プラットフォームを構築し、RSA形質の測定に実質的に適用しているため、方法が中心的である。
abstractHere, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype.
Reproduction assets foundThe paper deposits its paper-specific root images (N61 and MSD417) and coleorhiza microscopic images in Zenodo with explicit DOIs. The R analysis scripts are only in Supplementary Document S1 with no public URL, so they do not qualify as a public code asset.Dataset · publicThe microscopic images of coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131.Open asset ↗Zenodo · 10.5281/zenodo.18091131pdf-page:14 lines:1-71Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
1 Abstract Accurate quantification of plant disease is essential for resistance breeding, variety testing, and precision agriculture, yet visual ratings are limited by subjectivity, low precision, and restricted throughput. Image-based phenotyping can address these limitations, but field applications face substantial challenges due to spatial heterogeneity, symptom-level diagnostic requirements, and the need for very high-resolution imagery with limited spatial coverage. This introduces a fundamental trade-off: high-resolution images provide precise local measurements of disease, but spot-level estimates can be highly variable within experimental units. We analyzed a large image data set of wheat foliar diseases to characterize the distribution, spatial dependence, and aggregation behavior of spot-level severity estimates in plots. We combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area. Our results highlight focus bracketing as a promising approach for simultaneous diagnosis and quantification of disease in field plots. Autocorrelation in severity estimates both within focal image stacks and across plot positions was comparable, with 10 focal stack images or 10 positions per plot contributing approximately 2.5 independent observations each. Modeling plot-level severity as a latent Beta-distributed variable enabled robust estimation of mean severity and associated uncertainty. This supports both hypothesis testing and efficient sampling across the full range of disease severity associated with genotypic diversity and seasonality of developing epidemics. The proposed imaging approach is non-invasive and, in principle, transferrable to autonomous ground-based phenotyping platforms, offering the potential to shift the dominant source of uncertainty in estimating disease severity from measurement-related limitations toward biologically and environmentally driven variability in disease expression.
Why it matches plant phenotyping methods高解像度画像とフォーカスブラケティングを用いて植物病害の重症度を定量化し、圃場プロット単位の推定精度と不確実性を評価する手法が研究の中心であるため。
abstractWe combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area.
Reproduction assets foundThe paper states that R code to reproduce the full analysis (Beta-distribution modeling, autocorrelation/AR(1) mixed models, effective sample size estimation for wheat disease severity phenotyping) is publicly available on the authors' GitHub repository. The repository name appears truncated in the supplied text ('plotCode · publicR-code to reproduce the full analysis is available at https://github.com/and-jonas/plot-spot-Open asset ↗and-jonas/plot-spot-pdf-page:9 lines:1-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples. An efficient sample preparation method assembles wheat tillers into bundles from which individual tillers are robustly detected automatically, using classical image analysis. A custom-made user interface ('TillerCounter' program) allows adjusting the automatic detections interactively, which leads to highly accurate tiller counts comparable to the ground truth obtained by manual counting. The key contributions of our work include:1.An efficient method for imaging straw tillers based on bundle assembly.2.An extensive study of the obtained image quality and comparison with the ground truth data from manual counting.3.Demonstration of the approach's high accuracy using correlation analysis (Pearson correlation coefficient R = 0.973 compared to ground truth) and error analysis (root mean squared relative errors below 5 %).
Why it matches plant phenotyping methods小麦分げつ数という植物形態形質を、画像取得・古典的画像解析・専用ソフトウェアで自動推定し、手動計数を基準に精度検証しているため、フェノタイピング手法が中心です。
abstractWe present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples.
Reproduction assets foundThe paper's authors publicly released the TillerCounter GUI source code on GitHub, which implements the Hough-transform-based tiller counting analysis used in this study. The paper also cites original image/count data at Zenodo (10.5281/zenodo.14446564), but no Zenodo URL is present in the allowed URL list, so only theCode · publicThe source code of the TillerCounter GUI is given at https://github.com/agroscope-ch/TillerCounterGui. Original data is given at Zenodo repository: 10.5281/zenodo.14446564Open asset ↗agroscope-ch/TillerCounterGuihtml-lines:163-195Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Abstract An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and thickness profile. New shape descriptors based on thickness profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態形質を3D画像から抽出するパイプラインを開発し、形質の相関・遺伝子型間比較で検証しており、表現型取得法が研究の中心である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe preprint explicitly shares sample 3D spike scan data and the trait-extraction analysis code in the authors' public GitHub repository, with separate Data and code availability statements.Dataset · public1003/1)
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The authors declare that there are no conflicts of interest regarding the publication of this paper.
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1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code · public1003/1)
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The authors declare that there are no conflicts of interest regarding the publication of this paper.
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Sample data are shared in the following link:
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Abstract To address challenges in food security, a better understanding of crop performance under varying and changing environmental conditions is required. Plant time warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield. PTW leverages image time series, genetic markers, and environmental covariates to learn genotype-specific physiological responses to temperature and vapour pressure deficit. Compared with mere genomic prediction models, PTW demonstrates superior performance when predicting yield in unseen environments across 48 year–locations in Europe. The PTW model captures non-linear growth responses varying with phenological stages and identifies distinct patterns associated with yield performance and stability. Specifically, varieties with higher yield stability exhibit reduced sensitivity to vapour pressure deficit around 1.5 kPa and distinctive temperature responses during emergence and senescence. The learned response pattern enables retrospective and prospective yield predictions, providing a foundation for location-specific variety recommendations and targeted breeding strategies. The integration of phenomic, genomic, and enviromic data has the potential to substantially advance research in climate adaptation strategies for crop production by addressing generalization challenges of predictions to novel environmental conditions.
Why it matches plant phenotyping methods画像時系列を含む高スループット圃場フェノタイピングデータから小麦収量を予測する深層学習モデルを開発・評価しており、フェノタイピング解析が研究の中心である。
abstractPlant time warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield.
Optimizing biomass partitioning is essential for achieving sustainable yield improvement in wheat, particularly under increasing environmental stress. Traits such as spike partitioning index (SPI), harvest index (HI), and fruiting efficiency (FE) are central to understanding how assimilates are allocated between vegetative and reproductive organs. However, their complex physiology and the difficulty of manual phenotyping have limited their routine use in breeding programs. This study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat. Three trials of facultative soft wheat lines (2022-2024) and an independent validation set of advanced breeding lines were used to develop genomic prediction (GP), phenomic prediction (PP), and integrated multi-omic models combining genomic, phenomic, and environmental covariates (ECs). Kernel-based best linear unbiased prediction (BLUP), and machine-learning based, random forest regression and partial least squares regression were implemented to estimate predictive ability (PA). Phenomics-driven models markedly outperformed GP across most traits, achieving PA up to 0.61 for SPI, 0.56 for FE, 0.71 for grains/m 2 (GN), and 0.66 for grain yield (GY). Hyperspectral data provided higher accuracy than vegetation indices, and multi-omic integration slightly improved prediction (PA up to 0.73 for GN). These results demonstrate that UAV-based hyperspectral phenotyping can effectively capture canopy-level physiological signals associated with biomass partitioning, offering a scalable and data-driven approach for in-season selections. This can help wheat breeding programs to optimize biomass partitioning in modern wheat cultivars for long-term yield resilience and genetic gain.
Why it matches plant phenotyping methodsUAVハイパースペクトルデータによる作物形質推定と予測モデルの開発・検証が研究の中心であり、単なる生物学的実験の測定ではない。
abstractThis study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat.
Summary Anatomical and histochemical imaging of grass root systems relies on tissue sectioning and cell wall staining dyes because molecular reporter lines are limited for most organisms. Distinct staining dyes require variable incubation time and concentration across different tissues and organisms. As a result, staining with multiple dyes becomes time consuming or challenging. Here, we report a rapid method to perform simultaneous triple staining on a glass slide. The entire protocol requires ∼4 hours and a smaller volume of stain than traditional methods. We tested this method using the roots of two economically important crops, Triticum aestivum (wheat) and Zea mays (maize), as proof of concept. We have also demonstrated the presence of exodermis in wheat roots. Additionally, we identified the formation of polar lignin caps in maize exodermis using our simultaneous triple staining method. This method empowers a quantitative approach to cell biology by elucidating cell-type specific spatio-temporal distribution of cell wall materials in monocot root systems.
Why it matches plant phenotyping methods単子葉植物の根における細胞壁物質の細胞型別・時空間分布を定量的に可視化する同時三重染色法の開発が中心であり、植物状態の取得・解析手法に該当する。
abstractHere, we report a rapid method to perform simultaneous triple staining on a glass slide.
Timely acquisition of crop chlorophyll contents is essential for effective field management decisions and comprehensive crop nutritional monitoring. Unmanned Aerial Vehicle (UAV) has been extensively utilized for canopy chlorophyll content monitoring. However, prior studies predominantly concentrated on the horizontal variability of SPAD, with few researches dedicated to monitoring the vertical distribution of SPAD. This study aimed to develop a high-resolution vertical SPAD distribution model for winter wheat by integrating UAV-based Cross-Circle Oblique (CCO) photography with the Structure from Motion (SFM) and Multi-View Stereo (MVS) methods. The canopy was divided into upper, middle, and lower layers based on plant height. Nadir and CCO photography were used to capture images, with Nadir photography constructing the upper canopy SPAD model and CCO data used for the vertical distribution model (including upper, middle and lower layers). Shapley values were applied to evaluate feature importance in different machine learning models (GBR, gradient boosting regression; RF, random forest; SVM, support vector machine; RR, ridge regression). Finally, a vertical SPAD distribution model for winter wheat was created with a 0.1-meter gradient. The results demonstrated that the accuracy of SPAD vertical distribution inversion using single machine learning algorithms (KNN: k-nearest neighbor, RR: ridge regression) was lower than that achieved by ensemble learning methods (stacking, RF: random forest). Ensemble learning enhanced R 2 and RMSE by 0.13 and 1.23 for the training set, and by 0.09 and 0.56 for the test set, respectively. CCO photography exhibited high accuracy in capturing the SPAD spatiotemporal distribution of winter wheat, with R 2 values for the training sets across all three growth stages surpassing 0.8, and R 2 values for the test sets ranging from 0.6 to 0.81. The highest accuracy in SPAD vertical distribution inversion was achieved during the heading stage, with R 2 and RMSE values for the training and test sets of 0.89, 2.58, and 0.80, 3.34, respectively. Therefore, UAV-based CCO photography combined with the SFM-MVS algorithm demonstrates preliminary potential for vertical SPAD phenotyping and precision nutrient management; however, further validation across different growing seasons and wheat varieties is necessary to confirm its broad generalizability.
Why it matches plant phenotyping methodsUAV斜視画像とSfM-MVS、機械学習を統合し、コムギの垂直SPAD分布を推定する方法の開発・検証が研究の中心である。
abstractThis study aimed to develop a high-resolution vertical SPAD distribution model for winter wheat by integrating UAV-based Cross-Circle Oblique (CCO) photography with the Structure from Motion (SFM) and Multi-View Stereo (MVS) methods.
Wheat emergence rate and emergence uniformity are key indicators for evaluating seed vigor and sowing quality, and they play an important role in wheat growth and yield formation. Traditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data. In this study, RGB images and a two-stage deep learning algorithm were used to extract and analyze seedling traits of 420 wheat varieties under two nitrogen levels, and the results were applied to genome wide association studies to elucidate the genetic basis. The two-stage algorithm integrates a Bidirectional Feature Pyramid Network, small object detection layer, large size image input, and FasterNet to improve detection and instance segmentation speed and accuracy. The proposed method achieved an emergence rate accuracy of 0.929, with R 2 = 0.914 and RMSE = 2.448 compared to manual measurements, and required less than 0.2 s per image for analysis. By employing this two-stage algorithm for processing and analysis, varieties (e.g., Gao8901 and ShiYou20) that consistently exhibited high emergence rates and uniformity under multiple nitrogen treatments were identified. Furthermore, genome-wide association study identified the major loci qEmergence rate-3A and qUniformity-6B governing seedling emergence rate and uniformity, which likely enhance wheat seedling traits by modulating energy supply or related signaling molecules. The emergence-rate and uniformity data generated by the two-stage algorithm significantly accelerated the discovery of relevant genes and enabled the identification of wheat varieties with high emergence rate and uniformity, providing valuable insights and practical references for high-quality breeding and gene mining.
Why it matches plant phenotyping methodsRGB画像と二段階深層学習による出芽率・均一性の自動取得手法を開発し、手動測定との精度比較および大規模品種適用を行っており、表現型取得法が研究の中心である。
abstractTraditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data.
Reproduction assets foundThe authors openly provide test code, base models, and sample test data for the WS-YOLO two-stage wheat seedling phenotyping pipeline in a public GitHub repository. Raw phenotype datasets are only available upon request, so they do not qualify as public assets.Code · publicThe test code, base models, and sample test data are openly available in the GitHub repository: https://github.com/AIWheatLab/WheatSeedling.Open asset ↗AIWheatLab/WheatSeedlinghtml-lines:375-402Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Estimating canopy structure - leaf inclination distribution (LIDFa), leaf area index (LAI), and fractional vegetation cover (FCover) - is vital for breeding, yet the added value of multi-angular UAV sensing over nadir-only baselines remains insufficiently quantified. This study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy. Using transfer learning across cultivars and dates, we compared the retrieval performance of multi-angle versus nadir-only baselines for LIDFa, LAI, and FCover. BRDF model simulations agreed well with airborne BRF (optimal R 2 > 0.80, RRMSE R 2 = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa ( R 2 = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy ( R 2 ≥ 0.73), with MA models providing marginal gains ( R 2 = 0.75). Methodologically, CNN-based transfer learning proved most effective for LAI and FCover, while a Random Forest model using raw multi-angle spectra yielded the best results for LIDFa. Optimal viewing configurations were trait-dependent, generally favoring forward scattering directions with zenith angles between 15° and 45°. These results indicate that kernel-driven BRDF modeling effectively captures spectral anisotropy in dense wheat canopies, and that multi-angular observations provide a distinct advantage for retrieving structural parameters with complex scattering behaviors, such as LAI and LIDFa.
Why it matches plant phenotyping methods小麦育種材料のキャノピー構造形質を対象に、UAVマルチアングルセンシング、BRDFモデル、CNN/RFによる推定フレームワークを開発・比較しており、形質取得手法が研究の中心である。
abstractThis study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy.
Reproduction assets foundThe paper's data availability statement explicitly deposits the complete source code for BRDF modeling and the transfer learning pipeline, plus a subset of preprocessed field data, in a public GitHub repository matching an allowed URL. Additional data are available only on request.Code · publicThe complete source code for BRDF modeling and the transfer learning pipeline, along with a subset of the preprocessed field data used in this study, are openly available in the GitHub repository at https://github.com/ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materials.git . Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request.Open asset ↗ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materialslines:451-474Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Abstract Very high‑resolution satellites (VHRS) have potential for early crop disease detection and enhanced food security. The capability of multispectral SkySat and Pleiades‑Neo imagery for early detection and monitoring was assessed for wheat stem rust (SR) at the plot level. In randomized trials with contrasting fungicide and irrigation treatments, six bread wheat varieties with differing SR susceptibility were monitored using VHRS. 113 multispectral features were evaluated for their association with SR progression and associated yield loss. Several features demonstrated moderate to strong correlations with SR disease levels. Across early disease stages (healthy-mild-moderate), spectral sensitivity was dominated by Blue (B)‑based features, with Red-Blue (R-B) and Green-Blue (G-B) two-band features at mild and moderate levels, respectively. During late stages (severely-fully diseased), spectral sensitivity was driven by R-based features (e.g., R-B, R-G on both sensors) and by Deep Blue (DB), Red-Edge-DB (RE-DB), and R-DB combinations on Pleiades‑Neo. Early SR detection under moderate disease pressure was possible using ratio (RSI) and normalized difference (NDSI) spectral indices with B-G combinations. Key SkySat features such as RSI(NIR,B), RSI(R,B), and RSI(G,B) were sensitive across scenarios. This work delivers the first VHRS-based SR detection, advancing monitoring from plot to regional scales.
Why it matches plant phenotyping methods超高解像度衛星のマルチスペクトル画像からコムギ赤さび病の進行・重症度を推定する方法を、複数センサーとスペクトル特徴で評価・検証しており、植物状態の取得が研究の中心である。
abstractThe capability of multispectral SkySat and Pleiades‑Neo imagery for early detection and monitoring was assessed for wheat stem rust (SR) at the plot level.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.
Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。
abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.
Plant phenotyping relevance match · UnverifiedbioRxiv · OpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
To address challenges in food security, a better understanding of crop performance under varying and changing environmental conditions is required. Plant Time Warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield. PTW leverages image time series, genetic markers, and environmental covariates to learn genotype-specific physiological responses to temperature and vapor pressure deficit. Compared to mere genomic prediction models, PTW demonstrates superior performance when predicting yield in unseen environments across 48 year-locations in Europe. The PTW model captures non-linear growth responses varying with phenological stages and identifies distinct patterns associated with yield performance and stability. Specifically, varieties with higher yield stability exhibit reduced sensitivity to vapor pressure deficit around 1.5 kPa and distinctive temperature responses during emergence and senescence. The learned response pattern enable retrospective and prospective yield predictions, providing a foundation for location-specific variety recommendations and targeted breeding strategies. The integration of phenomic, genomic, and enviromic data has the potential to substantially advance research in climate adaptation strategies for crop production by addressing generalization challenges of predictions to novel environmental conditions. HighlightWe present a novel deep learning model that seamlessly combines high-throughput image data, genomic data, and weather data, enabling better crop predictions for future climates.
Why it matches plant phenotyping methods画像時系列を用いた高スループット圃場フェノタイピングを、ゲノム・環境情報と統合して収量を推定する深層学習手法PTWが研究の中心であり、手法開発・評価に該当する。
abstractPlant Time Warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield.
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract Unmanned aerial systems (UAS) equipped with multispectral and RGB sensors offer valuable data for monitoring crop health and assessing disease severity. However, the wide range of available photogrammetric software complicates software selection for high‐throughput plant phenotyping. This study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping. Over two seasons at Oklahoma State University research sites, imagery from a DJI Phantom 4 Pro Multispectral was used to generate eight vegetation indices (VIs), which were evaluated using correlation () and root mean square error (RMSE). Normalized VIs showed strong consistency across software, with values between 0.85 and 0.99 and RMSEs ranging from 0.004 to 0.07. Non‐normalized indices exhibited greater variability but retained high correlations ( > 0.76). Ground‐truth validation used single‐view imagery and disease severity ratings. Bayesian models quantified spectral measurement differences, their distributions (mean, standard deviation, skewness, and excess kurtosis) across processing approaches, and evaluated disease classification performance using ordinal logistic regression. Normalized VIs were highly consistent across software (posterior median differences <0.01 units, overlapping 95% highest density intervals), while single‐view imagery showed 15%–25% higher pixel‐level variability than software outputs. Non‐normalized indices showed greater processing sensitivity. RGB indices demonstrated near to perfect consistency. Disease classification accuracy ranged from 35% to 48% with minimal software differences (<2%). All three software produce biologically consistent results, ensuring stable genotype rankings regardless of processing choice. ODM performed comparably to proprietary alternatives while offering cost‐effectiveness, transparency, and reproducibility advantages.
Why it matches plant phenotyping methodsUAS画像から抽出する小麦表現型について、複数の写真測量ソフトウェアの一貫性・誤差・再現性を比較検証しており、フェノタイピング手法の技術評価が中心です。
abstractThis study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping.
Accurate growth stage recognition is vital for optimising crop inputs and improving yield efficiency. However, single-modal methods often fail to distinguish phenologically adjacent stages due to canopy similarity, spectral saturation, or structural ambiguity, leading to mistimed agronomic actions and resource loss. Moreover, the high computational demands of complex deep neural networks hinder practical implementation. To address this challenge, this paper proposes a lightweight multimodal data fusion network for wheat growth stage recognition. Specifically, the RGB images, multispectral (MS) data, and digital surface model (DSM) acquired by Unmanned Aerial Vehicles (UAV), along with derived spectral vegetation index (VI), are used to capture multimodal canopy features, including colour, spectral reflectance, and spatial structure. Furthermore, the approach utilises MobileNetV3-Small, a lightweight convolutional neural network, as the backbone to construct the multimodal data fusion framework. This framework enables efficient feature extraction and integration, achieving precise and rapid wheat growth stage recognition with minimal computational overhead. The results demonstrate that, compared to single-modal models, the proposed multimodal fusion model significantly enhances growth stage recognition accuracy, achieving an accuracy of 99.57 %, a precision of 99.58 %, a recall of 99.57 %, and an F1 score of 99.57 %. Notably, it improves stage differentiation in critical transitions such as Booting to Heading, reducing field misclassification risks and supporting quick decision-making. Comparative analysis with MobileNetV3-Large, ResNet-18, MNASNet, EfficientNet-B0, and ConvNeXt-Tiny demonstrates that MobileNetV3-Small offers the best trade-off between accuracy and resource efficiency, with only 1.53 M parameters and an inference time of 6.03 ms on RTX 4090 and 25.11 ms on Jetson Orin NX. This efficiency enables real-time deployment on resource-constrained edge devices, such as onboard UAV processors or in-field embedded systems. Overall, this study effectively overcomes the challenges of recognising adjacent growth stages and computational constraints, offering a robust theoretical foundation and an efficient, accurate solution for wheat growth stage recognition.
Why it matches plant phenotyping methodsUAV画像・マルチスペクトル・DSMを用いて小麦の生育段階という植物状態を推定する融合手法を開発・評価しており、表現型取得・抽出が研究の中心である。
abstractthis paper proposes a lightweight multimodal data fusion network for wheat growth stage recognition.
WheatField / plotRGB / grayscaleThermalPanicle / ear / spikeSeed / grainPhysiological trait estimationSegmentationGrowth / development / phenologyWater status / transpiration
Grain filling plays a vital role in determining both the yield and quality of wheat. Therefore, timely and accurate monitoring of the grain filling course (GFC) is essential for assessing the feasibility of harvest timing optimization. Traditional methods based on field sampling are time-consuming and destructive. This study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery. Wheat ears were first segmented using a temperature-threshold approach, after which colour and temperature features were extracted. Grain water content (GWC) was then estimated using a Normalised Relative Ear Temperature (NRET) index, while days after anthesis (DAA) were retrieved using a piecewise linear model derived from ear colour features. Finally, a grain filling index (Kf) was developed using DAA corresponding to 25 % moisture content (DAA25%) to quantify the GFC. Results showed that thermal images acquired at 17:00 showed the greatest separability between ears and background canopy and the highest sensitivity to irrigation differences. Both NRET and DAA based models provided accurate GWC estimates (R² = 0.86 and 0.91; RMSE = 3.13 % and 4.21 %; rRMSE = 0.07 and 0.09, respectively). The Kf index effectively captured differences in GFC under different irrigation treatments and detected early maturity under water stress (p < 0.05). This study demonstrates the potential of combining thermal and RGB imagery for high-resolution, non-destructive monitoring of wheat grain filling and for supporting timely harvest management.
Why it matches plant phenotyping methodsRGB画像と熱赤外画像を統合し、穂の分割・特徴抽出から穀粒水分含量と登熟進行を推定する非破壊フェノタイピング手法が研究の中心である。
abstractThis study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery.
Aiming at the hysteresis problem of traditional contact-type mechanical throughput detection method during combine harvester operation, this study proposes a multimodal data-driven online throughput prediction method. By building a multimodal sensor system that integrates vehicle-mounted cameras, GPS, grain moisture content, and feeding auger power sensors, a throughput prediction framework based on wheat ear biomass characteristics was established: Firstly, the WEC-MVFF wheat ear online counting density map estimation model is designed, and MobileViT is used to build a feature extraction backbone network. The multi-scale fusion module and the centralized conversion module are combined to realize the collaborative extraction of shallow texture features and deep semantic features of dense wheat ears in the field. Secondly, divide the image into regions of interest and establish a throughput prediction model based on multimodal information of wheat ear number (image) − moisture content (sensor) − travel speed. At the same time, a detection model based on feeding auger power is constructed as a comparison benchmark. Field tests show that the WEC-MVFF model maintains an average counting accuracy of more than 90 % under different wheat ear density, travel speed and light intensity conditions. The model’s online counting advantage is verified through ablation study and comparative tests with other counting models. The throughput prediction method achieved an MAE of 0.70 kg/s and 0.77 kg/s in test area 1 and 2 respectively, the first-order difference fluctuation was stable in the range of ±0.50 kg/s, and the prediction frame rate of 10-13fps met the real-time requirements. Compared with the single-modal image prediction method, the accuracy of the multimodal method considering moisture content was improved by 0.80 kg/s and 0.75 kg/s in the two test areas, respectively. Compared with traditional mechanical quantity detection methods, it has higher accuracy and stability while achieving early prediction, providing reliable feedforward information support for the intelligent control of harvesters.
Why it matches plant phenotyping methods小麦穂の画像計数とマルチモーダルセンサを用いて、穂密度・バイオマス特性および収量流量を推定する手法を開発・検証しており、植物形質の取得・推定が研究の中心である。
Accurate wheat growth stage predictions are important for efficient crop management practices, such as when to apply chemical inputs or fertilise. Recent studies have developed accurate machine learning (ML) approaches for predicting the growth stages of wheat and other crops, but these have generally focused on a few key stages and used limited datasets, restricting comprehensive validation across diverse growing seasons and/or regions. This makes it difficult to test their scalability, which is a critical consideration for real-world application. The National Variety Trials (NVT) program represents a key opportunity, providing observations of Zadoks stages since 2005 across the Australian grain belt. To develop a scalable, data-driven approach for predicting wheat growth stages using a national dataset and ML. The dataset contained over 80,000 wheat Zadoks stage observations from the NVT program from 2005 to 2023 across 169 sites in Australia. Models were developed with XGBoost, using 11 weather, remote sensing (RS), genetic and crop management features. Three experiments were designed to evaluate models: 70:30 split, leave-one-year-out (LOYO) and leave-one-site-out (LOSO). The optimal spatial extent was determined by comparing national, regional and subregional models, and a null model was developed to assess quality of predictions if only using features related to temperature. All three spatial extents tested yielded strong results, but the national performed best overall. It showed high accuracy across all three experiments, with strong agreement between observed and predicted Zadoks stages (00−99) (Lin’s concordance correlation coefficient [LCCC] = 0.76–0.80), minimal error (RMSE = 5.9–6.8 stages), and 58–66 % accuracy ±5 Zadoks stages across the three experiments. This model also consistently outperformed the null model, demonstrating that including non-temperature-related features (e.g. solar radiation, variety) led to more accurate growth stage predictions. No other known studies have used such a comprehensive dataset of growth stage observations, both in size and spatiotemporal coverage, to model crop growth stages. The use of this dataset enabled the development and validation of a model that is both accurate and scales well to unseen years and sites. This study therefore highlights the potential for a ML-based operational tool to support crop monitoring across diverse growing seasons and regions. Future work could explore incorporating more RS features and more observations of underrepresented Zadoks stages (e.g. seedling growth).
Why it matches plant phenotyping methods小麦の生育ステージという植物状態を、機械学習で推定する手法の開発と、年・地点外挿を含む大規模な検証が研究の中心であるため。
abstractTo develop a scalable, data-driven approach for predicting wheat growth stages using a national dataset and ML.
This study presents a novel application of atomic force microscopy (AFM) for characterising the ultrastructure of dry (15 % moisture) native and mature wheat grains (Triticum aestivum L.). A key contribution is the standardisation of a meticulous sample preparation protocol that minimises artefacts. This protocol involves dry-cutting the grains using a device that enables precise surface smoothing via ultramicrotomy, ensuring perfect alignment for AFM scanning without the need for resin-embedding. The research provides a comprehensive histological description ranging from the outer pericarp to the starchy endosperm. The outer layers (pericarp, seed coat, and nucellar epidermis) appear as compact, continuous structures in the dry state, with stronger inter-layer adhesion. The study also discovered a previously undescribed left-handed helical twist in the tube cells of the inner pericarp, a feature that is hypothesised to be lost in conventional resin-embedding techniques. AFM is demonstrated to be a powerful tool for revealing intricate, hydration-dependent ultrastructural adaptations in plant tissues.
Why it matches plant phenotyping methods成熟コムギ粒の組織微細構造という植物形質を対象に、AFM imaging とアーティファクトを低減する試料調製法を中心的に開発・実証しているため、植物フェノタイピング手法として採用する。
abstractThis study presents a novel application of atomic force microscopy (AFM) for characterising the ultrastructure of dry (15 % moisture) native and mature wheat grains (Triticum aestivum L.).
In precision agriculture, the analysis of UAV-based multispectral imagery enables spatial differentiation of crop condition, supporting targeted management decisions.This study compares the performance of two unsupervised segmentation algorithms (K-means and Gaussian Mixture Models) in analyzing RGB images of winter wheat, supported by NDVI-based interpretation.Segmentation was performed on RGB orthomosaics acquired at two phenological stages, followed by NDVI analysis to assign physiological meaning to each segment.The average NDVI per cluster was used to reconstruct NDVI maps and objectively assess vegetation condition within segments.In the early growth stage, segmentation primarily reflected spectral variability in the soil background due to low biomass and weak plant-soil contrast.NDVI analysis revealed that seemingly regular clusters corresponded to bare inter-row soil rather than emerging plants -highlighting the limited diagnostic value of RGB segmentation alone at this stage.In the later growth stage, both algorithms accurately delineated field plots and intra-field variability.Using five clusters, the analysis identified zones ranging from dense, healthy vegetation to bare soil.These results demonstrate that combining RGB-based unsupervised segmentation with NDVI analysis is an effective tool for mapping spatial heterogeneity in mature crops, while offering limited standalone value in early growth stages without additional spectral verification.
Why it matches plant phenotyping methodsUAV画像のセグメンテーション手法を比較・評価し、NDVIに基づく作物の生育状態と圃場内変異を抽出しているため、植物表現型取得法が研究の中心である。
abstractThis study compares the performance of two unsupervised segmentation algorithms (K-means and Gaussian Mixture Models) in analyzing RGB images of winter wheat, supported by NDVI-based interpretation.
Abstract Time-resolved phenotyping of disease symptoms enables dissection of resistance mechanisms and improves diagnosis, but acquiring phenotypic data at satisfactory scale remains challenging. Advances in imaging and image processing have improved measurement precision, robustness, and throughput, but further improvements are needed for practical application. We present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms. All images are geometrically aligned with a median precision of 0.16 mm (≈5 pixels). The dataset includes transformation matrices, symptom segmentation masks, metadata on treatments, weather, crop phenology, and disease occurrence, and a lightweight Python toolkit for loading, aligning, inspecting, and editing image sequences. These resources enable detailed investigation of leaf-level disease dynamics such as lesion, pustule, and fruiting body emergence rates, lesion growth, and dynamic interactions of disease development with spatial and environmental contexts. They offer a broad basis for developing improved methods for image alignment and symptom detection, segmentation, and tracking, possibly by tackling these connected challenges within a single end-to-end framework.
Why it matches plant phenotyping methods葉の病徴を対象とした高解像度時系列画像データセットで、幾何位置合わせ、病徴セグメンテーション、追跡用ツールを提供しており、植物病害表現型の取得・解析基盤が中心である。
abstractWe present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicWe provide a lightweight Python toolkit to facilitate loading, inspection, and curation of the image sequences and their associated processing products in the associated Git repository (https://github.com/and-jonas/sympathique-wheat).Open asset ↗github.com/and-jonas/sympathique-wheathtml-lines:317-337Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Key message We present an imaging-based deep learning phenotyping pipeline that classifies heat-stressed wheat anthers and quantifies size traits using YOLO, enabling fast, precise, scalable measurements to support breeding heat-resilient wheat varieties. Abstract Terminal heat stress is a major abiotic stress causing significant yield loss in wheat. Anther size, a key trait of terminal heat stress tolerance, is least studied in wheat due to complex and tedious scaling trait, and short live span. To provide user friendly approach to plant breeders, integration of the modern digital imaging and deep learning techniques together is the current need of high-throughput phenomic era. In this study, we introduced a hybrid approach that amalgamates the strengths of deep learning models for both binary classification and precise morphological analysis of anther images of 177 wheat accessions under normal and heat stress environments. ResNet18 with 94% accuracy, outperformed the traditional models like CNN and MobileNetV2, achieving high classification performance. For morphological trait extraction, we employed YOLOv8, a cutting-edge object detection model known for its high speed, accuracy, and computational efficiency. YOLOv8 successfully localized anthers and measured width and length with strong agreement to experimental measurements, as validated by Bland–Altman analysis. Its precise detection capability and lightweight architecture makes it ideal for high-throughput phenotyping using digital imaging approach. To further boost the interpretability of our deep learning models, we utilized Grad-CAM, a powerful technique for visualizing class-specific features in the network’s decision-making process. This facilitated in categorizing the key visual features within the anther images that had the greatest influence on the model’s decision-making process. This cohesive workflow not only sets a new benchmark in image-based classification and morphological measurement but also proposes an accessible tool for rapid, real-time phenotyping, supporting data-driven breeding strategies aimed at improving wheat resilience under terminal heat stress.
Why it matches plant phenotyping methodsコムギ葯の画像取得、深層学習による分類・形態形質抽出、実測値との技術検証を中心とする明確な植物フェノタイピング手法研究。
abstractWe present an imaging-based deep learning phenotyping pipeline that classifies heat-stressed wheat anthers and quantifies size traits using YOLO
BACKGROUND: Durum wheat (Triticum durum L.) productivity is strongly limited by salinity stress, particularly during early growth stages, due to disruptions in growth, water relations, and nutrient uptake. Seaweed extracts (SWEs), especially those derived from Ascophyllum nodosum, are widely used as biostimulants to enhance stress tolerance; however, their effects on durum wheat under salinity remain insufficiently characterized. In parallel, artificial neural networks (ANNs) provide effective tools for modeling complex plant responses to environmental stress. RESULTS: Salinity significantly reduced growth and physiological parameters, including biomass, chlorophyll content, and relative water content. SWE applications (2 and 4 g L⁻¹) effectively mitigated these negative effects. Biochemical traits such as proline accumulation, total phenolic content, and total antioxidant capacity were markedly enhanced under salinity. SWE treatments also improved macro- and micronutrient uptake in roots and shoots. ANN models successfully predicted multiple plant traits with high accuracy (R² > 0.90 for several key parameters). These models were implemented in a web-based R Shiny application to enable real-time prediction of plant responses. CONCLUSIONS : SWE application alleviates salinity-induced stress in durum wheat by improving growth, antioxidant capacity, and nutrient acquisition. The integration of ANN modeling with experimental data provides a reliable and practical approach for predicting plant responses, supporting artificial intelligence-assisted strategies for sustainable wheat production under saline conditions.
Why it matches plant phenotyping methods塩ストレス・生物刺激剤実験を背景とするが、ANNによる複数の植物生理・生化学・栄養形質の予測とWebアプリ実装が題名および結果の中心であり、再利用可能な計算的形質推定ワークフローに該当する。
titleArtificial neural network-based estimation of physiological, biochemical, and nutrient parameters in durum wheat under NaCl and biostimulant treatments.
Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R2 of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R2 of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R2 across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application.
Why it matches plant phenotyping methods作物キャノピーの分光反射から植物含水量を推定する新規三帯域指数と機械学習モデルを開発・比較しており、植物生理状態の取得手法が研究の中心である。
abstractThis study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW.
Wheat, being a major staple crop worldwide, is often attacked by rust diseases, which cause severe yield losses. The early detection and diagnosis of fungal infections, yellow rust, and brown rust are critical in minimizing their consequences. A web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases. The diseases that we examine in wheat plants are brown rust (BR) and yellow rust (YR), and healthy plants are classified in the third category. A dataset of labeled images of YR, BR, and healthy wheat plants was used to train the CNN. The model achieved a remarkable 96% classification accuracy. In addition to disease diagnosis, a recommendation module that gives advice on proper treatment based on disease names or symptoms is also provided. This twofold functionality allows for timely disease management and identification and facilitates the treatment of other wheat diseases besides rust diseases. Integrating the trained CNN model into an intuitive web application makes it user-friendly for end users, notably farmers, to have a practical tool in protecting wheat crops.
Why it matches plant phenotyping methods小麦植物画像から病害状態を分類するCNN手法を開発・評価しており、植物病害表現型の取得・推定が中心。治療推薦機能もあるが、画像ベース病害診断が主要な技術的貢献である。
abstractA web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases.
Reproduction assets foundThe paper's wheat disease image dataset (YR, BR, healthy; 3679 images) is a publicly available Kaggle dataset explicitly used for the CNN training, with an authors-provided URL matching an allowed URL.Dataset · publicThe images of YR and BR were taken from a Kaggle dataset, which is available at https://www.kaggle.com/datasets/sinadunk23/behzad‐safari‐jalal. The dataset includes 3679 images divided into three different categories, as shown in Table 2.Open asset ↗Kaggle · sinadunk23/behzad‐safari‐jalalhtml-lines:249-257Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · Crossref · checked 6 Sept 2026
ABSTRACT Plant growth is a dynamic process affected by genes and growing environment, with all kinds of interactions between them. These complex relationships make the prediction of plant growth challenging. We propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN). While PINNs have been widely applied to physical dynamical systems, their use in modelling the dynamics of plant growth systems is still largely unexplored. We illustrate the construction of a PINN on plant height data in wheat and compare its performance with alternative models for longitudinal plant data. All temporal prediction models only require time and temperature as input. Among a set of competing models, our PINN had the lowest average root mean squared error (RMSE) of prediction and the smallest standard deviation across multiple random initialisations. Therefore, we conclude that incorporating biological growth constraints into data-driven growth models can enhance prediction accuracy of longitudinal plant traits. Highlights Integrating plant growth equations into a temporal neural network improves plant height growth prediction over ordinary differential equations and machine learning models, especially when training data are limited.
Why it matches plant phenotyping methods植物高の時系列形質を予測するPINNを開発し、代替モデルと精度比較しているため、植物表現型の計算手法が中心である。
abstractWe propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
Abstract Continuous, high-frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season-long trait assessment. This study introduces AGIcam, an open-source IoT camera system for automated and continuous in-field plant phenotyping and yield prediction. The platform integrates solar-powered Raspberry Pi units with a modular software stack, comprising Node-RED, InfluxDB, Grafana, and Microsoft Azure, for automated data acquisition, transfer, and visualization. In the 2022 growing season, 18 AGIcam systems were deployed in spring and winter wheat breeding trials, maintaining an uptime of over 85% while capturing frequent RGB and NoIR imagery. Time-series vegetation indices derived from these images were used to predict yield using random forest and Long Short-Term Memory (LSTM) models. The LSTM approach achieved the highest accuracy approximately one week after heading, with mean prediction errors of 3.41% for spring wheat and 1.62% for winter wheat. These results highlight the potential of IoT-based platforms such as AGIcam to enable real-time, scalable, and effective phenotyping solutions for data-driven crop improvement.
Why it matches plant phenotyping methodsAGIcamは圃場での植物表現型取得を目的とするIoTカメラ基盤であり、画像取得、時系列形質抽出、収量予測を技術的に評価しているため、方法・プラットフォームが中心である。
abstractThis study introduces AGIcam, an open-source IoT camera system for automated and continuous in-field plant phenotyping and yield prediction.
High-throughput, low-cost phenotyping remains a critical bottleneck in wheat breeding, genetics, and crop management. This is particularly evident in the measurement of complex yield components (i.e., spike and spikelet counts), disease and grain-quality traits related to Fusarium Head Blight (FHB) and Fusarium-Damaged Kernels (FDK), and microscale physiological traits such as density and size of stomata and aperture. We introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications. WheatAI v1.0 provides an accessible, browser-based interface that supports multiscale data ingestion from smartphones, Unmanned Aerial Vehicles (UAVs), and portable microscopes. The core functionalities of the platform include plot- and field-scale assessment via UAV- and smartphone-based wheat spike detection and counting, as well as smartphone-based spikelet counting. Additionally, it offers grain quality assessment through FDK ratio estimation and kernel morphometric measurements, such as length, width, and area, derived from smartphone images of kernel samples. For leaf-level analysis, WheatAI provides microscale phenotyping through automated stomatal counting, size, and aperture measurement from digital microscopy images. The system supports both single-image and bulk processing via a guided upload-and-run workflow. This platform is designed to reduce labor costs and rater subjectivity while accelerating field-to-lab decision cycles. By providing standardized, image-based outputs, WheatAI enables breeders, agronomists, and producers to implement high-throughput selection and precision scouting at scale.
Why it matches plant phenotyping methodsWheatAIは、画像から収量構成要素、病害関連形質、穀粒形態、気孔形質を抽出する高スループット植物フェノタイピング基盤そのものであり、方法・ソフトウェアの開発が中心です。
abstractWe introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications.
• Multi-sensor phenotyping links soil, canopy, and atmosphere in real time • PLSR with VIP retrieves photosynthetic rate (A) and stomatal conductance (Gs) • Temporal dynamics captured with GAMs under contrasting water regimes • Targeted blue and red bands, together with a wide NIR spectrum, dominate trait prediction beyond NDVI proxies • Scalable for breeding and on-farm monitoring with minimal ground truthing Understanding the soil-plant-atmosphere continuum (SPAC) is essential for breeding and advancing precision agriculture. Despite advances in hyperspectral monitoring, few studies have captured dynamic photosynthetic traits, such as net photosynthetic rate (A) and stomatal conductance (Gs), limiting insight into their temporal fluctuations and utility in breeding for stress resilience. This study integrates plant, soil and atmosphere sensor data, with statistical modelling to monitor season-long, fine-scale physiological and environmental variables, including A, Gs, vapor pressure deficit, soil moisture and crop water stress. A multi-sensor high-throughput phenotyping platform (HTPP) with a novel soil moisture system enabled high-resolution monitoring. Partial least squares regression (PLSR) models were used to predict photosynthetic traits from hyperspectral bands (∼400-1000 nm) and selected 20 vegetation indices (VIs). Temporal dynamics of both observed and predicted values were fitted using generalized additive models (GAMs) to describe the seasonal trajectories of photosynthetic traits, crop stress status and soil moisture across genotypes and water regimes. In wheat field trials, hyperspectral data predicted A and Gs with high accuracy (Root mean square error of prediction 3.71 and 58.93, respectively; R-squared 0.72 and 0.70, respectively) and the predicted temporal dynamics closely matched ground-truth measurements. Additionally, soil moisture and crop water status were monitored throughout the season, along with physiological traits. This approach provides scalable, data-driven solutions to support breeding for resilient cultivars and improvements in crop management, as the predicted data can be integrated into mechanistic crop models to establish empirical relationships with parameters that vary throughout the growing season.
Why it matches plant phenotyping methods植物の光合成速度と気孔コンダクタンスをマルチセンサー・ハイパースペクトルデータから推定し、精度検証と時系列解析を行う高スループット表現型計測手法が中心である。
abstractA multi-sensor high-throughput phenotyping platform (HTPP) with a novel soil moisture system enabled high-resolution monitoring.
High-dimensional time series phenotypic data is becoming increasingly common within plant breeding programmes. However, analysing and integrating such data for genetic analysis and genomic prediction remains difficult. Here we show how factor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction. We use a subset of Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) elite yield wheat trial of 2014-2015, consisting of 1,033 genotypes. These were measured across three irrigation treatments at several timepoints during the season, using manned airplane flights with hyperspectral sensors capturing 62 bands in the spectrum of 385-850 nm. We perform multivariate genomic prediction using latent variables to improve within-trial genomic predictive ability (PA) of wheat grain yield within three distinct watering treatments. By integrating latent variables of the hyperspectral data in a multivariate genomic prediction model, we are able to achieve an absolute gain of .1 to .3 (on the correlation scale) in PA compared to univariate genomic prediction. Furthermore, we show which timepoints within a trial are important and how these relate to plant growth stages. This paper showcases how domain knowledge and data-driven approaches can be combined to increase PA and gain new insights from sensor data of high-throughput phenotyping platforms.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトルセンサーによる植物表現型時系列データから潜在特徴を抽出し、収量予測に統合する解析手法が研究の中心であるため。
abstractfactor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction
Reproduction assets foundThe paper's Data and code statement provides public GitHub repositories containing the authors' analysis scripts for the hyperspectral latent-factor/Procrustes workflow and the glfBLUP R package implementing the genomic prediction methodology. The hyperspectral phenotype dataset itself is only available upon request, iCode · publicy of secondary trait data and successful integration in multivariate genomic prediction.
As such, this method can contribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning mOpen asset ↗KunstJF/glfBLUP-Procrusteslines:388-492Code · publicntribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning methods for genomic prediction .
Springer , Cham, Switzerland .
External Links: ISBN 978-3-030-89009-4 978-3-030-8901Open asset ↗KillianMelsen/glfBLUPlines:388-492Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Wheat, the third most widely consumed cereal crop worldwide, faces substantial yield and quality losses as a result of rust disease, notably leaf rust, stem rust, and stripe rust. These rust disease, caused by Puccinia triticina , Puccinia graminis , and Puccinia striiformis , respectively, are capable of causing significant yield losses in wheat in the absence of timely detection. Conventional disease identification relies heavily on manual visual inspection, which is time consuming, labor intensive, and prone to error, especially in large scale agricultural systems. To address these limitations, this study proposes a deep learning-based framework for the early detection and classification of wheat rust diseases. A real-time dataset was developed using field images collected from various wheat-growing regions and augmented with publicly available data. The dataset comprises images of healthy leaves and those affected with the three major rust diseases. A modified convolutional neural network (CNN) architecture was employed for extract features and disease classification. Experimental results demonstrate that the proposed approach achieves high classification accuracy, highlighting its effectiveness as a reliable tool for automated wheat rust detection in precision agriculture. By enabling rapid and accurate disease identification, the system supports timely decision-making, reduces potential yield losses, and improves crop management practices, thereby contributing to food security and sustainable agricultural production.
Why it matches plant phenotyping methods小麦葉の画像からさび病の有無・種類を推定する深層学習手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractA real-time dataset was developed using field images collected from various wheat-growing regions and augmented with publicly available data.
Reproduction assets foundThe paper's own wheat rust image dataset (field images from North Punjab, Pakistan plus Kaggle-sourced images, with disease severity, GPS, variety, and weather metadata) is publicly deposited on Kaggle via an explicit repository link in Table 1. No author analysis code or trained model checkpoint is publicly released.Dataset · publict, Stripe Rust
Collection Region North Punjab, Pakistan
Collection Period Feb–March 2025
Collection Method Field observation + Kaggle image samples
Plant Growth Stage Tillering to heading
Field Data Includes Disease severity, GPS, wheat variety, weather data
Usage Disease classification, model training, analysis
Repository Link https://www.kaggle.com/datasets/sabaunnisa/wheat-rust-disease
We have divided the datasets 1294 into 962 training
images and 332 testing images. In the current study, a 3:1
ratio was used to create the training, and validation sets for
the image dataset, meaning 75% of the data 722 used to
training and 25% 240 to validation. A fixed random seed
(seed = 42) was used toOpen asset ↗Kaggle · sabaunnisa/wheat-rust-diseasepdf-raw-page:4 lines:1-66Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis . Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.
Why it matches plant phenotyping methods気孔の検出・密度・サイズ推定を目的とする深層学習画像解析手法を開発し、複数モデルおよび困難画像で性能比較・検証しており、植物表現型取得が研究の中心である。
titleStomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the datasets generated and analyzed (phenotype/trait and vegetation index data from the metribuzin tolerance phenotyping experiments) in the Washington State University Research Exchange repository with a public DOI. No author analysis code repository is URLDataset · public20-
67037-30671, 2022-67013-36426, and 2022-68013-36439.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L I B I L I T Y S TAT E M E N T
The datasets generated and analyzed for this study are avail-
able in the Washington State University Research Exchange
repository (https://doi.org/10.7273/000007507).O RC I D
Melinda Zubrod https://orcid.org/0000-0001-7024-8421
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Ahmadi, Z., Mehrabadi, M., Fazli, M., Khalesro, S., Abedi, R., &
Mokhtassi-Bidgoli, A. (2025). Enhancing tolerance of wheat culti-
vars to meOpen asset ↗Washington State University Research Exchange · 10.7273/000007507pdf-raw-page:12 lines:1-81Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Fusarium head blight (FHB), a frequent disease in wheat cultivation, can lead to substantial yield losses and the production of mycotoxins in grains. Therefore, the development of wheat varieties resistant to FHB is an important strategy to reduce related losses. In this respect, manual surveys of FHB are time-consuming and labor-intensive. To overcome this issue, this paper proposes a method for detecting and evaluating wheat FHB using color imaging and deep learning. Initially, a lightweight convolutional neural network model based on the You Only Look Once (YOLO) v8s artificial intelligence (AI) model was designed to detect wheat spikes from color images. Testing revealed that the model's mean average precision in spike detection reached 0.964. Moreover, another lightweight model was developed for detecting wheat spikelet and FHB. To enhance the detection capability of the model for small objects, space-to-depth convolution (SPD-Conv) and BiFormer attention modules were integrated. The results indicated that the model can accurately detect spikelet and FHB, with a mean average precision of 0.936. Finally, based on the wheat spikelet detection results, the rate of diseased wheat spikes (RD_S) and the disease index for wheat (DI_W) were calculated to evaluate the severity of wheat FHB. For RD_S and DI_W, the coefficients of determination between phytologists' evaluations and the estimates derived from the proposed method were 0.71 and 0.93, respectively. These results demonstrate that the proposed method facilitates the accurate and efficient detection of wheat FHB and contributes to the quantitative evaluation of FHB in the field.
Why it matches plant phenotyping methods小麦のFHB症状をカラー画像と深層学習から検出・定量し、専門家評価との一致で検証する手法開発が中心であるため含める。
abstractthis paper proposes a method for detecting and evaluating wheat FHB using color imaging and deep learning.
To accelerate the pace of wheat ( Triticum aestivum L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
Why it matches plant phenotyping methods自動マルチスペクトル画像と機械学習・コンピュータビジョンを統合し、個々の小麦種子の形態・スペクトル形質を高スループットに抽出するパイプラインが研究の中心である。
abstractwe first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds.
Reproduction assets foundThe paper's data availability statement names authors' public source code for the multispectral seed imaging analysis pipeline on GitHub (allowed URL), qualifying as a paper-specific public code asset. The multispectral imagery deposit (BioImage Archive S-BIAD2408, DOI 10.6019/S-BIAD2408) is also paper-specific and perCode · publicSource codes that support the results of this paper is available at https://github.com/The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipeline/releases .Open asset ↗The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipelinelines:562-570Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Drought is a significant challenge to winter wheat production. Its impact can be mitigated by preventing plant moisture stress through precision agriculture. Remote sensing and machine learning have proven effective for managing moisture stress in winter wheat. This study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat under irrigation treatments: W0 (no irrigation), W1 (45-65%), W2 (55-75%), W3 (65-85%), W4 (75-95%) of field capacity, and Z (irrigation and rainfall). Our findings show that the ratio stress index (RSI), with band combinations such as RSI7 (650, 428) , RSI8 (663, 422) , and RSI9 (671, 450) , performs better in tracking PMC and LMC, demonstrating high correlation and improved average prediction metrics for vegetation index (VI) models with R 2 , RMSE, and MAE of 0.838, 2.791, and 2.093 respectively, for LMC and VI-Tc input models with 0.850, 2.731, and 2.105 for PMC. Incorporating Tc into RSI models enhances prediction accuracy, increasing R² by up to 13.82% in the RSI-Tc-SVM-PMC model and decreasing RMSE and MAE by 15.89% and 18.33%, respectively. Therefore, a combination of RSI-Tc-SVM-ANN is recommended to monitor winter wheat moisture stress.
Why it matches plant phenotyping methods冬小麦の植物・葉の含水量および水分ストレスを、VIS/NIRと冠層温度のデータ融合および機械学習で推定する手法が研究の中心であり、植物生理状態の定量的フェノタイピングに該当する。
abstractThis study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat
The distribution of spikelets significantly affects wheat (Triticum aestivum L.) spike architecture. However, traditional methods lack the precision to study spikelet distribution effectively. We developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images. In addition to traditional spikelet number per spike (SNS), rachis length (RL), and spikelet density (SD, SNS/RL), we introduced spikelet distribution traits based on rachis internode lengths, providing quantitative insights into spike architecture. RachisSeg showed high consistency with manual measurements for SNS and RL, with the R2 values of 0.975 and 0.998, respectively. Using RachisSeg, we analyzed spikelet distribution patterns across wheat germplasm and found that traits such as spikelet distribution index (SDI) and apical-to-basal spikelet number ratio (AVB_SNS) were moderately correlated with grain yield per spike (GYPS) (r = 0.57 and 0.53, respectively), while internode width (IW) showed a strong positive correlation with GYPS (r = 0.75). Specifically, a denser spikelet arrangement in the upper spike negatively impacted grain number and weight in that section. Furthermore, comparative analysis revealed distinct spikelet distribution patterns among landraces, American cultivars, and Chinese cultivars. In a recombinant inbred line population, we identified 46 quantitative trait loci (QTLs) associated with rachis traits. A major QTL controlling SDI was detected on chromosome 6B, explaining up to 24.8% of the phenotypic variance. Candidate gene analysis suggested TraesCS6B02G417000 as a potential gene, whose mutant exhibited significant changes in RL and SDI. RachisSeg is a powerful tool for quantifying spikelet distribution, facilitating wheat genetic analysis, gene discovery, and breeding.
Why it matches plant phenotyping methodsRachisSegは、スキャン画像からコムギ穂軸・小穂分布形質を自動抽出する深層学習フェノタイピング手法として開発・検証されており、方法が研究の中心です。
abstractWe developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images.
Reproduction assets foundThe paper's authors publicly released the RachisSeg phenotyping pipeline (deep learning node detection and internode segmentation code) together with sample rachis images via their GitHub repository, explicitly stated in the Implementation and Data availability sections.Dataset · publicRachisSeg and sample rachis images is freely available online ( https://github.com/Jiang-Phenomics-Lab/RachisSeg ).Open asset ↗Jiang-Phenomics-Lab/RachisSeglines:514-549Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Near-real-time (NRT) daily crop monitoring at the field scale is crucial for precision agriculture, yet remains challenging due to limitations in the spatial or temporal resolution of existing remote sensing methods. While Sentinel-2 provides adequate spatial resolution for field-level applications, its temporal resolution is insufficient for capturing rapid crop dynamics, especially in cloudy regions. Existing spatiotemporal fusion techniques require multiple clear-sky images and lack true NRT capability, while ground-based sensors offer continuous monitoring but with limited spatial coverage. To address these limitations, this study develops the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, a novel approach based on a Bayesian dynamic linear model and Kalman filtering. The algorithm uniquely integrates Sentinel-2 imagery with continuous measurements from Internet of Things for Agriculture (IoTA) systems to generate daily 10-m Green Area Index (GAI) products. Its recursive framework supports both forward prediction in NRT mode following satellite overpasses and backward updating to refine historical profiles. Implemented over French wheat fields using 34 IoTA systems and Sentinel-2 time series from 2019, the algorithm effectively enhanced spatiotemporal completeness and accuracy (R = 0.75–0.98, RMSE = 0.1–0.49). A comprehensive leave-one-out Sentinel-2 evaluation demonstrated its superiority over the current Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm. Ground validation using handheld RGB cameras further confirmed the accuracy of the GAI products from the new algorithm (RMSE = 0.5). The NRT-GSF framework offers a robust and operationally solution for daily, high-resolution crop GAI mapping in NRT mode, and it can be extended to other traits or applications in the near-real-time context.
Why it matches plant phenotyping methods圃場規模の日次Green Area Index（GAI）という植物形態形質を、衛星画像と地上センサーから推定する融合アルゴリズムを開発し、比較評価と地上検証を行っているため、植物フェノタイピング手法が中心である。
abstractthis study develops the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm
Accurate estimation of the harvest index (HI), the ratio of grain yield to total aboveground biomass (AGB), is crucial for evaluating crop productivity and resource-use efficiency in wheat breeding programs. While traditional HI measurement methods use destructive field sampling, which is labour-intensive and impractical for large-scale breeding trials, recent advances in UAV-based remote sensing now offer non-destructive alternatives capable of delivering high-throughput, plot-level HI estimation. In this study, we present a high-throughput phenotyping framework that combines UAV-based multispectral imaging and ensemble machine learning to estimate HI under field environments. Multispectral data were collected at two key growth stages, anthesis and maturity, using a DJI M300 RTK drone equipped with a RedEdge-P sensor. Vegetation indices (VIs), including the normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI), were extracted using data from sensors and ground truth monitoring and used as predictors to estimate grain yield and AGB for calculating HI. An ensemble learning model, based on a stacking architecture comprising five regressors and a ridge regression meta-learner, was employed to enhance prediction accuracy. Results showed strong correlations between UAV-derived and ground-truth VIs ( R 2 > 0 . 94 , RMSE < 0 . 023). The ensemble model demonstrated high accuracy and strong generalization for HI estimation across both experimental sites and growing seasons. At the anthesis stage, the NDVI-based ensemble model achieved the best performance. For the Indian Head site, it yielded a testing R 2 of 0.87, RMSE of 4.18 g/p, and NRMSE of 2.73%, based on a training R 2 of 0.83. At the Swift Current site, the model produced a testing R 2 of 0.84, RMSE of 8.67 g/p, and NRMSE of 5.67%. Similarly, at the maturity stage, the NDRE-based ensemble model was the top performer. It recorded a testing R 2 of 0.86, RMSE of 7.10 g/p, and NRMSE of 4.64% at Indian Head, and a testing R 2 of 0.83 with an RMSE of 8.06 g/p, and NRMSE of 5.27% at Swift Current. Across all indices and stages, the ensemble model consistently outperformed individual models, achieving high testing R 2 values and low RMSE, which confirms its robustness and predictive power on unseen data. The proposed UAV machine learning framework demonstrates a reliable and non-destructive approach for field-level HI estimation, thereby improving germplasm selection efficiency for yield improvement. It offers a valuable tool for accelerating trait-based wheat breeding and precision agriculture applications.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、小麦の収穫指数を非破壊・高スループット推定する手法が研究の中心であり、検証結果も提示している。
abstractIn this study, we present a high-throughput phenotyping framework that combines UAV-based multispectral imaging and ensemble machine learning to estimate HI under field environments.
Point clouds generated by Structure from Motion (SfM) are often affected by significant noise caused by plant movement, as well as missing ground points caused by canopy occlusion. This reduces the quality of canopy and terrain extraction and makes it challenging to accurately estimate the height of crops from Unmanned Aerial Vehicle (UAV) images. To overcome these limitations, this study introduces a Density Consistency Filtering (DCF) algorithm, which adaptively models local density continuity to distinguish between valid points and noisy points. It effectively preserves local canopy structures while removing clustered noise. Furthermore, a color-spatial interpolation scheme based on ExG-RANSAC is developed to reconstruct missing ground points under dense canopies. Evaluated on six datasets from May to June 2019 covering key stages of winter wheat growth, the method achieved an RMSE of 7.7 cm, MAE of 6.2 cm, and R² of 0.91. After the early stem elongation stage, the method achieved an RMSE of 4.9 cm. The approach significantly improves estimation accuracy of the late growth stages, demonstrating strong potential for precision agriculture applications.
Why it matches plant phenotyping methodsUAV点群から冬小麦の草丈を推定するためのノイズ除去・地面点再構成手法を開発し、複数データセットで精度検証しており、植物形質取得手法が研究の中心である。
titleA Method for Estimating Winter Wheat Height Using UAV Point Cloud Data Enhanced by Density Consistency Filtering
Accurate prediction of complex agronomic traits such as grain yield and yield components remains a central challenge in winter wheat breeding because these traits are controlled by many genes and are strongly influenced by environmental variation. The integration of high-throughput phenotyping (HTP), genomics, and advanced machine learning approaches offers new opportunities to improve predictive accuracy and accelerate genetic gain in plant breeding. This study evaluates the use of an unmanned aerial vehicle (UAV)-based multispectral phenomics combined with genomic information, and deep learning approaches to enhance the prediction of grain yield (GY), test weight (TW), grain protein content (GPC), and tiller density (TD) in a winter wheat breeding program. The research was conducted during the 2022 to 2024 growing seasons at three locations in South Dakota: Brookings, Dakota Lakes, and Winner, across multiple breeding nurseries, including the Elite, Advanced, and Preliminary yield trials. UAV-derived spectral indices collected across multiple developmental stages were first integrated into deep neural network (DNN)-based phenomic prediction models and multi-trait genomic selection (MT-GS) frameworks. Significant associations were observed between UAV-based spectral indices and key agronomic traits. Phenomic prediction using DNN achieved strong accuracy for single-location trials (R² = 0.71, 0.62, and 0.49 for GY, TW, and GPC, respectively), with further improvement when models were trained on multi-location datasets (R² = 0.76, 0.64, and 0.75). Prediction accuracy for GY was highest at the Feekes 11 stage. Forward prediction of preliminary breeding lines using models trained on multi-location advanced lines improved accuracy by 32% relative to single-location training. Incorporating UAV-derived spectral indices as covariates in MT-GS models further improved predictive ability for GY (0.40) compared to single-trait genomic selection models (0.23), demonstrating the value of integrating phenomic information into genomic prediction frameworks. To address challenges associated with model transferability across environments, a deep transfer learning (DTL) strategy based on a one-dimensional convolutional neural network (1D-CNN) was implemented. Baseline cross-year and cross-location predictions showed poor performance (R² as low as -15.3); however, partial fine-tuning with 20-40% of target data substantially improved accuracy, achieving R² values up to 0.83 in cross-year and 0.29-0.69 in cross-location scenarios. Growth stage-specific modeling further revealed predictive performance highest at Feekes 10.5 and 11 (R² = 0.78-0.82), highlighting the importance of developmental timing in UAVbased trait prediction. Beyond predicting primary agronomic traits, UAV-based phenomics also provides opportunities to estimate important yield components that are difficult to measure at scale in breeding programs. Finally, UAV multispectral imagery was used to estimate early-season tiller density (TD). Among evaluated models, an attention-based convolutional neural network achieved the highest predictive performance (R² = 0.82; RMSE% = 15.20), outperforming conventional machine learning and standard deep learning approaches. Collectively, these findings demonstrate that integrating UAVbased HTP, genomic information, and deep learning approaches substantially improves the accuracy, generalizability, and scalability of complex trait prediction in winter wheat breeding. By enabling earlier and more reliable estimation of key agronomic traits across environments, these data-driven phenomic and genomic prediction frameworks accelerate breeding decisions and support the development of high-yielding and climate-resilient wheat cultivars.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による作物形質推定と、深層学習・転移学習モデルの開発および検証が研究の中心であり、再利用可能なHTPワークフローとして実質的な方法論的貢献がある。
abstractThis study evaluates the use of an unmanned aerial vehicle (UAV)-based multispectral phenomics combined with genomic information, and deep learning approaches to enhance the prediction of grain yield (GY), test weight (TW), grain protein content (GPC), and tiller density (TD)
The Moroccan agricultural sector is currently navigating a pivotal transformation driven by the “Generation Green 2020–2030” national strategy, which places a high priority on the digitalization of farming practices to bolster resilience against climate volatility and phytopathological risks. This study proposes a robust Smart Agriculture Framework engineered to automate crop disease diagnosis within mobile environments with limited resources. Unlike generic standard Deep Learning models often unsuited for local specificities, the methodology presented here is specifically tailored to Morocco’s agroecological context, targeting three strategic crops: Tomato (Souss-Massa region), Potato (Gharb plains), and Wheat (Chaouia region). A hybrid intelligent architecture is introduced that integrates a lightweight Convolutional Neural Network (CNN) with Particle Swarm Optimization (PSO-CNN) for autonomous hyperparameter tuning. The proposed framework was validated using a curated dataset of 15,000 images, rigorously augmented to reflect local field conditions, yielding a classification accuracy of 94.7%. This work effectively bridges the gap between theoretical AI architectures and practical Precision Farming, providing a rapid decision support system to minimize yield losses and align with the national objective of establishing a digitally empowered agricultural ecosystem.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNベースの診断手法を開発し、画像データセットで検証しており、表現型取得・推定が中心である。
abstractThis study proposes a robust Smart Agriculture Framework engineered to automate crop disease diagnosis within mobile environments with limited resources.
Abstract Process-based crop modelling platforms such as DSSAT are potentially valuable tools for crop breeding programmes, with the capacity to predict genotype-by-environment-by-management interactions. However, their application for breeding is challenged by the need to calibrate large numbers of genotypes within populations. In wheat (Triticum aestivum L.), using pre-existing DSSAT-CERES wheat ecotypes can introduce unrealistic parameter compensation during cultivar calibration. To address this, we developed a two-phase sequential calibration framework. This workflow uses phenotypic clustering to first define representative ecotypes using experiment-specific data before proceeding with cultivar-level parameter estimation. We demonstrate the utility of this framework to integrate direct measurements from proximal and remote sensing data collected on 14 genotypes grown under well-watered, drought, and heat stress field conditions. Incorporating experiment-derived ecotypes reduced compensatory adjustments in cultivar coefficients and improved simulation accuracy compared with default or non-representative ecotypes. Time-series data enhanced calibration, although the effect of different data combinations varied with environmental scenario and trait. Model simulations under stress conditions generally captured drought effects on biomass but underestimated heat stress impacts. This framework provides a systematic and scalable approach for integrating high-throughput phenotyping and process-based crop modelling.
Why it matches plant phenotyping methods作物モデルの遺伝型較正において、表現型クラスタリングと近接・リモートセンシングの時系列データ統合を中核とするスケーラブルな手法を開発・検証しているため。
abstractwe developed a two-phase sequential calibration framework
Timely monitoring of grain carbon and nitrogen accumulation dynamics is crucial for the growth monitoring and efficient field management of winter wheat. However, traditional destructive sampling methods are time-consuming, costly, and challenging to implement for large-scale rapid monitoring. Based on the source-sink theory, this study proposes a new method for predicting the dynamic changes of grain carbon and nitrogen accumulation in winter wheat grain by leveraging UAV-based inversion of agronomic parameters (APs). Remotely sensed aboveground biomass and plant nitrogen accumulation were employed as source indicators during the anthesis stage, along with the days after anthesis represented by phenological indices, as co-input variables for the model. Piecewise ordinary least squares regression was employed to analyze the temporal dynamics of grain carbon and nitrogen sink indicators. Combining feature selection and machine learning algorithms, the study developed a UAV multi-spectral image-driven APs inversion framework and visualized relevant grain indicators. The UAV-based grain carbon and nitrogen accumulation prediction model demonstrated excellent performance, with the grain weight accumulation showing R² = 0.86, nRMSE = 21.96 %, and RPD = 2.63; for grain nitrogen accumulation, R² = 0.71, nRMSE = 34.11 %, and RPD = 1.87; and for grain nitrogen content, R² = 0.70, nRMSE = 20.93 %, and RPD = 1.82. The grain carbon and nitrogen accumulation prediction model based on UAV multispectral images enables non-destructive prediction of the entire filling period, and has showcasing high accuracy and application potential.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から冬コムギ粒の炭素・窒素蓄積などの形質を非破壊推定するモデルと反転フレームワークが研究の中心であり、性能評価も実施している。
abstractthis study proposes a new method for predicting the dynamic changes of grain carbon and nitrogen accumulation in winter wheat grain by leveraging UAV-based inversion of agronomic parameters (APs).
Timely and accurate crop yield estimation is important for sustainable agricultural planning and resource optimization. This study is motivated by the need for a scalable, non-destructive, phenology-aware yield estimation pipeline that can outperform spectral index-based methods. A novel in-season crop yield estimation framework is presented that uses high-resolution UAV-based multispectral imagery and deep neural networks. The pipeline integrates automated phenological stage mapping using a custom Spatial Phenology Attention and Feature Cross (SPARC) Network, canopy structure modeling, and wheat head segmentation via a U-Net model fine-tuned on masks generated with SAM 2. UAV imagery is collected across 18 timestamps, processed to produce reflectance maps, vegetation indices (VIs), canopy height models (CHMs), and fractional cover maps. Plot-level phenological and morphological features are extracted to train multiple regression models for in-season yield estimation. Results show that combining temporal phenological features with structural head metrics significantly improve estimation accuracy, with Gradient Boosting Regression achieving an R2 of 0.89. The proposed approach not only improves the granularity and timeliness of in-season yield estimations but also enables scalable, non-destructive crop monitoring solutions, providing practical information for both farmers and breeders alike.
Why it matches plant phenotyping methodsUAV画像と深層学習を用いて、作物のフェノロジー、形態特徴、穂形状を抽出し、圃場区画レベルの収量を推定する技術パイプラインが研究の中心である。
abstractA novel in-season crop yield estimation framework is presented that uses high-resolution UAV-based multispectral imagery and deep neural networks.
Leaf area index (LAI) is a key indicator for measuring crop photosynthesis and growth status. In the monitoring of winter wheat LAI at the scale of large unmanned farms, satellite imagery is stable but lacks spatial resolution for precise monitoring, while UAV imagery is spatially detailed but prone to weather interference, resulting in poor spectral data consistency. To address this, this study took winter wheat in a large unmanned farm in Zouping City, Shandong Province as the research object and proposed a "coarse-fine fusion" two-step fusion method for UAV and Sentinel-2 imagery. Based on the fusion results, a feature set for winter wheat LAI inversion was constructed, and the SHAP model was used to evaluate feature contributions and screen the optimal combination. Machine learning models such as XGBoost and Random Forest were employed to invert LAI at key growth stages of winter wheat under different data fusion modes. Model hyperparameters were optimized through grid search to analyze the impact of data fusion methods on LAI inversion across growth stages. Experiments showed that the two-step fusion method significantly improved spectral consistency and accuracy, with the correlation coefficient between fusion results and Sentinel-2 NDVI values reaching 0.82. Nine key features were selected for model construction, among which the near-infrared band and plant height showed high positive contributions to LAI inversion. Under the two-step fusion data mode, the Random Forest algorithm performed best, achieving an overall R² of 0.895, MAE of 0.216 m²/m², and RMSE of 0.295 m²/m². Inversion accuracy varied across growth stages, with R² of 0.86 during the jointing stage, accurately reflecting dynamic LAI changes. This study provides an efficient and feasible solution for precision monitoring of large-area winter wheat, supporting precision agriculture and food security.
Why it matches plant phenotyping methodsUAV・衛星画像の融合、特徴選択、機械学習による冬小麦LAI推定手法を中心に開発・評価しており、植物形質の取得・推定が研究の主要目的である。
abstractproposed a "coarse-fine fusion" two-step fusion method for UAV and Sentinel-2 imagery
To address challenges in food security, a better understanding of crop performance under varying and changing environmental conditions is required. Plant time warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield. PTW leverages image time series, genetic markers, and environmental covariates to learn genotype-specific physiological responses to temperature and vapour pressure deficit. Compared with mere genomic prediction models, PTW demonstrates superior performance when predicting yield in unseen environments across 48 year-locations in Europe. The PTW model captures non-linear growth responses varying with phenological stages and identifies distinct patterns associated with yield performance and stability. Specifically, varieties with higher yield stability exhibit reduced sensitivity to vapour pressure deficit around 1.5 kPa and distinctive temperature responses during emergence and senescence. The learned response pattern enables retrospective and prospective yield predictions, providing a foundation for location-specific variety recommendations and targeted breeding strategies. The integration of phenomic, genomic, and enviromic data has the potential to substantially advance research in climate adaptation strategies for crop production by addressing generalization challenges of predictions to novel environmental conditions.
Why it matches plant phenotyping methods画像時系列を含む高スループット圃場フェノタイピングデータから、深層学習モデルで小麦の収量と生理応答を推定する手法が研究の中心であり、技術的検証も行っている。
abstractPlant time warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield.
The grain flow sensor is a core component for achieving precise online yield measurement in combine harvesters. However, the accuracy and stability of sensor monitoring are affected by factors such as the harvester structure, operational vibrations, dust, and electromagnetic interference. Improving sensor performance under these constraints has long been a key research focus in the industry. To enhance the monitoring accuracy and stability of grain mass flow rate in combine harvesters, this study proposes a flow-guided and weighing-based grain mass flow sensor (GMFS), which incorporates flow-guiding, buffering, and flow-stabilising functions. Based on dynamic analysis, a multivariable dynamic monitoring model for the grain mass flow rate was developed, integrating key parameters such as screw conveyor speed and grain weight. Dedicated circuits for weak signal amplification, noise filtering, and signal acquisition were developed, and an improved Kalman filter (KF) was employed to enhance monitoring precision. Building on this foundation, a GMFS prototype was integrated into combine harvesters and tested, and its buffering and deceleration performance was validated through simulations. The simulation results indicated that the average particle velocity magnitude at the GMFS outlet was lower and more stable than at the inlet, confirming the sensor’s effectiveness in buffering and decelerating grain flow. Bench test results showed that, under varying mass flow conditions, the GMFS achieved a signed mean relative error (MRE) of 0.64 % and a root mean square error (RMSE) of 0.041 kg·s⁻¹ for the steady-segment (Δt₁) flow rate, with 95 % confidence intervals (CIs) reported for the MRE. Dynamic tests on a combine harvester demonstrated comparable monitoring accuracy, with an MRE of 0.97 % (95 % CI: 0.31–1.63 %) and an RMSE of 0.107 kg·s⁻¹ for the steady-segment flow rate, and an MRE in full-interval total mass of –0.28 % (mean absolute error: 1.10 %). This study provides an accurate and stable sensor design for real-time monitoring of grain mass flow in combine harvesters and provides robust support for yield monitoring technology.
Why it matches plant phenotyping methodsコンバイン収穫時の穀粒質量流量（収量）をリアルタイム計測するセンサーを設計・実装し、シミュレーション、ベンチ試験、実機試験で精度を検証しており、植物の収量形質取得法が中心的である。
abstractTo enhance the monitoring accuracy and stability of grain mass flow rate in combine harvesters, this study proposes a flow-guided and weighing-based grain mass flow sensor (GMFS)
The characterization of wheat genetic resources constitutes a fundamental prerequisite for their effective use in breeding programs aimed at preventing future food shortages. Continued technological developments in plant phenotyping for remote and proximal sensing have enabled multidimensional data acquisition and analysis, making the screening of large numbers of genotypes more accessible and costeffective. Within this framework, 36 bread wheat landraces collected from different localities across Serbia were grown under rainfed conditions during the 2024/25 growing season at Rimski Šančevi, near Novi Sad (45.20° N, 19.51° E) and analyzed using several proximal non-destructive phenotyping devices. In the field trials, genotypes were evaluated at two growth stages for seven traits associated with plant productivity: green cover, leaf area index, maximum plant height, normalized difference vegetation index (Literal sensor, Hiphen), chlorophyll content, and nitrogen balance index (DUALEX optical leaf clip meter, Metos). After harvest, the landraces were assessed for thousand grain weight and grain size fractions (length, width, area) using the MARViN system (MARViTECH), and basic technological parameters (protein, moisture, carbohydrates, oil contents) using the GrainSense Analyzer (Oulu). Principal Component Analysis revealed a clear separation among the analyzed genotypes, reflecting their substantial genetic diversity with respect to the evaluated traits, and highlighting their potential as a valuable source of novel alleles for enhancing breeding value and developing high-yielding varieties with improved technological quality.
Why it matches plant phenotyping methods複数の近位非破壊センシング機器を用いて、遺伝資源の生育・形態・生理・収量関連形質を体系的に取得することが研究の中心であり、実質的なフェノタイピング手法の適用に該当する。
abstractanalyzed using several proximal non-destructive phenotyping devices
Wheat breeding strategies, focused on the creation of varieties with high yield potential under optimized, intensive fertilization, have resulted in a loss of genetic diversity and reduced the ability of these varieties to perform well in low-input farming systems. Recent advances in plant phenotyping have opened new possibilities for the in-depth characterization of old and neglected wheat varieties considering their value for cultivation under stress-prone conditions. The aim of this paper was to assess the environmental sustainability of old wheat varieties that were grown in South East Europe before and at the beginning of the Green Revolution. The 30 wheat varieties were grown under rain-fed conditions during the 2024/25 growing season in experimental trials at Rimski Šančevi, Serbia. Field evaluation was conducted at three growth stages using 15 traits associated with yield and resilience to abiotic stresses with two non-destructive phenotyping devices - the Literal sensor (Hiphen) and the DUALEX optical leaf clip meter (Metos). After harvest, thousand grain weight and grain size were assessed using the MARViN (MARViTECH), while basic technological parameters were obtained by the GrainSense Analyzer (Oulu). ANOVA revealed statistically significant differences among the analysed genotypes for the studied traits, while the re-evaluation of old varieties under contemporary climate conditions, applying high-throughput phenotyping instruments, enabled elucidation of their value and potential role in wheat production under climate change.
Why it matches plant phenotyping methods複数の非破壊センサーと高スループット機器を用いて、品種の収量・ストレス関連形質を体系的に取得する方法適用が研究の主要部分である。
abstractField evaluation was conducted at three growth stages using 15 traits associated with yield and resilience to abiotic stresses with two non-destructive phenotyping devices - the Literal sensor (Hiphen) and the DUALEX optical leaf clip meter (Metos).
Accurate estimation of daily actual evapotranspiration (ETₐ ₐcₜ) is important for many aspects of research and field management. ETₐ ₐcₜ can be calculated from the reference crop ET (ETₒ) and actual crop coefficient (Kc ₐcₜ) which are influenced by the actual crop growth conditions and soil water conditions. In this study, a new framework was developed to estimate the daily actual Kc ₐcₜ using multispectral and thermal data obtained from unmanned aerial vehicles (UAVs). With UAVs flights, the daily ETc ₐcₜ was calculated by the Surface Energy Balance Algorithm for Land model (SEBAL). Without UAVs flights, daily ETc ₐcₜ was corrected by crop coefficient under full water condition (Kc fᵤₗₗ wₐₜₑᵣ) and water stress coefficient (Kₛ), based on remote sensing data, SEBAL model, and soil water balance equation. This framework was tested on winter wheat grown under six irrigation treatments from no irrigation (I0) up to five irrigations (I5) for four seasons from 2019 to 2023. The six irrigation treatments created a wide range of soil moisture and crop growing conditions. The results showed that the best timing to estimate daily ETc ₐcₜ was using the remote sensing data obtained at 11:00 local time with an R² of 0.88 and an RMSE of 0.53 mm/day. The daily ET c ₐcₜ estimated by the new framework on days without UAV flights was consistent with the ET c ₐcₜ calculated using soil water balance equation, with R² value varying from 0.74 to 0.80 under the different irrigation treatments. The results from this study demonstrated that the new framework based on UAV remote sensing data could estimate the daily ET c ₐcₜ in real time and could be further used to estimate daily ET c ₐcₜ on days without UAV flights.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とSEBAL等を統合し、作物の実蒸発散量・水ストレス状態を推定する枠組みを開発・検証しており、測定手法が中心である。
abstractIn this study, a new framework was developed to estimate the daily actual Kc ₐcₜ using multispectral and thermal data obtained from unmanned aerial vehicles (UAVs).
Accurate simulation of the crop growth process was the foundation for the development of smart agriculture. However, the uncertainty of crop growth models limits their practical application. This study integrates the Soil Water Atmosphere Plant (SWAP) model with the Iterative Ensemble Smoother (IES) algorithm to develop the SWAP-IES optimization approach and explores various uncertainty factors of the system, including the ensemble size, observational errors setting, combination of observation variables and their corresponding observation stages, and uncertain parameters selection. The results suggested that, under water stress conditions, an ensemble size of 50 was recommended. It was advisable to choose leaf area index (LAI) and soil moisture content (SW) as observation variables, focusing on monitoring data from the flowering to the milk stage. The suitable observational error settings for LAI and SW were 0.3-0.5 m² m⁻² and 0.03-0.05 cm³ cm⁻³, respectively. For uncertain parameters, it was recommended to select the five crop parameters (RGRLAI, SPAN, CVO, EFF, and CVL) and three soil parameters (θₛ, Kₛ, and n) for simulation. The SWAP‐IES, validated with 2020 and 2021 spring wheat (Triticum aestivum L.) experiments, demonstrated high accuracy in simulating yields, with root mean square error values of 0.56 and 0.61 t ha⁻¹, respectively. The SWAP-IES optimization approach could significantly reduce the uncertainty in the simulation process and improve simulation accuracy by optimizing the system settings strategy.
Why it matches plant phenotyping methodsSWAP-IESという計算的な作物成長・収量推定手法を開発し、観測変数や不確実性設定を検討したうえで春コムギ実験により検証しており、植物形質（収量・LAI）の推定手法が中心である。
abstractThis study integrates the Soil Water Atmosphere Plant (SWAP) model with the Iterative Ensemble Smoother (IES) algorithm to develop the SWAP-IES optimization approach
Timely and accurate in-season estimation of aboveground biomass (AGB) and yield in winter wheat is crucial for optimizing resources and ensuring food security. Light use efficiency (LUE) models have proven effective in estimating crop gross primary productivity and yield across sites and years due to their strong physiological and ecological mechanisms. However, existing studies are limited to satellite applications and have not utilized unmanned aerial vehicle (UAV) imagery. This study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments. Subsequently, the scalability of the LUE-ML yield prediction approach was assessed in farmer's fields from five counties of Jiangsu Province, China. The results demonstrated that while the AGB for the heading stage was estimated by combining the retrieved LAI and 20-day accumulated meteorological features, the AGB during the post-heading period could be estimated accurately using the stage-skipping or stage-progressive strategy, with the latter ( R val 2 = 0.93) outperforming the former ( R val 2 = 0.84). The combination of one spectral index, LUE-derived AGB, and three 20-day accumulated relative meteorological features (Comb. #6) performed the best ( R cal 2 = 0.89; R val 2 ≥ 0.79) for yield prediction among all combinations. When extended to farmer-field yield prediction across the province, Comb. #6 also achieved acceptable performance. This study suggests the use of LUE-ML models represents a significant step forward towards mechanistic estimation of AGB and yield for cereal crops from UAV imagery.
Why it matches plant phenotyping methodsUAV画像から冬コムギの地上部バイオマスと収量を推定するLUE-ML手法を開発・評価し、圃場で性能検証しているため、植物形質取得・推定が研究の中心である。
abstractThis study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments.
Reproduction assets foundThe paper's Data Availability statement explicitly hosts the core code for the two UAV-LUE AGB estimation strategies and related test data in a public GitHub repository; other data are only available upon request.Code · publicThe core code for the two strategies and related test data in the UAV-LUE method for estimating wheat AGB are hosted in a public repository: https://github.com/qtaocheng/agb-estimation-uav-lue-two-strategies . Other data that support the findings of this study are available from the corresponding author (T.C.) upon reasonable request.Open asset ↗qtaocheng/agb-estimation-uav-lue-two-strategieslines:507-519Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Monitoring wheat growth, as one of the most important food grain sources for human nutrition, and forecasting yields are done through different phenological phases. Reliable estimates on yields play a crucial role in securing sufficient food supplies for the world's growing population. Currently, farmers estimate a wheat yield during the later stages of growth and are often biased in this process. Plant breeding scientists use a more accurate approach that collects data on the number of wheat ears manually counted at various locations throughout the field. A sufficiently precise count of wheat ears is one of the most important parameters for reliable early-stage prediction of wheat yield. To support the development of an affordable and trustworthy automated wheat ear detection approach, this work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties. Additionally, it evaluates six deep learning models for wheat ear detection. Among the F-RCNN-based models, RetinaNet, YOLOv8, and a Vision Transformer-based detector, RT-DETR, achieved the highest mean Average Precision (mAP@50) of 91%, with significantly higher computational complexity. BioS-Wheat complements Global Wheat Head Detection datasets, introducing a meaningful shift in data complexity with high sowing density and minimal row spacing, emphasizing the impact of agronomic diversity on model performance by an increased object occlusion and dense spatial arrangements. Enriched and agronomically diverse datasets support model robustness at different varieties, growth stages, and locations. This work offers a good baseline for establishing the procedure for image crowdsourcing, further dataset expansions, and model improvements.
Why it matches plant phenotyping methods小麦穂の画像検出による個体群形質推定を対象とし、注釈付きデータセットの構築と複数モデルの評価が中心であるため、表現型計測手法として収載する。
abstractthis work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Conventional plant phenotyping relies on visual scoring and manual measurements, which are labor-intensive, time-consuming, and prone to human error. To address these limitations, Unmanned Aircraft Systems (UASs) are increasingly being applied in breeding trials to capture various phenotypic traits. High Throughput Phenotyping (HTP) offers enhanced speed, accuracy, and efficiency, while potentially reducing costs in plant breeding programs. This study explores UAS-based phenotyping in wheat breeding trials with the aim to integrate HTP platforms across breeding pipelines. UAS images were acquired using a Parrot Bluegrass drone equipped with a sequoia multispectral sensor, processed via Agisoft Metashape (open-source) and Pix4D mapper (licensed), and analyzed using PlotPhenix (licensed) for Vegetation Indices (VIs) and plot segmentation. Comparisons between UAS-based and ground-based measurements revealed that grain yield is significantly negatively correlated (r = -0.74) with yellow rust disease severity. Multispectral-derived indices, particularly **Red, Red Edge, and NIR bands, showed positive correlations with grain yield (ranging from 0.22 to 0.23), though RGB-generated indices exhibited stronger correlations. The findings confirm that UAS-generated indices effectively assess yellow rust disease severity and predict grain yield. UAS-based phenotyping enhances efficiency and accuracy in trait collection and disease assessment, facilitating the development of improved wheat varieties and promoting the integration of UAS technologies into breeding programs.
Why it matches plant phenotyping methodsUAS・マルチスペクトル画像を用いた作物形質取得、区画分割、疾病重症度評価、収量予測を中心に扱う高スループット表現型解析研究であり、方法の適用とプラットフォーム統合が中心です。
titleApplication of Unmanned Aircraft Systems (UASs) for Disease Assessment and High Throughput Field Phenotyping of Plant Breeding Trials
The goal of the study is to ascertain the age of wheat crops in Punjab, Pakistan, by utilizing contemporary remote sensors in conjunction with machine learning and estimation methods. A range of vegetation indices, such as NDVI, EVI, SAVI, GDVI, and IVI using Sentinel satellite data, were used to track crop health and crop growth stage. To identify the most relevant features, two distinct selection approaches were applied: Univariate Linear Regression Tests and the Random Forest Feature Importance method. Polynomial regression models of degrees 1 to 3 were then used. The results demonstrated highly precise crop age estimation, reflected by strong R² values, which are 0.68- 0.92, and low Root Mean Square Error (RMSE), 1.14 to 0.5, is observed among polynomial degree 1 to 3, respectively. These findings are very important in making wise judgments on matters that are to be done in respect to water supply and nutrient management. The article draws attention to the power of data-driven by remote sensors and strong feature modeling and selection methods to improve precision agriculture. The approach will help in promoting sustainable agricultural activities because farmers will be able to take timely measures that can enhance productivity and resilience to environmental stresses in the area. The solution is an important development of present-day agricultural monitoring and management in Punjab and similar agro-ecological areas.
Why it matches plant phenotyping methodsSentinel衛星センシングと特徴量選択・多項式回帰を用いて小麦の作物齢・生育段階を推定し、精度も評価しているため、植物状態の取得・推定手法が中心である。
abstractutilizing contemporary remote sensors in conjunction with machine learning and estimation methods
Wheat rusts are the most important diseases leading to substantial yield losses. Early and precise detection of wheat rusts for early mitigation and disease control is imperative. This review summarizes the advances in high-throughput phenotyping (HTP) approaches for rust detection. Additionally, various genomic interventions leading to the development of rust resistance in wheat are discussed in detail. High-throughput phenotyping (HTP) approaches enable early, non-destructive, and repeatable detection of wheat diseases. However, they need initial investment, expertise, and computational resources. RGB imaging achieves ~80% accuracy by capturing infected leaf coloration, while hyperspectral and fluorescence imaging can predict rust with over 90% accuracy, 3–8 days before visible symptoms. LiDAR, UAVs, and robotic platforms automate large-scale field phenotyping, and spectral indices (NDVI, PRI), thermal, and chlorophyll sensors detect early physiological changes. AI and machine learning models, including CNNs and SVMs, enhance diagnostic precision and reduce bias, while mobile apps, lateral flow devices, and IoT-based systems facilitate affordable, real-time rust detection and forecasting. Genomic interventions complement phenotyping, with marker-assisted selection (MAS) enabling precise tracing of rust resistance genes, and genomic selection (GS) allowing early multi-trait prediction. QTL mapping and GWAS identify major and minor resistance loci, while introgression from wild relatives and MAS reduce linkage drag and introduce novel alleles. Transgenic approaches, RNA interference (RNAi), and CRISPR/Cas9 gene editing enhance resistance through targeted gene modification, and gene pyramiding combines multiple loci for durable protection. Wheat pan-genome resources further support precise trait targeting, and speed breeding integrated with MAS, GS, or gene editing accelerates rust-resistant line development. Efficient seed system pathways ensure rapid dissemination, adoption, and resilience. The development and commercialization of rust-resistant wheat varieties under harsh climatic conditions are crucial for mitigating yield losses, reducing fungicide use, safeguarding farmer livelihoods, and ensuring sustainable food security.
Why it matches plant phenotyping methodsコムギさび病の高スループット表現型解析手法を中心にレビューしており、画像・分光・熱・LiDAR・AIなどによる植物病徴の検出方法を扱うため。
abstractThis review summarizes the advances in high-throughput phenotyping (HTP) approaches for rust detection.
Agriculture is a cornerstone of the economies of many countries, and wheat is a staple cereal crop that sustains nearly half of the worldwide population. However, production of wheat is highly vulnerable to biotic stress such as pathogens and pests, as well as adverse environmental conditions. These factors significantly affect yield and quality, posing critical threats to food security and economic resilience. Conventional disease detection methods often involve intense human labor, prolonged procedures, and are predisposed to subjectivity. Therefore, the development of an automated, accurate, and real-time disease monitoring system is imperative for modern precision agriculture. We propose a hybrid deep learning based Multi-Disease Detection Framework for Wheat Diseases (MDDM-WD) for the identification of multiple wheat diseases using UAV imagery. The framework leverages the pre-trained VGG-16 convolutional neural network for deep feature extraction via a transfer learning approach. These features are subsequently classified using Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), XGBoost, and Bernoulli Naïve Bayes (BNB) algorithms of machine learning. The model is trained and evaluated on a custom-curated dataset, containing wheat diseases: stripe rust, powdery mildew, scab (Fusarium head blight), and yellow dwarf. Evaluation of experiments demonstrates that the classification performance is enhanced significantly through our hybrid approach, with accuracy ranging from 74 to 97%, precision from 73 to 96%, and recall from 73 to 95.7%. The SVM-based variant of the model achieved the highest performance, yielding 96% precision, 95.7% recall, 96% F1-score, and 97% accuracy. The proposed two-phase fine-tuned system demonstrates its effectiveness and efficiency in detecting multiple wheat diseases. The MDDM-WD model offers a resource-efficient and scalable approach for early disease detection, supporting informed decision-making for farmers, agronomists, and policymakers in advancing sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像からコムギの病徴・病害状態を直接推定する深層学習フレームワークを開発・評価しており、植物表現型取得法が中心です。
abstractWe propose a hybrid deep learning based Multi-Disease Detection Framework for Wheat Diseases (MDDM-WD) for the identification of multiple wheat diseases using UAV imagery.
Reproduction assets foundThe paper's wheat disease image analysis is based on two public datasets (DAE-Mask GitHub dataset and a Kaggle multi-class crop disease image dataset), explicitly declared as open-source and publicly available in the Data Availability statement. No author code or trained model is released.Dataset · publicThe two datasets analyzed during the current study are open-source and publicly available online in the repositories https://github.com/YcZhangSing/Dataset-of-DAE-Mask and https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-images.Open asset ↗https://github.com/YcZhangSing/Dataset-of-DAE-Maskhtml-lines:699-732Dataset · publicThe two datasets analyzed during the current study are open-source and publicly available online in the repositories https://github.com/YcZhangSing/Dataset-of-DAE-Mask and https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-images.Open asset ↗https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-imageshtml-lines:699-732Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
High-precision plant phenotyping requires efficient 3D reconstruction with high fidelity, yet existing methods such as MVS and NeRF all have problems of feature dependence and error accumulation during 3D reconstruction, which leads to geometric distortion in reconstruction and restricts the reconstruction efficiency. To address this bottleneck, this study first determined the multi-view image acquisition strategy. Further, based on the self-built multi-view dataset of chili peppers, it proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm. Experimental results indicate that the Peak Signal-to-Noise Ratio ( PSNR ) improves by 52.0 %, 26.4 %, and 4.2 % compared to NeRF, Instant-NGP, and 3D Gaussian Splatting respectively. Additionally, the Structural Similarity Index Measure ( SSIM ) increases by 22.9 %, 12.8 %, and 4.3 % respectively over above methods. The point cloud data reconstructed based on this algorithm also has advantages in the measurement of phenotypic parameters. Compared with the actual measured values, the R² of the phenotypic parameters such as pepper plant height, canopy width, and leafstalk angle obtained in this study are 0.997, 0.954 and 0.978 respectively, and the RMSE are 0.236 cm, 1.082 cm and 2.344° respectively, and the MAE are 0.209 cm, 0.880 cm and 1.965° respectively. The accuracy was significantly better than that of the existing phenotypic calculation methods. Verification across different growth stages of wheat and maize was performed universally, with all errors remaining below 1.1 %, providing new ideas and technologies for high-precision, low-cost, and high-throughput crop phenotypic research.
Why it matches plant phenotyping methods植物の多視点画像から3D再構成し、草丈・群落幅・葉柄角などの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的である。
abstractit proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm.
WheatRootMorphology / geometry measurementRoot system architecture
Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this platform, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput platform to support such research.
Why it matches plant phenotyping methods根の解剖学的形質を高スループットに画像取得する低コスト基盤を開発しており、植物フェノタイピング手法が研究の中心です。
abstractHere, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging
Reproduction assets foundThe paper's supplementary materials (hosted at the publisher DOI page) explicitly include the 3D design files (STL) for the RAT platform and the Python script used to control image acquisition, which are paper-specific phenotyping hardware/analysis assets. The phenotype datasets generated and analysed are only 'on the'Code · public3D design files (STL) are provided in the supplementary material. The Python script used to control image acquisition using the specific USB microscope used in this study is available in the supplementary materialsOpen asset ↗lines:229-267Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
WheatAerial / UAVMultispectral / hyperspectralGrowth / development / phenologyLeaf traits
Wheat growth monitoring plays a vital role in agricultural decision-making and food security. This study aims to develop an accurate and efficient monitoring method for wheat growth by integrating satellite remote sensing and machine learning techniques. Based on preprocessed Sentinel-2 satellite images and measured wheat leaf area index (LAI) data, a set of 11 vegetation indices-such as NDVI, NDRE, and RVI-were selected and ranked through Pearson correlation analysis. A comprehensive index system was then constructed by selecting the top eight indices using a stepwise optimization approach. Three machine learning models-Linear Regression (LR), Backpropagation Neural Network (BPNN), and XGBoost-were applied to evaluate the performance of the index system, with the Particle Swarm Optimization (PSO) algorithm employed to optimize each model. The results demonstrate that the PSO-optimized XGBoost model achieved the highest accuracy (R² = 0.94, MSE = 0.075), exhibiting strong stability and robustness to data fluctuations. These findings suggest that the proposed approach provides a reliable solution for wheat growth monitoring.
Why it matches plant phenotyping methods衛星リモートセンシングと機械学習により小麦のLAIを推定する監視手法の開発・評価が研究の中心であり、植物キャノピー形質の取得方法を扱っている。
abstractThis study aims to develop an accurate and efficient monitoring method for wheat growth by integrating satellite remote sensing and machine learning techniques.
Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust ( Puccinia graminis f. sp. tritici Eriks. and E. Henn.) of wheat ( Triticum aestivum L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.
Why it matches plant phenotyping methods小麦茎锈病の無症状感染を対象に、ハイパースペクトル画像の前処理パイプラインを開発・評価し、植物病害状態の早期推定性能を検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection
Reproduction assets foundThe paper's data availability statement points to a public Google Drive repository containing the study's hyperspectral datasets used for wheat stem rust early detection. No separate author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:616-634Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Bacterial Leaf Streak (BLS) and Fusarium Head Blight (FHB) are among the most damaging diseases of wheat (Triticum aestivum), with severe consequences for grain yield, quality, and ultimately food safety and security. Rapid and precise assessment of disease severity in the fields is crucial for effective field management, potential yield loss evaluation, and high-throughput phenotyping. This research examined the utility of UAV-based multispectral imagery in combination with both traditional machine learning and modern deep learning approaches to estimate wheat disease severity under field conditions. Data collection was carried out at two wheat experimental fields in South Dakota, USA, where Unmanned Aerial Vehicle (UAV) multispectral imagery was acquired in parallel with plot-level measurements of BLS and FHB severity. Spectral and textural metrics extracted from the UAV imagery served as inputs for machine/deep learning-based regression analyses. Regression models evaluated in this work comprised traditional machine learning methods Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and three deep learning architectures: Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and multi-head self-attention (MHSA)-enhanced CNN (Att-CNN). In addition, a deep transfer learning framework was tested by transferring an Att-CNN model trained on BLS to FHB severity estimation. The results showed that deep learning methods, particularly CNN-based architectures, consistently outperformed conventional machine learning approaches. Incorporation of a MHSA mechanism into the CNN architecture further enhanced performance, especially for BLS severity estimation. Att-CNN achieved the best results for both diseases, with R² = 0.83 and RRMSE = 30.55 % for BLS, and R² = 0.70 and RRMSE = 37.05 % for FHB. While estimation of FHB severity remained more challenging, transfer learning from BLS substantially improved prediction accuracy, raising R² from 0.70 to 0.79 and reducing RRMSE from 37.05 % to 31.31 %. The study highlights the considerable potential of UAV multispectral imagery, though with notable limitations, for monitoring crop diseases. This work also demonstrates the added value of attention-based deep learning and transfer learning techniques in addressing complex applications in agricultural remote sensing.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて、圃場のコムギ病害重症度という植物状態を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractThis research examined the utility of UAV-based multispectral imagery in combination with both traditional machine learning and modern deep learning approaches to estimate wheat disease severity under field conditions.
Introduction Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) has recently emerged as a low-cost, continuous, and high-resolution technique for monitoring crop growth. However, conventional GNSS-R approaches that rely on signal-to-noise ratio (SNR) observables are limited by data availability, particularly from legacy receivers, and the effectiveness of observable combination methods in this context has not yet been established. Methods This study presents the first successful attempt to retrieve wheat height using ground-based GNSS-R with code pseudorange and dual-frequency carrier phase observables. Six observable combination schemes from GPS and BDS were evaluated through a field experiment at the Fengqiu Agro-ecology Experimental Station in China. A GDD-parameterized Logistic growth model was employed as a continuous reference. A multi-system, multi-satellite fusion strategy was developed, incorporating principal frequency power weighting within each system and residual reciprocal weighting across systems. Results The observable combination method effectively captured wheat growth dynamics. The best-performing combinations-GPS C5I+L5I+L2P and BDS C2I+L2I+L6I-achieved correlation coefficients (R) of 0.935 and 0.957, and RMSE values of 0.081 m and 0.086 m, respectively. Dual-system fusion further enhanced retrieval accuracy, reducing RMSE by 22.6% compared with the best single-system combination and by 34.6% relative to an SNR-based method. Discussion These findings demonstrate the feasibility and superiority of pseudorange and dual-frequency carrier phase combinations for SNR-independent GNSS-R crop monitoring. The proposed strategy offers a robust, scalable, and accessible tool for precision agriculture and continuous crop growth tracking, particularly in contexts where SNR data are unavailable or unreliable.
Why it matches plant phenotyping methodsGNSS-Rを用いてコムギ高さと成長動態を推定する観測量の組合せ・融合戦略を開発し、既存のSNR法と精度比較しており、植物形質取得手法が中心である。
abstractThis study presents the first successful attempt to retrieve wheat height using ground-based GNSS-R with code pseudorange and dual-frequency carrier phase observables.
Doubled haploid (DH) technology is crucial for accelerating crop breeding. While the functional conservation of MATRILINEAL ( MTL ) in cereals enables haploid induction (HI) in wheat ( Triticum aestivum ), distinguishing haploid from diploid seeds remains a major bottleneck. Here, we developed an efficient haploid identification (HID) system for wheat by seamlessly integrating a two-fluorescent protein-based HID toolbox with an MTL mutant HI wheat line. This system enables efficient HI and utilizes dual-fluorescence screening, offering high accuracy while being independent of the genetic background of the recipient material. Our work demonstrates that engineered intraspecific HI is achievable in self-pollinating crops such as wheat, paving the way for applications that shorten the breeding cycle, facilitate quantitative genetic studies, and accelerate the fixation of desirable alleles. With further optimization and development, this system holds promise as a commercially viable pipeline for haploid production to facilitate wheat breeding.
Why it matches plant phenotyping methods小麦種子の倍数性（半数体・二倍体）を蛍光により識別するHIDシステムの開発が研究の中心であり、植物の状態を測定・抽出する方法に該当する。
abstractHere, we developed an efficient haploid identification (HID) system for wheat by seamlessly integrating a two-fluorescent protein-based HID toolbox with an MTL mutant HI wheat line.
Repeated occurrences of extreme weather events, such as low temperatures, due to global warming present a serious risk to the safety of wheat production. Quantitative assessment of frost damage can facilitate the analysis of key genetic factors related to wheat tolerance to abiotic stress. We collected 491 wheat accessions and selected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage. Image descriptors can complement the visual estimation of frost damage. Combined with genome-wide association study (GWAS), a total of 107 quantitative trait loci (QTL) (r 2 ranging from 0.75% to 9.48%) were identified, including the well-known frost-resistant locus Frost Resistance (FR)-A1/ Vernalization (VRN)-A1. Additionally, through quantitative gene expression data and mutation experience verification experiments, we identified two other frost tolerance candidate genes TraesCS2A03G1077800 and TraesCS5B03G1008500. Furthermore, when combined with genomic selection (GS), image-based descriptors can predict frost damage with high accuracy (r ≤ 0.84). In conclusion, our research confirms the accuracy of image-based high-throughput acquisition of frost damage, thereby supplementing the exploration of the genetic structure of frost tolerance in wheat within complex field environments.
Why it matches plant phenotyping methods小麦の霜害を画像記述子で定量評価し、その精度を検証しているため、画像ベース植物フェノタイピングが研究の中心です。
abstractselected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage.
Reproduction assets foundThe paper's data processing code is publicly available on GitHub. Genotype and phenotype data are only available on reasonable request, so they do not qualify as public assets.Code · publicThe data processing code presented in this study is available on the website https://github.com/yurui2024/Frost-tolerance .Open asset ↗yurui2024/Frost-tolerancelines:156-271Plant phenotyping relevance match · UnverifiedOpenAlex · checked 6 Sept 2026
Grain yield is one of the most critical indicators for evaluating the performance of wheat breeding. However, the assessment process, from early-stage breeding lines to officially registered varieties that have passed the DUS (Distinctness, Uniformity, and Stability) test, is often time-consuming and labor-intensive. Multispectral remote sensing based on unmanned aerial vehicles (UAVs) has demonstrated significant potential in crop phenotyping and yield estimation due to its high throughput, non-destructive nature, and ability to rapidly collect large-scale, multi-temporal data. In this study, multi-temporal UAV-based multispectral imagery, RGB images, and canopy height data were collected throughout the entire wheat growth stage (2023–2024) in Xuzhou, Jiangsu Province, China, to characterize the dynamic growth patterns of both breeding lines and registered cultivars. Vegetation indices (VIs), texture parameters (Tes), and a time-series crop height model (CHM), including the logistic-derived growth rate (GR) and the projected area (PA), were extracted to construct a comprehensive multi-source feature set. Four machine learning algorithms, namely a random forest (RF), support vector machine regression (SVR), extreme gradient boosting (XGBoost), and partial least squares regression (PLSR), were employed to model and estimate yield. The results demonstrated that spectral, texture, and canopy height features derived from multi-temporal UAV data effectively captured phenotypic differences among wheat types and contributed to yield estimation. Features obtained from later growth stages generally led to higher estimation accuracy. The integration of vegetation indices and texture features outperformed models using single-feature types. Furthermore, the integration of time-series features and feature selection further improved predictive accuracy, with XGBoost incorporating VIs, Tes, GR, and PA yielding the best performance (R2 = 0.714, RMSE = 0.516 t/ha, rRMSE = 5.96%). Overall, the proposed multi-source modeling framework offers a practical and efficient solution for yield estimation in early-stage wheat breeding and can support breeders and growers by enabling earlier, more accurate selection and management decisions in real-world production environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル・RGB・草冠高データから生育形質を抽出し、機械学習で収量を推定するワークフローが研究の中心であり、育種ラインの表現型評価に実質的に適用している。
abstractMultispectral remote sensing based on unmanned aerial vehicles (UAVs) has demonstrated significant potential in crop phenotyping and yield estimation due to its high throughput, non-destructive nature, and ability to rapidly collect large-scale, multi-temporal data.
This extended abstract details our solution for the Global Wheat Full Semantic Segmentation Competition. We developed a systematic self-training framework. This framework combines a two-stage hybrid training strategy with extensive data augmentation. Our core model is SegFormer with a Mix Transformer (MiT-B4) backbone. We employ an iterative teacher-student loop. This loop progressively refines model accuracy. It also maximizes data utilization. Our method achieved competitive performance. This was evident on both the Development and Testing Phase datasets.
Why it matches plant phenotyping methods小麦穂を画像から分割する手法の開発が中心であり、植物器官の表現型取得に直接関係する。
titlePseudo-Label Refinement for Robust Wheat Head Segmentation via Two-Stage Hybrid Training
Accurate monitoring of wheat phenology is critical for ensuring wheat production. Recent advances in deep learning have enabled the automated detection of wheat phenology in the field. In particular, deep learning models using multi-temporal image series have addressed the challenge of low accuracy in models that only use spatial features by incorporating dynamic aspects of the wheat growth process. However, utilizing multi-temporal image series introduces challenges such as model parameter redundancy, complex inference processes, and difficulties in real-time deployment. To address these issues, this study presents an optimization method for deriving wheat phenology from single-temporal images (WPDSI) that combines knowledge distillation and multi-layer attention transfer. The proposed approach employs knowledge distillation. In this framework, a teacher model extracts spatiotemporal features from multi-temporal image-series and generates soft labels to guide a student model trained on single-temporal images. This reduces model complexity and input data requirements. Multi-layer attention transfer allows the student model to inherit feature representations from multiple layers of the teacher model. This enhances its ability to capture key phenological characteristics and supports interpretability through attention mechanisms. The proposed method achieves an overall accuracy (OA) of 0.927, comparable to models trained on multi-temporal image series. Furthermore, the model demonstrates strong generalization on unseen datasets, enhancing real-time performance and computational efficiency while maintaining high accuracy, providing a practical solution for deriving wheat phenology in the field. The dataset is available at https://github.com/phenology-detection/WPDSI.
Why it matches plant phenotyping methods小麦の生育ステージを単一時点画像から推定する深層学習手法を開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractthis study presents an optimization method for deriving wheat phenology from single-temporal images (WPDSI)
Reproduction assets foundThe paper's wheat phenology image dataset is explicitly stated as publicly available at the authors' GitHub repository (https://github.com/phenology-detection/WPDSI), matching an allowed URL. No separate code availability is stated beyond this repository, so it is treated as the paper-specific public asset.Dataset · publicData availability
The dataset is publicly available at https://github.com/phenology-detection/WPDSI .Open asset ↗phenology-detection/WPDSIlines:270-275Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
RiceWheatField / plotMesh / voxelNeRF / 3D Gaussian SplattingLiDAR / point cloudPanicle / ear / spikeLeafRootSeed / grain
Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. It utilizes radiance field information to lift 2D masks, segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction. On a rice dataset, IPENS achieves a grain-level segmentation mean Intersection over Union (mIoU) of 63.72%. For phenotypic trait estimation, it achieves a grain voxel volume coefficient of determination R 2 = 0.7697 (Root Mean Square Error, RMSE = 0.0025), leaf surface area R 2 = 0.84 (RMSE = 18.93), and leaf length and width prediction accuracies of R 2 = 0.97 and R 2 = 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset, IPENS further improves segmentation performance to a mIoU of 89.68%, with exceptional phenotypic estimation results: panicle voxel volume R 2 = 0.9956 (RMSE = 0.0055), leaf surface area R 2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reaching R 2 = 0.99 and R 2 = 0.92 (RMSE = 0.23 and 0.15). Without requiring annotated data, IPENS rapidly extracts grain-level point clouds for multiple targets within three minutes using single-round image interactions. These features make IPENS a high-quality, non-invasive phenotypic extraction solution for rice and wheat, offering significant potential to enhance intelligent breeding.
Why it matches plant phenotyping methods植物形質抽出のためのNeRF-SAM2融合手法を開発し、作物データセットで分割性能と形質推定精度を検証しているため、方法開発・検証が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub. The rice/wheat MMR/MMW phenotype datasets (multi-view images, point clouds, annotations) are only available upon reasonable request, so they are not public.Code · publicCode is available at https://github.com/Vincent-Songwentao/IPENS-Code.git .Open asset ↗https://github.com/Vincent-Songwentao/IPENS-Code.gitlines:472-496Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Computers and Electronics in Agriculture.
Three-dimensional high-throughput plant phenotyping technology offers an opportunity for simultaneous acquisition of plant organ traits at the scale of plant breeders. Wheat, as a multi-tiller crop with narrow leaves and diverse spikes, poses challenges for organ segmentation and measurement due to issues such as occlusion and adhesion. Therefore, building on previous research, this paper establishes a phenotyping pipeline and develops a 3D phenotypic automated analysis system for individual wheat plants at different growth stages. This system enables automated and precise three-dimensional phenotypic acquisition and analysis of wheat plant architecture, spike morphology, and flag leaf traits. To address the challenges posed by the significant structural differences among wheat spikes, leaves, and stems, as well as their compact spatial distribution, we propose a point cloud segmentation model based on deep learning called ICFMNet. ICFMNet relies on an instance center feature matching module, which extracts features from each instance’s central region and matches them with global point-wise features by computing feature similarity. This approach enables precise instance mask generation independent of the spatial structure of the point cloud. In the analysis of wheat phenotypes, we introduce a contour-based method to accurately extract the barren segment from 3D-scale wheat spikes. Furthermore, we perform the analysis of a total of 19 phenotypes, including flag leaf phenotypes and whole-plant phenotypes. In the organ point cloud segmentation tests for wheat spikes, stems, and leaves, the semantic segmentation achieves mPrec, mRec, and mIoU values of 95.9 %, 96.0 %, and 92.3 %, respectively. The instance segmentation attains mAP and mAR scores of 81.7 % and 83.0 %, respectively. Moreover, in comparison to five other segmentation network models, ICFMNet demonstrates superior segmentation performance. To better assess barren segment localization accuracy, additional evaluations are conducted using two metrics: interval overlap and interval error, achieving values of 92.33 % and 0.1123 cm, respectively. Experimental results indicate that our method excels in terms of accuracy, efficiency, and robustness, providing a reliable systematic platform for precise identification and breeding research of wheat plant types. The source code and trained models for ICFMNet are available at https://github.com/xiao-pl/ICFMNet.
Why it matches plant phenotyping methods小麦個体・器官の3D形質を自動取得・抽出するセグメンテーションおよび解析パイプラインの開発と技術評価が研究の中心である。
abstractthis paper establishes a phenotyping pipeline and develops a 3D phenotypic automated analysis system for individual wheat plants at different growth stages.
High-throughput phenotyping of senescence dynamic traits is crucial for plant breeding, yet quantifying these dynamics remains limited due to the challenges in multitemporal measurements and validations, hindering the understanding of the association between senescence dynamics and nitrogen fertilization and how it differs between genotypes. Here, we proposed a novel method for extracting senescence dynamic traits (SDTs), quantifying the senescence dynamics based on the uncrewed aerial vehicle (UAV) multispectral sensing and generalized additive model (GAM). We investigated the extent to which senescence dynamic traits distinguish between winter wheat varieties and nitrogen (N) rates. A field trial was conducted to test the variability in senescence dynamics across wheat varieties under three nitrogen treatments (0, 120, and 180 kg· N ha −1 ). In addition to the UAV-derived canopy reflectance, we measured leaf anthocyanin concentrations, SPAD, and yield traits and analyzed their relationships with the SDTs. Results revealed that leaves exhibited an earlier onset of senescence under low N levels. The GAM-derived area under the curve (AUC) variables based on chlorophyll and anthocyanin dynamics were both found to be highly correlated with yield traits. The proposed SDTs demonstrated potential for characterizing varietal senescence types, highlighting their utility for multitemporal senescence monitoring and high-throughput field phenotyping. This study opens new possibilities for further field phenotyping research testing the effect of plant varietal differences in senescence dynamics on their nitrogen uptake and use efficiency.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングとGAMを組み合わせ、コムギの老化動態形質を抽出・定量する手法の開発と圃場検証が中心である。
abstractHere, we proposed a novel method for extracting senescence dynamic traits (SDTs), quantifying the senescence dynamics based on the uncrewed aerial vehicle (UAV) multispectral sensing and generalized additive model (GAM).
Leaf protein content (LPC) is a critical physiological parameter for assessing crop nitrogen status, optimizing fertilization strategies, and predicting crop yield. Although hyperspectral remote sensing offers a nondestructive alternative, it still faces challenges such as overlapping protein and water absorption spectral features. This study presents a “physics-constrained + data-driven” hybrid modeling framework, LPCNet, for remote sensing–based LPC estimation. The core innovations of LPCNet include (1) leveraging the physics-based PROSPECT-PRO and SAIL radiative transfer models to generate a simulated spectra dataset, addressing the challenges of small sample sizes and distribution bias in field measurements through pretraining and transfer learning; (2) incorporating leaf chlorophyll content (LCC) as an auxiliary training target within a multitask learning framework, which exploits the strong absorption features of LCC in the visible–near infrared (VNIR) range to enhance the ability of the model to interpret weak LPC absorption signals in the shortwave infrared (SWIR) range; and (3) employing a multiscale convolutional network with a feature fusion mechanism to explicitly model the complex nonlinear relationships between spectral reflectance and LPC. This study utilized field-measured data from three growing seasons of wheat and potato to develop and validate the LPCNet model. The results demonstrate the following: (1) the LPCNet model pretrained with a simulated spectra dataset notably outperforms nonpretrained models; (2) the pretrained and LCC-assisted strategy further improves LPC-estimation accuracy to RMSE = 0.000100 g/cm² (R² = 0.866), showing a substantial advantage over traditional RF (R² = 0.760, RMSE = 0.000134 g/cm²). This study proposes a hybrid deep learning modeling framework utilizing hyperspectral remote sensing for high-precision monitoring of crop LPC.
Why it matches plant phenotyping methodsハイパースペクトル計測から作物葉のタンパク質含量という植物生理形質を推定するモデルを開発し、複数年の圃場データで検証しており、表現型取得・推定手法が中心である。
abstractThis study presents a “physics-constrained + data-driven” hybrid modeling framework, LPCNet, for remote sensing–based LPC estimation.
Precision agriculture increasingly relies on unmanned aerial vehicle (UAV) imagery for high-throughput crop phenotyping, yet existing deep learning detection models face critical constraints limiting practical deployment: computational demands incompatible with edge computing platforms and insufficient accuracy for multi-scale object detection across diverse environmental conditions. We present LSM-YOLO, a lightweight detection framework specifically designed for aerial wheat head monitoring that achieves state-of-the-art performance while maintaining minimal computational requirements. The architecture integrates three synergistic innovations: a Lightweight Adaptive Extraction (LAE) module that reduces parameters by 87.3% through efficient spatial rearrangement and adaptive feature weighting while preserving critical boundary information; a P2-level high-resolution detection head that substantially improves small object recall in high-altitude imagery; and a Dynamic Head mechanism employing unified multi-dimensional attention across scale, spatial, and task dimensions. Comprehensive evaluation on the Global Wheat Head Detection dataset demonstrates that LSM-YOLO achieves 91.4% mAP@0.5 and 51.0% mAP@0.5:0.95—representing 21.1% and 37.1% improvements over baseline YOLO11n—while requiring only 1.29 M parameters and 3.4 GFLOPs, constituting 50.0% parameter reduction and 46.0% computational cost reduction compared to the baseline.
Why it matches plant phenotyping methodsUAV画像からコムギ穂を検出する軽量深層学習フレームワークを開発・評価しており、植物器官の画像ベース表現型取得が中心である。
abstractPrecision agriculture increasingly relies on unmanned aerial vehicle (UAV) imagery for high-throughput crop phenotyping
Plant density is an important variable for management and phenotyping of small-grain cereal crops such as wheat and barley. While many image-based estimation methods exist to replace laborious manual counting, most of them rely on empirical relationships that may not generalize well to different sites, growth stages, species and varieties. In this study, we propose a novel small-grain cereal plant density estimation method that uses leaf tip density dynamics derived from submillimeter-scale images acquired at 45° view zenith angle. This method contained two steps. In the first step, a P2PNet deep learning detection model was trained to estimate leaf tip count in a surface of known area to get the leaf tip density. An occlusion correction method was then applied on this density, leading to an estimation error of about 20% at critical growth stages. In the second step, a wheat leaf dynamic model was used to simulate the evolution of leaf tip density over thermal time as functions of several variables, including mean time of plant emergence, phyllochron and plant density. This model was then inverted using a lookup table approach to estimate plant density from leaf tip density dynamics. The results obtained on three test datasets indicated that two observations performed before the appearance of the second and third leaves could be sufficient to attain a relative plant density estimation error of about 10%. We also discussed that this method should be able to work on other datasets without recalibration, and estimate other variables such as phyllochron at early growth stages. The code will be available at: https://github.com/wdwzytc/WheatPlantDensity.
Why it matches plant phenotyping methodsRGB画像から葉先密度を抽出し、植物密度を推定する画像ベースの表現型計測法を開発・評価しており、方法が研究の中心である。
abstractwe propose a novel small-grain cereal plant density estimation method that uses leaf tip density dynamics derived from submillimeter-scale images
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
This study systematically evaluated the capability of near-infrared hyperspectral imaging (HSI) for rapid and non-destructive detection of wheat grain quality across 14 varieties from multiple ecological zones in Ningxia. By integrating machine learning approaches, predictions were made on the protein content of these wheat varieties. Results demonstrated that the Convolutional Neural Network (CNN) model achieved the highest coefficient of determination (R²) on both training and test datasets, indicating superior fitting performance and predictive accuracy. Among the feature wavelength extraction methods, the iterative Variable Importance in Projection on Latent Structures (iVISSA) technique stood out. After applying this method, the CNN model attained a test set R² of 0.9058 and a Root Mean Square Error (RMSE) of 0.4283, significantly enhancing model performance. These findings suggest that iVISSA effectively identifies feature wavelengths highly correlated with protein content, thereby improving the model's precision and generalization capability. In conclusion, near-infrared hyperspectral imaging combined with machine learning models offers a powerful tool for accurately predicting wheat grain protein content, providing valuable technical support for wheat quality assessment in Ningxia.
Why it matches plant phenotyping methods小麦穀粒のタンパク質含量という植物形質を、近赤外ハイパースペクトル画像と機械学習で推定する手法が研究の中心であり、特徴波長選択とCNN性能評価も行っている。
abstractThis study systematically evaluated the capability of near-infrared hyperspectral imaging (HSI) for rapid and non-destructive detection of wheat grain quality across 14 varieties from multiple ecological zones in Ningxia.
Efficient and high-throughput prediction of crop yield and accurate assessment of varietal drought tolerance are essential for modern precision breeding and agricultural resource management and optimization. In this study, we propose a Stages-based Multimodal Data Fusion Model (S-MDFM) by integrating low-cost, high-throughput UAV-based multimodal imagery and multivariate data extracted from images. A staged error metrics (SEMs) propagation mechanism is constructed to capture the dynamic characteristics across growth stages and their dependencies with final yield, thereby improving the accuracy of cross-stage yield prediction (R2 = 0.8536, rRMSE = 16.12 %), validated prediction accuracy using data from the subsequent year in the same cropping area (R2 = 0.8370, rRMSE = 16.63 %). Based on wheat yield under varying water treatment gradients in drought stress experiments, the Drought Stress Tolerance Index (DSTI) and Drought Stress Susceptibility Index (DSSI) were employed to construct a Drought Resistance Index Differential (DRID) evaluation system. This dual-index approach enables multi-level screening of wheat varieties for drought resistance and quantitatively captures the synergistic relationship between yield performance and water adaptability and is capable of performing multi-level screening and quantifying the synergistic relationship between yield performance and water adaptability in wheat varieties, with an identification rate of drought-tolerant and high-yielding cultivars reaching 83.3 %. A multimodal fusion strategy based on discontinuous time-phase observations provides technical support for yield assessment and precise identification of drought-resistant and high-yielding varieties of wheat at different fertility stages, and the method provides a scalable multimodal data integration framework for varietal selection and breeding under drought-stressed field conditions, which is of great practical value for precision agriculture breeding applications.
Why it matches plant phenotyping methodsUAVマルチモーダル画像から画像特徴を抽出し、段階的データ融合モデルでコムギ収量を予測し、乾燥耐性品種を定量スクリーニングする手法が研究の中心であり、翌年データによる検証も行っている。
abstractwe propose a Stages-based Multimodal Data Fusion Model (S-MDFM) by integrating low-cost, high-throughput UAV-based multimodal imagery and multivariate data extracted from images.
Better matching of the timing and amount of fertilizer inputs to plant requirements will improve nutrient use efficiency and crop yields and could reduce negative environmental impacts. Deep learning can be a powerful digital tool for on-site, real-time, non-invasive diagnosis of crop nutrient deficiencies. A drone-based RGB image dataset was generated together with ground truthing data in winter wheat (2020) and in winter rye (2021) during tillering and booting in the long-term fertilizer experiment (LTFE) Dikopshof. In this LTFE, the crops were fertilized with the same amounts for decades. The selected treatments included full fertilization including manure (NPKCa+m+s), mineral fertilization (NPKCa), mineral fertilization but no nitrogen (N) application (_PKCa), no phosphorus (P) application (N_KCa), no potassium (K) application (NP_Ca), or no liming (Ca) (NPK_), as well as an unfertilized treatment. The image dataset consisting of more than 3600 UAV-based RGB images was used to train and evaluate in total of eight CNN-based and transformer-based models as baselines within each crop-year and across the two crop-year combinations, aiming to detect the specific fertilizer treatments, including the specific nutrient deficiencies. The field observations showed a strong biomass decline in the case of N omission and no fertilization, though the effects were lower in the case of P, K, and lime omission. The mean detection accuracy within one year was 75% (winter wheat) and 81% (winter rye) across models and treatments. Hereby, the detection accuracy for winter wheat was highest for the NPKCa+m+s (100%) and the unfertilized (96%) treatments as well as the _PKCa treatment (92%), whereas for treatments N_KCa and NPKCa the accuracy was lowest (about 50%). The results were similar for winter rye. In the cross-year and cross-cereal species transfer (training on winter wheat, application on winter rye, and vice versa), the mean accuracy was about 18%. The results highlight the potential of deep learning as a digital tool for decision-making in smart farming but also the difficulties of transferring models across years and crops.
Why it matches plant phenotyping methodsUAV RGB画像から作物の栄養欠乏・施肥状態を推定するデータセットと深層学習モデルを構築・評価しており、植物状態の取得・推定手法が研究の中心である。
abstractDeep learning can be a powerful digital tool for on-site, real-time, non-invasive diagnosis of crop nutrient deficiencies.
Improving water and nutrient use efficiency is essential for increasing crop yields and addressing global population growth. Optimal irrigation and nitrogen topdressing levels can enhance crop water and nitrogen use efficiency. UAV remote sensing has emerged as an efficient tool for optimizing water and nitrogen management due to its ability to monitor crop traits in real-time. This study proposed a UAV-based hyperspectral imaging method to optimize water-nitrogen management in winter wheat. By analyzing the interaction between nitrogen fertilizer and irrigation, a coupling decision model was developed for precise water-nitrogen application. Leaf water content (LWC) and chlorophyll content (SPAD) were estimated using machine learning algorithms combined with sensitive band selection methods, such as Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS). The SPA-Random Forest (RF) model performed best for LWC estimation (R² = 0.83, RMSE = 5.39 %), while the VIs-RF model was optimal for SPAD estimation (R² = 0.65, RMSE = 4.34 %). Conversion models linked LWC to soil water content (SWC) and SPAD to leaf nitrogen content (LNC), achieving R² values of 0.79 and 0.78, respectively. The proposed water-nitrogen coupling model exhibited strong adaptability and stability during key growth stages by integrating hyperspectral inversion data with field measurements. This model enables dynamic water and nitrogen application rate adjustments across the growing period to achieve target yields, optimize application strategies, and enhance use efficiency. The findings underscore the significant potential of UAV-based hyperspectral technology in optimizing water-nitrogen management. This method provides a reference for improving water-nitrogen use efficiency from the perspective of water-nitrogen coupling on yield.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像から葉含水量・クロロフィル含量を推定する手法を提案・評価しており、植物形質の取得・推定が水窒素管理への応用とともに中心的である。
abstractThis study proposed a UAV-based hyperspectral imaging method to optimize water-nitrogen management in winter wheat.
Timely and accurate acquisition of winter wheat yield information is crucial for ensuring food security and formulating agricultural policies. Although deep learning methods have become increasingly prominent in crop yield estimation, they often face challenges in simultaneously capturing both fine-grained local patterns and long-term temporal dependencies in time series data. By utilizing EVI, LAI, and fraction of photosynthetically active radiation (FPAR) from MODIS, along with temperature (TEM) and precipitation (PRE) data from ERA5-Land, we propose a novel dual-branch hybrid model named TCN–Transformer (TCT), which synergistically integrates temporal convolutional network (TCN) and transformer architectures to concurrently capture both localized temporal patterns and long-term dependencies. Bayesian optimization was employed for automated hyperparameter tuning, enabling accurate estimation of winter wheat yield under diverse agricultural management conditions. The experimental results demonstrate that optimal performance is achieved by the proposed TCT model in terms of estimating the county-level winter wheat yields across North China on the test set (R2 = 0.80, RMSE = 645.75 kg/ha). It significantly outperforms the individual temporal models (the TCN, LSTM, and transformer) and other comparative models, including traditional machine learning methods (Ridge, RF, LightGBM, and XGBoost) and an advanced hybrid model (CNN-BiLSTM). Specifically, compared with the individual models, the TCT improved R2 by 0.03 to 0.1 and reduced the RMSE by 29.33 to 156.07 kg/ha. It also outperforms CNN-BiLSTM (R2 = 0.78, RMSE = 668.23 kg/ha), achieving lower errors and more robust bias control. To elucidate the decision-making mechanism of the model, the Shapley additive explanations (SHAP) method was employed to analyze the feature importance values across the study region and the temporal feature weights at 8-day intervals. The results reveal that the EVI is the most representative feature, with the model accurately identifying critical growth stages from T20 (February 26) to T28 (May 1), corresponding to the greening to milk phases, respectively. The feature contribution dynamics were further visualized, revealing a transition from FPAR dominance during early greening (T20–T22) to EVI dominance during jointing (T23–T25), EVI‒PRE interactions during heading-milk (T26–T29), and finally LAI‒PRE dominance at maturity (T30–T32). Furthermore, the one-year leave-one-out cross-validation confirms the robustness of the TCT model, the simulation of yield spatial distribution for unseen years is consistent with the official yield data. Additionally, the proposed interpretability framework not only performed excellently in this study but also demonstrated strong generalizability and flexibility, indicating its broad application potential in other crop types and agricultural domains.
Why it matches plant phenotyping methods冬小麦の収量という植物形質を、マルチソースリモートセンシング時系列から推定する新規TCN–Transformer手法を開発・比較検証しており、形質抽出手法が中心である。
abstractwe propose a novel dual-branch hybrid model named TCN–Transformer (TCT)
Accurate wheat yield estimation at the farm scale is crucial for food security, market strategies, trade planning, and storage decisions. However, predicting crop production using remote sensing at farm scale presents significant challenges. This research aimed to develop a field-scale wheat yield prediction model using multi-temporal vegetation indices derived from Sentinel-2 MSI imagery for the rabi seasons of 2018–19 and 2019–20 from Badsu village in Alwar district, Rajasthan. Vegetation indices derived from cloud-free Sentinel-2 images spanning the crop growth cycle were processed to generate multiple vegetation indices, grouped into greenness, chlorophyll content, and dryness indicators. Spearman’s rank correlation (ρ) assessed relationships between indices and wheat yield across various phenological stages and their combinations. Linear and multiple linear regression (MLR) models were developed using the most significant indices. Findings indicate that Wide Dynamic Range Vegetation Index (WDRVI), Normalized Green-Red Difference Index (NGRDI), and Normalized Difference Water Index-2 (NDWI2), representing greenness, chlorophyll, and water stress, respectively, exhibited strong correlations with yield, except during harvesting and crown root initiation. The best-performing model achieved an RMSE of 0.47 tons/ha and an R² of 0.74, demonstrating the effectiveness of remote sensing indices for precise wheat yield estimation at the field level in diverse agricultural Conditions.
Why it matches plant phenotyping methodsSentinel-2由来の植生指数を用いて圃場スケールのコムギ収量を推定するモデルを開発・評価しており、植物の収量形質の取得・推定手法が研究の中心である。
abstractThis research aimed to develop a field-scale wheat yield prediction model using multi-temporal vegetation indices derived from Sentinel-2 MSI imagery
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Wheat quality detection plays a crucial role in the processing of grain storage, and moisture distribution is one of the main factors that affect wheat quality. The uniformity of moisture distribution in wheat grains significantly impacts their morphological structures, nutrient distribution, storage period, and stress resistance. This study detects the moisture distribution in wheat grains by using terahertz time-domain spectroscopy (THz-TDS) to scan wheat grains soaked for different times (0, 2, 4, 6, 8, and 10 h) and dried for different times (0, 1, 2, 3, 4, and 5 h). The scanned results are used to observe the water content changes in wheat grains on both temporal and spatial scales. This study calculates the average spectrum of wheat grains to observe the regular changes in the terahertz time-domain spectrum of wheat grains under different soaking and drying degrees. These changes exhibit opposite trends. The frequency domain spectra are obtained through Fast Fourier Transform (FFT), and comparing the imaging effects at different frequency points, it can be observed that there is a good consistency between frequency-domain imaging and time-domain imaging. The experimental results indicate that THz-TDS can be used to effectively observe the moisture distribution in wheat grains during the soaking and drying processes.
Why it matches plant phenotyping methodsTHz-TDSによる小麦粒内の水分分布という植物器官の状態を画像化・評価する手法が研究の中心であり、吸水・乾燥過程での画像化性能を検討している。
abstractThis study detects the moisture distribution in wheat grains by using terahertz time-domain spectroscopy (THz-TDS)
Feeding a growing global population requires that we must try to increase production and to reliably predict yields before a harvest. Since one anticipates farm-level yields, one enables better resource management, market planning, and sustainable agricultural decisions. The objective of this study is to develop also evaluate a deep learning regression framework because it predicts the yields of wheat, corn, as well as soybean farms using multi-temporal Landsat-5 and Landsat-7 imagery together with annual ground-truth yield records from the Kellogg Biological Station Long-Term Ecological Research (KBS LTER) site in Michigan, United States. The dataset spans across 11 cropping years (2001, 2012, with 2002 excluded) and the dataset covers 24 farms. There were two models: one had training directly on seven spectral bands, and one trained on vegetation indices (VIs) (NDVI, SAVI, EVI2, GRNDVI). A 2×2-pixel window data augmentation strategy was used to address the limited sample size and farm-level yields were then reconstructed via weighted aggregation of window-level predictions. The band-based model did achieve a higher accuracy of about 89.44%. That figure is superior to the result of 87.22% for the VI model. Wheat yields were most accurately predicted (88.3%) when crops were assessed. Soybean (87.01%) then corn (85.08%) followed this result. This study provides an effective and reproducible framework for farm-level yield prediction under limited data conditions with a fully connected deep learning model, combined with systematic window-based augmentation and weighted yield reconstruction. Landsat imagery with its single-site scope and its 30 m resolution did constrain the framework yet it highlights the potential of combining deep learning with optimisation principles that are regression-based. The framework offers too a scalable basis for integration with multi-source datasets as well as decision-support systems in precision agriculture.
Why it matches plant phenotyping methods衛星画像から農場レベルの作物収量を推定する深層学習フレームワークを開発・評価しており、収量という植物形質の取得・推定手法が研究の中心である。
abstractThe objective of this study is to develop also evaluate a deep learning regression framework because it predicts the yields of wheat, corn, as well as soybean farms using multi-temporal Landsat-5 and Landsat-7 imagery
This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study's results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production.
Why it matches plant phenotyping methods小麦病害の症状をハイパースペクトル画像から検出・分類する手法の開発が研究の中心であり、機械学習モデルの精度評価も行っている。
abstractdeveloping automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI)
The prevalence of diseases in wheat crops poses a significant threat to global food security, as it reduces yield and quality. Addressing these challenges is critical for sustainable agriculture. This study proposes and evaluates a hybrid deep learning (DL) model, EffiXB3, which combines Xception and EfficientNetB3 architectures, enhanced with edge-aware features, to improve disease classification in wheat crops. EffiXB3 employs a dual-input stream architecture, where one stream processes structural features, while the other incorporates textural features through Canny edge detection. The performance of individual models, Xception and EfficientNetB3, was assessed alongside the hybrid EffiXB3 model in a multi-class classification task involving five wheat leaf categories: Blast, Brown Rust, Healthy, Leaf Blight, and Septoria. Xception and EfficientNetB3 achieved classification accuracies of 95% and 93%, respectively. The proposed EffiXB3 model outperformed both, achieving an accuracy of 98.5%. The integration of edge-aware features substantially improved robustness and classification performance, particularly in differentiating visually similar disease patterns. The findings demonstrate the effectiveness of hybrid DL models with edge feature integration in diagnosing agricultural diseases. EffiXB3 offers a promising approach for enhancing disease detection in wheat, contributing to improved crop management and food security.
Why it matches plant phenotyping methodsコムギ葉の病徴を画像から分類する深層学習手法を開発・評価しており、植物の疾病状態推定が中心的な方法論的貢献である。
abstractThis study proposes and evaluates a hybrid deep learning (DL) model, EffiXB3, which combines Xception and EfficientNetB3 architectures, enhanced with edge-aware features, to improve disease classification in wheat crops.
Research is presented on solving a pressing problem in agricultural engineering: the development of an energy-efficient onboard system for the automatic detection of phytopathological plant diseases using computer vision and deep learning methods. The study was conducted in the context of the growing need for intelligent agricultural monitoring technologies capable of functioning in field conditions with limited computing resources. The object of the study is wheat crops examined in the agricultural landscapes of the Republic of Bashkortostan in various phases of vegetation. The scientific novelty lies in the construction of a modified neural network detector architecture based on a lightweight version of YOLO, including low-cost convolutional blocks GhostConv and MBConv, attention modules SE and CBAM, as well as a multi-level feature aggregation structure BiFPN with an additional output P2 to increase sensitivity to small-scale disease symptoms. Unlike the basic YOLOv5s architecture, the proposed solution is optimized for operation on NavQ Plus, Jetson TX2, and Raspberry Pi 4 computing modules. The model was trained on a sample of 7,500 images manually labeled by agricultural specialists for brown and yellow rust. To validate the performance, key metrics were used: Precision, Recall, F1-score, average IoU, FPS, and power efficiency (FPS/W). The experimental results showed the following achievements: F1-score up to 0.978, IoU up to 0.82, processing speed up to 16.8 FPS and power efficiency of 2.7 FPS/W on the NavQ Plus platform. A comparative analysis with the YOLOv5s baseline model confirmed the superiority of the proposed architecture across all key parameters. The developed model can serve as the foundation for building intelligent precision farming solutions, enabling early disease detection and adaptive application of crop protection products with minimal energy and computational costs.
Why it matches plant phenotyping methods植物の病徴を画像から検出・定量する軽量深層学習モデルとUAV搭載システムを開発し、性能比較・検証しているため、植物病害フェノタイピング手法が中心である。
abstractthe development of an energy-efficient onboard system for the automatic detection of phytopathological plant diseases using computer vision and deep learning methods
Seed germination is a critical phase strongly affected by abiotic stresses including drought and artificial seed ageing. Traditional indices like Germination Percentage (GP) and Mean Germination Time (MGT) often fail to capture complex stress responses and priming efficacy. This study introduces eight novel indices that quantitatively measure distinct physiological mechanisms: The Priming Efficiency Index (SPEI), Stress Performance Stability Index (SPSI), Germination Recovery Ratio (SGRR), and Combined Vigor Index (SCVI), among others. Tested on wheat under drought stress and priming treatments, the indices demonstrated 34.2% improvement in germination recovery with gibberellin priming compared to 25.8% with hydro-priming. The SCVI showed a 20.7% enhancement in integrated seedling performance, while SGRR achieved complete stress recovery (1.004) with gibberellin treatment. Validation across triticale and pumpkin revealed consistent performance, with cross-species correlations exceeding 0.89. Statistical analyses confirmed the novel indices' superior discriminatory power, requiring 37.6% smaller sample sizes than traditional metrics while maintaining 94% rank stability under data perturbations. These indices provide robust, mechanistically informed tools for precision phenotyping in breeding programs and seed technology research.
Why it matches plant phenotyping methods発芽・幼植物性能を定量化する新規指標を開発し、複数作物で性能検証しており、表現型測定法が研究の中心である。
abstractThis study introduces eight novel indices that quantitatively measure distinct physiological mechanisms
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract Recent advances in unmanned aerial vehicles (UAVs) and multispectral sensor technologies have transformed high-throughput phenotyping as an efficient alternative to traditional approaches. In this study, we employed UAV-based multispectral imaging to monitor early-stage drought stress in wheat. A panel of 221 historical spring wheat cultivars from Pakistan, representing over a century of breeding history, were evaluated under irrigated and drought conditions. UAV flights were conducted twice at early growth stages to capture multispectral imagery, which was processed in Pix4D mapper for image alignment and orthomosaic generation. Plot segmentation and trait extraction were performed in QGIS. Eight drought-responsive vegetative indices (VIs) were analyzed to assess genotypic variation. Significant to highly significant differences were observed among genotypes, treatments, and their interactions across all VIs. Broad-sense heritability estimates were moderate to high for most traits, with predominantly additive gene action. Vegetative indices such as NDVI, GNDVI, EVI, and SAVI showed strong correlations with each other and effectively detected early canopy stress under drought. Principal component analysis based on 23.897K SNPs indicated a mixed genetic population. Genome-wide association studies identified 115 significant QTNs linked to VIs under both conditions, corresponding to 74 loci, including 26 pleiotropic loci. Of these, 10 pleiotropic loci detected under drought were annotated and six putative candidate genes were identified which showed expression in multiple tissues based on transcriptome data. Two genes i.e., TraesCS1D01G217600.1 ( HSP70 ) and TraesCS6D01G254000.1 ( ZmMDAR3 ), showed differential expression pattern among control and drought treatment. These findings highlight the potential of UAV-based phenotyping for early drought detection and provide candidate genes for improving drought tolerance in wheat. Future research should focus on the functional roles of these genes under drought stress.
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、コムギの干ばつストレスという植物状態を評価するフェノタイピング手法が研究の主要な基盤であり、単なる補助的測定ではない。
abstractUAV-based multispectral imaging to monitor early-stage drought stress in wheat
Abstract Background Wheat ( Triticum aestivum ) is one of the most economically important crops in the United States. However, over the past two years, wheat production has suffered up to a 40% reduction in final yield due to pathogen infections worldwide. A major emerging threat in the Great Plains and Canadian Prairies, including South Dakota, North Dakota, and Minnesota, is bacterial leaf streak (BLS)/black chaff disease caused by Xanthomonas translucens spp., which has led to substantial yield losses in the last decade. Absence of both effective chemical controls and competitive highly resistant varieties makes BLS disease management very difficult. A critical step missing in this process is the establishment of a reliable and reproducible infection protocol for resistance evaluation under both controlled and field conditions. Currently, no protocols are published, and the methods published as part of research manuscripts lack detailed procedures, equipment specifications, and have major drawbacks for applications limited to controlled environment and discrepancies in field disease ratings scales. Therefore, we are presenting here a robust and reproducible BLS disease infection protocol and, disease severity rating scale for estimation of BLS disease in both controlled and field conditions. Results After Three days of inoculation with X. translucens pv. undulosa ( Xtu ), wheat plants developed initial water-soaked symptoms at inoculation sites. Over seven days, symptoms progressed to chlorosis and necrosis, frequently covering entire leaves of highly susceptible genotypes, whereas limited to no symptoms on resistant genotypes. Disease severity was consistently scored on a 1–9 scale, enabling clear differentiation of resistant, moderately resistant, and susceptible genotypes. Pathogen re-isolation confirmed infection fidelity. Field validation at the booting stage produced comparable symptom progression on flag leaves, with severity scored at 7, 14, and 21 days post-inoculation. The same protocol was successfully adapted for Pantoea ananatis and Xanthomonas prunicola , demonstrating the adaptability of the method. The protocol was repeated across five independent trials and produced reproducible results in both controlled and field environments. Conclusion We describe a simple, reproducible, and cost-effective inoculation protocol for evaluating BLS severity in wheat. The method reliably distinguishes resistance responses across environments and can be extended to other bacterial pathogens affecting small grains. Its affordability, accessibility, and reproducibility make it a valuable tool for large-scale germplasm screening and resistance breeding. Key Features • A detailed and systemic infection protocol is devised for different cultivars of wheat. • Plants can screen at seedling and adult-plant stage. • No specific equipment required. • Using an inexpensive pipeline to ensure uniform symptoms. • This protocol is validated for other bacterial species that are reported to cause bacterial leaf streak symptoms on small grains ( Pantoea spp and Xanthomonas prunicola on small grains (wheat and Barley).
Why it matches plant phenotyping methods植物の病徴・重症度を再現性よく取得・評価する感染プロトコルと重症度評価尺度を開発し、管理環境および圃場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す。
abstractwe are presenting here a robust and reproducible BLS disease infection protocol and, disease severity rating scale for estimation of BLS disease in both controlled and field conditions.
Abstract Wheat stem sawfly (WSS, Cephus cinctus Norton) threatens wheat ( Triticum aestivum L.) production in the US Great Plains. Increased stem solidness improves resistance to WSS but developing solid‐stemmed cultivars requires time‐consuming and destructive phenotyping. To expedite development of WSS‐resistant cultivars, a high‐throughput phenotyping method is needed. Therefore, we assessed phenomic prediction using uncrewed aerial systems (UAS) to predict stem solidness, WSS infestation, and yield in wheat. Multispectral and red‐green‐blue UAS data were collected in‐season at two naturally infested locations in western Nebraska from 2023 to 2024. The spectral reflectance indices from the UAS data were compared with agronomic traits (i.e., yield and plant height) and WSS traits (i.e., stem solidness and WSS infestation). Ridge regression, k ‐nearest neighbors (KNN), and random forest (RF) models were then trained to use spectral indices to predict yield, stem solidness, and WSS infestation. Correlations between WSS and spectral traits were temporally and environmentally dependent. The best prediction model depended on the biological trait. RF performed the best for yield ( r = 0.469), KNN for stem solidness ( r = 0.231), and ridge regression for WSS infestation ( r = 0.194). Predicting traits based on spectral data in a new environment was poor for both stem solidness ( r = 0.02–0.38) and WSS infestation ( r = 0.01–0.22). Our ability to predict WSS resistance was low, and UAS‐based phenotyping was not viable with current technology.
Why it matches plant phenotyping methodsUASマルチスペクトル/RGBデータと機械学習によって、茎の充実度、害虫被害、収量を推定し、環境間の性能を評価するフェノタイピング手法の検証が中心である。
abstractwe assessed phenomic prediction using uncrewed aerial systems (UAS) to predict stem solidness, WSS infestation, and yield in wheat
Introduction Excessive fertilization not only causes environmental pollution and degrades water and soil quality but also increases production costs and reduces agricultural sustainability. Methods Based on two consecutive years of field experiments, this study developed a two-step data assimilation strategy for nitrogen (N) topdressing recommendations for winter wheat. First, a data assimilation system was established by minimising the discrepancy between aboveground dry biomass (AGB) estimated from remote sensing and that simulated by the crop growth model using a particle swarm optimization approach. Second, target yields under varying growth conditions were constructed using the DSSAT model and N economic return curves to enable optimised N fertilization recommendations. Results AGB monitoring model was developed, achieving satisfactory results in both the calibration and validation datasets, with determination coefficient (R²) (normalised root mean square error (nRMSE)) values of 0.94 (13.62%) and 0.82 (15.42%), respectively. Based on the data assimilation system, the data assimilation stability for AGB and yield are relatively high. The nRMSE values for AGB are 11.20% and 19.44% for the training and validation datasets, respectively. The nRMSE values for yield are 6.35% and 11.22% for the training and validation datasets, respectively. The data assimilation-based recommended fertilization shows a negative power-law relationship with AGB at the jointing stage (R² = 0.65). Under different yield levels, fertilization was reduced by 6.69%-34.08% compared with that under high yield levels. Conclusion This study balances yield and production costs by developing a data assimilation strategy for N fertilization recommendations, which can maintain high productivity and sustainability.
Why it matches plant phenotyping methodsリモートセンシングから小麦の地上部乾物量を推定するモデルを開発・検証し、データ同化に組み込んでいるため、施肥推薦が主目的でも植物形質取得法が実質的に中心的役割を持つ。
abstractFirst, a data assimilation system was established by minimising the discrepancy between aboveground dry biomass (AGB) estimated from remote sensing and that simulated by the crop growth model using a particle swarm optimization approach.
Fusarium head blight (FHB) poses a significant threat to global wheat yields and food security, underscoring the importance of timely detection and severity assessment. Although existing approaches based on semantic segmentation and stereo vision have shown promise, their scalability is constrained by limited training datasets and the high maintenance cost and complexity of visual sensor systems. In this study, AR glasses were employed for image acquisition, and wheat spike segmentation was performed using Depth Anything V2, a monocular depth estimation model. Through geometric localization methods—such as identifying abrupt changes in stem width—redundant elements (e.g., awns and stems) were effectively excluded, yielding high-precision spike masks (Precision: 0.945; IoU: 0.878) that outperformed leading semantic segmentation models including Mask R-CNN and DeepLabv3+. The study further conducted a comprehensive analysis of differences between diseased and healthy spikelets across RGB, HSV, and Lab color spaces, as well as three color indices: Excess Green–Excess Red (ExGR), Normalized Difference Index (NDI), and Visible Atmospherically Resistant Index (VARI). A dynamic fusion weighting strategy was developed by combining the Lab-a* component with the ExGR index, thereby enhancing visual contrast between symptomatic and asymptomatic regions. This fused index enabled quantitative assessment of FHB severity, achieving an R2 of 0.815 and an RMSE of 8.91%, indicating strong predictive accuracy. The proposed framework offers an intelligent, cost-effective solution for FHB detection, and its core methodologies—depth-guided segmentation, geometric refinement, and multi-feature fusion—present a transferable model for similar tasks in other crop segmentation applications.
Why it matches plant phenotyping methods深度推定、幾何補正、特徴融合によってコムギ穂の病徴領域を抽出し、赤かび病重症度を定量推定する方法が研究の中心である。
abstractwheat spike segmentation was performed using Depth Anything V2, a monocular depth estimation model.
WheatField / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Root hydraulic properties affect water uptake in wheat ( Triticum aestivum L.) and are strongly influenced by root anatomy, yet how they vary along root axes and interact with cultivar differences remains underexplored. We investigated crown roots of six German winter wheat cultivars spanning one century of release, sampled from a field experiment. Roots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to GRANAR–MECHA to model radial ( K r ) and axial conductance ( k x ). Longitudinal anatomical gradients were pronounced: tissue dimensions, metaxylem number, and apoplastic barriers decreased from the base onwards, resulting in K r increasing and k x decreasing with distance from the base. Cultivar differences were also apparent: modern cultivars had smaller tissues and fewer metaxylem vessels, reducing both axial and radial conductance and lowering whole-root water uptake capacity (∼20–30%). By integrating field sampling with high-throughput image analysis and mechanistic modeling, this study establishes an integrated phenotyping approach that links root anatomy to water uptake and uncovers anatomical traits relevant to hydraulic function. The results show that longitudinal gradients and cultivar-associated anatomical differences contribute to variation in hydraulic properties and persist along fully mature root segments. Highlight High-throughput imaging–modeling shows that longitudinal gradients and cultivar-associated anatomical differences along crown roots shape radial and axial conductance, leading to reduced whole-root water uptake capacity in modern winter wheat
Why it matches plant phenotyping methods根の高スループット画像解析と機械論的モデリングを統合し、解剖形質から水理特性を推定するフェノタイピング手法が研究の中心であるため。
abstractRoots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to GRANAR–MECHA to model radial ( K r ) and axial conductance ( k x ).