RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Field / plotFlowerClassificationObject detectionGrowth / development / phenology
Abstract Dioecious crops face significant pollination challenges due to the asynchrony in flowering between male and female plants. This asynchrony varies spatially across orchards, requiring targeted interventions in zones where synchrony is lacking. Assisted pollination addresses this deficiency, albeit at a substantial operational cost that could be optimised through spatial phenological mapping. Manual assessment proves economically infeasible at commercial scales, while existing computer vision systems are unable to classify phenological stages and integrate geospatial information. This study presents the Mobile Phenological Mapping (MPM) Framework, which integrates GNSS-synchronised smartphone video with automated phenological detection to generate plant-level phenological distribution maps validated in commercial kiwifruit (Actinidia chinensis) orchards. This modular framework comprises (i) training and validation data acquisition, (ii) model optimisation, (iii) operational pipeline, and (iv) performance evaluation. MPM employs hierarchical deep learning across three stages: structure detection, gender classification, and phenological stage classification. Video frames are georeferenced through timestamp matching with GNSS metadata, enabling spatial phenological mapping. Operational validation across four commercial orchard zones demonstrated mean absolute percentage errors of 17.2% for structure detection and 20.1% for gender classification. The framework reduces monitoring time from 113 to 1.6 hours per hectare, decreasing labour costs from €2 060 (113 hours × 18.20 € per hour) to €29 (1.6 hours × 18.20 € per hour) per hectare based on the Portuguese hourly labour cost for minimum wage workers. When integrated with routine orchard operations, video acquisition incurs negligible additional cost. MPM provides growers with precision phenological maps for targeted pollination interventions. While validated in a kiwifruit orchard, the modular architecture can be adapted to other crops by replacing the training data.
Why it matches plant phenotyping methodsスマートフォン動画とGNSSを用いて植物体レベルの性別・生育段階を自動検出し、フェノロジー分布を作成する手法と、その性能評価・商業園での検証が研究の中心である。
abstractThis study presents the Mobile Phenological Mapping (MPM) Framework, which integrates GNSS-synchronised smartphone video with automated phenological detection to generate plant-level phenological distribution maps validated in commercial kiwifruit (Actinidia chinensis) orchards.
Growth chamberMultimodalStereoRootStem / branchTrackingGrowth / development / phenology
Understanding plant behaviour requires the integration of multiple phenotypic and physiological signals measured over time under controlled conditions. However, different plant signals are typically studied using separate experimental setups, limiting temporal alignment and integrative analyses.We present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging. Its modular architecture is designed to accommodate additional acquisition modules, such as electrophysiological signalling, as future extensions. The platform integrates a controlled growth environment with stereovision imaging, high-resolution time-of-flight mass spectrometry and custom rhizocameras. These components are connected through a unified network infrastructure that ensures synchronized acquisition and centralized data handling.We validate the performance of each acquisition module through multi-week recordings, demonstrating high-temporal stability, reliable stereovision synchronization, effective isolation of VOCs signals and robust operation of below-ground imaging. We further illustrate the analytical potential of the platform using a one-day continuous multimodal acquisition combining shoot kinematics, above-ground VOC emissions, rhizocameras observations and environmental data.Mind(the)Plant provides a novel methodological framework for studying plant behaviour, signalling and phenotypic plasticity in ecological and evolutionary research. By enabling coordinated measurements of multiple plant response modalities, the platform supports investigations of dynamic plant-environment and plant-plant interactions from a behavioural perspective.
Why it matches plant phenotyping methods植物の複数の表現型・生理シグナルを同期取得する施設を開発し、各取得モジュールの性能を検証しているため、表現型計測プラットフォームが研究の中心です。
abstractWe present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging.
Reproduction assets foundThe paper's data availability statement explicitly deposits data, code and processing pipelines (supporting the multimodal plant phenotyping measurements and analysis) in a public Zenodo archive with an authors' URL matching an allowed URL.Code · publicf Interest Statement
The authors have no conflicts of interest to declare.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70411 .
Data availability Statement
Data, code and processing pipelines supporting this study are available at https://doi.org/10.5281/zenodo.22095454 ( Simonetti & Castiello, 2026 ).
References
Avesani S, Bonato B, Simonetti V, Guerra S, Ravazzolo L, Gjinaj G, Dadda M, Castiello U. Comparing proton transfer reaction (PTR) and adduct ionization mechanism (AIM) for the study of volatile organic compounds. Molecules. 2026;31(3):402. doi: 10.3390/molecules31030402.
Baluška F, LeOpen asset ↗zenodo · 10.5281/zenodo.22095454lines:482-508Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Olive ( Olea europaea L.) breeding initiatives rely heavily on the extensive and correct characterisation of genetic resources to successfully achieve their goals, such as addressing climate change and emerging diseases challenges. However, the historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses. Methods Within the Horizon 2020 GEN4OLIVE project, five international olive germplasm banks established a consensus-based methodological framework to systematically evaluate over 500 olive cultivars. We harmonised 14 evaluation protocols covering six fundamental dimensions: phenological and agronomic traits, abiotic stress resilience, biotic stress resilience, olive oil yield and chemical quality, table olive quality assessment, and morphological characterisation and photography. While most protocols were adapted from previously published literature to ensure ease of implementation across different facilities, novel methodologies for frost tolerance and standardised photography were developed de novo. Results The implementation of these consensus methods across five countries proved highly successful. This methodological framework enabled the generation of the largest harmonised, publicly available dataset on olive genetic resources to date, effectively making possible the correct comparation and ranking of the olive cultivars based on their specific characteristics. Conclusions This compendium of methods provides a robust, highly replicable reference point for the standardisation of olive germplasm characterisation and use of shared benchmark cultivars as an effective way for data normalization and comparation. It facilitates future global pre-breeding efforts, ensures international data interoperability, and supports the discovery of resilient cultivars to secure the future of the olive sector.
Why it matches plant phenotyping methodsオリーブ遺伝資源の標準化フェノタイピングプロトコルを体系化し、複数機関で実装・検証して大規模データセットを生成した方法論中心の研究である。
abstractthe historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses.
Reproduction assets foundThe article declares two paper-specific public assets: the GEN4OLIVE phenotypic dataset from evaluating over 500 olive accessions across five germplasm banks, hosted on the project's Olive Varieties Database, and a Zenodo-deposited methodological handbook (Extended Data) containing the 14 protocols, visual assessment, Dataset · publicData and software availability
The phenotypic dataset generated from the evaluation of over 500 olive varieties across the five Mediterranean
germplasm banks using this compendium of protocols and methodologies, is publicly available via the GEN4OLIVE
project repository.
• Repository: GEN4OLIVE Olive Varieties Database.
• Link: https://www.uco.es/ucolivo/gen4olive/olivevarieties (GEN4OLIVE Database, 2025).
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Open Research Europe 2026, 6:322 Last updated: 14 SEP 2026Open asset ↗GEN4OLIVE Olive Varieties Databasepdf-raw-page:8 lines:1-44Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published2 Sept 2026TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Key message Yield-Graph enables accurate maize yield prediction from incomplete multi-stage phenotypic and environmental data by modeling higher-order environment-trait interactions, with robust applicability across growth stages, regions, and crop species. Accurate yield prediction before maize harvest is crucial for advancing agricultural management and ensuring food security. Unlike conventional approaches that rely on traits from a single growth stage, this study models multiple traits across different developmental stages, all targeting final yield, thereby uncovering their stage-specific contributions and demonstrating the feasibility of early yield prediction. We introduce Yield-Graph, an innovative framework that evaluates phenotypic data at distinct developmental stages for yield prediction. The method employs a bipartite graph structure to impute missing trait values at each stage and leverages a hypergraph attention mechanism to capture high-order sample relationships. Comprehensive benchmark experiments demonstrate that Yield-Graph matches the top-tier predictive accuracy of exhaustively optimized tree models. Moreover, the framework exhibits strong robustness across growth stages, high adaptability to regional variations, and effective generalization across datasets. These findings highlight the potential of graph-enhanced multi-stage modeling for early-stage yield prediction, offering a scalable solution for precision agriculture and intelligent crop management.
Why it matches plant phenotyping methods作物収量という植物形質を、複数時期の表現型データから推定するグラフニューラルネットワーク手法を開発し、ベンチマーク評価しているため、計算型フェノタイピング手法が中心である。
abstractWe introduce Yield-Graph, an innovative framework that evaluates phenotypic data at distinct developmental stages for yield prediction.
Accurate and efficient monitoring of tea plant growth parameters via remote sensing is essential for precision plantation management. However, spectral indices relying solely on reflectance often exhibit limited sensitivity in capturing complex tea canopy characteristics. This study developed a data-driven framework integrating spectral reflectance, frequency-domain harmonic components, and spatial texture features to construct tri-feature fusion indices (TFIs) and establish machine learning and deep learning models for tea growth monitoring. Ten-band multispectral imagery was acquired using a UAV alongside synchronous field measurements of leaf and plant biomass and nitrogen accumulation. TFIs were constructed through exhaustive feature combinations and optimized via a data-driven search strategy. Subsequently, random forest (RF), multilayer perceptron (MLP), convolutional neural network (CNN), and transformer models were evaluated using a leave-one-site-out cross-validation (LOSO-CV) strategy. The selected TFIs showed strong associations with tea growth parameters within the investigated dataset, with R2 values up to 0.63 and 0.62 for leaf dry matter and leaf nitrogen accumulation, respectively. Models incorporating selected TFIs achieved cross-validated R2 values of 0.56 for leaf dry matter (MLP), 0.59 for plant dry matter (MLP), 0.73 for leaf nitrogen accumulation (MLP), and 0.68 for plant nitrogen accumulation (CNN). These models exhibited competitive predictive performance comparable to RF, although no statistically significant differences in mean absolute error were observed under site-held-out evaluation. Furthermore, model-derived spatial maps provided insights into fine-scale spatial heterogeneity and potential interannual variations in tea growth parameters across representative plantations from 2024 to 2025. Overall, this study provides a UAV-based framework for tea growth parameter estimation by integrating multi-domain information without requiring additional environmental observations.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から茶植物の乾物量・窒素蓄積を推定する特徴量融合および機械学習・深層学習フレームワークを開発し、サイト外交差検証で評価しており、植物形質の取得・推定手法が中心である。
abstractThis study developed a data-driven framework integrating spectral reflectance, frequency-domain harmonic components, and spatial texture features to construct tri-feature fusion indices (TFIs) and establish machine learning and deep learning models for tea growth monitoring.
Abstract - Plant diseases can significantly affect plant growth, productivity, and overall health. This research presents a mobile-based plant analysis system designed to identify plants, assess their health condition, and analyze visible diseases from plant images. The proposed system allows users to capture an image using a mobile camera or upload an existing image. The image is processed and analyzed using machine learning and deep learning techniques. A Convolutional Neural Network (CNN) can be used to learn visual features such as leaf shape, color, spots, and disease symptoms for plant identification and disease analysis. The system also provides a health assessment and disease severity indication to support users in understanding the condition of a plant. A Flutter-based mobile application provides the user interface, while Python and Flask can be used for image-processing and model-serving tasks. The proposed approach aims to provide a simple and accessible tool for preliminary plant identification, health assessment, and disease analysis. Key Words: plant identification, plant health assessment, disease analysis, CNN, deep learning, Flutter.
Why it matches plant phenotyping methods植物画像から健康状態と病害症状・重症度を推定する機械学習システムの開発が中心であり、植物の病害状態という表現型を画像から取得・評価する方法を扱っている。
abstractThis research presents a mobile-based plant analysis system designed to identify plants, assess their health condition, and analyze visible diseases from plant images.
Cauliflower emergence rate and seedling growth are key indicators of field conditions and varietal potential. Traditional manual surveys are unsuitable for continuous monitoring across multiple varieties. This study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth. Built on YOLOv11, the model incorporates a lightweight feature extraction structure and an optimized detection-scale configuration. It reduces computational complexity while maintaining detection accuracy, thereby improving the efficiency of cauliflower seedling detection. DualSlim-YOLO achieved P, R, F1-score, mAP@0.5, and mAP@0.5:0.95 of 95.35%, 96.75%, 96.05%, 98.55%, and 86.65%, respectively. The number of parameters was reduced by 38.61%, while the inference speed increased by 22.16%, demonstrating good lightweight performance. Based on this model, UAV images of 171 cauliflower varieties acquired at 7, 21, and 28 d after transplanting were used for seedling detection and emergence rate estimation. In addition, 18 time-series seedling phenotypic traits were extracted, enabling a comprehensive quantitative evaluation of emergence dynamics and early-growth performance across multiple cauliflower varieties. This method effectively screens cauliflower varieties for high emergence rates, rapid emergence, and excellent seedling growth performance. It provides technical support for high-throughput, nondestructive seedling phenotyping and early germplasm screening under field conditions.
Why it matches plant phenotyping methodsUAV画像と軽量YOLOモデルを用いて、カリフラワー苗の検出、出芽率推定、18種類の時系列表現型形質抽出を行う手法が中心であり、モデル性能も検証している。
abstractThis study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 11 Sept 2026
Published30 Aug 2026bioRxiv (Cold Spring Harbor Laboratory)
Abstract The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery–based phenotyping, from data acquisition to plot-and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (G×E) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams—including remote sensing, environmental, and management data—toward data-driven decision making in agriculture.
Why it matches plant phenotyping methods航空画像から作物形質を自動抽出するエンドツーエンドのフェノタイピング基盤を開発・実証しており、形質取得と解析ワークフローが研究の中心です。
abstracta scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery–based phenotyping, from data acquisition to plot-and genotype-level inference.
Alfalfa / lucerneX-ray / CTRoot2D/3D reconstructionVisualization / data managementGrowth / development / phenology
The symbiotic relationship between the legume Medicago sativa and the soil bacteria Sinorhizobium meliloti results in the formation of nitrogen-fixing root nodules. Traditional destructive methods, including paraffin sectioning, vibratome sectioning, and cryosectioning, have been applied to visualize how bacteria occupy the nodule, making it extremely difficult to obtain reliable three-dimensional information. These approaches are often combined with fluorescent labeling or staining, which can introduce additional stress affecting plant growth and nodule formation. MicroCT has emerged as a relatively quick, easy, and robust tool for plant biology that can non-destructively visualize plant histological features in three dimensions (3D), thereby avoiding destructive artifacts during sample preparation and ensuring high-fidelity 3D reconstruction. While microCT has been applied to legume root nodules, a detailed established protocol that documents the process from plant harvest and sample preparation to scanning and software visualization is lacking. In this study, we show a step-by-step microCT workflow using Medicago sativa as a model. The protocol includes nodule excision from roots, fixation, contrast enhancement, mounting, scanning, and three-dimensional reconstruction. Critical parameters affecting elements such as image quality, tissue preservation, and contrast are highlighted. Using this approach, it is possible to visualize the overall tissue organization, bacteroid-infected cells, and vascular bundles in three dimensions without physically sectioning the nodules. The pipeline described here provides a reproducible method for non-destructive, high-resolution imaging of native root nodules and is likely adaptable to other legume species, offering researchers a practical tool for studying nodule structure and bacterial organization within nodules in 3D.
Why it matches plant phenotyping methods根粒の組織構造と感染細胞を3Dで取得するMicroCT撮像・再構成プロトコルが研究の中心であり、植物器官の形態状態を測定する実質的なフェノタイピング手法である。
abstractIn this study, we show a step-by-step microCT workflow using Medicago sativa as a model.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 5 Sept 2026
Abstract Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments – including different growth irradiances, heat treatment and drought stress – with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.
Why it matches plant phenotyping methods3Dマルチスペクトル画像・ハイパースペクトル画像と機械学習を統合し、植物形態・スペクトル形質を非破壊かつ高スループットに取得・検証する方法が研究の中心である。
abstractNon-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function
Abstract Background and aims Plant volatile organic compounds (VOCs) change dynamically with plant development and in response to environmental conditions. However, their potential as non-invasive indicators of phenological progression remains poorly explored. In this study, we developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology. Using soybean ( Glycine max (L.) Merr.), we investigated whether development-associated temporal variation in VOC emissions could delineate and predict developmental phases. Methods We collected VOCs daily under controlled environmental conditions from 16 to 43 days after sowing, spanning the transition from vegetative to reproductive stages, using an automated sampling system coupled with thermal desorption-gas chromatograph-mass spectrometer (TD- GC-MS). To characterise temporal changes in VOC profiles associated with phenological progression, we analysed the daily VOC data using a multi-step pipeline combining statistical filtering and similarity-based network analysis. We defined VOC-derived developmental phases from similarity patterns in the VOC profiles, then developed and evaluated machine-learning models to predict these phases. Key results Seven VOCs exhibited distinct phase-dependent dynamics, including green leaf volatiles and monoterpenes showing characteristic temporal changes during phenological progression. Network-based clustering of VOC profiles resolved five developmental phases closely aligned with conventional developmental stages. A machine-learning model predicted these phases from the VOC profiles with high predictive accuracy on independent test data, demonstrating that phenological progression could be quantitatively inferred from VOC emission patterns. Conclusions Our findings support VOC profiling as a reliable and non-invasive approach for assessing phenological progression in soybean. By extracting temporally structured VOC signals, this framework captures developmental information that may be difficult to obtain through visual observation alone, particularly after canopy closure. VOC profiling offers a practical tool for monitoring crop developmental dynamics and has broader potential for plant phenotyping and precision crop management.
Why it matches plant phenotyping methods自動VOCサンプリング、時系列VOCプロファイリング、機械学習を統合し、VOCから植物の発育段階を非破壊推定する方法を開発・評価しており、フェノタイピング手法が中心である。
abstractwe developed a framework integrating automated VOC sampling, time-resolved VOC profiling, and machine-learning analysis for the non-invasive assessment of plant phenology.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe peak area matrix obtained from the MS- DIAL analysis (Supplementary Dataset S1) was filtered to remove unreliable features.Open asset ↗lines:66-69Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Abstract The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (G×E) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams-including remote sensing, environmental, and management data - toward data-driven decision making in agriculture.
Why it matches plant phenotyping methods植物の航空画像から形質を自動抽出するエンドツーエンドのフェノタイピング基盤を開発・実証しており、形質取得ワークフローとプラットフォームが研究の中心である。
abstracta scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference.
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Microplastics (MPs) are persistent and ubiquitous contaminants in aquatic ecosystems, yet their interactions with submerged aquatic plants remain poorly understood. While MP-induced phytotoxicity has been extensively investigated in terrestrial plants, quantitative evidence for MP uptake and internal accumulation in submerged species is still limited. In this study, we investigated the phytotoxicity and accumulation patterns of fluorescent microplastics (FMPs) in two submerged aquatic plants, Bacopa lanigera and Rotala indica, using fluorescence spectroscopy. Plants were exposed to FMPs of two particle sizes (50 nm and 1 µm) across three exposure concentrations (0.001%, 0.01%, and 0.05%). Plant growth, chlorophyll content, fluorescence emission, and FMP accumulation were systematically evaluated. Our results demonstrated clear size- and concentration-dependent responses. Smaller particles (50 nm) showed significantly higher uptake and induced stronger phytotoxic effects than 1 µm particles, with pronounced growth inhibition and chlorophyll reduction observed at the highest concentration (0.05%). Fluorescence-based analysis enabled quantitative estimation of both surface-associated and internalized FMPs within plant tissues. Maximum surface accumulation reached 207 ppm, while internal (cross-sectional) accumulation reached up to 75 ppm, regardless of plant species. Under the respective experimental conditions, B. lanigera exhibited higher estimated FMP accumulation, whereas R. indica showed greater growth inhibition. These findings provide quantitative evidence of microplastic uptake and internal accumulation in submerged aquatic plants and highlight particle size as a critical determinant of phytotoxicity. Moreover, this study establishes a fluorescence-based methodological framework for estimating microplastic concentrations in aquatic plant tissues, contributing to improved ecological risk assessment of microplastics in freshwater ecosystems.
Why it matches plant phenotyping methods蛍光分光法による植物組織内マイクロプラスチック蓄積の定量が中心的な技術貢献であり、植物の蓄積状態と毒性関連表現型を評価している。
Maize ( Zea mays L.) is a highly adaptable crop grown worldwide across diverse climates and management practices, with uses across multiple sectors. Consequently, the traits prioritized in maize research and breeding programs vary depending on the specific objectives. Core traits, however, such as flowering time, plant and ear height, and stalk and root lodging, which are important for evaluating and improving the performance and stability of maize genotypes, are routinely evaluated across breeding programs, regardless of their goals. Standardized measurement of these core traits is essential to ensure data reliability and comparability, enabling the integration of phenotypic data across different experiments. Such efforts ultimately support better decision-making and accelerate the development of improved maize genotypes. This is particularly important in public sector programs, where large-scale evaluations, critical for assessing the value of specific genotypes, are often only feasible through collaboration across programs. Here, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials. These methods promote consistency and accuracy in the evaluation of these traits, and support communication and coordination among groups in the public sector and other research settings. Further, such standardization facilitates the integration and comparison of data across programs, enabling robust longitudinal and multienvironment analyses.
Why it matches plant phenotyping methodsトウモロコシの主要形質を対象に、圃場試験での表現型データ収集を標準化するプロトコル自体を提示しており、測定方法が研究の中心である。
abstractHere, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials.
Background Global soybean production is constrained by scarce arable land, and standardized evaluation tools remain lacking for natural mixed saline-alkali stress, the predominant abiotic stress under field conditions. Objective This study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification. Methods Seventy-one soybean germplasms were tested under 90 mmol/L mixed saline-alkali stress (NaCl:Na 2 SO 4 :NaHCO 3 :Na 2 CO 3 = 1:9:9:1, pH 8.2) simulating natural saline-alkali soil. We quantified the saline-alkali tolerance coefficients (SATC) of 13 morphological and physiological indicators, followed by coefficient of variation (CV), principal component analysis (PCA), subordinate function, cluster analysis and regression modeling. Results Significant inter-germplasm variations in saline-alkali tolerance were detected, and indicators with CV > 0.35 ( e.g ., root length (RL), root fresh weight (RFW)) were screened as primary indices. PCA extracted five principal components with 87.18% cumulative variance contribution, and the integrated analytical pipeline categorized germplasms into five tolerance grades: eight highly tolerant, 24 moderately tolerant, 13 generally tolerant, 16 sensitive and 10 highly sensitive accessions. A high-precision prediction model was constructed ( D = 0.290 X 1 - 0.026 X 2 + 0.438 X 3 + 0.402 X 4 + 0.180 X 5 + 0.153 X 6 + 0.813 X 7 - 1.123; R 2 = 0.998, where X 1 - X 7 represent the SATC of germination rate (GR), RL, RFW, total fresh weight (TFW), shoot dry weight (SDW), root dry weight (RDW), and total dry weight (TDW), respectively). A Chi-squared Automatic Interaction Detection (CHAID) decision tree model was further developed and validated using 10-fold cross-validation, yielding a cross-validation risk value of 0.003, which was comparable to the resubstitution risk value (0.002), indicating good generalization ability and low risk of overfitting. A novel five-dimensional overlapping analysis identified RFW as the core evaluation indicator, with RDW, TFW and R/S as key auxiliary indicators. Conclusion This study delivers a standardized, reproducible technical framework for large-scale screening of saline-alkali-tolerant soybean germplasms. It facilitates global saline-alkali land utilization, accelerates worldwide soybean stress-tolerance breeding, and provides a transferable paradigm for stress tolerance evaluation in other major crops.
Why it matches plant phenotyping methodsダイズの耐塩・耐アルカリ性を評価するための形態・生理形質の統合評価体系、予測モデル、指標選定、交差検証を中心的に開発・検証しており、再利用可能な植物表現型評価手法に該当する。
abstractThis study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification.
Abstract Purpose Timely, accurate, and field-scale crop yield mapping is essential for precision crop management, yet most existing studies rely on late- or full-season data, limiting in-season decision-making. This study aims to develop a stage-aware earliest possible corn yield mapping framework that balances early data availability, predictive accuracy, and spatial fidelity by integrating Sentinel-2 imagery, vegetation indices (VIs), and accumulated growing degree days (AGDD). Methods Corn yield data were collected from a commercial farm over three growing seasons (2018–2020). The final modeling dataset included 51,794 Sentinel-2 10 m aggregated yield samples across three seasons: 2018 ( n = 16400), 2019 ( n = 18534), and 2020 ( n = 16860). Sentinel-2 raw spectral bands and derived VIs were organized for V4, V6, R1, R5, and R6 growth stages based on AGDD- and DAP-defined corn growth-stage windows, also confirmed visually on-ground, allowing observations from different planting and harvest dates across the three growing seasons to be aligned by crop developmental stage rather than calendar date. A stage-wise Pearson correlation and frequency-based selection identified informative and non-redundant VI subsets across crop development. Four machine learning models: Random Forest (RF), XGBoost (XGB), k-Nearest Neighbors (kNN), and a Neural Network (NNET; multi-layer perceptron) were trained using 13 input configurations, including individual growth stages and multi-stage combinations capturing phenological progression. Models were tuned via randomized search, trained on 2018–2019 data, and independently validated on the 2020 season. Yield predictions were mapped directly at 10 m Sentinel-2-pixel resolution without spatial interpolation to preserve fine-scale variability. Results Model performance was strongly influenced by phenological stage selection. Among single-stage inputs, R1 was the earliest stage where reliable yield mapping could be availed (RF: R² = 0.56, RMSE = 29.50%). While combining V6 with R1 stage inputs substantially improved predictive performance (RF: R² = 0.70, RMSE = 24.13%) for the yield mapping at the R1 stage, where V6-stage signals provided complementary yield-related information. The full-season combination (V4 + V6 + R1 + R5 + R6) produced the highest accuracy (RF: R² = 0.72, RMSE = 23.28%) but would be less suitable for early in-season decision-making. Early- or late-stage-only inputs (V4, R5, R6) showed weaker and less stable cross-year performance. Among algorithms, RF consistently generalized best to the independent 2020 dataset and is recommended for operational use. XGB showed strong training performance but reduced cross-year stability, kNN yielded moderate accuracy, and NNET achieved accuracy comparable to RF while closely reproducing observed spatial patterns such as center-pivot effects and edge gradients. Direct 10 m mapping preserved yield heterogeneity and avoided smoothing artifacts common in interpolation-based approaches. Conclusion Stage-aware feature selection and phenology-informed input combinations are critical for balancing yield prediction timeliness and accuracy. For operational in-season yield mapping, the RF model using the V6 + R1 stage combination provides a practical and reliable solution, enabling early, accurate, and spatially detailed yield estimates with robust cross-year performance. This study presents a deployable framework for integrating satellite time series and weather data into conventional (non-sequential) machine learning models to support proactive, within-season decision-making in precision agriculture.
Why it matches plant phenotyping methods衛星画像・気象データと機械学習を統合し、作物の収量という明示的な植物形質を圃場内10 m解像度で推定する手法を開発・独立年で検証しており、フェノタイピング手法が中心である。
abstractThis study aims to develop a stage-aware earliest possible corn yield mapping framework that balances early data availability, predictive accuracy, and spatial fidelity by integrating Sentinel-2 imagery, vegetation indices (VIs), and accumulated growing degree days (AGDD).
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
Cell polarity and tip growth rely on the dynamic spatial organization of signaling and structural components. Quantitative characterization of these spatiotemporal dynamics is critical for understanding polarized cell growth, yet manual quantification is labor-intensive and existing computational tools often lack the flexibility and robustness needed to analyze molecular and structural dynamics in tip-growing cells. Tip Quantification (TipQuant) identifies the cell apex by detecting the site of maximum expansion and automatically quantifies fluorescence distribution along the plasma membrane and within the apical cytoplasm from live-cell imaging data, enabling analysis of the spatiotemporal dynamics of molecular and structural components in tip-growing cells. TipQuant accurately identified cell apices and quantified the spatiotemporal behavior of fluorescently labeled proteins and cellular structures in Arabidopsis thaliana pollen tubes and Fusarium graminearum hyphae, reproducing manual measurements while reducing user bias and improving efficiency, consistency, and analytical flexibility. The tool also revealed a strong positive correlation between rho-like GTPase from plants activity and apical Ca 2+ influx in Arabidopsis pollen tubes, demonstrating its utility for analyzing dynamic cellular processes. TipQuant is a robust analytical tool for quantifying spatiotemporal dynamics in tip-growing cells, providing a flexible alternative to manual image analysis and enabling studies of the molecular mechanisms underlying polarized growth.
Why it matches plant phenotyping methodsTipQuantはライブセル画像から植物の細胞先端位置、膜上の蛍光分布、先端細胞質内の動態を自動定量する解析ツールであり、画像ベースの植物表現型・状態取得が研究の中心です。
abstractTip Quantification (TipQuant) identifies the cell apex by detecting the site of maximum expansion and automatically quantifies fluorescence distribution along the plasma membrane and within the apical cytoplasm from live-cell imaging data
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
Abstract Understanding the allometric relationships between leaf area and other plant traits is essential for non-destructive growth monitoring and efficient crop management. However, no comprehensive study has yet modeled leaf area in quinoa ( Chenopodium quinoa Willd.) using simple morphological traits across different sowing dates. This study aimed to quantify allometric relationships between leaf area and plant height, leaf dry weight, stem dry weight, panicle dry weight, and total dry matter, and to evaluate whether these relationships are modified by sowing date. A two-year field experiment was conducted with 12 sowing dates under a randomized complete block design with three replications. Leaf area index (LAI) dynamics were described using a logistic model, and allometric relationships were fitted using power-law equations. The results showed that LAI followed a logistic trend across all sowing dates, with maximum values ranging from 2.7 to 7.9. Plant height provided the most reliable prediction of leaf area (R² = 0.83, b = 1.2), followed by leaf dry weight (R² = 0.72, b = 0.97). The allometric coefficients for stem dry weight (b = 1.54, R² = 0.74) and panicle dry weight (b = 1.95, R² = 0.71) showed greater variability. A striking finding was the exceptionally high allometric coefficient (b = 4.95) recorded on May 6 of the second year, indicating a pronounced shift in resource allocation toward leaf area expansion. Total dry matter was a weak predictor (R² = 0.54), likely due to leaf fall during the growing season. The hypothesis that sowing date modifies allometric relationships was confirmed, as evidenced by considerable variation in allometric coefficients across sowing dates. This study provides, for the first time, a comprehensive set of allometric models for quinoa across multiple sowing dates. Plant height and leaf dry weight are recommended as simple, rapid, and non-destructive indicators for leaf area estimation, facilitating improved crop monitoring and management under diverse environmental conditions.
Why it matches plant phenotyping methods草丈や乾物重から葉面積を非破壊推定するアロメトリックモデルを中心に開発・評価しており、植物形質の取得手法が実質的な主題である。
abstractThis study aimed to quantify allometric relationships between leaf area and plant height, leaf dry weight, stem dry weight, panicle dry weight, and total dry matter, and to evaluate whether these relationships are modified by sowing date.
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:266-273Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Boll-opening concentration is critical for mechanical cotton harvesting, yet it is still assessed mainly by manual records and single time-point indicators that miss temporal dynamics. To bridge the lack of a unified workflow linking vision foundation models, multi-temporal boll-opening monitoring, and harvest decision-making, we developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX. DINO-BollGX couples a DINO v3 backbone, a RetinaNet detection head, and an adaptive refinement-and-suppression module for robust open-boll detection under complex field conditions. Using multi-temporal UAV imagery collected over two years for 383 cultivars, we reconstructed plot-scale time series of open-boll counts, derived dynamic features describing progression and intensity changes, and proposed a Cotton Boll-Opening Temporal Stability Index (CTSI) to quantify boll-opening rhythm and concentration; CTSI was further integrated with a time-based risk function to generate harvest decision curves. Under unified data and training settings, DINO-BollGX achieved precision = 0.91, F1 = 0.88, and AP@0.50 = 0.80, and provided accurate boll-count estimation (R 2 =0.98; MAE=3.10), outperforming YOLOv11, YOLOv12, YOLOv13, and RT-DETR. On an independent cross-year dataset acquired at 5 m altitude, it obtained precision = 0.98 and F1 = 0.87. An internal consistency analysis showed that CTSI had the expected negative association with Window_days (r = −0.83) and positive associations with Max_count (r = 0.78) and the boll-opening efficiency index (r = 0.92), reflecting the co-occurrence of temporal compactness and main-phase opening intensity in the cultivar population. CTSI ranged from −2.72 to 4.69 across cultivars, enabling identification of highly synchronized boll-opening. Harvest decision curves indicated that the relative net income index peaked at day 67 after the first observation and a compact optimal harvest window near the end of monitoring; on a fixed harvest date, Kuche 130292 (CTSI=4.69) produced 486 open bolls versus 182 for Xinluzao 36 (CTSI=0.53) and 90 for Andizhan-60 (CTSI=-2.72). Overall, the framework integrates dynamic boll-opening phenotyping with harvest timing optimization, supporting scalable cultivar screening and mechanization-ready deployment, with potential extension to harvest decision scenarios in other crops.
Why it matches plant phenotyping methodsUAV画像と基盤モデルを用いて綿花の開絮を検出・定量し、時系列表現型指標を構築・検証する方法が研究の中心であるため。
abstractwe developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX.
Reproduction assets foundThe paper publicly releases its cotton boll-opening UAV image dataset (3638 patches, 94,774 YOLO-format bounding-box annotations) on GitHub, directly supporting the paper's phenotyping analysis. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicentary information for evaluating cross-scale detection performance and characterizing macroscopic spatial patterns. The 5 m imagery acquired on 18 Sept 2024 is used exclusively for cross-year generalization assessment. All cropped images and the corresponding YOLO-format annotation files have been publicly released on GitHub ( https://github.com/mianchen0529/cotton-boll-dataset/tree/main ) to facilitate further research on cotton phenotyping, agricultural remote sensing, and intelligent analytics.
2.3.
Model construction
2.3.1.
Overall architecture of the DINO-BollGX network
The proposed DINO-BollGX network consists of four stages: image preprocessing, feature extraction, object prediction,Open asset ↗mianchen0529/cotton-boll-datasetlines:63-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
ArabidopsisCell / cellular structureRootPhysiological trait estimationGrowth / development / phenology
Decoding how plants integrate multiple hormone signals to coordinate growth requires tools capable of resolving pathway interactions at cellular resolution in living tissue. Here we present ACE (Auxin–Cytokinin–Ethylene) and ACE2 , proof-of-concept single-locus reporters to simultaneously capture activity of multiple hormones. Deploying ACE alongside well-established reporters, exogenous hormone treatments, and reverse-genetic perturbations of hormone biosynthesis, signaling, and transport in three-day-old etiolated Arabidopsis seedlings, we dissect the spatiotemporal hierarchy governing primary root elongation and root apical meristem (RAM) size. We demonstrate that both ethylene- and cytokinin-triggered root growth inhibition involve a boost of TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1 (TAA1)-mediated auxin biosynthesis and AUXIN RESISTANT1 (AUX1)-dependent auxin redistribution. Two spatially distinct auxin responses underlie the respective root growth effects: ethylene expands TAA1-dependent auxin biosynthesis from the root vasculature into the epidermis and promotes AUX1-mediated auxin import into the transition and elongation zones to inhibit cell elongation, while cytokinin confines ethylene-dependent TAA1-boosted activity to the vasculature and drives auxin accumulation in lateral root cap cells to reduce RAM size. Together, these data establish a reciprocal regulatory loop between these hormones, positioning ethylene as a convergence node in auxin–cytokinin crosstalk, and cytokinin as a modulator of the ethylene–auxin interaction. Critically, the changes in cross-activated reporter patterns described for different genetic backgrounds, alongside quantitative assessment of hormone-specific inhibition of the mutants’ growth, were consistent with the multi-hormone network established over two decades of research, and added cell-type-resolved spatial detail and a proposed hierarchy for the etiolated seedling root. Finally, a second-generation reporter, ACE2 , overcomes key technical limitations of ACE , expanding the platform’s capacity toward a higher-order multi-hormone monitoring system. These resources expand the Arabidopsis genetic toolkit and provide a generalizable framework instrumental for dissecting multi-hormone signaling hierarchies at the cellular level.
Why it matches plant phenotyping methods多ホルモン活性を生体組織で同時可視化するACE/ACE2レポーターを開発・改良し、遺伝背景や根成長阻害との整合性を検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present ACE (Auxin–Cytokinin–Ethylene) and ACE2 , proof-of-concept single-locus reporters to simultaneously capture activity of multiple hormones.
Calibrating the mutagenic dose is the first practical step of any radiation mutation-breeding programme, and it is usually summarised by the median lethal dose (LD50) or the median growth-reduction dose (GR50). We asked whether an accessible, image-based phenotyping pipeline can quantify the early radiation response of cowpea (Vigna unguiculata L. Walp.) seedlings finely enough to estimate GR50 and to rank organ- and pigment-level sensitivities. Seeds of the traditional Paraguayan landrace kumandá pyta’i were exposed to Cobalt-60 gamma rays at 0, 100, 200, 300, 400, 500, 600, and 700 Gy, grown in a greenhouse, and photographed at the early seedling stage. A single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits (total, root, and shoot length, root:shoot ratio, tortuosity, and a two-dimensional biomass proxy) and colorimetric traits (CIE L*a*b*, a normalised greenness index, and colour-class pixel fractions). Because the data departed from normality, dose effects were tested with Kruskal–Wallis, Spearman rank correlation, and Dunn post hoc tests, and GR50 was estimated by regression of each trait expressed as a percentage of the control. Total length, shoot length, and the biomass proxy declined significantly with dose (Spearman ρ = −0.40, −0.51, and −0.47; all p < 0.001), preceded by a low-dose stimulation at 100 Gy. Estimated GR50 values were ≈390 Gy for shoot length, ≈510 Gy for total length, and ≈550 Gy for the biomass proxy, within the range reported for other cowpea genotypes. Shoot elongation was more radiosensitive than root elongation, so the root:shoot ratio did not decline; tortuosity showed no dose response. Among pigment traits, the loss of greenness was the most robust signal (a* increased, ρ = +0.62, p = 5 × 10−10; green pixel fraction fell from 0.32 to near zero by 500 Gy). These results show that single-photograph phenotyping resolves a coherent, statistically supported dose response and yields a GR50 estimate usable for dose calibration. For kumandá pyta’i, doses of roughly 300–400 Gy (below GR50) are the most defensible starting window for mutation induction. The framework is reproducible and low-cost, but it is based on one greenhouse experiment and a single genotype, and should be validated across independent trials and cultivars.
Why it matches plant phenotyping methods画像取得・セグメンテーション・解析による形態および色彩形質の抽出を中心に、放射線応答とGR50を推定する低コスト画像ベース表現型解析法を提示しているため。
abstractA single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits
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.
Field / plotMultispectral / hyperspectralClassificationGrowth / time-series analysisGrowth / development / phenology
Aquatic plants are vital for lake ecosystem functioning and water-quality stability, yet their community dynamics and phenology rhythms remain insufficiently understood, due to the lack of effective strategies for fine-scale species mapping and phenology extraction. In this study, based on Sentinel-2 MSI imagery, we developed an integrated framework combining machine learning and a priori ecological knowledge to quantify the spatiotemporal changes in aquatic plant distribution, species composition and phenological dynamics for eight dominant species in five regulating lakes along the Eastern Route of the South-to-North Water Diversion Project in China. Results showed that the proposed framework enabled accurate aquatic plant identification, achieving an overall classification accuracy of 96.16% and over 90% accuracy for each species. Since 2016, aquatic vegetation coverage has substantially declined in most lakes, mainly due to the retreat of submerged vegetation. Community structure has shifted from submerged-plant dominance to emergent and floating-leaved dominance in two of them. Phenologically, we found that most aquatic vegetation exhibited a longer growing season, characterized by earlier growth onset (-0.28 days/year) and peak timing (-0.67 days/year) and delayed senescence (0.54 days/year). Correlation analysis indicated that aquatic vegetation dynamics was associated with climate variation, nutrient enrichment, turbidity, and water diversion, with warming and solar radiation likely promoting the growth of some emergent species, while nutrient enrichment and turbidity could be linked with submerged vegetation decline and earlier phenological shifts. Overall, this study provides an effective framework for species-level mapping and phenological monitoring of aquatic vegetation, offering valuable support for the management and conservation of lake ecosystems.
Why it matches plant phenotyping methodsSentinel-2画像と機械学習等を統合した、植物種分布・構成・フェノロジーを抽出する手法を開発し、精度検証と大規模適用を行っているため、植物フェノタイピング手法が中心である。
abstractwe developed an integrated framework combining machine learning and a priori ecological knowledge to quantify the spatiotemporal changes in aquatic plant distribution, species composition and phenological dynamics for eight dominant species in five regulating lakes
The uniformity of maize plant spacing serves as a critical indicator for assessing sowing quality, seed vigor, and field seedling emergence stability. However, manual measurement is inefficient, and complex field conditions make automatic seedling detection and plant spacing measurement challenging. Aiming at the challenges of missed detection, insufficient accuracy for small targets, and large errors in plant spacing calculation under complex field conditions, this study constructs a high-quality dataset containing 693 maize seedling images and implements preprocessing enhancement for images degraded by haze or dust. An intelligent maize seedling detection and plant spacing measurement method based on improved YOLOv8 is proposed. The Global Attention Mechanism (GAM) is embedded into the backbone network to strengthen cross-dimension information interaction between channels and spaces, suppress background interference, and reduce the missed detection rate. The Bi-directional Feature Pyramid Network (BiFPN) is adopted to replace the original PAFPN for enhanced multi-scale feature fusion and deep semantic representation. A new 160 × 160 high-resolution small-object detection layer is added to significantly improve the detection performance of weak and small seedlings. Experimental results demonstrate that the improved model achieves a precision, recall, mAP50, and mAP50-95 of 89.4%, 90.3%, 94.4%, and 49.4%, respectively, which are 2.6, 0.5, 1.5, and 2.7 percentage points higher than those of the original YOLOv8 model. These results indicate that the proposed model improved maize seedling detection performance under complex field conditions. Automatic plant spacing calculation is realized based on detection outputs; the average plant spacing of the dataset is 30.42 cm, with a relative error of only 4.93% compared with the preset sowing spacing of 32 cm. The proposed method can efficiently accomplish field seedling identification, plant spacing quantification, and sowing quality evaluation, providing reliable technical support for precision maize sowing, seeder parameter optimization, and intelligent field management, which is of great significance for promoting the intelligent upgrading of grain crop production.
Why it matches plant phenotyping methodsトウモロコシ幼苗の検出画像から株間という植物形態・配置形質を自動抽出するYOLOv8手法を開発し、データセット構築と性能検証まで行っており、フェノタイピング手法が中心である。
abstractAutomatic plant spacing calculation is realized based on detection outputs
BarleySeed / grainPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
Background and aims Laser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination. Methods Malting barley (Hordeum vulgare subsp. distichum L. cv. Sinfonia) seeds were analysed using five coefficients-Generalised Differences (GD), Fujii, Lasca, Frequent Motion Image (FMI), and Moment of Inertia (MI)-to assess their ability to discriminate between treatments and tissue regions, and to track changes during imbibition. Whole and longitudinally cut seeds from two treatments (control [untreated] and autoclaved [heat-inactivated]) were analysed, focusing on embryo/endosperm activity ratios. LBSA was also evaluated as a function of imbibition time and seed moisture content. Key results Four coefficients (GD, Fujii, FMI, and MI) successfully differentiated control and autoclaved seeds, as well as embryo and endosperm regions in control seeds, revealing distinct activity patterns. In control seeds, LBSA increased with imbibition time and was well described by polynomial models (quadratic for Fujii and MI; cubic for GD and FMI). GD, Fujii, and FMI required a minimum seed moisture content of 25% to detect activity, while MI was responsive only above 31.5%. In contrast, Lasca was exclusively sensitive to hydration level, fitting a relaxation curve independent of treatment. Conclusions LBSA constitutes a robust, non-destructive methodology for monitoring early germination processes. By combining coefficients, it is possible to infer physiological traits such as embryo specificity, hydration thresholds, and dynamic metabolic reactivation. This positions LBSA not only as a diagnostic tool for seed viability but also as a physiologically informative proxy for studying germination and tissue-level dynamics.
Why it matches plant phenotyping methodsレーザーバイオスペックル画像法を用いて種子の生存性、含水率、発芽動態を測定・識別し、複数係数の性能評価とモデル化を行う手法中心の研究である。
abstractLaser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination.
This study presents a multi-scale framework for reconstructing snow avalanche (SA) frequency and assessing vegetation structural responses in data-scarce mountain environments. The approach integrates dendrogeomorphological reconstructions, satellite-based spectral disturbance detection, and UAV-based Structure-from-Motion (SfM) photogrammetry, complemented by field data, and was applied to two avalanche paths in the Piatra Craiului Mountains (Southern Carpathians, Romania). Tree ring analyses allowed reconstruction of spatially explicit minimum avalanche chronologies for the 1980–2025 period. These reconstructions were combined with a DEM-based upslope algorithm to derive spatially variable avalanche return periods, revealing the highest frequencies in release and upper-track sectors and progressively longer return periods toward lower-track zones. Sentinel-2 imagery was used to assess the surface footprint of a reconstructed avalanche event in 2018. Among the tested spectral indices, the Moisture Stress Index (MSI) showed the most spatially coherent response, while the combined MSI-NDMI-NBR approach reduced index-specific noise. UAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states. Vegetation was classified using a machine-learning-based object-oriented approach (Random Forest) integrating spectral, geometric, structural, and textural parameters. The multi-parameter feature set yielded very high classification accuracy (Cohen’s Kappa ≈ 0.95). Across avalanche return-period gradients, both UAV-derived and field-based metrics showed a systematic associations between tree height and avalanche frequency, whereas tree age and stem diameter exhibited more variable, path-dependent responses. The proposed framework provides a transferable basis for linking avalanche disturbance regimes with vegetation structure and surface stability in mountain landscapes lacking long-term observational records.
Why it matches plant phenotyping methodsUAV-SfMと機械学習による植生構造・樹高の高解像度推定が研究枠組みの主要部分であり、分類精度も評価しているため、植物状態の画像ベース表現型計測として含める。
abstractUAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states.
Abstract In peanut ( Arachis hypogaea L.), plant stand establishment, seedling vigor, and canopy growth are key determinants of crop performance; yet traditional ground‐based assessment methods can be destructive, labor‐intensive, and limited in throughput. This study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut. Six runner‐type peanut cultivars were evaluated in 2024 and seven in 2025, with each cultivar represented by two seed size classes (small and large), to generate variation in these traits. Within‐row vegetation discontinuity‐based plant stand ratings for estimating plant stand count ( R 2 = 0.81–0.90), together with canopy coverage for assessing seedling biomass ( R 2 = 0.77–0.82) and light interception ( R 2 = 0.96–0.98), were the best‐performing vegetation metrics. These vegetation metrics provided similar or greater cultivar separation compared with ground‐based measurements. In contrast, several vegetation indices exhibited strong correlations with ground‐based measurements but provided inconsistent cultivar rankings and statistical groupings. MS imagery outperformed RGB imagery for plant stand and seedling biomass assessment. Overall, these results demonstrate that UAV‐derived canopy metrics provide reliable, high‐throughput tools for early‐ to mid‐season crop assessment and offer scalable alternatives to traditional ground‐based approaches for agronomic, crop physiological, and plant breeding research.
Why it matches plant phenotyping methodsUAV画像から植物体の出芽・苗勢・バイオマス・光 interception を推定する植 phenotyping 手法を開発・評価しており、取得指標の性能検証が研究の中心です。
abstractThis study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut.
MaizeField / plotClassificationGrowth / development / phenology
Integrating precision agriculture (PA, a data-driven agricultural management system) with deep learning (DL) models can effectively support various activities, including yield prediction, crop health monitoring, field task automation, and decision-making. Taking advantage of such data-driven methodologies typically requires desktops, high-performance computing systems, and cloud clusters for data analysis, but their portability limits in-field applications. However, a single board computer, such as Raspberry Pi, offers a compact, lightweight, cost-efficient, easy-to-use, and feature-rich portable computing device which is ideal for in-field decision-making in PA applications. One such in-field application is crop growth stage classification for better crop management. Therefore, in this study, eight corn growth stages were classified using PhenoCam (near-surface [proximal] remote sensing network camera)imagery collected from ten PhenoCam sites. Four lightweight DL models were developed, ELiteCrop0, ELiteCrop1, ELiteCrop4, and MobNetCropV2, and evaluated across five image vertical clipping levels(0 %–40 %) using a supercomputer. The optimized model was subsequently deployed on a Raspberry Pi5 for edge inference. Model training accounted for the majority of the total CPU time, exceeding 97 %, while the testing times ranged from 0.01 min to 0.12 min, enabling real-time applications. Among the models, ELiteCrop0 achieved the most balanced performance with a confusion-matrix diagonal ratio(CMDR) of 0.93, followed by ELiteCrop1 (CMDR = 0.92). Overall, model performance decreased with increasing vertical clipping; therefore, a moderate image clipping (0 %–10 %) was recommended for improved computational efficiency. Analysis with a supercomputer produced an intrasite (same train sites)accuracy of 0.90–0.93 (Raspberry Pi: 0.78–0.81) and an intersite (new test sites) accuracy of 0.48–0.50(Raspberry Pi: 0.41–0.43), indicating challenges with model generalization. Raspberry Pi successfully processed ≈ 1000 images/min under safe operating conditions (68◦C). Future work should focus on extending the multi-site dataset to improve cross-site performance. Hence, this study presents a scalable and cost-effective solution for real-time corn growth stage monitoring in PA.
Why it matches plant phenotyping methodsトウモロコシの生育段階という植物状態をPhenoCam画像から推定する深層学習モデルを開発・評価し、Raspberry Piへ展開して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。
abstractTherefore, in this study, eight corn growth stages were classified using PhenoCam (near-surface [proximal] remote sensing network camera)imagery collected from ten PhenoCam sites.
leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants from different accessions grown under different water and nitrogen conditions. From these time series, leaf-level growth dynamics were reconstructed and used to calculate key developmental traits, including phyllochron and relative expansion rates. These measurements were further integrated into the GreenLab model to infer plant-scale parameters such as radiation use efficiency (RUE) and leaf demand parameters. Statistical analyses revealed significant genotype, treatment, and interaction effects on several traits, with stable genotype rankings but contrasted sensitivities to environmental conditions. While whole-rosette growth captured strong and consistent responses, individual leaf contributions to global growth remained limited, highlighting the integrative nature of rosette-scale dynamics. Model-derived parameters provided additional insight into plant strategies, showing that genotypes differ in both the duration and timing of leaf demand, with generally stable patterns across environments. In parallel, QTL mapping was performed using leaf-level measurements and phyllochron traits. This analysis identified shared and trait-specific genetic loci, including loci associated with leaf emergence dynamics that were not detected using traditional rosette-scale integrative traits. Overall, this study demonstrates that combining deep learning-based image analysis with mechanistic modeling enables large-scale and quantitative characterization of plant development, providing biologically interpretable traits and new insights into their genetic and environmental determinism. It highlights the potential of combining high-throughput image-based phenotyping, deep learning, and mechanistic modeling to generate biologically interpretable traits and to assess genetic and environmental influences on these traits.
Why it matches plant phenotyping methods深層学習による葉の自動セグメンテーション・追跡を開発的に適用し、時系列画像から葉レベルおよび植物体レベルの発達形質を定量化しているため、表現型取得・抽出が研究の中心である。
abstractleaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants
Reproduction assets foundThe paper explicitly states that the authors' leaf segmentation software (AraLeaf_segmentation), the GreenLab Arabidopsis model (AraGreenlab), and the statistical analysis code (AraParameters_statistical_analyses) are open-source and publicly hosted on forge.inrae.fr, and that the data and results used in the paper areCode · publicThe leaf segmentation software (AraLeaf_segmentation) and the GreenLab model of Arabidopsis thaliana (AraGreenlab) are open-source software, and distributed under the GNU GPL v3 licence. The two software packages, and the code to run statistical analyses (AraParameters_statistical_analyses) are available at:https://forge.inrae.fr/phenoscope-public/plant_phenomics_suppmat.Open asset ↗phenoscope-public/plant_phenomics_suppmathtml-lines:419-444Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Germination percentage is an endpoint measure and therefore does not describe when an individual seed begins visible growth or how rapidly its radicle and plumule expand. We developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture. A single industrial camera moved along a 1 m rail and imaged three culture boxes at 1 h intervals for up to 80 h. The archive comprised 1,062 full-frame images and 6,372 seed-level repeated observations under the six-seed field-of-view configuration. A physical grid maintained seed identity through time and enabled individual regions of interest to be extracted. Whole-seed foregrounds were obtained with a pretrained U 2 -Net, and a masked RGB intensity rule separated newly emerging tissue from the darker hull. For each tracked seed, projected emerging-tissue area and interval growth rate were calculated. Three representative normally germinating seeds first showed measurable tissue at 48 h, yet subsequently followed distinct trajectories: final projected areas ranged from 2,605 to 4,700 pixels and peak interval growth rates ranged from 106.88 to 287.92 pixels h −1 . B-1 accumulated 63.71% of its final visible area during 72–80 h, whereas B-3 accumulated 73.51% during 60–72 h. Thus, seeds with the same observed emergence interval can differ substantially in the timing and magnitude of post-emergence expansion. The workflow converts repeated images into biologically interpretable temporal phenotypes and provides a basis for nondestructive studies of rice seed vigor and germination heterogeneity.
Why it matches plant phenotyping methods連続画像から個々のイネ種子の発芽・組織面積・成長速度を抽出する時間分解フェノタイピングワークフローを開発しており、表現型取得と解析手法が研究の中心である。
abstractWe developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture.
Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C4 carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment.
Why it matches plant phenotyping methodsソルガムの干ばつ適応に関するセンシング型フェノタイピング手法を体系的にレビューし、形質推定の精度・移植性・検証、およびモデルと機械学習の統合を扱うため、方法論が中心である。
abstractThis systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement.
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.
Field / plotRGB / grayscaleLeafSegmentationGrowth / development / phenology
Bud flushing timing is a crucial attribute for forest geneticists and physiologists studying adaptive variation in tree species. In clonal populations such as Norway spruce seed orchards, accurate assessment of bud flushing is essential, as this attribute strongly influences survival and fitness. However, traditional bud-flushing assessment relies on visual scoring scales that are inherently subjective and susceptible to observer bias, leading to inconsistencies when multiple technicians collect data. To address these limitations, we introduce a novel quantitative phenotype derived from segmented RGB images for quantifying bud flushing that objectively measures the ratio of light-green current-year needles to the total visible needle area of each grafted ramet. By extracting and summarizing digital image data for each ramet, this approach provides a standardized and reproducible assessment of bud flushing phenology. We evaluated clonally replicated ramets in two sites across two seasons and compared this image-derived flushing ratio with conventional visual scoring and color indices. Using mixed models, we quantified genetic control and cross-orchard stability of clonal performance. The image-derived flushing ratio tracked visual-score progression but showed higher broad-sense heritability (0.54–0.70) than visual scoring (0.45–0.65) and canopy color indices (0.20–0.51). Although site means differed strongly, clonal ranking remained stable between orchards for the image-derived flushing ratio and for visual scoring, whereas color indices showed lower stability and greater uncertainty. Overall, these results support the image-derived flushing ratio as a structure-based phenotype that improves the precision and cross-site transferability of genetic evaluation of bud flushing in Norway spruce seed orchards.
Why it matches plant phenotyping methodsRGB画像のセグメンテーションから bud flushing の定量形質を開発し、従来の視覚評価や色指数と比較検証しており、植物表現型取得法が研究の中心である。
abstractwe introduce a novel quantitative phenotype derived from segmented RGB images for quantifying bud flushing
The precise identification of microspore and pollen at the optimal developmental stages to be induced towards embryogenesis (vacuolated microspores and young pollen) is essential for induction of in vitro androgenesis in plants. Such identification is not always easy, and it is especially difficult in recalcitrant species such as Vicia faba . The present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry. We measured flower bud and anther length and width, grouping them at different intervals, and performed a detailed microscopical and anatomical analysis of buds, anthers and microspores/pollen at different stages. Our results demonstrated that flower buds in V. faba exhibit complex and irregular morphologies, with considerable variation in both sepal length and shape. Furthermore, the determination of microspore and pollen developmental stages in this species is constrained by pronounced developmental asynchrony and strong genotype dependence. Although anther length measurements correlate closely with microspore and pollen developmental stages, their practical use can be challenging. Therefore, measuring flower bud length, while excluding sepals, remains the most practical criterion for routine applications. Combining this refined morphometric approach with microscopic validation appears to be the most effective strategy for improving the identification of flower buds containing microspores or pollen at developmental stages suitable for androgenesis induction in this recalcitrant legume species.
Why it matches plant phenotyping methods花蕾・葯の形態計測と顕微鏡検証を用いて、微小胞子・花粉の発達段階を推定する実用的な植物フェノタイピング手法を評価しており、方法開発・検証が中心である。
abstractThe present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry.
The simultaneous diagnosis of diseases and evaluation of age quality grades in tea leaves are critical for precision agriculture and the economic valuation of tea products. Although deep learning has shown promise in agricultural vision tasks, current multi-task models often suffer from performance degradation due to feature conflicts: tea leaf disease recognition relies heavily on macro-structural lesions, whereas tea leaf-age quality grading depends on micro-textural features such as trichome density and color uniformity. To address this discrepancy, we propose a novel dual branch fusion network. Our architecture fundamentally decouples the feature extraction process by utilizing a dual branch mechanism. The first branch employs global average pooling to capture first-order spatial statistics; it can retain the global structural layout necessary for macro-lesion detection. The second branch introduces a dimensionality-reduced self-bilinear pooling module to compute second-order covariance matrices; it can effectively capture the fine-grained textural patterns essential for micro-grade classification. These decoupled features are subsequently fused and optimized through a weighted multi-task loss function. Experimental results on a comprehensive tea leaf dataset demonstrate that the proposed dual fusion framework significantly outperforms baseline models. The proposed network can rescue the disease classification accuracy drop observed in standard bilinear models while maintaining exceptional grading performance. Furthermore, the proposed network maintains a compact parameter footprint and low computational complexity. This balance renders it suitable for deployment on agricultural Internet of Things edge devices where inference speed is critical.
Why it matches plant phenotyping methods茶葉の病害状態と葉齢品質を画像から推定する深層学習手法を開発し、データセット上でベースラインと比較評価しており、表現型取得・推定法が中心である。
abstractwe propose a novel dual branch fusion network
Reproduction assets foundThe paper's Data Availability Statement publicly releases the two tea leaf image datasets used for its phenotyping tasks (disease recognition and leaf-age quality grading) via Mendeley Data. The authors' analysis code and trained models are only promised 'upon acceptance' with no public URL, so they do not qualify.Dataset · publicThe tea leaf disease recognition dataset analyzed in this study is available from https://data.mendeley.com/datasets/744vznw5k2/3 (accessed on 11 February 2026)Open asset ↗744vznw5k2/3lines:514-565Dataset · publicthe tea leaf grading dataset is available from https://data.mendeley.com/datasets/7t964jmmy3/1 (accessed on 11 February 2026)Open asset ↗7t964jmmy3/1lines:514-565Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published4 Aug 2026Journal of Agriculture and Ecology Research InternationalCited by 0 · OpenAlex ↗
Crop stress develops through a sequence that begins with molecular and biophysical perturbation, progresses through physiological dysfunction, and only later becomes visually apparent. Precision agriculture therefore requires sensors that can shorten the interval between stress onset and actionable diagnosis while preserving spatial context. This critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection. Literature published from 2000 to 1 June 2026 was selected through live searches of accessible scholarly indexes, DOI registries, publisher records, institutional repositories, and citation networks, with foundational studies retained where necessary. The evidence shows that nano-enabled interfaces can measure early biochemical, ionic, volatile, electrical, and microclimatic signals at high temporal resolution, whereas geospatial technologies reveal the distribution, persistence, and management relevance of stress across canopies and fields. Optical nanotube sensors, surface-enhanced Raman probes, electrochemical microneedles, ion-selective wearables, and flexible leaf sensors have demonstrated biologically meaningful signals before visible symptoms in controlled or pilot field settings. Yet most remain constrained by sparse sampling, crop-specific calibration, bio-interface effects, power and communication burdens, uncertain durability, and limited agronomic validation. Geospatial methods are operationally more mature, particularly thermal and multispectral imaging for water stress and hyperspectral imaging for disease and nutrient-related changes, but they often infer stress through non-specific proxies that are confounded by canopy structure, atmosphere, soil background, phenology, and co-occurring stresses. The strongest future architecture is therefore not a contest between nanoscale and landscape-scale sensing. It is a multiscale system in which physiologically specific plant sensors anchor and interpret spatial imagery, while remote sensing directs where high-specificity measurements and interventions are most valuable. Progress depends on prospective field trials, reference measurements, uncertainty-aware data fusion, interoperability, lifecycle safety assessment, and decision thresholds linked to economic and agronomic outcomes.
Why it matches plant phenotyping methods植物ストレス状態の検出に用いるナノセンサー、ウェアラブルセンサー、熱・マルチスペクトル・ハイパースペクトル画像などを中心に批判的に統合した方法レビューであり、単なる農業応用紹介ではなく、センサー性能、校正、検証、データ融合を論じている。
abstractThis critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of "time-series perception-dynamic simulation-feature identification-early prediction", providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.
Why it matches plant phenotyping methodsUAV LiDAR/RGBによる草丈推定と時系列成長曲線から、トウモロコシの抽だい期を識別・予測する技術フレームワークが研究の中心であり、植物形質取得と検証を伴うため採用。
abstractMulti-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize.
Accurate and efficient acquisition of seedling density and growth information is of great significance for guiding modern agricultural field management. Although drone imagery has been widely used in seedling monitoring, the inherent trade-off between operational efficiency and image resolution limits the effectiveness of remote sensing-based seedling detection. To address this challenge, this study proposes an integrated analytical method combining super-resolution reconstruction and object detection. The approach first employs the Real-ESRGAN model to enhance low-resolution image quality, then utilizes the YOLOv12 model to accurately localize cotton seedlings, and finally generates visualizations of seedling density and growth uniformity. Experimental results demonstrate that super-resolution reconstruction enhances the detection algorithm's capability for small targets, increasing the object detection precision by 3.3%. With the incorporation of super-resolution reconstruction, the seedling counting accuracy reaches 92.08%, representing a 46.15% improvement over the method without super-resolution, thereby effectively enhancing the algorithm's counting capability. Furthermore, this method achieves image detail equivalent to that obtained at 7.5 meters flight altitude while operating at 30 meters, reducing data acquisition time to 1/16 of the original requirement. In practical applications, the visualized results of seedling density and growth uniformity provide precise decision-making support for thinning, replanting, and differentiated field management. The proposed method is not only applicable to cotton but can also be extended to staple crops such as corn, wheat, and rice, with additional potential applications in forestry and ecological monitoring.
Why it matches plant phenotyping methodsUAV画像の超解像化と物体検出により、ワタ苗の密度・生育均一性を定量化する手法が研究の中心であり、性能評価も行っているため。
abstractThe approach first employs the Real-ESRGAN model to enhance low-resolution image quality, then utilizes the YOLOv12 model to accurately localize cotton seedlings, and finally generates visualizations of seedling density and growth uniformity.
Accurate plant population estimation is critical for crop monitoring, yield prediction, and field management in precision agriculture. However, challenges such as complex backgrounds, overlapping plants, and the need for large-scale coverage make seedling counting from UAV imagery difficult. In this study, we propose a novel automated pipeline for maize seedling counting based on high-resolution UAV orthomosaic images. The pipeline begins with an automatic field-based plot extraction method that isolates individual planting regions without the need for manual intervention. A lightweight object detection model, YOLOv8n-CA, is then employed, incorporating Coordinate Attention to enhance feature localization while maintaining fast inference. To further accelerate the process, we introduce the ‘In-Range Sliding’ strategy, which limits inference to only planting regions, reducing computational overhead. Extensive experiments demonstrate the effectiveness of our approach, achieving a mean absolute error (MAE) of 0.587 and a coefficient of determination ( R 2 ) of 94.19 % at the plot level. Additionally, our system enables spatial feature extraction such as seedling spacing, supporting more refined agronomic analysis. This work provides an efficient and scalable solution for plant counting, offering valuable insights for large-scale, rapid post-processing agricultural monitoring.
Why it matches plant phenotyping methodsUAV画像と深層学習によるトウモロコシ幼苗の計数・圃場区画抽出パイプラインを開発し、計数精度を検証しているため、植物表現型取得法が中心である。
abstractwe propose a novel automated pipeline for maize seedling counting based on high-resolution UAV orthomosaic images.
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.
Accurate, field-scale mapping of crop growth stages is critical for supply-sensitive vegetable production, where timely harvests require detailed phenological information. Consecutive growth stages often involve rapid and subtle morphological changes and are influenced by challenging open-field conditions, which frequently result in misclassification when stages are treated as independent, discrete categories. To address this issue, CropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery. CropMap incorporates the structured biological progression of crop development into the learning objective through tree-based label constraints, allowing the model to recognize phenological continuity and reduce confusion between adjacent stages. The framework is evaluated on the publicly available National Information Society Agency of Korea (NIA) field crop growth-stage dataset, a large-scale, multi-institutional UAV dataset containing 337,665 multispectral patches across six hierarchically related growth stages of Chinese cabbage and radish, curated by the NIA. CropMap achieves a test-set mean Intersection over Union (mIoU) of 0.5382, representing a 5% relative improvement over the best-performing transformer baseline (SegFormer; mIoU = 0.5124). Performance varies across classes: background separation is strong (IoU = 0.9128) and the rosette stage is well distinguished (IoU = 0.6354), while the leaf expansion stage remains the primary challenge (IoU = 0.3541), reflecting the inherent difficulty of mapping this spectrally and morphologically transitional class. These findings indicate that hierarchy-aware learning reduces inter-stage confusion for most phenological classes, but transitional growth stages remain a significant limitation for field-scale deployment. The framework provides a foundation for stage-resolved crop monitoring to support harvest timing and supply forecasting in high-value vegetable systems.
Why it matches plant phenotyping methods作物の生育段階という植物状態を、マルチスペクトルUAV画像と階層型セマンティックセグメンテーションで推定する手法が研究の中心であり、公開データセット上で性能評価も行っている。
abstractCropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery.
Biomass is a key trait in pasture plant breeding and agronomy, but measuring Dry Matter Yield or Fresh Weight across large numbers of samples is labour intensive and costly. Efficient biomass assessment systems must balance accuracy, speed, and cost, while ideally enabling non-destructive measurements. We developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution. The system operates under ambient light conditions at a ground speed of 2.7 km per hour. It was evaluated in small-plot perennial ryegrass trials at two field sites in New Zealand, across two seasons at site A and one season at site B. At site A, correlations between LiDAR-derived height and fresh weight ranged from 0.33 to 0.74 across individual measurement cycles, with an overall multilevel R² of 0.72. At site B, the multilevel correlation increased to R² = 0.88. Weekly LiDAR scans at site B were used to estimate plot-level growth rates for 60 plots, demonstrating improved temporal resolution. Statistically significant differences in growth rate within regrowth cycles were detected among plots. The platform reliably differentiates perennial ryegrass plots based on biomass and offers higher temporal resolution than traditional methods.
Why it matches plant phenotyping methodsLiDARプラットフォームを開発し、草高から牧草バイオマスと成長率を非破壊推定・検証しており、植物形質取得手法が研究の中心です。
abstractWe developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution.
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
Quantifying the canopy growth dynamics and light interception capacity under different management practices laid the physiological foundation for potato yield formation. However, the traditional manual measurement methods are labour-intensive, time-consuming, and incapable of capturing time-series dynamics. To address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model. Furthermore, how Nitrogen(N)-Potassium(K) interaction affects the temporal canopy growth dynamics, light interception, and tuber yield was determined. The results indicated that: (1) Among the 11 secondary indices extracted from the canopy growth dynamic curves, the interaction of N and K had the greatest effect on the maximum canopy duration and the total canopy growth curve integral. The direct path coefficients of N and K inputs on these two parameters were 0.847 and 0.805, and 0.234 and 0.148, respectively. (2) There was a strong linear relationship between the integral area under the curve (S∫) and the total plant dry weight, with R² at 0.90 in 2023-2024. A simplified net photosynthetically active radiation utilisation assessment framework that achieved high accuracy with minimal parameter was built. (3) Prolonging the maximum canopy continuous coverage time is the main way to improve potato yield. The overall effect of N input on yield was significantly higher than that of K fertiliser, with a total effect value of 1.428. Optimising the N-K interaction improves nutrient precision and light interception. The integration of UAV remote sensing and the crop physiological-ecological model enables the tracking of potato canopy dynamics, which is helpful for optimising management practices to improve potato yield.
Why it matches plant phenotyping methodsUAV RGB画像と生理モデルを統合した高スループット手法を開発し、ジャガイモのキャノピー成長動態と光 interception を時系列で推定することが中心である。
abstractTo address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model.
Japanese agriculture faces pressing challenges, including a declining and aging farming population and the need to adapt to climate change. To address these issues, Smart Agriculture is being introduced to improve production efficiency. Among these, unmanned aerial vehicles (UAVs) have gained attention for their ability to rapidly monitor entire fields. We proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values. The experimental site consisted of five paddy fields within an 80 m × 50 m plot in Iwate Prefecture, Japan, equipped with weather and water sensors. Ground-truth data (overall length, culm length, panicle number, and stem number) were collected approximately one week before harvest. UAV monitoring was conducted four times using a multispectral camera, and growth analysis was performed with six VIs. Correlation analysis revealed a positive relationship between crop growth and the daily average water level during the drainage period, and a negative relationship with the daily temperature range in mid-June. A combined cluster-label representation, constructed from clustering results of all VIs for each mesh, enabled integrated analysis and visualization of multi-index patterns. Grid size optimization showed no significant differences in correlation trends between 1 m × 1 m and 5 m × 5 m resolutions. For non-crop area removal, a comparison of three image segmentation methods demonstrated that the Otsu Method achieved the highest performance. Finally, to facilitate practical use in the field, we prototyped a report interface for the diagnosis system. Future work will focus on developing a comprehensive field diagnosis system to clarify field environments, with the aim of addressing fragmentation and enclaves in Japanese farms.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数、画像分割、クラスタリングを統合した作物生育診断システムの開発・評価が中心であり、作物形質との相関検証や実用インターフェースも扱っている。
abstractWe proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values.
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Aerial / UAVWhole plant / canopy / plot / fieldGrowth / time-series analysisTrackingVisualization / data managementGrowth / development / phenology
ABSTRACT Accurate monitoring of plant phenology is essential for agricultural decision‐making, as deciding the right time of fertilizer application, the maturity of the plant, and climate variation. Traditional manual monitoring often fails to capture the temporal variations across large fields. UAV‐based imaging combined with deep learning can provide a solution for automated phenological assessment. In this study, we propose an AI‐driven framework on UAV‐captured Indian mustard plants to predict the phenological progress. We applied deep learning methods, EfficientNet‐B0, and a proposed hybrid model of a Vision Transformer + LSTM to predict phenological growth from UVV‐captured images of the Indian mustard plant. Both models were trained under a supervised regression setup with extensive augmentation and optimization strategies. The results of the study show that the ViT + LSTM outperforms the EfficientNet‐B0 model in terms of prediction accuracy for mustard plant phenology. The R 2 = 0.9974, minimal errors MSE = 0.0111, and RMSE = 0.149 indicate that the ViT + LSTM provides more accurate phenological predictions on temporal and spatial dependencies. Correlation Analysis confirmed a strong linear and monotonic relationship with the ground truth, with r = 0.9989 and ρ = 0.9946. Gram‐CAM visualization showed that the ViT + LSTM captures the meaning area of the plant. These results were further validated using statistical measures such as Pearson's r and Spearman's ρ , which confirmed the reliability and consistency of the model's predictions.
Why it matches plant phenotyping methodsUAV画像から植物の生育フェノロジーを推定する深層学習手法を開発し、複数モデルの精度比較と統計的検証を行っており、フェノタイピング手法が中心である。
abstractUAV‐based imaging combined with deep learning can provide a solution for automated phenological assessment.
With the rapid development of artificial intelligence, UAV remote sensing, and agricultural Internet of Things technologies, crop growth monitoring is evolving from manual inspection and single-source analysis toward intelligent decision-making based on multisource perception. However, existing methods still suffer from limited robustness under environmental variations, insufficient integration between UAV imagery and sparse ground sensor observations, and weak capability for transforming predictions into practical agricultural management recommendations. This study proposes a UAV–ground sensor collaborative lightweight framework for crop growth assessment and agricultural decision support. The proposed framework integrates UAV RGB and multispectral imagery with ground sensor observations through a region-level aerial–ground alignment mechanism and a sensor-guided attention fusion module, enabling environmental conditions to enhance visual feature interpretation. Furthermore, a fact-constrained decision module is developed to generate management recommendations based on crop status, environmental risks, and field information. Experimental results demonstrate that the proposed method achieves superior performance in crop growth classification and yield-trend prediction, reaching Accuracy, Precision, Recall, and F1-score values of 92.47%, 91.86%, 91.39%, and 91.62%, respectively, with an RMSE of 0.381 and an R2 of 0.902. The lightweight framework requires only 6.18M parameters and 0.91G FLOPs, achieving 39.56 ms inference latency and 25.28 FPS on edge devices. The proposed framework also improves decision reliability, achieving an expert agreement rate of 89.34% and a risk identification accuracy of 90.18%. Economic analysis indicates that the proposed framework reduces labor cost, water consumption, and fertilizer input by 49.7%, 26.7%, and 23.0%, respectively, while increasing net benefit by 46.1% compared with conventional field management practices. These results demonstrate that the proposed method provides an accurate, interpretable, and deployable AI-driven solution for intelligent crop management in smallholder and medium-sized farming systems.
Why it matches plant phenotyping methodsUAV画像と地上センサーを融合し、作物生育状態を評価する取得・推定フレームワーク自体を開発しており、植物状態の推定方法が中心的です。
abstractThis study proposes a UAV–ground sensor collaborative lightweight framework for crop growth assessment and agricultural decision support.
Accurate monitoring of agronomic phenology is essential for yield estimation and food security assessment. However, currently available maize phenology datasets usually represent only a limited number of growth stages, restricting their application in process-based crop modeling and stage-specific agricultural management. Here, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution. By coupling MODIS spectral information with meteorological drivers in an energy-driven XGBoost framework, we retrieved eight key agronomic stages: Emergence, Three-leaf, Seven-leaf, Jointing, Flowering, Silking, Milking, and Maturity. Validation against observations from 91 agrometeorological stations during 2009-2024 demonstrates robust performance, with an overall RMSE of less than 5 days and R² values greater than 0.63 across all stages. Beyond overall accuracy, the dataset shows strong spatial consistency and temporal stability, preserves coherent regional phenological gradients, and captures interannual variations over the 24-year period. This long-term, multi-stage dataset provides a valuable benchmark for crop model calibration, climate change impact assessment, and the development of adaptive agricultural strategies in one of the world's major maize-producing regions.
Why it matches plant phenotyping methodsMODISスペクトル情報と気象データ、XGBoostを組み合わせてトウモロコシの8つの生育段階を推定し、観測データで検証した高解像度フェノロジーデータセットであり、植物形質の取得・抽出手法とベンチマークが中心です。
abstractHere, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution.
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.
The ready-to-eat lettuce industry is rapidly expanding, increasing the need for reliable, scalable methods to assess seed germination and early growth under realistic soil conditions. This study presents an automated imaging-based approach for quantifying germination dynamics and seedling vigor using a low-cost multi-camera system under greenhouse conditions. Lettuce seeds were grown in soil either inoculated or non-inoculated with the soil-borne pathogen Rhizoctonia solani. Top-view images were acquired using commercial surveillance cameras and processed through a calibrated pipeline including geometric correction, color normalization, vegetation segmentation, clustering, and temporal tracking of emergence events. Seedling vigor was quantified through projected leaf area estimation. The proposed method enables accurate estimation of germination kinetics and growth dynamics under field-like conditions. Automated counts were validated against manual measurements at both intermediate and final time points, achieving high agreement in both cases. At the final assessment, the method reached R² = 0.98 and RMSE = 1.12, while at the midterm evaluation it achieved improved performance with R² = 0.998 and RMSE = 0.5, reflecting the lower complexity of plant structure at earlier growth stages. Results showed that pathogen inoculation significantly reduced both germination rate and seedling vigor, with up to 70% reduction in biomass accumulation. The proposed framework provides a robust, low-cost solution for high-throughput phenotyping of early plant development in soil-based systems, supporting scalable agricultural experimentation.
Why it matches plant phenotyping methods低コスト多カメラ画像システムと画像解析パイプラインを開発・検証し、発芽動態と幼植物活力を定量化しているため、植物フェノタイピング手法が中心です。
abstractThis study presents an automated imaging-based approach for quantifying germination dynamics and seedling vigor using a low-cost multi-camera system under greenhouse conditions.
Long-duration flood inundation can substantially suppress crop growth and cause yield loss, particularly in semi-arid agricultural regions increasingly affected by extreme rainfall. Timely crop damage assessment is critical for disaster response and insurance-related decision-making, but direct yield-loss observations are often unavailable during or shortly after flooding. This study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations. The study was conducted on the Tumochuan Plateau, Inner Mongolia, China, where severe rainfall beginning on 23 July 2025 caused widespread cropland inundation. Sentinel-2 EVI time series from 2022 to 2025 were fitted using a Savitzky–Golay (SG) filter, and annual area under the EVI curve (AUC) loss in 2025 relative to the 2022–2024 historical mean was used as a proxy for flood-induced crop damage. Optical features from Landsat-8/9 and Sentinel-2, together with SAR backscatter features from Sentinel-1, Lutan-1, and Gaofen-3, were incorporated into machine learning regression models. SAR features improved pixel-wise prediction, with the Random Forest model achieving the highest R2 of 0.62 using early-period features and 0.77 using later-period features. Village-scale aggregation further improved performance, yielding an early-period R2 of 0.84 across 123 and 0.78 across 122 villages. These results demonstrate the feasibility of SAR–optical and phenology-guided regression for early crop damage assessment under long-duration inundation.
Why it matches plant phenotyping methodsSAR・光学画像とフェノロジー指標を用いて作物被害を推定する回帰手法が研究の中心であり、作物状態（洪水被害）を定量化・検証しているため。
abstractThis study proposes a phenology-guided regression framework for early crop damage assessment using multi-source SAR–optical observations.
MilletNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldGrowth / time-series analysisVisualization / data managementGrowth / development / phenology
Finger millet ( Eleusine coracana (L.) Gaertn.) is a small seeded cereal known for its health benefits and its ability to grow in water-limited and saline environments. Despite its health and adaptation advantages, there is no standardized BBCH scale developed for finger millet, precluding standardization of a defined growth and development scale. The initial step in any breeding program and related research, is the precise, consistent and standardized description of crop growth stages. Therefore, we present a standardized phenological scale to describe and compare different stages of growth and development. In this study, we used four-finger millet accessions from the U.S.D.A. National Plant Germplasm System (NPGS) collection. Using a standardized BBCH chart as the baseline, we describe nine main stages of development to provide a simplified and user-friendly phenological growth staging scale for finger millet. A novel feature of this study was the use of Neural Radiance Fields (NeRF) to generate three-dimensional (3-D) renderings that align growth stages with 3-D imaging, paving the way for future phenological staging studies to incorporate 3-D visualization as a tool for standardization, researcher training, and reduction of observer bias across environments.
Why it matches plant phenotyping methods指のキビの生育段階を標準化する手法を開発し、NeRFによる3D画像化を生育ステージ判定・標準化に組み込んでいるため、植物フェノタイピング手法が中心である。
titlePhenological growth stages of Finger millet (Eleusine coracana (L.)) using 3-D phenotyping with relevance to breeding applications
The Florida sugarcane industry is transitioning from manual to mechanical planting systems that use comparatively smaller seedcane pieces (billets) as planting material. A major limitation of mechanical planting is the increased seedcane requirement owing to mechanical damage and the increased vulnerability of seedcane pieces to soilborne pathogens that cause sett rots, particularly pineapple sett rot caused by Thielaviopsis spp. Current sugarcane breeding programs in Florida screen for major diseases, such as rusts, smut, ratoon stunting, and viruses, early in the breeding process but not for pineapple sett rot. This study aimed to isolate and identify Thielaviopsis spp. in the Everglades Agricultural Area (EAA), develop a single-bud inoculation protocol for greenhouse-based disease screening, and phenotype the current widely grown sugarcane varieties in Florida against Thielaviopsis spp. The pathogen was confirmed as T. ethacetica , consistent with previous reports from the EAA. A reproducible inoculation method was established and validated through symptom assessment, pathogen reisolation, and molecular confirmation. Using this protocol, six widely grown Florida sugarcane varieties showed significantly reduced germination (by more than 50%) and reduced above- and belowground morphological characteristics under infection, indicating susceptibility. Varietal differences were observed, with CP 03-1912 showing the highest mortality percentage and reduced growth under T. ethacetica infection. These findings highlight the vulnerability of current varieties to pineapple sett rot, especially under mechanical planting systems where smaller seedcane pieces are used. Furthermore, the developed inoculation protocol provides a scalable tool for early stage evaluation of resistance in breeding programs, offering potential to accelerate the development of varieties better adapted to mechanical planting.
Why it matches plant phenotyping methodsサトウキビの病害抵抗性を評価するための単芽接種・症状評価プロトコルを開発し、再現性を検証した研究であり、植物病害表現型の取得法が中心である。
abstractdevelop a single-bud inoculation protocol for greenhouse-based disease screening
Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (R2 = 0.82; MAE = 6.36 q ha−1; MAPE≈7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (R2 = 0.86; MAE = 6.56 q ha−1; MAPE≈7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons.
Why it matches plant phenotyping methodsUAVマルチ／ハイパースペクトル画像と地上測定を統合し、トウモロコシ収量を予測する方法を開発・比較・検証しており、植物形質の取得・推定が研究の中心である。
abstractA series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations.
Background Covered smut in barley caused by Ustilago hordei leads to yield reduction and quality loss of stored grains and is especially challenging in organic production. However, screening for resistance remains challenging. The goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes. We considered favorable pathogen growth conditions, a sufficient sample size to detect differences among genotypes through a power analysis, sources of disease escape or avoidance, and the infection effect on agronomic traits. Results In the first experiment, twenty genotypes treated with various inoculum concentrations were screened for disease incidence under a speed breeding system. Generally, low infection levels were found, likely due to disease escape or avoidance. Based on a power analysis, we modified the protocol to include more plants and improved pathogen growth conditions under a normal greenhouse system. With the modified protocol, the incidence of covered smut was significantly different among genotypes. The protocol also reduced the number of plants required to detect at least one infected plant. Artificial inoculation significantly decreased germination rates while head emergence, days to heading, and plant height were affected by disease infection in the most susceptible genotypes. We also found that covered smut incidence varied with tiller emergence order. The genotypes 'DH160779' (RES check), PI 270630', 'CIho15270', and 'MTV-color-158' presented potential resistance to covered smut. Conclusion The protocol has a high power to differentiate moderately resistant barley genotypes and we confirmed that specific agronomic traits were affected by disease incidence in susceptible genotypes.
Why it matches plant phenotyping methodsオオムギ病害の抵抗性スクリーニングプロトコルを評価・改良し、検出力と遺伝子型間の識別性能を検証しているため、植物病害表現型の取得法が研究の中心です。
abstractThe goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes.
Reproduction assets foundThe paper's disease-screening and agronomic-trait measurement data are publicly deposited on Zenodo, as stated in the Availability of data and materials section. No author analysis code or trained models are explicitly deposited.Dataset · publicThe data used and/or analyzed in the current study are available through the Zenodo, which is available at Gopinathan, G. (2025). Optimization of a protocol for covered smut in barley [Dataset]. Zenodo. [ 47 ] (https:/doi.org/ https://doi.org/10.5281/zenodo.17906264 ).Open asset ↗Zenodo · 10.5281/zenodo.17906264lines:190-223Plant 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.
Precise characterization of alfalfa growth dynamics is essential for breeding accessions with superior regrowth capacity, persistence, and yield stability. However, traditional plot level and coarse scale observations suffer from low signal to noise ratios particularly before canopy closure when phenotypic data are strongly affected by weeds and soil background. Moreover, existing studies rarely capture the dynamic mechanisms of crop development across the entire growth cycle. To address this, the DINO-Pheno-Cluster framework is introduced as a foundation model driven and mechanism decomposed phenotyping approach. This decoupled framework first utilizes DINO-XMem, a few-shot individual plant segmentation network based on DINOv3 and a dual memory mechanism. It subsequently applies parameterized dynamic modeling guided by growth process knowledge. Validation utilized high frequency Unmanned Aerial Vehicle (UAV) imagery from 12 time points across three growing seasons covering 127 alfalfa accessions. DINO-XMem achieved an 89.54% mean Intersection over Union (mIoU) under a 10-shot setting and maintained 86.77% under extreme 1-shot conditions. It successfully resolved dense canopy oversegmentation outperforming fully supervised baselines by 4.08% to 11.97% in mIoU. Crucially, the extracted high purity time series trajectories were parameterized into specific biological indicators including maximum growth rate, comprehensive regeneration index, and seasonal stability index. Gaussian Mixture Model (GMM) clustering based on these mechanistic traits identified four distinct functional ideotypes comprising High yield/High regrowth, Upright/Sparse, High stability/Persistent, and Short/Dense, all validated by ground measured biomass. This workflow establishes a precision screening tool for multi harvest crops advancing crop phenomics toward process level analysis.
Why it matches plant phenotyping methods植物の時系列UAV画像から個体を分割し、成長・再生・安定性などの形質を抽出するフェノタイピング手法の開発と検証が中心である。
abstractthe DINO-Pheno-Cluster framework is introduced as a foundation model driven and mechanism decomposed phenotyping approach
Accurate estimation of crop water requirements is essential to improve irrigation efficiency for forage maize production. This study compared satellite- and UAV-derived normalized difference vegetation index (NDVI) models for estimating crop coefficients (K c ) and evaluated their operational performance for irrigation scheduling. K c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season in two forage maize hybrids (N83N5 and Matador) under three irrigation strategies: conventional producer irrigation (ID1), satellite-based irrigation scheduling (ID2), and UAV-based irrigation scheduling (ID3). Both NDVI sources exhibited strong relationships with K c , with higher calibration accuracy for the UAV model (R 2 = 0.9414) than for the satellite model (R 2 = 0.8278). The UAV-based model applied 23-30% less irrigation water, maintaining high water productivity but also reducing crop growth, forage yield, and nutritional quality. In contrast, satellite-based irrigation scheduling promoted greater crop growth and produced the highest forage yield, reaching 59.8 t ha -1 in hybrid N83N5 while maintaining efficient water use. This treatment also improved forage quality by increasing dry matter and starch concentrations while reducing fiber fractions. The findings highlight the complementary potential of satellite and UAV imagery in precision irrigation and underscore the trade-offs between spatial detail, temporal resolution, and operational scalability. Furthermore, the results demonstrate that a stronger K c -NDVI relationship does not necessarily translate into improved irrigation scheduling performance. Under the conditions evaluated, the satellite-based model provided the best balance between water use, forage yield, and nutritional quality.
Why it matches plant phenotyping methods衛星・UAV画像からNDVIを用いて作物係数を推定する手法を開発し、別年・圃場条件で検証しており、植物群落状態の取得・推定が研究の中心である。
abstractK c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season
Abstract Architectural analysis provides a powerful analytical framework for understanding the ontogenetic trajectories of tree species and their adaptive responses to environmental constraints. Despite their ecological, economic, and cultural importance in West African agroforestry systems, the architectural development of Khaya senegalensis (Desr.) A. Juss. (Meliaceae) and Pterocarpus erinaceus Poir. (Fabaceae), two overexploited taxa classified as Vulnerable on the IUCN Red List, had never been formally described. This study presents the first complete characterization of their architectural development, from the seedling stage to senescence, based on architectural and retrospective analyses conducted on 360 individuals per species across seven localities along a south-north bioclimatic gradient in Côte d'Ivoire, covering contrasting vegetation zones ranging from dense humid forest to dry Sudanian savanna. Both species display well-defined ontogenetic trajectories comprising four phases: juvenile establishment, architectural construction, reproductive transition, and crown restructuring associated with ageing. Distinct architectural models were identified: K. senegalensis conforms to Rauh's model, characterized by a monopodial orthotropic trunk with indefinite growth and rhythmic acrotonic branching; P. erinaceus follows Troll's model, in which the orthotropic trunk progressively gives rise to a sympodial plagiotropic system with mixed terminal and lateral flowering. Architectural units, defined as the minimal structural organization enabling a species to reach reproductive maturity, were established at the adult stage: that of K. senegalensis comprises four axis categories and five branching orders, while that of P. erinaceus comprises three axis categories and up to six branching orders in old trees. Significant variation in growth-unit morphology among habitats and localities ( P ) revealed the architectural plasticity of both species in response to ecological gradients. The calculated Favourable Development indices ( FDi ) identified Bouaké and Katiola as optimal zones for K. senegalensis , and Bouaké and Toumodi for P. erinaceus , providing objective spatial criteria for reforestation planning. These findings demonstrate that architectural traits are robust indicators of development, adaptive strategies, and crown functioning. By linking structural organization to productivity, resilience, and regeneration potential, this study provides a scientific basis for integrating architectural analysis into reforestation programmes, sustainable forest management, and the design of agroforestry systems for threatened African tree species facing growing climatic and anthropogenic pressures. Complementary regression analyses further showed that phytomer number, rather than internode elongation, primarily governs growth-unit length in both species, and that growth unit diameter scales positively with growth unit length; a multivariate analysis of variance (MANOVA) confirmed that ontogenetic stage and locality, but not habitat alone, robustly structure growth-unit morphology.
Why it matches plant phenotyping methods樹木の成長段階・分枝構造・成長単位形態を対象に、建築学的および回顧的解析を中心的手法として適用し、植物構造形質と発達状態を定量・比較しているため。
abstractThis study presents the first complete characterization of their architectural development, from the seedling stage to senescence, based on architectural and retrospective analyses conducted on 360 individuals per species across seven localities
Published23 Jul 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dßprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.
Why it matches plant phenotyping methods樹冠全体のシュート伸長という植物形質を取得するための3D再構成手法を開発・評価しており、精度・解像度・完全性の検証も中心的に扱っている。
abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Quantifying blueberry fruit yield and maturity is important for evaluating yield potential in breeding trials, but manual measurement remains slow, labor-intensive, and costly. Object detection and classification networks offer a high-throughput solution, yet few studies validate image-based counts against hand-harvested ground truth while explicitly accounting for canopy occlusion. Hence, this study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts across 32 diverse southern highbush blueberry genotypes. Among the models evaluated, YOLOv8x achieved the highest detection performance, with an mAP50 of 0.82 and an mAP50–95 of 0.66. External validation produced F1 scores ranging from 0.74 to 0.91 for berry maturity classes. However, image-based detections systematically underestimated hand-harvested fruit counts, with R² values ranging from 0.40 to 0.61. Fruit occlusion varied widely among genotypes, from 42% to 90%, indicating that canopy structure strongly affects berry visibility. Incorporating image-derived canopy architecture, color, and texture features improved predictions of berry counts and maturity. Partial Least Squares regression provided the best performance, increasing R² values of hand-harvested fruit counts, ranging from 0.52 to 0.74. These results show that accounting for canopy occlusion improves image-based estimation of blueberry yield and supports more accurate high-throughput phenotyping.
Why it matches plant phenotyping methodsブルーベリーの収量・成熟度を画像から推定する検出パイプラインを開発し、手収穫値との外部検証および樹冠遮蔽・構造を考慮した改良を行っており、植物表現型取得法が中心である。
abstractthis study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.
Why it matches plant phenotyping methodsカスタマイズしたロボット搭載3Dマルチスペクトル画像システムを実装し、植物形態・スペクトル・ストレス状態を高精度に取得することが研究の中心である。
abstractthe present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns
Accurate high-throughput evaluation of seed germination under abiotic stress is often hindered by subjective manual scoring and insufficient temporal resolution. This study introduces an integrated phenomics framework leveraging an explainable deep learning model for real-time monitoring of lettuce (Lactuca sativa L.) germination dynamics under salinity stress and nano-silicon priming. Utilizing a custom X-Y motorized imaging system, we captured continuous time-lapse data across 16 treatment combinations (0-60 mM NaCl × 0-300 mg L⁻¹ nano-SiO₂). We developed an ultra-lightweight architecture, YOLO26n-Ghost-EMA, which integrates Ghost convolutions and Efficient Multi-scale Attention. This model achieved 99.46% mAP@50 with a 4.5 ms inference time, providing a high detection accuracy while maintaining a lightweight architecture and favorable accuracy-efficiency trade-off compared with standard YOLO variants. while reducing computational demand by 35-50%. To ensure biological validity, Explainable AI (XAI) via Grad-CAM confirmed that the model precisely targets radicle protrusion zones, eliminating 'black-box' opacity. Response Surface Methodology (RSM) quantified the potent ameliorative effect of nano-SiO₂, identifying 100 mg L⁻¹ as the optimal concentration to recover germination from 58.57% to 84.28% under severe salinity (60 mM NaCl). By bridging real-time computer vision and plant stress physiology, this framework provides a scalable, high-resolution solution for precision seed biology and rapid assessment of abiotic stress.
Why it matches plant phenotyping methods深層学習とカスタム撮像システムによる発芽動態の高スループット・リアルタイム定量が研究の中心であり、植物状態（発芽・幼根突出）を画像から抽出する手法を開発・検証している。
abstractThis study introduces an integrated phenomics framework leveraging an explainable deep learning model for real-time monitoring of lettuce (Lactuca sativa L.) germination dynamics under salinity stress and nano-silicon priming.
Strawberry production in hydroponic systems requires precise and spatially explicit information on fruit load, maturity, and size to support harvest planning and quality control. This study presents an integrated computer vision and GNSS RTK framework that detects individual strawberries, estimates their maturity and physical dimensions, assigns fruits to operational size–maturity categories, and generates high-resolution yield maps in an open-field hydroponic system. RGB-D images are processed with YOLOv8-seg and a BoT-SORT-based tracking module to obtain instance-level masks, from which a color-based ripeness index and 3D point clouds are derived. After DBSCAN-based filtering, fruit length and width are computed in metric space and combined with ripeness percentage to assign each strawberry to one of nine operational size–maturity categories using predefined threshold rules. The system was evaluated in a commercial hydroponic crop in Arcabuco, Boyacá, Colombia, achieving accurate segmentation, with median IoU values up to 0.83 for bounding boxes and 0.71 for masks, and mean absolute errors of 2.51 mm in length and 1.85 mm in width with respect to Vernier caliper measurements. Yield maps aggregated in 1 m2 cells and by crop row revealed marked spatial variability in fruit density, maturity state, and size–maturity composition, allowing the identification of zones with a high concentration of fruits ready for harvest versus areas dominated by immature fruits. The proposed framework provides a practical and low-cost decision support tool for hydroponic strawberry management and may be adapted to other high-value horticultural crops after recalibration under different crop architectures, cultivars, and environmental conditions.
Why it matches plant phenotyping methodsRGB-D画像と追跡・3D解析により、イチゴ果実の成熟度、寸法、果実密度および収穫状態を個体レベルで推定し、精度検証も行う中心的なフェノタイピング手法研究である。
abstractThis study presents an integrated computer vision and GNSS RTK framework that detects individual strawberries, estimates their maturity and physical dimensions, assigns fruits to operational size–maturity categories, and generates high-resolution yield maps in an open-field hydroponic system.
This study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024) over the core winter rapeseed production region of the Middle and Lower Yangtze River Plain in China. The data originate from systematic observations at 50 national-level agrometeorological stations across six provinces: Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan. The dataset provides complete records of the specific dates for each phenology stage from sowing to maturity, including eight key phenology periods: Sowing (SO), Emergence (EM), Five-leaf (FV), Bud Formation (BF), Stem Elongation (SE), Flowering (FL), Green Ripening (GR), and Maturity (MA), along with the calculated durations of six distinct growth lengths. We implemented a multi-level quality control protocol encompassing internal logical checks, statistical outlier detection, climatological validation, time series homogenization, and expert arbitration. This protocol effectively constrained data uncertainty and corrected non-climatic discontinuities. Univariate linear regression was further employed to quantify the decadal change trends of each phenology period and growth length, supplemented by Kernel Density Estimation (KDE) to characterize their probability distribution features. The final dataset is presented as structured tables (in xlsx format) and high-resolution diagnostic plots (including trend and density plots), with a total volume of approximately 470 MB, systematically organized by province and station. This dataset fills a critical gap in long-term, standardized rapeseed phenology data for the region. The integrated analysis of phenology dates, growth stage durations, and their trends across the entire network provides an indispensable, high-quality empirical foundation. It is designed to support in-depth investigations into the nonlinear response mechanisms of overwintering crops to climate warming, improve crop model parameterization and validation, and inform regional adaptive management strategies.
Why it matches plant phenotyping methods44年間のナタネの生育段階日を標準化・品質管理して公開するデータセット研究であり、植物状態（フェノロジー）の測定データ整備が中心です。
abstractThis study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024)
Reproduction assets foundThe paper's rapeseed phenology dataset (1981–2024, 50 stations) is openly deposited in Science Data Bank under DOI 10.57760/sciencedb.34086, containing structured xlsx tables and diagnostic plots. No custom code was created per the authors.Dataset · publicThe dataset described in this work has been deposited in the Science Data Bank (ScienceDB) under
accession code https://doi.org/10.57760/sciencedb.34086 [27].Open asset ↗Science Data Bank · 10.57760/sciencedb.34086pdf-page:12 lines:1-68Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
PotatoField / plotGrowth / time-series analysisGrowth / development / phenologyWater status / transpiration
Real-time measurement of belowground tuber growth has not been conducted in field crops. Here, a strain-gauge sensor was used to monitor potato tuber growth and estimate mean daily tuber water loss. Over the 13 days preceding harvest, tuber thickness increased by 0.90 mm, corresponding to a daily gain of 1.33 g, or a 4.8% increase. The greatest diurnal fluctuation was 0.453 mm, corresponding to a transpirational water loss of 9.4 ml, or 3% of the tuber’s water content. Daily transpiration showed a positive correlation with air temperature and vapor pressure deficit. This sensor will enable more precise input control and higher temporal resolution than current methods for below-ground crops, supporting improved crop management, yield prediction, and harvest decisions.
Why it matches plant phenotyping methods埋没ジャガイモ塊茎の成長・厚さ変動・水分損失をひずみゲージでリアルタイム測定する手法が研究の中心であり、植物器官形質の取得法として適格。
abstractHere, a strain-gauge sensor was used to monitor potato tuber growth and estimate mean daily tuber water loss.
Abstract Biotic stress is a major, yet under-quantified, driver of global soybean yield losses, and field-based phenotyping under pest pressure remains a critical bottleneck for crop improvement. Using multi-temporal data from soybean genotypes grown under insecticide-protected and unprotected conditions in Brazil, we present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure. We introduce a three-dimensional metric that jointly captures productivity, feature-level similarity as a proxy for tolerance, and phenological response through days to maturity. This unified formulation enables field-based quantification of pest resilience and replaces labor-intensive and often unreliable direct pest collection and counting. To operationalize this framework, we integrate vegetation indices and self-supervised visual embeddings into a common representation space linking feature stability, performance response and phenological development. This approach enables robust identification of genotypes that maintain feature integrity, minimize developmental delay and sustain yield under pest pressure, with genotypic differences peaking during the pod-fill (R3–R4) and grain-fill (R5.1–R5.5) stages. Overall, this work establishes a scalable, field-ready paradigm for quantifying crop resilience to biotic stress and provides a practical pathway to accelerate breeding for stable yields under real-world agricultural conditions.
Why it matches plant phenotyping methodsUAVによる大規模な圃場フェノタイピング基盤と、植生指数・視覚埋め込みを統合した新しい耐虫性表現型の定量手法が研究の中心である。
abstractwe present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure
Reproduction assets foundThe paper explicitly states that the analysis code is publicly available in the authors' GitHub repository (jianglong26/soybean-insect-resistance), which directly reproduces this paper's phenotyping pipeline (orthomosaic processing, VI/DINOv3 feature extraction, similarity analysis, genotype ranking). The paper also声明sCode · public540 The code used for analysis is available at https://github.com/jianglong26/Open asset ↗pdf-page:16 lines:1-45Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Fall dormancy (FD) and forage yield (FY) are two key traits in alfalfa ( Medicago sativa L.) breeding programs. However, genetic progress has remained limited over the past decades, largely due to the complexity of alfalfa breeding and the reliance on labor-intensive phenotyping methods. High-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation. The objectives of this study were to i) estimate FD using UAV-derived canopy height and RGB vegetation indices and ii) evaluate the predictive performance of machine learning (ML) models for FY estimation. A total of 210 alfalfa populations with diverse genetic backgrounds were evaluated over two growing seasons (2023 to 2025) across seven harvests under Mediterranean conditions in central Chile. FY was measured manually, while FD was estimated using both manual and UAV-based approaches. A total of 19 RGB-derived indices (VIs) including plant height (PH) were extracted and used as predictor variables. Five complex predictive ML models were evaluated: PLS, PCR, SVM, ANN, and MLR. The results showed that UAV-derived FD was significantly correlated with FD obtained through conventional methods ( R 2 = 0.88). The automated UAV-based FD phenotyping framework demonstrated slightly higher precision ( R 2 = 0.92) and broad-sense heritability ( H 2 = 0.69) compared to manual measurements ( R 2 = 0.87–0.89; H 2 = 0.64), providing a more reliable selection tool for breeders. Among the tested ML models, SVM and ANN achieved the highest accuracy ( R 2 ≈ 0.73) for FY prediction. These findings demonstrate that integrating low-cost RGB imagery with complex modeling offers a promising avenue that could assist in refining future selection strategies for this genetically complex species.
Why it matches plant phenotyping methodsUAV画像からアルファルファの休眠性と収量関連形質を推定する高スループット表現型解析手法を開発・検証し、手動測定との比較と機械学習モデル評価を行っているため、方法が研究の中心である。
abstractHigh-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation.
Apple orchard health monitoring is important for supporting timely crop management under the temperate conditions of Kashmir Valley. The present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices, namely the Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Modified Soil Adjusted Vegetation Index and Enhanced Vegetation Index, in a 2 ha apple orchard at SKUAST-K, Shalimar, Kashmir, during the 2020 growing season. Sentinel-2 imagery acquired from April to September was processed using SNAP and QGIS, and mean vegetation index values were extracted for the orchard area. Monthly weather data, including average temperature and rainfall, were obtained from the on-campus meteorological observatory. Ground observations at 20 georeferenced points were used to support the interpretation of canopy development, phenological stage and visible plant health condition. All four vegetation indices showed a seasonal increase from April to July-August, followed by a decline during September-October, corresponding to canopy development, fruit maturation, harvest and senescence. Based on the monthly values presented in the study, temperature showed positive associations with the vegetation indices, with the strongest relationship observed for MSAVI, followed by NDVI and SAVI. Rainfall showed weak and non-significant associations with the indices during the study period. The results indicate that Sentinel-2-derived vegetation indices can reflect seasonal canopy dynamics in apple orchards under the studied conditions. MSAVI appeared particularly useful for representing canopy development, while field observations remained necessary for interpreting pest, disease and phenological effects.
Why it matches plant phenotyping methodsSentinel-2画像から植生指数を算出し、リンゴ樹冠の季節動態・健康状態を評価する測定ワークフローが研究の中心であり、植物状態の推定に直接用いられている。
abstractThe present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices
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:78-112Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Orthoimagery derived from unmanned aerial vehicles (UAVs) has become a valuable data source for crop-growth monitoring. Individual plant-level (IPL) information enables high-throughput analyses by capturing plant-to-plant variability within fields. However, reliable IPL-based analysis requires accurate extraction of plant-specific regions, which remains challenging in soybean cultivation due to weed interference and canopy overlap. This study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery. A segmentation model was developed using combinations of RGB and multispectral orthoimagery, and a furrow line detection algorithm was designed to generate IPL ROIs aligned with crop rows. The ensemble model combining U-Net, DeepLabV3+, and SegFormer achieved the most stable performance (F1-score up to 0.94 and IoU up to 0.89). The furrow-guided ROI generation algorithm also accurately estimated crop counts, showing strong agreement with manual observations (R² = 0.90 and RMSE = 6.35). The generated IPL ROIs enabled accurate quantification of growth-related features, with strong agreement between automatically generated and manually delineated ROIs (R² > 0.90). Overall, the proposed preprocessing framework provides a practical and scalable solution for UAV-based high-throughput phenotyping in soybean and other ridge-based cropping systems.
Why it matches plant phenotyping methodsUAV画像から個体単位の植物領域を抽出し、成長形質を定量化する前処理・セグメンテーション手法が研究の中心であるため。
abstractThis study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery.
Reproduction assets foundThe authors state that the complete implementation of their IPL soybean preprocessing pipeline (semantic segmentation + furrow line detection) is publicly available on Zenodo. The annotated sample dataset, however, is only available upon request from the corresponding author, so it is not a public asset.Code · publicThe complete implementation of
this pipeline is publicly available at https://doi.org/10.5281/zenodo.21095307.Open asset ↗zenodo · 10.5281/zenodo.21095307pdf-page:7 lines:1-62Plant 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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
CottonField / plotMultimodalFruitClassificationPhysiological trait estimationGrowth / development / phenology
Cotton fiber quality is shaped during boll development, boll opening, fluffing, and harvesting, but current assessment still relies largely on manual field inspection and postharvest laboratory testing. This limits timely harvest scheduling and plot-level quality management. To address this problem, we propose a self-supervised multimodal sensing framework for linking preharvest cotton boll status, environmental conditions, and postharvest fiber quality. First, the Cotton Boll Visual Phenotype Self-Supervised Encoding Module learns maturity-related visual representations by reconstructing masked image patches, so that boll cracking, lint exposure, and surface texture can be captured from unlabeled field images. Second, the Agricultural Sensor Temporal Masked Modeling Module reconstructs masked sensor observations to model temporal patterns in temperature, humidity, light, soil moisture, rainfall, and other environmental variables. Third, the Vision–Environment Cross-Modal Contrastive Fusion Module aligns image features with environmental features and produces a joint representation for downstream prediction. Field experiments were conducted using cotton boll images from different maturity and abnormal states, environmental sensor records, management information, and postharvest fiber quality measurements. The framework was evaluated for maturity classification, harvest-window recognition, and fiber quality prediction. The results showed that the proposed method performed consistently better than representative machine learning, single-modal deep learning, and multimodal fusion baselines, while few-shot and ablation experiments supported the value of self-supervised pretraining and multimodal fusion. These findings indicate that the proposed approach can provide useful information for preharvest cotton maturity assessment and harvest-quality management.
Why it matches plant phenotyping methods綿花の成熟状態を画像・環境センサーから抽出し、成熟度分類や収穫時期認識を行うマルチモーダル手法の開発・評価が中心であり、植物状態の表現型推定に該当する。
abstractwe propose a self-supervised multimodal sensing framework for linking preharvest cotton boll status, environmental conditions, and postharvest fiber quality.
Abstract Genomic selection has accelerated genetic gain in many breeding programs worldwide but genotype-by-environment-by-management (GxExM) hampers further progress for systems where these interactions are important and not well represented in the training data. Process-based crop growth models (CGMs), which encode physiological relationships between plants and their environments, can extrapolate to novel conditions but cannot directly leverage genomic information. Coupling these complementary approaches in the Crop Growth Model–Whole Genome Prediction (CGM-WGP) framework addresses both limitations, yet applications in horticultural crops remain scarce. In this study, we apply CGM-WGP to predict flowering time in broccoli ( Brassica oleracea var. italica ) and common bean ( Phaseolus vulgaris L.), two horticultural species with contrasting physiological responses to temperature and photoperiod. Genotype-specific thermal time requirements and photoperiod parameters were jointly estimated with genome-wide marker effects and predictions were compared against a Reaction Norm Genomic Best Linear Unbiased Prediction (RN-GBLUP) benchmark across four cross-validation scenarios of increasing predictive difficulty. RN-GBLUP achieved the highest accuracy under sparse-testing scenarios where training data covered all target environments, while CGM-WGP outperformed RN-GBLUP when predicting untested environments and untested genotype-environment combinations (broccoli: Pearson r = 0.66, RMSE = 9.4 days; bean: r = 0.86, RMSE = 5.2 days). These results demonstrate that CGM-WGP can be applied to horticultural crops using genome-wide markers alone, but without requiring prior identification of quantitative trait loci. CGM-WGP also provides a modular foundation that can be extended to predict the timing of other developmental transitions and output traits such as biomass and yield.
Why it matches plant phenotyping methods開花期という植物形質を対象に、CGM-WGPによる予測手法を適用し、RN-GBLUPとの交差検証で精度比較・評価しており、形質推定ワークフローが研究の中心である。
abstractCoupling these complementary approaches in the Crop Growth Model–Whole Genome Prediction (CGM-WGP) framework addresses both limitations
The selection of genotypes adapted to water stress requires experimental facilities that allow environmental control without compromising physiological and yield relevance. The objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe, which comprised sixty-two high-volume prismatic lysimeters arranged in rows 1.2 m long and spaced 0.6 m apart. Soil water dynamics were monitored weekly using a weighting system. To validate PlaFe, two soybean genotypes were exposed to two water scenarios for forty days from R2 + 7d, during two growing seasons. Two irrigation treatments were applied: irrigation to keep soil water content over 60–70 % of field capacity (EH0), and irrigation equivalent to 35 % of that applied in EH0 (EH1). Water consumption, crop biomass, and pod number were determined at maturity. On average, water stress reduced both biomass and pod numbers by 40 %. However, reproductive efficiency varied among genotypes. Canopy temperature increased by 0.56 °C as daily water consumption decreased, demonstrating its potential to assess drought. These results demonstrate PlaFe’s potential for the accurate evaluation of crop response and adaptation to diverse water scenarios without compromising the complex plant-environment interactions inherent to field conditions.
Why it matches plant phenotyping methodsPlaFeという屋外半制御型フェノタイピングプラットフォームを設計・検証しており、植物の水消費、バイオマス、莢数、群落温度などの表現型評価が研究の中心である。
abstractThe objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe
BarleyLaboratory / benchtopRootMorphology / geometry measurementSegmentationGrowth / development / phenologyRoot system architecture
Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of barley root system architecture and accompanied visible projected root hair area (VP root hair area) over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hairs from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for projected root area and 0.65 for VP root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm -3 ). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a training-free method for integrated analysis of root traits e.g. projected root size, root growth, root diameter and root hairs under controlled physical conditions similar to soil, facilitating studies of root-soil interactions that require both spatial resolution and temporal continuity.
Why it matches plant phenotyping methods根系形態と根毛面積を画像から定量する画像解析ワークフローおよび生育・撮像システムを開発し、アルゴリズム性能も検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R
FruitClassificationGrowth / development / phenology
Papaya is a very popular tropical fruit variety because it is rich in nutrients. However, the method of assessing the ripeness of papaya fruit is still often done manually, which can cause errors in the separation and distribution process. Thus, this study aims to develop an automatic system to classify the ripeness level of papaya fruit using the Convolutional Neural Network (CNN) method based on the ResNet50 architecture. The dataset used consists of papaya fruit images divided into four stages of ripeness, namely unripe, half-ripe, and unfit. The images then undergo a preprocessing process that includes resizing the image to 224 × 224 pixels, adjusting pixel values, and data augmentation through techniques such as rotation, zoom, and horizontal flipping to increase the variety of training data. The model is trained using a transfer learning approach by utilizing existing weights from the imagenet dataset. Model performance evaluation is carried out through the use of a confusion matrix and a classification matrix that includes accuracy, precision, recall, and F1 score. The results of the training process show that the model achieved a training accuracy of 91.72% and a validation accuracy of 83.56%, with a validation loss value of 0.4958. These findings indicate that the model can classify papaya fruit images with relatively good and consistent performance. This research is expected to support the automation process in identifying the ripeness level of papaya fruit in the agricultural and food industry sectors. Keywords: image classification, papaya, CNN, Resnet-50, deep learning.
Why it matches plant phenotyping methodsパパイヤ果実の成熟度という植物器官の状態を画像から自動推定するCNN手法の開発・評価が研究の中心であり、植物フェノタイピング手法として採用する。
abstractthis study aims to develop an automatic system to classify the ripeness level of papaya fruit using the Convolutional Neural Network (CNN) method based on the ResNet50 architecture.
Purpose In recent years, there has been a growing use of unmanned aerial vehicle (UAV) based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards. However, using LiDAR time-series collected throughout the growing season to assess PAI variations in response to phenology, represents an understudied area of investigation. Furthermore, establishing the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point cloud data remains poorly defined. Here, we assess the capability of a UAV-based LiDAR system to characterize cherry trees throughout the growing season, with a focus on monitoring PAI and the vertical structure of individual trees. Methods A time-series of 14 point cloud acquisitions with a density of 3300 points/m2 was collected between February and December 2022, covering all phenological stages of a cherry orchard in southern France. A voxel-based method was applied to create a three-dimensional grid within which PAI was estimated for each voxel. PAI was mapped by accumulating the individual voxel-based PAI values within each vertical voxel column. Results The results demonstrate that a voxel size of at least 0.7 m is required to retrieve reliable PAI estimates (RMSE = 0.58 m2.m−2, MAE = 0.48 m2.m−2, bias = 0.19 m2.m−2, rRMSE = 23%, and R2 = 0.51), while a voxel size of 1 m produced the most accurate PAI estimates (RMSE = 0.5 m2.m−2, MAE = 0.41 m2.m−2, bias = 0.07 m2.m−2, R2 = 0.59), when assessed against field-based PAI measurements obtained with a LAI-2200 Plant Canopy Analyzer. The temporal variation of canopy PAI illustrated the progression of key phenological stages, including flowering, leaf development, ripening and senescence, as well as the response of the canopy to drought stress (reduction in PAI due to leaf rolling) during the summer. The maps of PAI successfully described the variations in leaf canopy density for different cherry varieties and allowed assessment of the vertical PAI profile at the individual tree level, which provides valuable insight into tree condition. Conclusion This study confirms that seasonal UAV-LiDAR monitoring is a viable, informative approach for capturing orchard canopy dynamics at the individual tree and sub-canopy level, linking canopy structure to phenology, varietal differences, and stress responses across the growing season.
Why it matches plant phenotyping methodsUAV-LiDARとボクセル法による樹冠PAI・垂直構造の推定手法を開発・検証し、時系列および個体レベル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。
abstractHere, we assess the capability of a UAV-based LiDAR system to characterize cherry trees throughout the growing season, with a focus on monitoring PAI and the vertical structure of individual trees.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Field / plotCell / cellular structureWhole plant / canopy / plot / fieldGrowth / development / phenologyStress response / tolerance
Abiotic stress tolerance has been significantly weakened in modern crops during the domestication process. Regaining tolerance has become a critical task in light of current climate trends and their impact on global food security. Abiotic stress tolerance is an extremely complex trait and is conferred at various levels of plant functional organization and developmental stages, with regulatory mechanisms operating across multiple scales, from individual cells to tissues and the entire plant. The emergence of advanced molecular tools such as single-cell RNA sequencing and spatial omics technologies has revolutionized the field, advancing our understanding of plant responses to hostile environments. However, the implementation of this knowledge in crop breeding programmes is handicapped by the lack of appropriate phenotyping platforms. Here, we argue that current phenotyping methods may be excellent tools for functional validation of previously discovered traits but have limited predictive value in stress biology. We also propose that bridging the mismatch between omics technologies and phenotyping is the only way to account for cell-specific operation of key genes conferring stress tolerance and implementing them in breeding programmes. Some practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.
Why it matches plant phenotyping methods細胞ベースの植物フェノタイピング手法を扱い、蛍光色素や電気生理学的方法の例、限界、展望を論じる方法論レビューである。
abstractSome practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.
Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.
Why it matches plant phenotyping methods樹冠全体のシュート伸長を測定するための3D再構成手法を開発・評価し、センサー評価、取得・処理戦略、精度・完全性の検証を中心に扱っているため。
abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.
Introduction Accurate and transferable rice yield prediction is essential for precision agriculture and food security, yet existing remote sensing-based models often suffer from limited generalization across regions, cultivars, and field scales. Methods This study developed an interpretable automated machine learning framework for rice yield prediction using UAV-based multispectral imagery collected at the maturity stage. A total of 143 rice samples, including 79 experimental plots and 64 production fields across 15 counties in Sichuan Province, China, were investigated. 20 vegetation indices and 36 gray-level co-occurrence matrix texture features were extracted from multispectral orthomosaics, and three feature selection strategies: Pearson correlation coefficient (PCC), Random Forest feature importance (RF-I), and AutoGluon feature importance(AutoGluon-I), were systematically compared. Four regression approaches, including CatBoost, ExtraTrees, Random Forest, and an AutoGluon stacked ensemble, were evaluated using R 2 , RMSE, and MAE. Results The results showed that the AutoGluon ensemble consistently outperformed individual machine learning models, improving testset R 2 from 0.403-0.670 to 0.528-0.736. The best performance was achieved by combining Pearson correlation-based feature selection with AutoGluon, yielding a training R 2 of 0.821 and a test R 2 of 0.736, with RMSE and MAE values of 0.749 and 0.568 t ha -1 , respectively. Shapley Additive Explanations (SHAP) analysis further revealed that texture features, particularly red-band contrast and angular second moment features, contributed substantially to yield prediction, indicating the importance of canopy structural heterogeneity at maturity. Discussion Overall, the proposed PCC-AutoGluon-SHAP framework provides a lightweight, accurate, and interpretable approach for UAV-based rice yield estimation across heterogeneous field conditions, offering practical potential for scalable precision agriculture and regional yield monitoring.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からイネの収量を推定する画像・特徴抽出・機械学習フレームワークを開発し、複数モデルと特徴選択法を比較検証しており、植物表現型取得・推定が研究の中心である。
abstractThis study developed an interpretable automated machine learning framework for rice yield prediction using UAV-based multispectral imagery collected at the maturity stage.
Introduction The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consuming, and susceptible to subjectivity, highlighting the need for objective and reproducible decision-support tools. This study evaluated the effectiveness of REMATTOOL-R (Relative Maturity Adjustment Tool in R) in validating the existing hybrid advancement framework adopted under the AICRP on Maize. Methods Multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late conducted during Kharif 2020-21 across five locations representing the Central West Zone (CWZ) of India were analysed. The dataset comprised 45 entries, including 40 experimental hybrids, four commercial checks, and one filler entry. REMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits. Least-square means generated from mixed-model analysis were used to identify superior hybrids based on a predefined grain yield superiority threshold (≥5%) over the standard check while maintaining comparable maturity and agronomic performance. Results REMATTOOL-R enabled rapid visualization and integrated assessment of multiple agronomic traits, allowing objective identification of superior hybrids. Five experimental hybrids-PM 21109L (Entry 30), R8050 (Entry 35), PM 21111L (Entry 32), BIO 978 (Entry 4), and DKC 9226 (Entry 9)-recorded ≥5% higher grain yield than the standard check Bio 9682 while maintaining statistically comparable days to 50% anthesis, grain moisture at harvest, and harvested plant stand. All five hybrids identified by REMATTOOL-R corresponded with the official AICRP decisions for advancement from NIVT to Advanced Varietal Trial-I (AVT-I), while three hybrids (R8050, PM 21111L, and DKC 9226) progressed further to AVT-II during subsequent testing cycles, confirming the reliability of the analytical framework. Discussion The findings demonstrate that REMATTOOL-R provides an efficient, transparent, and reproducible framework for the simultaneous evaluation of grain yield, maturity, and adaptation-related traits in maize multi-environment trials. By complementing the existing AICRP hybrid evaluation procedure, the tool facilitates objective advancement decisions and reduces subjectivity associated with manual interpretation of complex datasets. REMATTOOL-R therefore represents a valuable decision-support approach for coordinated maize breeding programmes and has considerable potential for application in large-scale hybrid evaluation systems.
Why it matches plant phenotyping methodsREMATTOOL-Rという解析ツールを開発・評価し、収量、成熟期、収穫時水分、植立本数を統合してハイブリッドの表現型・適応性を客観的に評価することが中心である。
abstractREMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits.
Plant varieties are essential for the survival of human beings and animals, as they act as an alternative source of food, fiber, fodder, and other raw materials for domestic needs and industries in any society. Early identification of plant leaf diseases is very important for keeping the health of crops intact. Crops being infected can affect the overall yield of crops, which may be detrimental to the earnings of farmers. With the emergence of artificial intelligence technology, it has become possible to deploy systems for quicker identification of illnesses. This work has been carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves. For this purpose, the dataset has been created after retrieving data from the PlantVillage dataset. The clinical reliability of different deep learning models of various representational capacities has been tested while using ImageNet pre-trained parameters. The experimental results show that MobileNetV2 achieves the highest accuracy of 96.15%, outperforming deep CNN (76.92%) and medium CNN (61.54%). The test accuracy and class-wise F1-scores for the CNN are observed to be substantially very high. The generalization ability and result of DCNN and MCNN are moderate and poor, respectively, as observed. Additionally, the proposed CNN only uses the disease-affected areas on the leaf, thus making the result more interpretable
Why it matches plant phenotyping methods葉の画像から病徴を推定する深層学習手法を開発・比較し、モデル性能と解釈性を評価しているため、植物表現型取得が中心である。
abstractThis work has been carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves.
LeafGrowth / time-series analysisGrowth / development / phenology
A growing body of literature has illustrated the importance of understanding the circadian clock due to its connection to a wide array of molecular and physiological processes. Numerous methods have been developed in order to monitor the status of the circadian clock in living tissue; however, most methods are either costly or labor intensive, and often require precise conditions that lowers throughput and limits flexibility in the size, age, or type of plant to be assayed. Here, we present an affordable and adaptable methodology for assaying circadian period using the well-characterized circadian output of leaf movement. Employing fully automated time-lapse photography using Raspberry Pi cameras and predominantly automated image post-processing, this methodology minimizes manual input to expedite circadian analysis, thus improving throughput. Additionally, the top-down setup used in this method is appropriate for a wide range of sizes and ages of plants, allowing for an expansion of the scientific questions that can be assayed by this methodology.
Why it matches plant phenotyping methods葉の運動リズムを自動画像計測・解析して概日時計の周期を評価する、植物表現型取得法の開発が中心である。
abstractwe present an affordable and adaptable methodology for assaying circadian period using the well-characterized circadian output of leaf movement
Abstract Agroforestry systems (AFS) offer a promising strategy to address environmental challenges while supporting rising food demands. However, the complex interactions between trees and crops complicate research, particularly regarding their effects on crop yields. This study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study. Growth parameters, namely the Normalized Difference Vegetation Index (NDVI) and plant height, were derived as proxies for yield and analyzed in relation to the distance from tree stripes. Additionally, direction-dependent regression analyses were conducted to assess whether spatial variations in the field could be attributed to the trees. Two distinct patterns emerged: first, a pronounced increase in NDVI was observed at close proximity to the trees, correlated with tree height and schematically illustrated for two representative tree stripes; second, at greater distances, fluctuations in NDVI were associated with the trees but lacked consistent directional trends. Considerable inconsistencies were also observed in plant height variations. The discussion highlights potential drivers of the close-range NDVI increase, the applicability of UAS for AFS research, and limitations in generalizing findings from a single case study. Overall, the results demonstrate that tree effects on crop growth and vitality are detectable but marginal in terms of their influence on maize yields at this site, while showcasing the utility of UAS-based approaches for field-scale analysis of AFS.
Why it matches plant phenotyping methodsマルチスペクトルUASからNDVIと植物高を抽出し、樹木からの距離に伴う作物形質を解析する手法の実質的適用が研究の中心であり、単なるルーチン測定を超える。
abstractThis study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study.
NeRF / 3D Gaussian SplattingLiDAR / point cloudLeafWhole plant / canopy / plot / field2D/3D reconstructionGrowth / time-series analysisTrackingArchitecture / morphology / geometryGrowth / development / phenology
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intricate non-rigid deformations, exhibit changing topology as new organs emerge, and often lack explicit temporal correspondences between consecutive data acquisitions due to newly formed tissue. Methods designed for general scenes struggle to model topology changes and asynchronous organ growth characteristic of plants. To address these challenges, we introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series. Our approach decomposes a plant into its constituent organs and aligns each organ into its own canonical coordinate frame, isolating intrinsic growth patterns from global plant motion. We then learn a shared continuous neural deformation field that models temporal dynamics across all organs, conditioned on learnable per-organ latent codes capturing organ identity and growth characteristics. The resulting modular yet unified representation naturally accommodates the asynchronous development of plant organs while remaining grounded in the practical setting of organ-level plant tracking. We evaluate GrowFields on growth sequences from four plant species, assessing geometric fitting and organ tracking accuracy using manually annotated leaf-tip trajectories. Results demonstrate consistent improvements in spatial precision, temporal coherence, and morphological fidelity over a range of existing representations.
Why it matches plant phenotyping methods植物の3D時系列観測から器官追跡と成長動態を抽出する、オルガン認識型4Dニューラルフィールド手法の開発・評価が中心である。
abstractwe introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series.
MaizeField / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
Understanding evapotranspiration (ET) partitioning into soil evaporation (E) and plant transpiration (T) is crucial for improving agricultural water use efficiency in water-scarce regions. The isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized. This study systematically compared these two methods using two consecutive years (2012-2013) of field isotopic observations in a summer maize field on the North China Plain, a core maize production area facing severe agricultural water scarcity. Stable isotope analysis showed that the local meteoric water line (LMWL) had a slope lower than the global meteoric water line. The 0-5 cm surface soil water evaporation lines had slopes of 5.84 (2012) and 8.06 (2013), confirming significant evaporative enrichment in the topsoil. Plant water isotopic composition closely resembled that of 40-100 cm deep soil water, indicating limited root uptake from the surface layer. IMB-estimated transpiration ratio (T/ET) exhibited distinct phenological patterns, increasing from 37 to 44% at jointing to a peak of 94-96% at filling, then declining to 84-85% at maturity. The two methods agreed well during filling to maturity (differences of 2-10%), but compared with the IMB method, AquaCrop substantially underestimated T/ET at jointing (0.9% vs. 43.8% in 2013) due to its canopy-cover-based transpiration algorithm. These findings identify the filling stage as the critical water demand period, providing a quantitative reference for precision irrigation management under similar climate and soil conditions.
Why it matches plant phenotyping methodsトウモロコシの蒸散比を対象に、同位体質量収支法とAquaCropモデルを比較・検証しており、植物の水利用状態を取得する測定手法の性能評価が中心である。
abstractThe isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
RiceSeed / grainPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyFruit / seed / panicle traitsWater status / transpiration
Grain filling is the decisive period for rice grain weight formation. However, traditional static traits fail to capture its complex, nonlinear dynamics, while direct panicle weighing is hindered by canopy occlusion. Given the intrinsic synchronization between grain filling and dehydration from anthesis to physiological maturity, monitoring grain moisture content (GMC) dynamics serves as a robust proxy for characterizing the filling process. Here, we propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration. Leveraging a 4-year dataset across 135 cultivar-environment combinations, we demonstrate that the GMC threshold for physiological maturity is relatively stable (≈25%). Concurrently, we developed 2 image-based models for GMC estimation, achieving high accuracies (R2 = 0.82 and 0.86). Integrating this physiological threshold with GMC estimation models enabled the successful reconstruction of the dehydration process. Validation on 26 independent cultivars across 2 sowing dates predicted physiological maturity with a root mean square error of 2.4 to 3.3 d. Traits extracted from these dehydration profiles accounted for 42% of the variance in grain weight, doubling the explanatory power of traditional traits. These gains are largely attributed to a new integrated trait, the moisture maintenance index, which showed a higher and more stable correlation with thousand-grain weight (r = 0.6). This framework offers a scalable approach for monitoring large-scale dehydration dynamics to deepen our understanding of grain weight formation, facilitating the genetic improvement of the filling process to enhance crop yield.
Why it matches plant phenotyping methods穀粒含水率の画像推定モデルと生理学的閾値を統合し、脱水動態や成熟期などの植物形質を高スループットに抽出・検証する枠組みが研究の中心である。
abstractwe propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration
To overcome the inefficiency and subjectivity of manual seedling surveys, this study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing. Centimeter-level multispectral imagery was captured to reconstruct 3D point clouds using SfM and MVS techniques. At the algorithmic level, an improved unsupervised pipeline was developed: the Otsu method was employed for plant segmentation, followed by a Fourier Transform to extract 2D spatial frequency features for precise crop row identification and automated spacing measurement. Subsequently, the Combined Entropy Uniformity (CEU) index was developed using Shannon entropy, and a proxy for canopy closure (CCP) was derived using a porosity model, thereby enabling the simultaneous relative quantification of seedling height consistency, spatial distribution uniformity, and canopy geometric structure variability. At the application level, the framework was validated through field trials involving 19 precision planters of diverse configurations. Performance was assessed using indices such as qualified spacing, miss-sowing, and the Coefficient of Variation of Plant Spacing (PSCV). Results indicate that: (1) Vacuum-type planters exhibited optimal stability at speeds of 7–9 km/h, achieving an average qualified spacing rate of 76.7% and a PSCV of approximately 24%, whereas finger-pickup planters were more sensitive to seed size variation and mechanical vibration. (2) The results from the Generalized Additive Model (GAM) suggest a possible nonlinear relationship between seeding rate and certain uniformity indices, indicating that appropriately adjusting operational parameters could help balance operational efficiency and seeding quality; however, this trend requires further validation with larger sample sizes and repeated observations. (3) Point cloud CEU metrics and canopy structure proxies based on the Gap Fraction model showed statistical correlations with certain manually collected indicators, indicating that this method has the potential for rapid screening of seeding quality and relative evaluation of seedling population structure at the field scale under the current experimental conditions.
Why it matches plant phenotyping methodsUAV画像・3D点群から作物の出芽、草丈均一性、空間分布、群落構造を抽出する解析ワークフローを開発し、19種のプランターで検証しており、植物表現型取得法が中心である。
abstractthis study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing.
Semi-arid regions with a high potential for rice and maize cultivation have become some of the most actively farmed areas. They now face the challenge of achieving food security despite the threats of crop water stress, nutrient loss, and environmental changes. In this paper, we develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models in Mahabubabad district, Telangana, India. Different types of crop stresses such as water stress, nutrient deficiency, and phenological anomalies were detected and classified using a hybrid CNN-LSTM architecture with an attention mechanism. The methodology was based on 874 field polygons with extensive in-situ data collection during 2023-24, incorporating multi-temporal spectral indices (NDVI, EVI, NDWI, REP), weather variables, and soil characteristics. The total classification accuracy reached 89.4% for paddy and 87.2% for maize over all stress types, showing that stress detection from satellite images is quite reliable. Water stress was the category that was detected most accurately (92.1% for paddy and 89.8% for maize), followed by nutrient stress (88.7% and 86.3%) and phenological stress (85.2% and 83.9%). The warning system made it possible to identify the problem 15-25 days before there were visible symptoms, making it possible for the farm management to respond in time. Activities of the farm that were most vulnerable to detection were air and water temperatures, precipitation, and crop growth stages for water stress 45-60 days after sowing, 30-45 days for nutrient stress, and during the reproductive phase for phenological stress. The system could be extended for industrial crop stress monitoring across the semi-arid agricultural systems which might lead to precision agriculture and climate-resilient farming practices.
Why it matches plant phenotyping methods衛星画像と深層学習を用いて作物の水ストレス・栄養ストレス・生育異常を直接推定し、精度検証と早期検出性能を評価しているため、植物表現型取得法が中心である。
abstractwe develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models
In Controlled Environment Agriculture (CEA), traditional fixed set-point control is replaced by dynamic control strategies. These strategies enable joint optimization of resource use efficiency and biomass output, leverage electricity price fluctuations to reduce energy costs, and employ targeted environmental stressors to enhance crop quality and physiological resilience. Implementation of dynamic control strategies, however, builds upon real-time monitoring, robust data integration and management, and high-fidelity predictive modeling. These capabilities can be effectively provided through a Digital Twin (DT). This study introduces a novel open-source DT framework designed to support dynamic control strategies in CEA, addressing challenges in scalability, generalizability and interoperability. The framework is centered on the IoT platform ThingsBoard, providing unified, scalable data acquisition and management across heterogeneous sensor and actuator networks through vendor-agnostic integration and standardized interfaces. A significant contribution is its physics-based modeling backend, built on ordinary differential equation models developed in Modelica and exported as Functional Mock-up Units (FMUs). To ensure model accuracy across varying biological conditions, a parameter estimation pipeline is developed to calibrate and adapt these FMUs against experimental data. Building on this, a dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control. Furthermore, the framework incorporates a Multirate Moving Horizon Estimation (MMHE) state estimator to estimate critical unmeasured variables, such as plant biomass. This estimator is specifically designed to handle multirate data, maintaining continuous estimates even when certain sensors provide frequent data while others are sparse or infrequent. Demonstrated through a simulation-based case study modeling lettuce growth in a vertical hydroponic farm, the DT framework's architectural feasibility and virtual modeling capabilities are verified. Using synthetic data generated from a known true parameter set, the calibrated growth model achieved a low cross-validation prediction error, with an RMSE of 0.221g and an NRMSE of 6.62% on an independent test set. The MMHE-based state estimator effectively maintained continuous biomass estimates despite sparse synthetic measurements and model mismatch. These findings underscore the framework's potential as a robust and extensible foundation for future physical DT implementations in CEA, enabling a 31 transition toward dynamic, data-driven, and energy-aware operations.
Why it matches plant phenotyping methods植物バイオマスという観測可能な植物形質を、デジタルツインの状態推定器と動的モデルで継続的に推定する方法を開発・検証しており、単なる栽培制御や routine measurement ではない。
abstracta dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control.
Integrating multi-omics data, including phenomic, genomic, and environmental inputs, offers a powerful approach for enhancing maize performance and predicting grain yield. In this study, crop health was quantified using temporal NGRDI (Normalized Green Red Difference Index) trajectories collected from 16 unoccupied (unmanned) aerial vehicle or system (UAV or UAS, drones and sensors) flights (from 19 to 117 d after planting) in maize trials conducted in Texas. Crop health indices (CHIs) were calculated through area under the curve (CHIAUC) and functional principal component analysis (CHIFPCA), capturing dynamic plant health responses throughout the growing season. Heritability estimates of NGRDI fluctuated between 0.3 and 0.7, averaging 0.51 ± 0.02, reflecting consistent genetic contributions to growth dynamics. CHIs derived from a favorable (irrigated) trial in Texas effectively separated high- and low-yielding hybrids across 41 environment-tester combinations, achieving significant differentiation in 27 (CHIAUC ) and 28 (CHIFPCA1) environments. In comparison, only 21 environments were differentiated when using grain yield alone. Genomic mapping of temporal NGRDI revealed key quantitative trait loci (QTLs) linked to maize growth, containing candidate genes including br2, phyC1, wus1, mads69, cct1, rap2, miR172, and gl15, associated with canopy development, flowering regulation, and drought adaptation. Integrating multi-omics data into phenomic- and environment-informed genomic prediction models improved yield prediction accuracy by approximately 18.5%, particularly for untested genotypes in both tested and untested environments. These findings demonstrate that multi-omics integration provides a scalable framework for enhancing maize performance and advancing grain yield prediction across diverse agricultural systems.
Why it matches plant phenotyping methodsUAV画像から時系列NGRDIを抽出し、作物健康・生育動態の表現型指標を構築して検証・予測に利用しており、フェノタイピング手法の適用が研究の中心的要素です。
abstractCrop health indices (CHIs) were calculated through area under the curve (CHIAUC) and functional principal component analysis (CHIFPCA), capturing dynamic plant health responses throughout the growing season.
BlueberryFlowerGrowth / time-series analysisGrowth / development / phenology
Flowering in perennial crops is a trait influenced by genetics, environmental cues and plant vigor. Here, we studied the genetic and environmental control of repeat flowering (RF) in blueberry ( Vaccinium corymbosum ). RF was measured in a full-sib population from a cross between repeat and non-repeat flowering cultivars (‘Hortblue Petite’ and ‘Nui’, respectively). Longitudinal phenotypes were used to model the area under the curve for the first and second flowering peaks. We found that RF was strongly influenced by bush size and vigor, which we then incorporated into the area under the flowering curve models. Quantitative trait loci linked to both first bloom and RF were detected at three hotspots on chromosomes 4 and 10, and genes of interest known to regulate flowering under both temperature and photoperiod control were discussed. The phenotyping protocol and statistical modelling method reported here are an effective strategy for the investigation of complex interactions between multiple genetic loci and environmental variables on developmental traits, such as flowering. Experimental designs with replicated multi-environment and multi-year measurements are now needed to corroborate our results and further elucidate the determinism of RF in blueberry.
Why it matches plant phenotyping methodsブルーベリーの反復開花を連続的・定量的に測定し、開花曲線のモデリングを組み合わせた新規フェノタイピング手法とプロトコルが明示され、開花形質の遺伝解析における方法論的貢献が中心的です。
titlewith a novel continuous quantitative phenotyping approach
Accurate yield estimation and crop load monitoring are essential for precision orchard management, supporting targeted fertilization, pruning, thinning, harvest planning, and marketing decisions. However, reliable in-situ monitoring remains challenging because commercial orchards are characterized by severe canopy occlusion, fruit overlap, heterogeneous tree architecture, variable illumination, and complex backgrounds. This review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions. First, yield-related indicators are summarized, including direct phenotypic traits such as fruit number, size, volume, and spatial distribution, as well as indirect structural and physiological proxies such as canopy volume, vegetation indices, flowering intensity, and spectral maturity attributes. Second, representative sensing devices and carrying platforms are reviewed, including red-green-blue (RGB) cameras, red-green-blue-depth (RGB-D) sensors, light detection and ranging (LiDAR), hyperspectral and multispectral systems, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs). Third, the evolution of estimation methods is discussed, from traditional image processing and machine learning to object detection, instance segmentation, multi-object tracking, point-cloud analysis, remote-sensing regression, and multi-modal fusion. The review shows that no single sensor or algorithm can satisfy all orchard monitoring requirements. Ground-based vision and depth sensing are more suitable for fine-scale fruit counting and sizing, whereas UAV and spectral sensing provide advantages for regional yield mapping and quality-enhanced assessment. Future research should emphasize occlusion-aware perception, robust cross-environment generalization, lightweight edge deployment, standardized benchmarks, and integrated quantity-quality monitoring frameworks for actionable crop load management.
Why it matches plant phenotyping methods果実数・サイズ・体積・空間分布などの植物形質を対象に、センシング機器と画像解析・深層学習による収量推定法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions.
Spatio-Temporal Fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, covering the entire soybean growing season from late June to early November. Near-daily Planet SuperDove imagery with 3 m resolution was used to temporally enhance UAV images, which were acquired at 0.05 m resolution but only at weekly to monthly intervals. Through the downscaling process, the UAV data were converted into a daily dataset with a target spatial resolution of 0.5 m. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms- Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (Fit-FC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)-within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) trajectories, from which the NDVI-based Vegetation Growth Metrics (VGM)85 and the EVI-based VGMmax were derived. The validation results indicated that ESTARFM achieved the highest NDVI performance among the evaluated algorithms, with a Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697. CACAO post-processing further improved these results, with CA-ESTARFM achieving an RMSE of 0.108 and a UIQI of 0.740, corresponding to a 4.4% reduction in RMSE and a 6.2% improvement in UIQI relative to the baseline ESTARFM. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using NDVI-based VGM85 and EVI-based VGMmax showed that CA-ESTARFM remained consistent with simple linear interpolation of UAV observations while retaining finer spatial structure and reducing localized noise in the derived growth metrics. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methodsUAV・衛星画像の時空間融合とCACAO処理により、NDVI/EVIおよび植生成長指標を抽出するワークフローを開発・比較検証しており、植物状態の取得手法が中心である。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Reproduction assets foundThe paper's STF/CACAO analysis code is openly available on Zenodo. The underlying Planet/UAV imagery data are only available from the corresponding author upon request, so they qualify as request_only.Code · publicThe code supporting this study is openly available at Zenodo
(https://doi.org/10.5281/zenodo.20923823).Open asset ↗Zenodo · 10.5281/zenodo.20923823pdf-page:20 lines:1-70Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published30 Jun 2026Arcitech: Journal of Computer Science and Artificial IntelligenceCited by 0 · OpenAlex ↗
The maturity level of green vegetables is an important factor affecting product quality, market value, and shelf life. Maturity identification is generally performed visually based on leaf color changes, making the assessment subjective and potentially inconsistent. This study aims to develop a classification model for green vegetable maturity levels using a combination of color feature extraction and a Convolutional Neural Network (CNN) to provide a more objective and accurate system. The research began with image acquisition of green vegetables categorized into three maturity levels: immature, mature, and overripe. Preprocessing included image resizing, normalization, and segmentation. Color feature extraction was performed using RGB and HSV color spaces to represent maturity conditions. The dataset was divided into training and testing sets with a 90:10 ratio and processed using a CNN architecture. Model performance was evaluated using accuracy, precision, recall, and F1-score. Results showed that the proposed model achieved 95.2% accuracy, 94.8% precision, 95.6% recall, and 95.1% F1-score. These findings indicate that combining color features and CNN effectively supports automated vegetable sorting and quality control systems.
Why it matches plant phenotyping methods緑色野菜の成熟度という植物器官の状態を、画像取得・色特徴抽出・CNNで自動推定する手法の開発が研究の中心である。
abstractThis study aims to develop a classification model for green vegetable maturity levels using a combination of color feature extraction and a Convolutional Neural Network (CNN) to provide a more objective and accurate system.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract High-throughput plant phenotyping captures development at scale, yet image-rich screens are still often reduced to static trait summaries. We tested whether nutrient availability, polyamine priming, concentration, and their transport reshape Arabidopsis rosette development by generating distinct morphologies or by changing residence along a common trajectory. We analyzed 138,223 time-resolved rosette images from Col-0 and five mutants involved in polyamine transport ( put1-5 ) primed to putrescine, spermidine, spermine, dose, and nutrient regimes using a self-supervised vision backbone, Poincaré embedding, hyperbolic Mapper, and manifold straightening. The data form a single connected developmental manifold with 410 nodes and 746 edges, organized from an early, low-nutrient-biased hub through high-betweenness transition corridors to two late, nutrient-enriched terminal regions. Polyamine identity stratifies this manifold by developmental phase: putrescine enriches early states, spermidine occupies transition corridors, and spermine marks late compact rosettes. Nutrient richness and dose change distal occupancy, whereas put genotypes alter dwell time within shared regions rather than producing separate topologies. Manifold straightening resolves these effects into a short early lateral deflection followed by convergence, yielding two scalar readouts, early transverse offset and distal occupancy, that summarize treatment action on a common morphodynamic scale. The framework converts large image screens into interpretable developmental geometry for image-based phenomics.
Why it matches plant phenotyping methods大規模なロゼット画像から発達形態を抽出し、自己教師あり視覚モデルとトポロジー解析で再利用可能な表現型指標を開発・適用しており、表現型取得・解析手法が研究の中心である。
abstractHigh-throughput plant phenotyping captures development at scale, yet image-rich screens are still often reduced to static trait summaries.
Field / plotWhole plant / canopy / plot / fieldObject detectionPhysiological trait estimationStress / disease detectionGrowth / development / phenologyPigment / colour / senescenceStress response / toleranceWater status / transpiration
Plant wearable sensors are emerging as flexible, non-invasive platforms for continuous assessment of plant physiological status and plant–environment interactions. This review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses. It summarises major sensing approaches, including capacitive, chemical, photodetector-based and piezoresistive sensors, with attention to their materials, fabrication strategies, operating principles and potential applications in plant health monitoring. Advances in flexible substrates, conductive materials, nanostructured sensing layers, biodegradable polymers and wireless communication have improved sensor compatibility with plant surfaces and enhanced the detection of physiological changes under field-relevant conditions. Integration with the Internet of Things, artificial intelligence, machine learning, cloud platforms and data analytics further supports continuous data acquisition and interpretation for precision crop management. These systems may contribute to early detection of biotic and abiotic stresses, enabling timely interventions and improved resource-use efficiency. However, broader adoption remains limited by sensor durability, environmental interference, power requirements, scalability, cost and the complexity of interpreting plant-derived signals. Continued interdisciplinary research is required to develop reliable, affordable, energy-efficient, biodegradable and multifunctional sensing platforms that support sustainable agricultural management under changing environmental conditions.
Why it matches plant phenotyping methods植物の生理状態・成長・クロロフィル・ストレス応答を測定するウェアラブルセンシング手法を中心に扱うレビューであり、植物フェノタイピング手法が中核である。
abstractThis review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses.
Against the backdrop of the rapid development of smart agriculture, pest and disease monitoring and crop growth assessment for large-scale farmlands are of substantial importance for precision management and risk early warning. However, traditional unimodal visual methods are highly susceptible to illumination variation, canopy occlusion, scale differences, and background interference in real field environments, and thus fail to make full use of environmental sensing information and spatial priors. To address these issues, a multimodal target perception framework for intelligent farmland inspection is proposed in this study. By jointly integrating UAV imagery, time-series data from ground Internet of Things sensors, and spatial positional information, joint modeling of pest and disease recognition and crop growth assessment is achieved through cross-modal alignment and collaborative encoding, multi-scale target perception, and dynamic multimodal fusion and decision-making. Experimental results demonstrate that, in the pest and disease recognition task, the proposed method achieved a Precision of 91.63%, a Recall of 90.27%, an F1-score of 90.94%, and an mAP of 93.15%, significantly outperforming comparison models such as Faster R-CNN with ResNet50 backbone, YOLOv8-m, Swin Transformer-Tiny, and Multimodal Transformer. In the crop growth assessment task, an Accuracy of 89.96%, a Precision of 89.11%, a Recall of 88.74%, and a Macro-F1 of 88.92% were achieved, again clearly exceeding those of ResNet50, EfficientNet-B3, ViT-B/16, and conventional multimodal fusion models. The ablation study further verified the effectiveness of the cross-modal alignment module, the multi-scale target perception module, and the dynamic fusion module, with the complete model reaching 90.94%, 93.15%, and 88.92% in Pest F1, Pest mAP, and Growth Macro-F1, respectively. Furthermore, the net economic return regression experiment at the unit-area level further demonstrates that the proposed method can effectively connect state information with economic outcomes, showing strong application potential in return prediction, performance evaluation, and resource allocation optimization. These findings indicate that the proposed method can effectively improve perception accuracy and robustness in complex farmland environments, thereby providing reliable technical support for intelligent inspection, pest and disease early warning, and precision management in agricultural scenarios.
Why it matches plant phenotyping methodsUAV画像、IoT時系列データ、空間情報を統合したマルチモーダル手法を開発し、作物生育状態の評価を技術的に検証している。害虫認識単独ではなく、植物の生育評価を含む取得・推定手法が中心である。
abstracta multimodal target perception framework for intelligent farmland inspection is proposed in this study.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionSegmentationStress / disease detectionArchitecture / morphology / geometryGrowth / development / phenology
Three-dimensional (3D) crop phenotyping is increasingly used to capture crop structure, but its value for crop protection is conditional rather than automatic. 3D approaches are operationally justified only when reconstructed geometry adds decision-relevant information beyond simpler 2D, spectral, scalar, or conventional baselines. This review examines 3D crop phenotyping through a reconstruction-trait-task-maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development. The literature is organized through four connected lenses: reconstruction routes that generate crop geometry, 3D traits that may alter protection reasoning, decision pathways that link traits to intervention variables, and maturity levels that distinguish static 3D models, validated phenotypic traits, process-coupled systems, protection outputs, and outcome-updated decision twins. The strongest decision-facing evidence currently comes from canopy-based dose adjustment, deposition prediction, drift reduction, and related spraying applications in which 3D traits are linked to intervention variables and field-facing comparators. Disease, stress, and architecture-aware modelling provide important but more heterogeneous evidence, while many point-cloud datasets, segmentation pipelines, neural reconstruction methods, and agricultural digital-twin frameworks remain upstream of practical crop-protection decisions because they do not yet connect 3D measurements to validated protection labels, comparator baselines, decision thresholds, intervention outputs, or outcome updating. A central conclusion is that high-fidelity 3D representation should not be conflated with decision-twin maturity. Protection-oriented digital twins require explicit coupling among synchronized crop geometry, functional or epidemiological models, decision rules, and recorded field outcomes. This review therefore identifies the evidence and reporting priorities needed to move 3D crop phenotyping toward validated, deployment-oriented, and feedback-aware crop-protection support.
Why it matches plant phenotyping methods3D作物フェノタイピングの再構成、形質抽出、検証、デジタルツイン成熟度を中心に扱うレビューであり、植物フェノタイピング手法が中核。
abstractThis review examines 3D crop phenotyping through a reconstruction-trait-task-maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development.
Accurate and rapid detection of maize seedling growth is critical in early breeding decisionmaking, smart management, and yield improvement. Traditional leaf age detection still relies heavily on labor-intensive and low-efficiency manual field surveys, underscoring the urgent need for high-throughput phenotyping. Integrating multisource sensor data from unmanned aerial vehicle (UAV) with measured information such as crop height can further enhance the estimation accuracy of crop phenotypic parameters. Accurate field plot segmentation is critical for field-scale phenotypic analysis. However, current approaches remain largely dependent on slow, manual segmentation. Automating this step would greatly reduce the workload of agronomists. This study used UAV RGB and multispectral imagery collected over maize inbred line population plots before the canopy closure stage to perform automatic plot segmentation on field orthophotos and combined measured plant height with relative flight dates to achieve high-throughput detection of leaf age during the maize seedling stage. First, this study proposed a maize plot automatic segmentation method based on orthophotos. Then, it extracted texture features, RGB, and multispectral vegetation indices of each plot. Combined with relative flight date and plant height, four datasets were constructed. Support vector regression (SVR), random forest regression (RFR), and automatic machine learning (AutoML) regression algorithms were used to build the leaf age detection model. The results showed that the orthomosaic from March 23 achieved the best plot-segmentation performance, with minimum intersection over union (IoU), mean IoU, and IoU standard deviation of 14.67%, 96.47%, and 8.65%, respectively. Incorporating relative flight dates and plant-height measurements improved model performance, and the AutoML demonstrated the greatest robustness, achieving a validation R2 of up to 0.862 and an RMSE as low as 0.715. This study proposed a leaf age estimation method that offers practical technical support for field-based maize seedling assessment and reduces manual labor demands.
Why it matches plant phenotyping methodsUAV RGB・マルチスペクトル画像、圃場区画 segmentation、特徴抽出、回帰モデルを統合し、トウモロコシの葉齢という植物形質を推定する方法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractThis study used UAV RGB and multispectral imagery collected over maize inbred line population plots before the canopy closure stage to perform automatic plot segmentation on field orthophotos and combined measured plant height with relative flight dates to achieve high-throughput detection of leaf age during the maize seedling stage.
Objective This study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence. Methods Six commercial varieties, including orange, yellow, white, and purple genotypes, were evaluated under field and laboratory conditions using multispectral drone imagery, high-resolution spectroradiometric signatures, red green blue (RGB) images, and CIELAB color measurements. A hierarchical modeling framework was developed across two phases: (i) spectral modeling using uncrewed aerial vehicle (UAV)-based multispectral indices, textural and geometric metrics, and laboratory-generated hyperspectral signatures; and (ii) a colorimetric index from RGB images. Results Using UAV-based multispectral field data, phenological prediction indices achieved high classification performance (F1-scores > 0.90) when modeled with a Random Forest classifier, supported by distinct spectral signatures associated with canopy development and senescence. In parallel, carotenoid content estimation using a Random Forest regression model demonstrated strong predictive accuracy ( R 2 = 0.897; RMSE = 0.584), with the Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI) identified as the most influential predictors. A complementary laboratory-based Random Forest regression model using high-resolution spectral signatures achieved near-perfect predictive performance ( R 2 = 0.987). SHapley Additive exPlanations (SHAP) analysis identified physiologically relevant wavelengths in the green (540-550 nm) and red-edge (∼700 nm) regions as the primary drivers of carotenoid concentration. Likewise, a novel colorimetric index (ICarot), derived from CIELAB parameters, enabled accurate image-based carotenoid estimation ( R 2 = 0.85). Conclusion This study introduces an innovative multi-sensor framework for precision agriculture and automated postharvest quality control, enabling rapid, objective, and scalable phenotyping in carrot production systems. Through the integration of spectral, colorimetric, and AI-based approaches, the proposed methodology effectively captures both internal nutritional attributes and external quality traits within a unified, non-destructive assessment pipeline.
Why it matches plant phenotyping methods複数センサー画像・分光計測とAIを統合し、ニンジンの生育段階およびカロテノイド含量を非破壊推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence.
Reproduction assets foundThe paper's Data Availability section explicitly deposits the study's data (and project materials) on GitHub and Zenodo, both with authors' public URLs matching allowed_urls. These qualify as paper-specific public assets for the carrot phenotyping measurements and analysis.Dataset · publicThe data is available at GitHub and Zenodo:
- https://github.com/agrocompuepidemlab/Carrot-value-chain-proyect/tree/mainOpen asset ↗github.com/agrocompuepidemlab/Carrot-value-chain-proyectlines:184-307Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. As key components of agricultural management, planting and harvesting schedules have strongly influenced crop production by defining the length of the crop growing season and shaping the environmental conditions crops experience. Accurate knowledge of these management data is crucial for enhancing crop yield estimates by capturing the timing of crop development relative to weather and soil conditions, assessing climate adaptation by tracking shifts in farming practices over time, and supporting agricultural carbon accounting. Yet, existing planting and harvesting date datasets are largely based on state-level statistics or rule-based calendars that overlook intra-regional variability and the influence of human decision-making. The absence of long-term, high-resolution planting and harvesting date information hinders our ability to reconstruct historical agricultural practices and assess their agronomic and environmental consequences. In this study, we introduce CropPlantHarvest, the first dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution from 2001 to 2024. Planting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates. Harvesting dates are retrieved using the Normalized Harvest Phenology Index (NHPI), a novel index that integrates Normalized Difference Vegetation Index (NDVI) and near-infrared (NIR) reflectance to detect harvesting events by capturing the distinct spectral transition from senescent crops to exposed crop residues. Validation against USDA crop progress reports and field-level dataset demonstrates high accuracy of CropPlantHarvest, with a mean absolute error of approximately 5 d for both crop species. This large spatial and temporal dataset captures management-driven variability in crop season timing and duration, supporting improved modeling of crop yields, greenhouse gas emissions, and resource use. It could also serve as a benchmark for refining remote-sensing phenology products and evaluating the agro-environmental impacts of evolving crop management decisions. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).
Why it matches plant phenotyping methods衛星観測と作物モデルによる圃場レベルの作付・収穫時期推定手法を開発し、NHPIを提案して独立データで検証した大規模データセット研究であり、植物の生育・収穫状態の取得が中心的です。
abstractPlanting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates.
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 · publicOur CropPlantHarvest dataset, which provides planting and harvesting dates for corn and soybean fields at 500 m spatial resolution across the U.S. Midwest from 2001 to 2024, can be accessed via Zenodo: https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).Open asset ↗Zenodo · 10.5281/zenodo.16967482lines:322-333Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
A booming population around the world raises the concern of shortages of food resources in this new era. Thus, monitoring and managing crop production is extremely essential, especially rice crops, as they are the fundamental food source for most countries. Several challenges need to be addressed in this case, such as the classification of farmland from various land usages, precise monitoring of rice seedlings, and segmentation of rice growth. By leveraging advanced technologies such as drone imagery and machine learning, this paper proposed a new integrated pipeline for rice field classification and growth monitoring: a combination of convolutional neural networks (CNNs), You Only Look Once (YOLO), and modified U-Net models. These models were used in stages, specifically for paddy field classification, rice seedling detection, and rice growth segmentation. Substantial measurements and analysis have been carried out to verify the performance of the proposed system, including an accuracy of at least 85%, low classification/segmentation loss below 0.35, and high detection recall above 0.9. Thus, the findings highlight how combining different machine learning models with aerial photography can revolutionise conventional farming methods for better efficacy.
Why it matches plant phenotyping methodsドローン画像とCNN・YOLO・改良U-Netを統合し、イネ苗の検出および生育セグメンテーションを行う技術パイプラインが中心で、性能検証も実施している。農地分類は除外対象になり得るが、植物の生育状態を直接抽出する手法部分が十分に実質的である。
abstractThese models were used in stages, specifically for paddy field classification, rice seedling detection, and rice growth segmentation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
RiceGrowth / development / phenologyStress response / tolerance
The reliance on traditional or manual phenotyping creates significant operational bottlenecks in rice breeding due to its resource-intensive and time-consuming nature. This review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations. HTP has great potential to accelerate rice breeding by revolutionizing phenomics. This review examined the various applications of HTP in rice research, phenomics, and breeding. The use of HTP in rice has been substantiated through a range of cutting-edge technologies, such as drones, imaging systems, and sensor networks, that facilitate precise monitoring of key traits at various growth stages, assessment of responses to biotic and abiotic stresses, and the identification of genes or quantitative trait loci (QTLs) associated with essential characteristics. Also, this review discussed HTPs' contributions to current rice breeding programs and documented notable challenges in scaling them. This review offers insights into optimizing HTP strategies to advance rice research, phenomics, and rice breeding.
Why it matches plant phenotyping methodsイネのハイスループット表現型解析技術の応用、技術、課題を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations.
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 · UnverifiedEurope PMC · checked 5 Sept 2026
Abstract Forest Landscape Restoration (FLR) has become an important strategy for reversing land degradation and improving ecosystem resilience in tropical drylands. However, quantitative evaluations of restoration effectiveness remain scarce in Timor-Leste, particularly those integrating satellite-based monitoring with field observations. This study assessed vegetation recovery following reforestation activities within a 148-ha restoration site in Balak, Manatuto, Timor-Leste, using multi-temporal Sentinel-2 imagery and field-based ecological measurements. Vegetation dynamics were evaluated using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 images acquired in May 2022 and May 2026. NDVI differencing was applied to quantify vegetation change, while field data collected from 29 monitoring plots were used to assess seedling survival and growth performance. Pearson correlation and linear regression analyses were employed to examine relationships between vegetation growth indicators and restoration performance. The results indicated substantial vegetation improvement across the restoration area. Mean NDVI increased from 0.288 in 2022 to 0.475 in 2026, representing a 65% increase in vegetation greenness. Approximately 77.6% of the sites experienced moderate to significant vegetation recovery based on ΔNDVI analysis, whereas only 2.3% showed vegetation decline. Field assessments revealed that 62.1% of monitoring plots were classified as high-recovery sites and only 6.9% as low-recovery sites. A significant positive relationship was observed between average plant height and growth percentage ( r = 0.423, R ² = 0.179, p = 0.022), indicating that vegetation structural development was associated with restoration performance. These findings demonstrate that the AFoCO-supported reforestation programme has effectively accelerated vegetation establishment and improved ecosystem condition within a degraded tropical dryland landscape. The integration of Sentinel-2-derived NDVI indicators with field measurements provides a practical, cost-effective, and scalable framework for monitoring FLR outcomes in data-limited regions and offers valuable evidence to support restoration planning and evaluation in Timor-Leste and comparable tropical dryland environments.
Why it matches plant phenotyping methodsSentinel-2 NDVIと圃場測定を統合し、植生回復・成長という植物状態を定量評価する監視フレームワークを中心的に適用しているため。
abstractusing multi-temporal Sentinel-2 imagery and field-based ecological measurements
O uso de rizobactérias promotoras de crescimento de plantas (RPCPs) apresenta-se como alternativa sustentável para a agricultura, porém a predição de seus efeitos envolve múltiplas variáveis. Este trabalho teve como objetivo desenvolver um aplicativo móvel apoiado por aprendizado de máquina para análise preditiva do impacto de RPCPs no crescimento de arroz. A metodologia abrangeu quatro fases: levantamento de requisitos com especialista, análise exploratória de uma base de dados com 6.038 registros experimentais, desenvolvimento e avaliação de modelos de classificação e implementação do sistema. Foram comparados os algoritmos KNN, Random Forest e XGBoost, sendo este último selecionado por apresentar maior acurácia (0,945) e menor desvio padrão (0,010) na validação cruzada. A arquitetura Cliente-Servidor integrou um aplicativo Android em Kotlin com Jetpack Compose a uma API RESTful em FastAPI, operando em duas modalidades: não destrutiva, baseada em medições de campo, e destrutiva, com métricas de biomassa seca. Os resultados indicam que a ferramenta pode auxiliar a tomada de decisão ao reduzir a necessidade de coletas destrutivas em determinadas situações, contribuindo para práticas agrícolas mais sustentáveis.
Why it matches plant phenotyping methodsイネの生育影響という植物形質を、非破壊測定および乾物バイオマスから機械学習で予測するアプリケーションの開発・評価が研究の中心であり、単なる生育実験ではない。
abstractdesenvolver um aplicativo móvel apoiado por aprendizado de máquina para análise preditiva do impacto de RPCPs no crescimento de arroz
StrawberryField / plotFlowerFruitObject detectionGrowth / development / phenology
To address the challenges of recognizing small strawberry targets and achieving accurate phenological perception in complex field environments, this paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net. The backbone is a Residual Efficient Layer Aggregation Network (R-ELAN) enhanced with a Multi-Scale Convolutional Attention (MSCA) mechanism, which emphasizes subtle color and texture variations to differentiate key phenological phases. For feature fusion, hypergraph convolution (from HyperC2Net) and a Mixed Aggregation Network (MANet) are incorporated, modeling the clustered morphology of strawberries and strengthening the representation of sparse small fruits. The detection head incorporates a lightweight Conv2Former module to capture long-range dependencies and spatial contextual information across growth stages, thereby enhancing the model's capacity to represent continuous phenological changes. A Shape-Normalized Wasserstein Distance (Shape-NWD) loss is introduced to stabilize optimization against minor pixel deviations. Experimental results demonstrated that HCMS-Net achieved a mean average precision (mAP) of 94.9% and an F1-score of 90.0%. Specifically, the average precision (AP) values for the flowering, young fruit, green fruit, veraison, and mature fruit stages reached 99.3%, 88.3%, 90.9%, 97.0%, and 98.2%, respectively. Heatmaps confirmed HCMS-Net's precise attention focus across all five phenological stages, effectively suppressing irrelevant backgrounds. Compared to ten mainstream detectors, HCMS-Net surpassed alternatives such as RT-DETR and the YOLOv5n to v13n by 3.4-8.0 percentage points in mAP. It even surpassed YOLOv12s by 2.7 percentage points, while containing only 32.86% of its parameters. The model offers high accuracy and efficiency for phenological period detection, supporting selective harvesting and intelligent agricultural management.
Why it matches plant phenotyping methodsイチゴの生育フェノフェーズを画像から認識する新規検出モデルを開発し、複数手法との性能比較・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 paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net
MaizeRoot2D/3D reconstructionGrowth / development / phenologyRoot system architecture
Maize (Zea mays L.) is a major cereal crop whose productivity across diverse agro-ecological environments is strongly influenced by belowground traits. The root system functions as the primary plant-soil interface, regulating water and nutrient uptake, providing mechanical support and enabling adaptive responses to abiotic and biotic stresses. Despite its central importance, maize root biology has historically received less attention than aboveground characteristics. The maize root system comprises primary, seminal, nodal and lateral roots differing in developmental origin, growth behaviour and physiological role. Key architectural traits-such as rooting depth, root growth angle and branching density-play a crucial role in root system development. These traits, along with anatomical features like cortical aerenchyma and root hair development, are governed by complex genetic networks involving regulatory genes, quantitative trait loci and hormone-mediated signalling pathways. Root growth and spatial distribution are further shaped by soil properties and agronomic practices, including irrigation and nutrient management. Recent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits. This review consolidates recent progress in maize root research with emphasis on root system architecture (RSA), developmental regulation and their functional relevance to crop productivity. Uniquely, it integrates structural, genetic and phenotyping advances in maize root research into a unified framework, while explicitly linking RSA with its functional significance, an aspect often treated separately in earlier reviews. Optimising maize root systems is therefore essential for improving productivity, resource-use efficiency and agricultural sustainability under changing climatic conditions.
Why it matches plant phenotyping methodsトウモロコシ根系研究のレビューであり、根系形態形質の定量評価に用いるハイスループット表現型解析、3D再構成、AI画像解析を明示的に扱うため、表現型解析手法レビューとして中心的です。
abstractRecent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP0.5) of 89.27%, improving mAP0.5 by 1.82 percentage points over YOLOv12. VSG-UNet achieved a mean intersection over union (mIoU) of 83.9% and a Dice coefficient of 81.8%, while reducing floating point operations (FLOPs) and parameters by 44.2% and 61.2%, respectively, compared with U-Net. After stage-aware calibration, the coefficient of determination (R2) between segmented area and leaf dry weight increased from 0.764 to 0.813, and the root mean square error (RMSE) decreased from 0.0210 g to 0.0190 g. These results demonstrated that the proposed framework provided a proof of concept approach based on RGB images for the nondestructive assessment of leaf area and leaf dry biomass in pepper seedlings under restricted experimental conditions.
Why it matches plant phenotyping methodsRGB画像による葉の検出・セグメンテーションと、葉面積から葉乾燥バイオマスを推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstracta cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping.
Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.
Why it matches plant phenotyping methods3D点群による非破壊フェノタイピングと連続重量計測を組み合わせ、植物形態・バイオマス・水利用形質を定量化し、測定精度も検証しているため、手法が研究の中心である。
abstractUsing 3D point-cloud phenotyping, this study compared the canopy structure and WUE
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。
In Japan, the quantity of domestically produced fruit has been gradually decreasing, while wholesale prices have continued to rise due to declining production volumes and a shift toward high-quality varieties. To address these trends, improving quality and reducing labor through automation have become urgent challenges. In precision viticulture, monitoring the growth of grape clusters plays a key role in yield estimation, disease management, and optimal harvest timing. Although recent advances in deep learning and 3D reconstruction have enabled accurate fruit detection and modeling in vineyards, tracking the same clusters on different days remains challenging because of branch movement, fruit growth, and varying imaging conditions. This study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters. Stable vine structures, such as trunks and main branches, are reconstructed using Structure from Motion, and their spatial correspondences are estimated through SIFT-based matching and similarity transformation. Once the coordinate systems of different days are aligned, the grape clusters detected by CenterNet are associated based on spatial proximity in the unified 3D space. Experiments over multiple observation days demonstrated that the proposed method successfully maintained the consistent tracking of grape clusters throughout the growth period. These results indicate that branch-based alignment effectively stabilizes multi-day observations and facilitates the temporal monitoring of fruit growth, supporting automated phenotyping and future field robot applications in viticulture.
Why it matches plant phenotyping methodsブドウ房の経日追跡を目的とする3D画像アライメント手法を開発し、果実成長の時系列モニタリングと自動フェノタイピングへの利用を実験的に検証しているため、フェノタイピング手法が中心である。
abstractThis study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters.
Maturity at harvest is a critical determinant of yield, storability, market value, and nutritional quality, making accurate and objective maturity assessment essential for sustainable crop and fruit production. The agricultural sector is under pressure to satisfy rising global food demand while reducing losses and environmental impacts, yet conventional maturity assessment methods remain largely manual, subjective, and labour-intensive. Against this backdrop, computer vision and deep learning have emerged as powerful tools for non-destructive, high-throughput evaluation of maturity traits in the field and along the supply chain. This review consolidates recent advances in deep learning-based maturity assessment across a wide range of crops, with a particular emphasis on citrus fruits, where external colour change, internal quality, and heterogeneous orchard conditions pose distinctive challenges. The paper analyzes state of the art architectures for classification, segmentation and detection, associated datasets and imaging modalities, and the metrics used to benchmark performance. By critically examining their advantages and limitations for real-world deployment, the review outlines key research gaps and future directions toward robust, scalable, and sustainable DL-driven maturity assessment systems for both citrus and other major crops.
Why it matches plant phenotyping methods作物の成熟度という植物形質を対象に、画像・深層学習による評価手法、データセット、画像モダリティ、ベンチマーク指標を体系的にレビューしており、フェノタイピング手法が中心です。
abstractThis review consolidates recent advances in deep learning-based maturity assessment across a wide range of crops
Plant 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.
Early, precise, and non-destructive stress detection is essential for maintaining crop productivity, particularly in high-density plant growth systems like controlled environment agriculture (CEA), where manual monitoring is often impractical. Using plant motion as a proxy for growth and plant health, we demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis in lettuce and five other CEA relevant crops. Leaf-movement dynamics under stress were imaged with a low-cost, scalable Raspberry Pi imaging setup and quantified using a repurposed open-source motion estimation algorithm; Tracking Rhythms in Plants (TRiP). Our system detected stress-induced changes in leaf-movement within 1 hour of stress, with the timing dependent on the nature of the stress. Sustained reductions in leaf-movement coincide with decreased biomass accumulation. This approach offers a non-invasive, rapid, scalable, and cost-effective solution for continuous crop monitoring, with potential for application in both terrestrial and space farming CEA systems. Abstract Figure Graphical abstract: Quantification of leaf-movement dynamics as a high-throughput proxy for plant physiological status, enabling early stress detection and timely intervention to mitigate yield penalties in CEA settings (image made with biorender.org).
Why it matches plant phenotyping methods低コスト撮像と既存アルゴリズムを用いて葉の動きを定量化し、植物ストレス・生理状態を早期推定する方法が研究の中心である。
abstractwe demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis
Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development.
Why it matches plant phenotyping methods衛星画像の植生指数を放物線フィッティングし、ブドウ園の植生活動・地上部バイオマス・構造発達を推定する分析手法が研究の中心であるため。
abstractthis study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices
The canopy closure stage is a critical phase of rice ( Oryza sativa L.) development that influences canopy structure and final grain yield. Accurate and continuous monitoring of canopy closure dynamics is therefore essential for variety screening and cultivation optimization. This study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics. UAV RGB images were acquired for 198 hybrid rice varieties during early growth stages and used to build a canopy segmentation dataset. Three semantic segmentation models, i.e., DeepLabv3+, U-Net, and PSPNet, were systematically evaluated. Results show that DeepLabv3+ performed the best and enabled precise extraction of rice canopy features, obtaining a mean intersection over union (mIoU) of 0.86. Based on the extracted canopy coverage, the Gompertz model was utilized to characterize temporal canopy closure trajectories for all varieties, achieving an average R 2 of 0.978. Subsequently, five key dynamic indicators were derived, including canopy closure limit value ( K ), initial growth coefficient ( a ), growth rate coefficient ( b ), maximum instantaneous growth rate ( MGR ), and days to maximum growth rate ( Tm ). K-means clustering analysis was performed on these indicators to categorize all rice varieties into three clusters, disclosing pronounced differences in early-stage canopy development characteristics. Correlation analysis further demonstrated that canopy closure dynamics were closely associated with grain yield. Overall, while acknowledging the limitations of a single-season and single-site dataset, this study provides a scalable and objective framework for quantifying rice canopy closure dynamics, offering valuable support for variety selection, cultivation optimization, and high-yield rice production.
Why it matches plant phenotyping methodsUAV画像と深層学習セマンティックセグメンテーションを用いてイネのキャノピー閉鎖動態を定量化する手法を構築・評価しており、植物形質抽出が研究の中心である。
abstractThis study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics.
Accurate diagnosis of potato nitrogen status is critical for optimized fertilizer management and sustaining productivity. We used data from nine field experiments (2010-2018) across major potato-producing regions in northern China to develop a regional critical nitrogen dilution curve via a Bayesian hierarchical model. The curve, Nc = 4.179 × DW -0.417 (DW = whole-plant dry matter), provided the basis for calculating the nitrogen nutrition index (NNI), which was related to canopy spectral indices from a GreenSeeker sensor. Relationships between spectral indices and NNI were strongly growth-stage dependent. The tuber initiation-bulking period, approximately 29-70 days after emergence (DAE), represented the effective phenological window, with 29-55 DAE as the primary operational window for quantitative spectral diagnosis. Stage-specific ratio vegetation index (RVI) showed the most consistent association with NNI, whereas pooled whole-season models had low predictive power. The Bayesian framework quantified uncertainty, emphasizing that near-threshold NNI values require cautious interpretation. The resulting regional-average reference supports rapid field diagnosis of potato N status while accounting for cultivar, year, and site variability. These findings provide practical guidance for stage-specific N management and demonstrate the importance of growth-stage-aware spectral assessment in operational decision-making.
Why it matches plant phenotyping methodsジャガイモの窒素栄養状態を対象に、Bayesian窒素希釈曲線とキャノピー分光センシングを開発・評価し、成長段階別の診断性能と不確実性を検証しているため、植物表現型取得法が中心である。
abstractWe used data from nine field experiments (2010-2018) across major potato-producing regions in northern China to develop a regional critical nitrogen dilution curve via a Bayesian hierarchical model.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in coastal areas due to insufficient water resources and inadequate irrigation infrastructure. Consequently, combined salinity-drought stress poses a major threat to rice production. To investigate the effects of combined salinity-drought stress, a two-season study was conducted utilizing soil media. The first season involved screening 58 rice genotypes, while the second season focused on validating the consistency of response in 20 selected tolerant and susceptible genotypes. These included established tolerant checks (Pokkali and Salumpikit) and susceptible checks (IR 29 and IR 20). Both drought and salinity treatments were given at an electrical conductivity (EC) of 10 dSm⁻¹ and 75% field capacity at the seedling stage. The experimental design was arranged in a modified lattice design in each season, with six blocks and three replications in the first season and two blocks and five replications in the second season. The data collected are leaf symptoms, biomass weight, and shoot length. A number of 330 images captured by a smartphone camera. Machine learning models were employed to predict drought-salinity tolerance criteria. The study revealed that XGBoost model achieved an accuracy of 90.62%. The study identified two genotypes, IR18A1925-SKI-0 and Inpari 30, that exhibited insignificance to Pokkali, based on assessment of shoot length, biomass, and leaf symptoms. These two genotypes were also consistently clustered with Salumpikit. These findings highlight potential of machine learning techniques in predicting rice tolerance to combined salinity-drought stress, with the XGBoost model demonstrating superior predictive capability in this study.
Why it matches plant phenotyping methodsスマートフォン画像から葉症状・バイオマス・草丈などの表現型を取得し、機械学習で複合ストレス耐性を予測する手法の適用が研究の中心である。
titleImage-based Phenotyping and Machine Learning Prediction of Rice Genotypes to Combined Drought-Salinity Stresses
Determining the drivers of ecological stability amid accelerating global environmental change is a critical goal of contemporary ecology. Various candidate drivers have been suggested, with recent attention turning to response diversity—the variation among organism-environment responses. However, despite conceptual interest in response diversity as a driver of stability, there remain few field tests of this relationship. Using multi-species competitive communities of floating aquatic macrophytes as an experimental model for measuring temporal stability and response diversity to nutrient loading, we show that response diversity does not promote temporal stability of total macrophyte cover, but that communities with an uneven distribution of species responses were more resistant to an exogenous shock. To quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology. We measured response diversity as the balance of positive and negative biomass growth responses to dissolved nitrate concentration, weighted by species’ relative contributions to biomass, and tested its effect on temporal stability and resistance to an unexpected pulse disturbance (a large typhoon that disrupted our outdoor mesocosms). Response imbalance predicted typhoon resistance, but species asynchrony and mean population stability best predicted community stability, with no direct or indirect effect of species responses. Overall, our results provide new experimental evidence for how the structure of species responses promotes stability, and we aim our LeafMosaic workflow to empower future field experiments using floating macrophytes to study response diversity and ecological stability.
Why it matches plant phenotyping methods浮遊水生植物の写真時系列から種組成と成長動態を抽出する、オープンソースでスケーラブルな機械学習ワークフローを開発しており、植物表現型取得・解析法が中心的です。
abstractTo quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
FlowerClassificationGrowth / development / phenology
Accurate recognition of flower growth stages is important for plant phenotyping but remains challenging due to subtle visual differences and limited labeled data. This study proposes a hybrid CNN/Transformer + GCN framework for fine-grained flower growth-stage classification. A new dataset, BD Flower Growth, is introduced with 3,889 original images from eight Bangladeshi flower species, categorized into three stages (early, mid, full), forming 24 classes. The dataset is divided into training and testing sets, with augmentation applied only to the training data. Deep backbone networks are used to extract feature maps, which are transformed into graph representations and refined using Graph Convolutional Networks (GCN). A systematic ablation study is conducted by varying GCN depth (3, 5 layers), node resolution ([Formula: see text], [Formula: see text]), and graph construction methods (4-neighbour, 8-neighbour, and KNN with [Formula: see text]). Experimental results show that performance depends strongly on both backbone and graph configuration. The best performance of 97% accuracy is achieved by EfficientNetV2, DenseNet201-based hybrid models, additionally Swin Transformer model shows the largest improvement, increasing from 84% to 97% after GCN integration. Across different settings, grid-based graphs (4- and 8-neighbour) consistently provide more stable and higher performance compared to KNN graphs, while moderate GCN depth (3-5 layers) offers the best balance accuracy. Cross-dataset evaluation on the Oxford 102 Flower dataset further demonstrates the generalization capability of the proposed approach. These findings highlight the effectiveness of hybrid graph-based learning and the importance of graph configuration in improving fine-grained classification.
Why it matches plant phenotyping methods花の生育段階という植物状態を画像から分類する手法の開発・比較検証が中心で、新規データセットとアブレーションおよびクロスデータセット評価も含むため。
abstractAccurate recognition of flower growth stages is important for plant phenotyping
CottonSeed / grainGrowth / time-series analysisGrowth / development / phenology
Seed vigor underpins uniform crop establishment, but its dynamic genetics are understudied. Combining high-resolution temporal phenotyping and genomics in upland cotton, we used the SeedRanger platform to record 17 image-based traits every 30 min over 120 h, revealing stage-specific heritability and identifying 541 seed-vigor loci. These loci show extensive pleiotropy and temporal coordination, forming a genetic network that preserves developmental continuity; 8.9% overlap regions under domestication selection, indicating concurrent optimization with fiber yield. Functional validation of FLA2, a candidate gene underlying a dynamic QTL, implicates auxin-mediated control of radicle elongation and cotyledon development. This temporal framework exposes dynamic genetic architecture and breeding targets for high-vigor crops.
Why it matches plant phenotyping methodsSeedRangerによる高頻度画像計測と17形質の抽出が研究の主要なデータ取得基盤であり、時間的な種子活力表現型を解析する実質的なフェノタイピング応用である。
abstractCombining high-resolution temporal phenotyping and genomics in upland cotton, we used the SeedRanger platform to record 17 image-based traits every 30 min over 120 h
Soil salinization has become a critical factor limiting global agricultural production. Characterizing the growth and developmental responses of okra to salt stress and developing efficient and accurate salt-stress phenotyping techniques can provide an important methodological reference for okra cultivation in saline lands and future multi-cultivar salt-stress phenotyping studies. Traditional manual measurement of plant phenotypic parameters suffers from low efficiency and insufficient detection accuracy, making it difficult to achieve rapid and non-destructive analysis of plant phenotypic traits under salt stress. Therefore, this study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model. Using dual-view feature fusion, we constructed a dedicated dataset. On the basis of PointNet++-MSG, the original MLP layers were replaced with C2F modules, and the SGE attention mechanism was integrated to enhance morphological feature extraction, thereby constructing a lightweight CSP-MSG Net architecture adapted to okra seedling point clouds for semantic segmentation of okra point clouds combined with DBSCAN clustering to complete instance segmentation, phenotypic parameters including plant height, stem diameter and canopy width were further calculated. This scheme enables high-throughput data acquisition, improves measurement accuracy, effectively reduces model parameters and computational overhead, and realizes lightweight operational performance. The results show that okra seedlings can still grow with increasing salt stress concentration, while the growth rates of the three measured traits are all inhibited, indicating that high-concentration salt stress impairs the growth activity of okra seedlings. To verify the calculation accuracy of the model, the phenotypic parameters predicted by the model were compared with manually measured values. The coefficients of determination for stem diameter, canopy width and plant height of okra seedlings reached 0.96, 0.99 and 0.99, respectively. These results strongly demonstrate the excellent reliability and effectiveness of the proposed method, providing methodological support for non-destructive and accurate phenotypic detection of okra seedlings under salt stress.
Why it matches plant phenotyping methodsオクラ幼苗の点群から草丈・茎径・樹冠幅を抽出する計算フェノタイピング手法を開発し、手動測定との比較で精度検証しているため、方法が研究の中心である。
abstractthis study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant height during the early growth stage of rice is a key indicator reflecting canopy establishment rate, tillering potential, and overall growth vigor, all of which critically determine final yield formation. Conventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics, limiting the understanding of early growth vigor and its genetic mechanisms. In this study, a UAV-based LiDAR system was employed to acquire canopy point clouds of 211 rice accessions across ten time points within 40 days after transplanting. High-resolution canopy height models (CHMs) were generated, and continuous plant height trajectories H(t) were reconstructed using piecewise cubic Hermite interpolation (PCHIP). The first derivative V(t) quantified growth rate dynamics and identified the timing of maximum growth (T max ), enabling precise differentiation of early growth patterns among geno-types. Genome-wide association analysis (GWAS) using a mixed linear model (MLM, Q+K) detected 604 significant SNPs, among which 33 were stably expressed across environments. Five candidate genes were identified within ±200 kb windows, mainly encoding proteins related to cell elongation, hormone signaling, and photosynthetic metabolism. The results highlight that LiDAR-based dynamic monitoring of plant height, coupled with genomic association analysis, provides a robust framework for quantifying rice early growth vigor and elucidating its molecular basis, offering valuable guidance for breeding high-vigor “early-establishing” rice cultivars.
Why it matches plant phenotyping methodsUAV-LiDARによるイネ草丈の時系列取得・CHM生成・成長率推定が研究の中心であり、GWASはその測定形質の応用分析。
abstractConventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics
Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R2 values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms.
Why it matches plant phenotyping methods植物成長のLAI、地上部バイオマス、フェノロジー、収量を予測する仮想センサー・デジタルツインを開発し、精度検証しており、表現型推定手法が研究の中心である。
abstractThis study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China.
Plant phenotyping based on unmanned aerial vehicles still faces challenges regarding the direct correlation between spectral information with field-collected variables, due to the influence of environmental factors and the considerable variation among maize phenological stages. Therefore, the objectives of this research were: I) to evaluate the interaction of nitrogen doses and evaluation environments (phenological stages and growing seasons) and variance components for field variables and vegetation indices; II) to identify the most suitable indices according to the evaluation environments; and III) to predict field variables based on relevant vegetation indices identified through the proposed methodology. The study was conducted using a randomized complete block design with four repetitions, in which treatments consisted of six nitrogen (N) topdressing doses (0, 50, 100, 200, 300, and 400 kg ha−1) during the 2022/2023 and 2023/2024 growing seasons. Evaluations of agronomic variables and image acquisition were performed in five distinct phenological stages throughout the maize crop cycle. The data were analyzed using deviance analysis and variance components, principal component analysis (PCA), and multivariate linear modeling for the prediction of field variables. Our results demonstrated that all indices were affected by the interaction between N doses and evaluation environments (phenological stages and growing seasons). Additionally, the most reliable were EXGRaw, TGI, GNDVI, NDRE, CIRE, GVI, CVI, BNDVI, PanNDVI, SRNIRRe, SFDVI, RGBindex, NDVI, SAVI, MSAVI, and OSAVI, which showed clustering patterns according to growing season condition and phenological stage. Finally, the variables predicted using the proposed methodology achieved coefficients of determination above 0.80, except for shoot biomass and 100-grain weight. Therefore, it can be concluded that vegetation indices are influenced by the evaluated environment; however, the proposed framework based on the deduction of fixed and random effects enables the prediction of field variables with high accuracy using relatively simple models.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を選定し、農業形質を予測する方法論の開発・評価が研究の中心であり、植物形質の取得・推定に直接関与している。
titleMethodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.
Reproduction assets foundThe paper's supplementary file contains the REML-BLUP adjusted values for all vegetation indices and field variables, which directly reproduce the paper's phenotyping measurements and underpin its computational analysis. The raw UAV imagery and field data are only available on request, and the EstimateBreed R package (Dataset · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15121782/s1 , Table_Supplementary_1. This table contains all vegetation indices and field variables with values adjusted using the RELM-BLUP methodology.
Author Contributions
C.d.S.L.: Conceptualization, methodology, validation, visualization, writing—original draft, writing—review and editing. A.J.T.S.: Data collection and iOpen asset ↗lines:76-146Plant phenotyping relevance match · UnverifiedbioRxiv · checked 11 Sept 2026
Cassava is a major staple crop in tropical regions, particularly in Sub-Saharan Africa, yet its productivity remains constrained by genetic and agronomic limitations. A major bottleneck in cassava breeding is the difficulty of accurately phenotyping agronomic traits under field conditions using conventional, labor-intensive methods. Here, we evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions. For this, multi-temporal UAV imagery was collected over two growing seasons (2018-2019 and 2019-2020) in a panel of 46 cassava genotypes planted in fields of the International Institute for Tropical Agriculture (IITA), Nigeria. Canopy height, canopy volume, and their relative growth rates (RGRh and RGRv) were extracted at the plot-level, and their seasonal dynamics and canopy-yield relationships were further assessed across developmental stages and environmental conditions. Repeatability (R) and broad-sense heritability (H2) were estimated using a linear mixed model (LMM) that partitioned genetic, genotype-by-year, and residual variance components, enabling the evaluation of both measurement reliability and genetic signal. Overall, UAV-derived growth dynamics were found to exhibit comparable patterns across genotypes, reflecting shared seasonal growth trajectories, while canopy-yield relationships varied with developmental stage and environmental conditions. In terms of genetic metrics, R was high for all UAV-derived traits (R = 0.68-0.69), indicating reliable genotype-level assessment across replicates and seasons. In contrast, H2 differed substantially among traits. Canopy volume (H2 = 0.64) and canopy height (H2 = 0.58) exhibited moderate-to-high heritability, reflecting strong genotype effects and comparatively moderate genotype-by-year interactions. However, their relative growth rates showed near-zero H2 values, driven primarily by genotype-by-year interaction, indicating a dominant environmental influence. These results demonstrate that UAV-derived canopy height and volume provide a consistent basis for genetic differentiation of cassava genotypes across environments, supporting their use in selection, whereas growth-rate traits are better suited for characterizing growth plasticity and genotype-by-environment interactions.
Why it matches plant phenotyping methodsUAV画像からキャッサバの樹冠高さ・体積・相対成長率を抽出し、再現性と遺伝率を検証することが研究の中心であり、実質的な植物フェノタイピング手法の評価に該当する。
abstractwe evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions.
Estimating the age and growth of long-lived desert succulents is challenging due to the absence of annual growth rings. This study utilizes Single-Image Photogrammetry (SIP) and a historical photograph taken by G. Sykes in 1965 and two replicated photographs obtained in 2016 and 2025 to analyze architectural changes over six decades in an iconic “Boojum tree” ( Fouquieria columnaris ) and an adjacent columnar cactus ( Pachycereus pringlei ) from a relictual population in Sonora, Mexico. Results indicate that F. columnaris exhibited a slow mean vertical growth rate of 1.71 cm yr −1 during the 1965-2016 interval which increased to 3 cm yr −1 for the recent 2016-2025 period. This acceleration aligned with values recorded at more favorable sites (i.e. 3.3 to 3.6 cm yr −1 ). Conversely, P. pringlei exhibited a minimal growth rate (1.01 cm yr −1 ), which is much lower than rates reported from repeat photography (5-10 cm yr −1 ) or radiocarbon dating of spines (3-23 cm yr −1 ). Together, these species demonstrate high ecological resilience near their physiological limits in the Sonoran Desert through divergent morphofunctional pathways. F. columnaris favors architectural stability and sustained growth, whereas P. pringlei relies on mechanical robustness and structural redundancy. These findings highlight the efficacy of SIP and historical records for long-term demographic monitoring in extreme arid environments.
Why it matches plant phenotyping methods単一画像フォトグラメトリを用いて植物の建築構造変化と成長速度を定量化することが研究の中心であり、植物表現型の実質的な測定法適用に該当する。
abstractThis study utilizes Single-Image Photogrammetry (SIP) and a historical photograph taken by G. Sykes in 1965 and two replicated photographs obtained in 2016 and 2025 to analyze architectural changes over six decades
Abstract Accurate prediction of maize yield is crucial for improving field management and enabling timely yield estimation. To improve the accuracy and determine the optimal timing of field-scale spring maize yield estimation in the Junggar Basin, this study focuses on spring maize in this region. In 2023, UAV-based multispectral images were acquired at three key growth stages: jointing, filling, and milk stages. Eighteen spectral features significantly correlated with yield were selected. Spring maize yield prediction models were constructed using XGBoost, CatBoost, RF, DT, SVR, GP, LR, and a stacked ensemble learning model, respectively, revealing differences in prediction accuracy across growth stages. Finally, SHAP was used for model interpretability analysis. The results show that: (1) The milk stage achieved the highest prediction accuracy (R² = 0.761, MAE = 0.067 kg·m⁻², RMSE = 0.089 kg·m⁻², MAPE = 5.055%), outperforming the jointing and early grain-filling stages, thereby resolving the uncertainty regarding the optimal timing for UAV-based yield estimation of spring maize in the Junggar Basin. (2) Compared with traditional machine learning algorithms, the stacked ensemble model exhibited stronger robustness and generalization ability. This study provides a technical reference for timely yield estimation and field management of spring maize in irrigated areas of the Junggar Basin, supporting regional food production stability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトウモロコシ収量を推定し、複数生育段階・機械学習モデルの精度と頑健性を比較して最適推定時期を評価しており、表現型推定手法が研究の中心である。
abstractUAV-based multispectral images were acquired at three key growth stages: jointing, filling, and milk stages.
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 · UnverifiedCrossref · checked 15 Sept 2026
Published4 Jun 2026Biomedical Engineering: Applications, Basis and CommunicationsCited by 0 · OpenAlex ↗
The agricultural sector is the most important asset in the country, which boosts development by reducing unemployment, food shortages, poverty, and unstable economic conditions. The recognition and classification of agricultural imagery are the fundamental requisites of contemporary farming methodologies. Thus, it helps to accurately identify the enumeration of plant growth, plant diseases and so on, leading to an increase in crop yield production. There are numerous methods for classifying crop images; however, the conventional methods struggle with issues like noise, distortion, and image quality. It is not capable of fully exploiting the rich potential of image characteristics due to the weather conditions and shooting angles. It does not have the ability to significantly extract the relevant information in the training process and enhance the negative classified outcomes. Multiple learning-based methods have been created to gain more accurate and trustworthy information from image classification. In order to overcome these challenges, a novel crop image classification model is implemented in this research work for classifying the crop images to improve yield production. The required high-quality crop images are collected from the standard datasets. These images are further fed into the feature extraction phase, the Visual Geometry Group 16 (VGG16), graph convolutional neural network (GCNN), and residual network (ResNet) models are utilized to extract the relevant primary information in the collected crop images for generating ensemble convolution features. Subsequently, the resultant features are fed into the classification process, where the multi-scale and dilated adaptive recurrent neural network (MDARNN) mechanism is utilized to effectively classify the crops. In this process, a random function improved dark forest algorithm (RFIDFA) is employed for tuning the RNN parameters, thus improving the classification process. Finally, the validation of the designed approach is performed using several performance measures and compared with the previous works to ensure the designed model’s superior performance. The designed method achieves the best outcome of 95.19% precision, 95.14% NPV, and 95.15% accuracy measures. The developed crop image classification method can continuously monitor the crop growth information to improve yield production. Also, it helps to optimally detect the disease and pest-affected crops in an earlier stage to reduce crop losses, allowing for proper treatment and preventing widespread infection. It is possible to determine the water needs of various crops to ensure efficient crop production and precise farming practices.
Why it matches plant phenotyping methods植物画像から生育状態や病害状態を分類する画像解析手法の開発・検証が中心であり、植物の状態を直接推定するため採用。
abstracta novel crop image classification model is implemented in this research work for classifying the crop images
Alternatives to soil-based horticulture, such as hydroponics, have been developed to respond to food distribution concerns for dense urban centers. A new system was developed to track an individual lettuce plant's growth in a hydroponic environment, utilizing streams of measured information and available models to continuously update the growth trajectory estimates for a plant. These "digital twin" models were integrated into an operating hydroponic greenhouse, with custom horticultural and sensor hardware to grow and measure relevant information. To aid in updating model parameters, plant yield was continuously measured with a custom neural network, using RGB-D images of the plants as an input. The network, trained on a collected dataset of 1300 images, was able to estimate mass within 1.5 g of the ground-truth value. After integration into the custom system, digital twin growth projections could approximate future yield between one and four days in the future, maintaining around a 2 g forecasting error.
Why it matches plant phenotyping methodsRGB-D画像から個体レタスの収量・質量を推定するニューラルネットワークと、センサー統合型の成長追跡基盤が研究の中心であり、植物形質の取得・予測手法を実質的に開発・検証している。
abstractA new system was developed to track an individual lettuce plant's growth in a hydroponic environment, utilizing streams of measured information and available models to continuously update the growth trajectory estimates for a plant.
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:511-575Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Because conventional vegetative propagation methods for Kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson) often are constrained by their need for extensive plantation areas, high labour inputs, and intensive weed management. Therefore, in vitro micropropagation has emerged as an effective approach for the large-scale production of uniform, disease-free kiwifruit plant material. In the present study the shoot growth dynamics and spatial competition between explants for kiwifruit (cv. Hayward) in vitro-grown were investigated considering different explant densities (3, 5, and 7) and two subculture durations (30 and 45 days). Growth performance was assessed integrating traditional measurements (shoot viability, number and length, callus formation, fresh and dry biomass) with high-resolution three-dimensional photogrammetric reconstruction. Image acquisition was performed using a smartphone-based system (iPhone+viDoc RTK rover), and dense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis. Consistent correlations were observed between manually measured growth traits and smartphone-derived morphometric parameters at both 30 and 45 days of subculture. Specifically, point cloud–based estimates of surface area, height, and volume were significantly associated with shoot number, shoot length, and biomass accumulation, supporting the reliability of 3D photogrammetry as a non-destructive tool for phenotyping in vitro kiwifruit growth. The proposed approach demonstrates the potential of 3D photogrammetry to enhance the objectivity, resolution, and repeatability of growth assessment in in vitro culture systems, offering new insights into shoot development and density-dependent interactions. Graphical Abstract
Why it matches plant phenotyping methodsスマートフォン撮影とSfMによる3Dフォトグラメトリで、キウイフルーツ苗条の表面積・高さ・体積を非破壊推定し、手動測定およびバイオマスとの相関で信頼性を検証しており、表現型取得手法が中心である。
abstractdense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis.
: 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.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Accurate acquisition of plant phenotypes is crucial for elucidating plant growth and development, underlying genetic mechanisms, and responses to environmental stimuli. Traditional three-dimensional (3D) phenotyping mainly captures geometric traits such as height, leaf area, and canopy volume, while overlooking physiological and biochemical information. Here, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology. High-quality plant point clouds were first reconstructed using PlantGaussian, and hyperspectral images(HSI) were mapped onto them to produce hyperspectral point clouds. In potted soybean experiments, we built predictive models linking hyperspectral reflectance to SPAD (chlorophyll content) and EWT (equivalent water thickness), and visualized their 3D distributions. The hyperspectral point clouds achieved strong predictive performance for SPAD ( R 2 = 0.78, RMSE = 2.05) and EWT ( R 2 = 0.80, RMSE = 1.07), thereby validating the approach. It further revealed clear vertical stratification within the canopy, highlighting significant spatial heterogeneity of SPAD and EWT in individual plants. Temporal monitoring from August 6 to 21, 2025, captured a sharp increase in EWT after heavy rainfall on August 11. Overall, our results demonstrate that hyperspectral point clouds enable accurate, non-destructive trait estimation and provide a powerful tool for exploring plant function, monitoring stress responses, and advancing precision agriculture.
Why it matches plant phenotyping methods植物の3D形態とハイパースペクトル情報を統合してSPAD・EWTを推定する手法を開発し、予測性能を検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology.
Accurate timing of rice panicle anthesis is critical for quantifying sterility risk under heat and humidity, yet minute-scale field measurement remains challenging because anthesis is transient and spikelets are tiny and difficult to detect. To address this, we present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model. For slender panicle detection and panicle pose normalization, YOLO-SnakePanNet was introduced by using Dynamic Snake Convolution with a lightweight box-rotated head. Ablation experiments show that YOLO-SnakePanNet achieved mAP@50 of 94.4%, improving by 3.6% over the YOLOv11 while reducing computation by 0.7 GFLOPs. For panicle-level anthesis pinpointing, PanicleTimeMAE was proposed by incorporating a pyramid-dilated temporal convolutional network and a confidence-aware smoothing gate into the transformer, reaching Acc@±1 of 0.85 on 5-min sampled sequences (±1 frame = ±5 min), yielding a 40% decrease in MAE over VideoMAEv2. Finally, correlation analysis between variety-level anthesis start time (T start ) and peak time (T peak ) and same-day meteorology showed that higher photosynthetically active radiation (r = -0.543/-0.573 for T start /T peak ) and temperature (r = -0.288/-0.272) advanced anthesis, whereas higher relative humidity (r = 0.397/0.438) and rainfall (r = 0.428/0.502) delayed anthesis. The variance decomposition within fixed-effects model for Tstart ( R2 = 0.651) and Tpeak ( R2 = 0.648) prediction shows that variance mainly attributed to meteorological effects (64%) and variety effects (33.5%). Overall, OPAL-Flow enables variety selection for heat- and humidity-resilient anthesis in rice breeding and supports ecophysiological dissection of anthesis regulation.
Why it matches plant phenotyping methodsイネ穂の開花時刻という植物形質を圃場画像・動画から推定する検出、追跡、超解像、時刻推定パイプラインを開発し、性能評価も行っているため、植物フェノタイピング手法が研究の中心である。
abstractwe present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model.
Reproduction assets foundThe paper's Data availability statement explicitly states that the source code and test samples for OPAL-Flow are publicly available on GitHub at the authors' repository. This is a paper-specific, publicly actionable code asset. The phenotype datasets (panicle detection dataset, start/peak annotation sequences) are notCode · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/OPAL-FLOW . Additional data can be made available upon reasonable request.Open asset ↗gfjiyue/OPAL-FLOWlines:578-590Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
Modern plant phenotyping faces the challenge of interpreting complex, high-dimensional data. Traditional analytical tools often fail to capture the non-linear, hierarchical, and temporal relationships that define plant responses under multifactorial conditions. We present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space. Unlike conventional Euclidean approaches, HTDA-Mapper preserves the hierarchical structure of phenotypic traits, improves cluster resolution, and reveals hidden growth trajectories across treatments and time, offering a powerful means to explore latent phenoms. The pipeline supports both quantitative data and images. When integrated with unsupervised contrastive learning, HTDA-Mapper identifies similarities and differences in raw image data without requiring manual labelling or post hoc processing. We applied this framework to a high-throughput phenotyping (HTP) dataset of over 27,000 images of Arabidopsis thaliana seedlings exposed to varying nutrient levels and priming agents at different concentrations over seven days. Using cubical complexes, HTDA-Mapper mapped relationships between treatment variables, compound concentrations, and phenotypic outcomes. Furthermore, it reliably detected compound-specific effects, uncovered dynamic trait–environment interactions, revealed phenotypic trajectories not captured by conventional methods, and facilitated biologically meaningful interpretation of the complex dataset. By preserving the geometry and temporal evolution of plant development, HTDA-Mapper sets a new standard for HTP analysis. Beyond phenomics, it is a versatile tool for other omics, such as transcriptomics and metabolomics, where structured, high-dimensional data is prevalent. HTDA-Mapper can accelerate data-driven crop improvement by uncovering effective compounds, robust genotypes, and adaptive growth strategies that enhance plant resilience.
Why it matches plant phenotyping methods植物フェノミクスの高次元画像・形質データを解析するHTDA-Mapperアルゴリズムを開発し、27,000枚超の植物画像データで適用・評価しているため、解析手法が中心的である。
abstractWe present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space.
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 · publicUpon acceptance, the codes and all material used in this research will be freely available at HYPERLINK: https://github.com/JZdrazilX/MML and data at ZENODO: 10.5281/zenodo.17952279.Open asset ↗JZdrazilX/MMLhtml-lines:222-260Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
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.
Despite numerous efforts to incorporate emerging innovations into the agricultural domain for increasing crop yield and actively managing the state of the fields, it remains difficult for the industry to implement cutting-edge technologies in practice. This paper proposes AgroVision – a web-based intelligent multimodal system for comprehensive analysis of crops around the world. Designed using a three-layer scalable architecture, the system includes four modules – CNN-based growth stages and plant diseases recognition, AI Chatbot with LLM capabilities and RAG support, as well as the video analysis tool for detecting plant density and weeds. The key technology behind the core image analysis functionality of AgroVision is represented by the efficient Vision Mamba (ViM) architecture, which allows for analysing multiple tasks simultaneously using only one image uploaded by the user. Based on the extensive dataset called "New Plant Diseases Dataset" containing over 87 thousand images divided into 38 classes, the ViM model demonstrates exceptional results achieving weighted average F1-Score of 97.1%. Considering that the inference latency of the model does not exceed 25-40 milliseconds, the system can be deployed at the edge, providing an easy-to-use solution for farmers.
Why it matches plant phenotyping methods植物の成長段階、病害、植物密度を画像から推定するウェブ型解析プラットフォームを提案しており、表現型取得・推定が研究の中心である。
abstractThis paper proposes AgroVision – a web-based intelligent multimodal system for comprehensive analysis of crops around the world.
The utilization of multi-source sensing data to achieve intelligent perception and refined management of farmland has become a vital research direction in modern agriculture. However, traditional inspection approaches based solely on visual information are highly susceptible to illumination variations, occlusion, and background interference, which makes stable pest detection and accurate crop growth assessment difficult to achieve. To address these problems, we propose a multimodal target perception network for intelligent farmland inspection. By integrating UAV imagery, ground environmental sensor data, and spatial location information, joint perception of farmland pests, diseases, and crop growth status is achieved. In the proposed framework, cross-modal alignment and collaborative encoding mechanisms, a multi-scale target perception structure, and a dynamic multimodal fusion strategy are introduced to collaboratively model information within a unified semantic space. Experimental results on a constructed multimodal farmland dataset demonstrate that the proposed method achieved 87.53% Precision and 89.16% mAP in the pest and disease detection task, and 88.04% Accuracy in the crop growth assessment task, significantly outperforming several mainstream visual detection models and multimodal fusion approaches. The results indicate that this intelligent perception framework can significantly improve the robustness of farmland inspection systems, providing an effective technical pathway for AI-driven precision agriculture decision-making. This technology breaks the barrier between production-side sensing data and e-commerce demand, providing a practical technical solution for agricultural production-marketing synergy, quality premium realization and digital rural revitalization.
Why it matches plant phenotyping methodsUAV画像と環境センサーを統合し、作物の生育状態および病害を推定するマルチモーダル手法を開発・評価しており、植物表現型の取得が中心的な貢献である。
abstractBy integrating UAV imagery, ground environmental sensor data, and spatial location information, joint perception of farmland pests, diseases, and crop growth status is achieved.
Blueberry production has rapidly expanded over the past decade, accompanied by growing demand for efficient and accurate methods to monitor the flowering and fruiting phases of blueberry development, which has a direct impact on yield potential. Accurate determination of blueberry phenology enables growers to make data-driven decisions on freeze protection applications and harvest windows. In addition, objective phenology data of blueberry mapping populations will provide high-quality phenotype data for the discovery of genetic mechanisms regulating blueberry flowering and fruiting times. Traditional approaches, such as manual counting and visual ratings, are labor-intensive and subjective in capturing variation across genotypes. Recent progress in computer vision and deep learning has enabled automated flower detection, but most existing studies on blueberries remain restricted to narrow flowering windows or close-up images, limiting their application at the bush level and across the seasonal development. In this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages. A comprehensive dataset was collected on three dates using a field phenotyping robot, covering five flowering stages. The integration of CFNet, a custom module fusing shallow spatial features, and PIoU loss improved the detection performance. Additionally, the Slicing Aided Hyper Inference algorithm was employed to address small-object detection in bush-level images. Experimental results demonstrated that BerryFlowerNet outperformed the baseline YOLO model and three additional detectors, achieving an average mAP0.5 of 0.644 across five independent training runs. The model achieved an accuracy of 0.88 when predicting blueberry flowering stages, indicating its effectiveness and accuracy. Additionally, the results of the bush-level image analysis showed the capability of the model to capture genotype-level differences in flowering dynamics. Overall, this approach offers new opportunities for growers and breeders to determine blueberry phenological development that is critical for optimizing on-farm management strategies and advancing precision phenotyping to facilitate the development of climate-resilient blueberries.
Why it matches plant phenotyping methodsブルーベリーの花房検出・計数と開花ステージ推定を行うCNNおよびフィールド表現型ロボットの開発・評価が研究の中心であり、植物の生育状態を直接推定する実質的な表現型手法である。
abstractIn this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages.
Climate change poses increasing challenges to Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production through unpredictable weather patterns that induce premature bolting and physiological disorders. Traditional breeding programs rely on labor-intensive visual assessment that cannot capture continuous developmental dynamics or precisely quantify stress responses across variable environments. This study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes across contrasting autumn (favorable) and spring (stressful) seasons in Taiwan. The system, based on a FieldScan gantry platform equipped with multispectral 3D scanners, operated autonomously 2-3 times daily, continuously monitoring morphological parameters (3D leaf area, digital biomass, plant height) and spectral indices (NDVI, PSRI) throughout the growth cycle. The system’s automated components -continuous data acquisition and real-time parameter extraction – generated approximately 100,000 data points from 63 morphological, spectral, and structural parameters during 6-week pre-harvest period. Subsequent quality and statistical analysis enabled objective genotype classification and breeding decisions. Automated measurements showed season-dependent associations with visual assessment scores (R² = 0.37-0.56 in autumn; R² = 0.73-0.80 in spring), with spring models substantially outperforming autumn models due to enhanced physiological differentiation under stress. Spring cultivation induced severe stress responses, evidenced by 71% increase in PSRI (0.12 vs. 0.07) and 26% increase in plant height, with bolting resistance emerging as the critical determinant of adaptation. A quantile-based multi-dimensional classification framework integrating seasonal composite scores and Euclidean distances stratified germplasm into actionable breeding categories: stable genotypes (3.7%), spring-specific types (0.7%), poor performers (13.4%), and intermediate materials (82.1%). Continuous temporal monitoring enabled early stress detection, with binned PSRI measurements predicting subsequent morphological development one week in advance (R² = 0.62). This integrated phenotyping framework provides efficient tools for accelerating climate-resilient breeding through objective genotype classification, early stress detection, and data-driven decision support, with potential adaptation to other vegetable crops and integration with IoT-based collaborative breeding network.
Why it matches plant phenotyping methods高スループット3D・マルチスペクトル表現型計測プラットフォームの検証と応用が研究の中心であり、形態・スペクトル形質の自動取得、抽出、予測性能、遺伝子型分類を評価している。
abstractThis study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes
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
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Rice is a significant food that plays a vital part in delivering nutrition to the world's population. Hence, approaches for assessing rice yield have received considerable study. The amount of rice seedlings (density) is a main agronomic module. It is related to harvest and also plays a significant part in the survival rate. Unmanned Aerial Vehicles (UAVs) are prepared with lightweight sensors, which creates a substantial effect in the field of crop phenotyping. The UAV was effectively used to measure germination rates and density in an accurate and effective method that would otherwise be laborious and expensive to obtain when compared to manual valuation. In image processing, mainly over the applications of deep learning (DL) models, there was a notable academic search for the value of UAV images for varied agricultural monitoring tasks. This work develops a Rice Seedlings for Assessing Germination Rates and Density using Aerial Images with Hierarchical Deep Network (RSAGRD-AIHDN) model. The goal of this paper is to assess germination rates and seedling density in rice fields using remote sensing (RS) or UAV-based imaging techniques for improved crop establishment monitoring. To accomplish that, the image pre-processing stage is initially applied with dual stages, such as image acquisition and pre-processing, to ensure high-quality and consistent inputs. Furthermore, the RSAGRD-AIHDN model employs the ConvNeXt method for the feature extraction process. For rice seed detection and classification, the RSAGRD-AIHDN model implements ensemble models, namely stacked autoencoder (SAE), bidirectional temporal convolution network (BiTCN), and Deep Q-Learning (DQL). The experimental assessment of the RSAGRD-AIHDN method is performed under the aerial dataset of rice seedlings. The experimentation of the RSAGRD-AIHDN method portrayed a superior accuracy value of 98.68% over existing approaches.
Why it matches plant phenotyping methodsUAV画像と深層学習モデルを用いて、イネの発芽率と苗密度という植物形質を推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThe UAV was effectively used to measure germination rates and density in an accurate and effective method
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。
abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle lettuce growth dataset used for evaluation, matching an allowed URL. No author code or model checkpoints are disclosed.Dataset · publicThe datasets used and/or analyzed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/jurijsruko/lettuce/data.Open asset ↗Kaggle · jurijsruko/lettucehtml-lines:469-500Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Abstract Artificial Intelligence and Machine Learning rely on large, high-quality datasets for accurate and robust models, yet data scarcity remains a major challenge, especially in smart farming. Phenology modeling, a key application, studies how plant biological events relate to climate and seasons. Accurate phenology models improve crop quality, support climate adaptation, and guide decisions such as pesticide use and harvesting, enhancing environmental and economic sustainability. However, agricultural data are highly diverse and heterogeneous, complicating model development. This study presents a proposed georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Aragón, Spain. Developed by a multidisciplinary team, the dataset combines 9 datasets from 8 sources (including meteorological time series, field phenology observations, and Copernicus Sentinel-2 multispectral imagery) covering the period 2016–2022. It supports both physical and Machine Learning-based phenology modeling and facilitates knowledge extraction in agronomy and plant biology. Its relevance lies in its comprehensive scope, the inclusion of 9 phenological stages, and a rigorous methodology ensuring reproducibility. This framework enables the creation of similar datasets for other regions or crops, advancing smart farming through scalable, data-driven solutions. The open publication of the code further supports this objective. We further anticipate its potential contribution to developing foundation models as well as to the creation of new knowledge in biology and agronomy.
Why it matches plant phenotyping methodsブドウの9段階のフェノロジーを対象に、圃場観測と衛星画像などを統合した再利用可能な地理参照データセットを構築しており、植物形質取得・モデル化の方法論が中心である。
abstractThis study presents a proposed georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Aragón, Spain.
Abstract Accurate mapping of agricultural residue burning using satellite data remains challenging due to the rapid temporal overlap and spectral similarity of mature crops, harvested fields, and burnt residues during peak harvest periods. This study presents a scalable, decision-rule–based methodology for the concurrent mapping of mature, harvested, and burnt paddy fields, integrating field-scale hyperspectral measurements with multi-temporal Sentinel-2 multispectral imagery. Hyperspectral observations captured systematic changes in crop reflectance associated with maturity, harvest intensity, and post-burn ash deposition, which were subsequently upscaled to Sentinel-2 spectral bands to evaluate a comprehensive set of vegetation and burn-sensitive indices. The analysis identified the Chlorophyll Absorption Ratio Index (CARI) as the most effective indicator for separating mature from harvested rice, while the delta Normalized Burn Ratio (dNBR) exhibited the highest sensitivity for distinguishing harvested fields from burnt residues. These indices were combined within a hierarchical decision-tree framework and applied to multi-date Sentinel-2 imagery to map rice burning dynamics across intensively cultivated districts in northern India. The approach achieved an overall classification accuracy of 92.57% with a kappa coefficient of 0.80, demonstrating strong spatial and temporal consistency with field observations. By explicitly addressing intra-seasonal spectral confusion in agricultural landscapes, the proposed framework advances burned-area mapping beyond single-index detection toward integrated crop-state discrimination. The methodology is computationally efficient, sensor-transferable, and suitable for operational implementation, offering significant potential for large-scale agricultural monitoring, emission assessment, and policy-driven residue management in rice-based cropping systems globally.
Why it matches plant phenotyping methods圃場規模のハイパースペクトル／Sentinel-2データからイネの成熟・収穫・焼却状態を識別する手法を開発・検証しており、植物の作物状態を抽出する方法が研究の中心である。
abstractThis study presents a scalable, decision-rule–based methodology for the concurrent mapping of mature, harvested, and burnt paddy fields
Field / plotMultimodalWhole plant / canopy / plot / fieldClassificationCountingGrowth / development / phenology
Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.
Why it matches plant phenotyping methodsキウイフルーツの生育段階を対象とした注釈画像・地理参照動画データセットであり、自動フェノロジー検出と空間検証のためのベンチマーク基盤が中心である。
abstractThe Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions.
Reproduction assets foundThe paper describes a public multi-modal Actinidia chinensis phenology dataset (annotated images, georeferenced videos, ground-truth counts) deposited on Zenodo, plus authors' MIT-licensed preprocessing scripts on GitHub. CVAT and FiftyOne are generic third-party tools and excluded.Dataset · publicThe Multi-Modal Actinidia chinensis Phenology Dataset described in this Data Descriptor is publicly available
at Zenodo: https://doi.org/10.5281/zenodo.17371025.Open asset ↗Zenodo · 10.5281/zenodo.17371025pdf-page:12 lines:1-92Code · publicCustom scripts for dataset preparation are publicly available under the MIT License at https://github.com/Open asset ↗GitHubpdf-page:12 lines:1-92Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Background The BBCH scale (Biologische Bundesanstalt, Bundessortenamt und Chemieindustrie) is a fundamental tool for standardizing plant phenological observations. As apple production shifts towards high-density dwarf orchard systems to enhance yield and facilitate mechanization, a significant gap exists in a unified phenological description framework for dwarfing rootstock apple trees, particularly under desert climate conditions. This study aims to systematically characterize the phenological growth stages of dwarf-rootstock apple using an extended BBCH scale to support precision orchard management. Result In our study, phenological monitoring was carried out systematically through the utilization of optical observation equipment and manual observations throughout the entire growth cycles. The extended BBCH scale with a three-digit coding system was applied, where the first digit indicates the principal growth stage (0-9), the second digit represents a mesostage (1 for spring, 2 for autumn growth), and the third digit describes secondary stages (0-9). We described and illustrated eight main growth stages: bud development (stage 0), leaf development (stage 1), shoot development (stage 3), inflorescence emergence (stage 5), flowering (stage 6), fruit development (stage 7), fruit ripening period (stage 8), and senescence and beginning of dormancy (stage 9). A total of 43 secondary stages were defined. Crucially, mesostages were introduced to differentiate the distinct spring and autumn vegetative growth flushes characteristic of the bimodal growth pattern observed under desert conditions. Conclusions The developed three-digit BBCH scale offers a standardized and refined framework for monitoring apple phenology in high-density dwarf orchards. It serves as a vital tool for optimizing the timing of key agronomic practices like irrigation, fertilization, pruning, and pest control, thereby supporting the intelligent implementation of precision agriculture. This framework effectively standardizes phenological observations across diverse environments, laying a foundation for improved yield, fruit quality, and sustainable orchard management.
Why it matches plant phenotyping methodsリンゴの生育段階を標準化・細分類するBBCHスケールを開発し、光学観察と手動観察によるフェノロジー取得を中心的に扱っているため、植物フェノタイピング手法として含める。
abstractThe BBCH scale (Biologische Bundesanstalt, Bundessortenamt und Chemieindustrie) is a fundamental tool for standardizing plant phenological observations.
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.
Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.
Why it matches plant phenotyping methods植物の光合成・機能を推定するリモートセンシング手法（SIF）を中心に、その機構とスケール間利用をレビューしており、植物フェノタイピング手法のレビューに該当する。
abstractThe emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales
The real-time quantitative estimation of herbaceous plant growth status holds significant potential for investigating fertilization effects, predicting growth curves, and enhancing crop yield. This study constructed a growth quantification model using an improved YOLOv5 architecture integrated with 3D point cloud processing, with pak choi as an exemplar crop. To improve the recognition accuracy while reducing the number of parameters, we employed a lightweight YOLOv5 model enhanced with Atrous Spatial Pyramid Pooling and Ghost convolution modules for individual pak choi plant localization and growth stage classification. We also developed a segmentation method based on the HSV color space to segment leaves. To estimate the total fresh weight of individual plants, we first calculated the leaf surface area by generating a triangular mesh from the corresponding leaf point clouds and predicted the chlorophyll content using a stacking ensemble model. Subsequently, to address the leaf occlusion issues, the leaf pixel ratio in the images, leaf surface area, and mean leaf chlorophyll content were collectively used as independent variables. Finally, a multiple linear regression model was developed to accurately estimate the total fresh weight of individual pak choi plants. Experimental results demonstrate that the modified YOLOv5 architecture achieves a 3.5% improvement in mAP@0.5 (reaching 96%) and a 4.66% increase in F1-score (attaining 90.26%), while significantly reducing the computational complexity compared to the baseline model. Statistical tests verified that the fitted equation could explain 79% of the variation in the total fresh weight, with an average relative error of 12.16%. This enables non-contact and accurate measurement of the pak choi growth status.
Why it matches plant phenotyping methodsYOLOv5、3D点群、葉面積・クロロフィル推定を統合し、個体の生体重という植物形質を非接触推定する手法が研究の中心である。
abstractThe real-time quantitative estimation of herbaceous plant growth status holds significant potential
Understanding cellular growth dynamics in plants requires precise, long-term imaging of developing tissues. Cauline leaves are produced during the transition from vegetative to reproductive development and provide a useful system for studying how laminar organs diversify in form and function. While other laminar organs, such as rosette leaves and sepals, have been extensively studied, early cauline leaf development remains technically challenging to capture due to their concealed position, curved morphology, and the presence of dense trichomes. Here, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana . This method enables reproducible, high-resolution imaging of cauline leaves, supporting robust quantitative analysis of growth across developmental stages at cellular scale resolution. Key features • Fine dissection method for exposing initiating cauline leaves in Arabidopsis thaliana . • Long-term confocal live imaging of cauline leaf development at cellular resolution. • Optimized imaging parameters for high-fidelity 2.5D segmentation and growth analysis in MorphoGraphX.
Why it matches plant phenotyping methodsカウリン葉の成長を細胞レベルで定量化するための解剖、共焦点イメージング、2.5Dセグメンテーション、画像解析パイプラインが中心的に開発・提示されている。
abstractHere, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana .
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 · public2. MorphoGraphX 2.0.1 ( https://morphographx.org/software/ ) (access date, 2026-02-26) [10–11]
3. All codes have been deposited to OSF: https://osf.io/uth78/ (access date, 2026-02-26)
Procedure
A. Plant growth
1. Sow the seeds in pots filled with moist, room-temperature soil. Add a layer of water to the bottom of the tray and cover with a lid to maintain high humidity.
Note: Space seeds sufficiently to avoid contact between the developing plants and to prevent leaf damage; typicallyOpen asset ↗OSFlines:109-143Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Plant disease and pest surveillance is undergoing a profound technological transition. Conventional crop protection has historically depended on episodic field scouting, expert visual inspection, and broad-spectrum preventative spraying, all of which are constrained by labour intensity, uneven diagnostic accuracy, and weak temporal resolution. In contrast, recent advances in artificial intelligence, deep learning, the Internet of Things, remote sensing, and edge computing have enabled crop-health monitoring systems that are more continuous, data-rich, and spatially explicit. This review analyses the evolution of AI-driven plant disease and pest surveillance, with particular attention to how image-based deep learning, connected environmental sensing, unmanned aerial vehicle platforms, cloud-edge infrastructures, and multimodal analytics are reshaping next-generation crop protection. The article argues that the central innovation is not merely automated diagnosis, but the emergence of surveillance ecosystems capable of recognising symptoms, estimating risk, localising hotspots, and informing more selective intervention. The review synthesises major developments in convolutional neural networks, object detection, semantic segmentation, transfer learning, domain adaptation, transformer-based computer vision, anomaly detection, environmental time-series modelling, and multimodal analytics. It also evaluates the practical obstacles that still limit real-world deployment, including dataset bias, annotation uncertainty, poor cross-domain generalisation, limited interoperability, energy and connectivity constraints, weak model explainability, and uneven economic accessibility. The article further considers how AI-based surveillance may strengthen integrated pest management by supporting earlier warning, more precise treatment timing, reduced blanket pesticide use, and stronger alignment between biological risk and management action. It concludes that the future of crop protection will depend less on isolated improvements in benchmark accuracy and more on the development of trustworthy, scalable, and biologically meaningful surveillance systems that can support sustainable decisions under real agricultural conditions.
Why it matches plant phenotyping methods植物病害の症状認識・リスク推定・ホットスポット局在化を行う画像解析、センサー、UAV、マルチモーダル手法を中心にレビューしており、植物の病害状態を推定するフェノタイピング手法レビューに該当する。
abstractThis review analyses the evolution of AI-driven plant disease and pest surveillance, with particular attention to how image-based deep learning, connected environmental sensing, unmanned aerial vehicle platforms, cloud-edge infrastructures, and multimodal analytics are reshaping next-generation crop protection.
Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.
Why it matches plant phenotyping methods複雑形質の表現型性能を遺伝子型・環境情報から予測する新規計算手法を開発し、オオムギの収量・開花期で評価している。ウェブインターフェースも提供され、形質推定ワークフローが中心である。
abstractwe developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions.
Reproduction assets foundThe paper deposits its barley genotype, phenotype, and environmental datasets at three DOI repositories, and its analysis source code on GitHub, plus a public Shiny web tool.Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000010lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000003lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000011lines:31-42Code · publicAll the data and source codes have been uploaded to GitHub and can be accessed under the GNU Open License at: https://github.com/pwang2019/GxE_Model .Open asset ↗github.com/pwang2019/GxE_Modellines:196-205Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
AppleField / plotRGB / grayscaleFruitClassificationObject detectionSegmentationGrowth / development / phenology
This article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red). The dataset serves as a resource for detecting and segmenting apples across different developmental phases. Each image includes pixel-level instance segmentation masks annotated in JSON format using the VGG Image Annotator (VIA), ensuring compatibility with deep learning frameworks. The dataset's real-world variability-spanning lighting conditions, occlusions, and clustered fruit arrangements-enhances its utility for training generalizable computer vision models in precision agriculture. It supports tasks such as fruit detection, segmentation and growth-stage classification, addressing the scarcity of annotated data for transitional maturity phases. With 2574 annotated apple instances, this dataset facilitates research on maturity grading and transfer learning for agricultural robotics. By standardizing annotations and incorporating diverse field conditions, this dataset reduces preprocessing overhead and accelerates the development of deployable AI solutions for orchard management. It is particularly valuable for improving model robustness in heterogeneous environments, thereby advancing data-driven horticultural practices.
Why it matches plant phenotyping methodsリンゴ果実の発育段階・成熟度という植物器官の状態を対象に、画素単位アノテーション付き画像データセットを構築しており、観測・抽出手法の再利用可能な基盤が中心である。
abstractThis article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red).
Reproduction assets foundThe paper is a data descriptor for a public apple image dataset (1406 RGB images, pixel-level instance segmentation masks in JSON) deposited on Mendeley Data with a direct URL and DOI, matching the allowed URL exactly.Dataset · publicRepository name: Wang, Dandan; Wang, Bo (2026), “A Multi-Stage, Pixel-Level Annotated Apple Dataset for Precision Agriculture Research”, Mendeley Data, V4
Data identification number: 10.17632/gfcmdbvw65.4
Direct URL to data:https://data.mendeley.com/datasets/gfcmdbvw65/4Open asset ↗Mendeley Data · 10.17632/gfcmdbvw65.4html-lines:1-97Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
ArabidopsisStem / branchPhysiological trait estimationGrowth / development / phenology
ABSTRACT Agriphotovoltaics (APV) combines crop production with solar energy generation to address increasing demands for food and energy while reducing land-use competition. Unlike conventional opaque photovoltaic systems, semitransparent organic photovoltaics (OPVs) selectively absorb light, potentially improving efficiency but also altering both light quantity and spectral quality, key factors affecting plant growth. Here, we developed a rapid bioassay based on hypocotyl elongation to evaluate plant responses to OPV-filtered light using Arabidopsis thaliana and Cardamine hirsuta , two species with contrasting shade strategies. Screening a diverse set of OPV materials revealed that plant growth responses depend more on spectral composition than on total light intensity alone. Certain materials, such as PTB7-Th and D18, produced growth patterns similar to neutral shading, while others promoted elongation. Our analyses identified blue light wavelengths, linked to cryptochrome activity, as more critical than red light wavelengths, linked to phytochrome activity, for maintaining normal development. These findings provide a scalable framework to assess OPV-plant compatibility and demonstrate that optimizing spectral quality alongside light intensity is essential for designing efficient APV systems that sustain crop performance while generating renewable energy.
Why it matches plant phenotyping methods植物の光応答を測定する迅速・スケーラブルな低胚軸伸長バイオアッセイを開発し、OPV材料評価に適用しており、表現型取得法が中心的です。
abstractHere, we developed a rapid bioassay based on hypocotyl elongation to evaluate plant responses to OPV-filtered light
Abstract Background: The stink bug complex is one of the most damaging pests of soybean, reducing yield and seed quality. Genetic resistance remains the most sustainable and effective management strategy, but its quantitative inheritance and labor-intensive field phenotyping make its implementation in breeding programs challenging. Objective: This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models. Methods: A population of 304 soybean lines was evaluated in alpha-lattice design trials across two seasons under natural infestations. Five resistance-related traits, grain yield (GY), healthy seed weight (HSW), number of days to maturity (NDM), tolerance (TOL), and leaf retention (LR), were manually scored and linked to UAV-derived vegetation indices (VIs) and texture indices (TIs). Three ML models (AdaBoost, SVM, MLP) were tested to predict these traits from aerial features. Results: Results showed that VIs, particularly Visible Atmospherically Resistant Index at the 25th percentile (VARI_P25), were consistently associated with resistance-related traits, while decision tree analysis highlighted TIs at 45° and 135° as complementary sources of structural information. Prediction ability was highest for GY, HSW, and NDM, especially in flights near flowering and maturity, but remained low for TOL and LR. Integrating multiple flights modestly improved accuracy, whereas cross-season predictions were unreliable. Nonetheless, indices such as VARI_P25 provided useful cross-season correlations for HSW and TOL, enabling early screening of less promising lines. Conclusion: This pioneering study demonstrates that UAV–ML pipelines can capture genetic signals of stink bug resistance in soybean, despite environmental complexity. These findings open new avenues for resistance phenotyping, supporting more efficient breeding strategies and accelerating genetic gains in soybean improvement.
Why it matches plant phenotyping methodsUAV画像と機械学習を用いて、ダイズの抵抗性関連形質や収量を推定するHTPパイプラインを技術的に評価しており、表現型取得・推定法が研究の中心である。
abstractThis study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models.
This study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops, using Cestrum nocturnum as a model species. A greenhouse experiment was conducted with plants grown in containers and irrigated with nutrient solutions at three electrical conductivity (EC) levels (2.0, 4.5, and 7.0 dS m - ¹). Plant responses were assessed through vegetative growth, visual quality, flowering intensity, continuous stem diameter variation (maximum daily stem shrinkage, MDS), cumulative evapotranspiration (ETa), and substrate bulk EC monitored with sensors. Increasing salinity reduced vegetative growth, particularly shoot biomass, while enhancing flowering intensity at 4.5 dS m - ¹, indicating a shift from vegetative to reproductive development. The moving average of MDS (avgMDS) responded to salinity, showing both increases and decreases depending on stress intensity, and, when expressed as signal intensity (SI: control/salinity), discriminated between stress levels, establishing alert (1.10) and critical (1.38) thresholds. Salinity decreased ETa by 35% and 65% at 4.5 and 7.0 dS m - ¹, respectively, and ETa-based SI defined alert (1.20) and critical (1.55) thresholds. The hourly moving average of bulk EC (avgECb) enabled continuous assessment of salinity dynamics, minimizing the influence of substrate moisture variability. The use of avgMDS, ETa, and avgECb enables the detection and interpretation of salinity stress by integrating plant physiological responses with substrate conditions, while the combined use of two or more indices improves the robustness of the assessment, providing a quantitative framework for salinity management in potted crops.
Why it matches plant phenotyping methods植物の生理応答とセンサー計測から塩ストレスを定量検出する指標を開発し、警戒・臨界閾値を設定して技術的に評価しているため、単なる生育測定ではない。
abstractThis study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops
LiDAR / point cloudFlowerLeafStem / branchSegmentationGrowth / development / phenology
The segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis. Compared to point cloud segmentation tasks in other fields, plant point cloud segmentation is more challenging due to the interwoven distribution of various parts such as stems, leaves, and flowers. In this paper, we propose a universal point cloud segmentation network PlantEFRSegnet that can be used for multi-species of plants. The proposed PlantEFRSegnet utilizes a newly designed edge point preservation downsampling module to identify and preserve the points at the edges of plant organs during the downsampling process, in order to assist the segmentation network in learning the contours of various plant organs. PlantEFRSegnet performs supervised feature repair on the point cloud features obtained through downsampling to mitigate the impact of feature loss on segmentation performance during feature embedding. The encoder of the segmentation network is composed of four local feature extraction modules. These four modules can not only extract features but also enhance the features corresponding to points with high contributions in local regions based on point attention mechanism. We evaluated the proposed PlantEFRSegnet on a laser-scanned plant point cloud dataset. Compared with the state-of-the-art approaches, the proposed PlantEFRSegnet achieved better segmentation results.
Why it matches plant phenotyping methods植物器官の3D点群を対象に、器官分割と植物成長フェノタイプ解析を行う新規ネットワークを開発・評価しており、フェノタイプ取得の計算手法が中心である。
abstractThe segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe experimental dataset used in this paper can be obtained through the following link: https://github.com/dllab23/PlantPointCloud (accessed on 11 May 2026).Open asset ↗dllab23/PlantPointCloudhtml-lines:785-806Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Ch ol , cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, β-aminobutyric acid, γ-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.
Why it matches plant phenotyping methods低コストRGB撮像とU-Netによる自動セグメンテーションを中核とする、ジャガイモ表現型取得プラットフォームの開発・検証であり、形態・色・植生指数形質を自動抽出している。
abstractWe present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Efficient phenotyping monitoring of cauliflower is crucial for its breeding and production. However, traditional manual measurement methods are time-consuming and labor-intensive, and existing deep learning (DL) methods mostly focus on the seedling stage, lacking systematic research covering the entire growth period. In this study, RGB images of cauliflower from seedling to harvest were collected. Through systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes. Evaluation results showed that YOLO12s-seg was the optimal model. It can achieved a segmentation mask mAP 50 of 99.4% for plants and curds in sparsely planted images and showed an advantage in identifying partially occluded early curds beneath inner leaves. Traits such as plant canopy width and curd diameter automatically extracted from segmentation results were highly consistent with manual measurements (R 2 > 0.90). Furthermore, the Richards model and Sine model were used to accurately fit the growth dynamics of leaf area and curd area, respectively. Based on growth kinetics, curds were classified into three types: mature and compact type, peak-burst type, and steady-increase type. Cluster analysis of 47 germplasms based on high-throughput phenotyping data revealed four groups and their growth characteristics: comprehensively coordinated type, mid-maturity compact type, large high-yield type, and curd-dominant type. Integrating the above functions, a platform for cauliflower growth monitoring and phenotypic analysis was developed. It provided full-process support from automatic image processing to growth dynamic analysis. This work provides an effective automated solution for high-throughput phenotyping analysis and growth dynamic monitoring of cauliflower, and offers a referable analytical framework for crop growth pattern research and intelligent breeding decision-making.
Why it matches plant phenotyping methods植物のインスタンスセグメンテーションから葉面積・草冠幅・花蕾形質を自動抽出し、手動測定との技術検証と成長動態解析、統合プラットフォーム開発を行っており、フェノタイピング手法が研究の中心である。
abstractThrough systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes.
This study proposes the Hydroponic Plant Growth Analysis System (HPGAS), a public-data-based preliminary framework for multimodal plant growth state analysis toward future filter-free aquaponic validation. The HPGAS integrates plant images, water quality signals, and environmental signals to estimate an image-centered growth index, growth stage, and proxy abnormal state probability. Because no public dataset jointly provides plant images, direct growth labels, fish metabolic variables, suspended solids, and nitrification-related measurements from a real filter-free aquaponic system, this study is not a direct operational validation. A two-stage evaluation was conducted using the Autonomous Greenhouse Challenge (AGC), HydroGrowNet, and two aquaponic Internet of Things (IoT) water quality datasets. Stage 1 implemented dataset loaders, image–sensor alignment, proxy label generation, and unimodal and fusion baselines. Stage 2 expanded handcrafted image and sensor-context features and adopted month-wise hold-out evaluation. The image-only model achieved the best growth index regression performance, with a root mean square error (RMSE) of 0.0492 ± 0.0187, whereas the fusion model showed a RMSE of 0.0837 ± 0.0196. Conversely, the fusion model achieved the best proxy abnormal state classification performance, with a F1 score of 0.9695 ± 0.0057 under the clean condition, decreasing to 0.9232 ± 0.0263 under sensor dropout and 0.9132 ± 0.0169 under image noise. Under sensor dropout, the fusion model was more stable than the sensor-only model, whereas under image noise it degraded more than the image-only model. These results indicate that multimodal fusion is most useful for proxy abnormal state classification and robust state interpretation, rather than universally superior scalar growth regression. The HPGAS provides a reproducible baseline for future real filter-free aquaponic experiments, while its operational validity remains to be tested using real filter-free aquaponic data.
Why it matches plant phenotyping methods植物画像とセンサーデータを統合し、成長指数・成長段階・異常状態確率を推定する再現可能な解析フレームワークを開発・評価しており、植物表現型の取得・推定手法が中心である。
abstractThis study proposes the Hydroponic Plant Growth Analysis System (HPGAS), a public-data-based preliminary framework for multimodal plant growth state analysis
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Introduction: Recent technological advances in high resolution image capture and analysis have led to increased adoption of high-throughput digital phenotyping in plant science research. High-throughput digital phenotyping provides a nondestructive method to quantify changes in plant growth and health in response to environmental factors or developmental cues. Moreover, it allows researchers to conduct large experiments in a time- and cost-efficient manner. The TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information. Leaf reflectance is presented as five vegetation indices (e.g., normalized difference vegetation index). Methods: This project evaluated nitrogen (N), phosphorus (P), and potassium (K) deficiency in greenhouse grown ornamental and vegetable plants using the TraitFinder. Plant species included celosia, coleus, marigold, petunia, and tomato. Plants were fertilized with a complete Hoagland's solution (control), and three modified solutions: Hoagland's solution without nitrogen (-N), phosphorus (-P), or potassium (-K). Each plant species was evaluated separately with eight replicate plants per treatment, organized as a randomized complete block design. Results: Treatment with -N, -P, and -K solutions resulted in reduced vegetative growth and decreased concentration of the corresponding macronutrient in leaf tissue for all species evaluated. We observed that the presence of flowers would negatively affect calculations of the vegetation indices due to their distinct spectral properties; therefore, flowers must be excluded to accurately quantify plant health parameters. In general, we observed a common trend where GLI (green leaf index) and NDVI (normalized difference vegetation index) decreased, and NPCI (normalized pigment chlorophyll index) and PSRI (plant senescence reflectance index) increased in response to macronutrient deficiency. The measure of GLI, NDVI, NPCI, and PSRI were different from the control plants, but these observations were dependent on the nutrient deficiency and species tested. Discussion: Our results underscore the importance of accounting for species-specific spectral signatures when assessing plant responses to nutrient deficiencies. This project also provides reference values for interpreting vegetation indices, offering valuable guidance for scientists implementing digital phenotyping in their experimental protocols. Digital phenotyping can significantly improve experimental throughput and provide quantitative insights into plant health.
Why it matches plant phenotyping methodsTraitFinderによる形態・スペクトル形質の取得と、花の除外や種特異的スペクトルへの対応を含むデジタルフェノタイピングの実質的な適用・評価が中心である。
abstractThe TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information.
Understanding plant growth dynamics requires imaging across day-and-night cycles to quantify growth, movement and development in the aerial plant body and to capture the rhythmic nature of these processes. This requires imaging in light during the day and in darkness at night without perturbing plant physiology. Nighttime imaging has typically depended on infrared (IR) illumination, producing monochrome datasets that require specialised hardware and separate analysis pipelines when combined with daytime RGB imaging. Here, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce). We show that high resolution colour images can be obtained under dimG using low- cost cameras, with sufficient consistency between full-spectrum and dimG images to allow direct comparison and unified image analysis. We show that very low-fluence green light (<0.5 μmol m -2 s -1 ) does not sustain circadian oscillations of gene activity under continuous exposure and does not perturb rhythms when applied during the dark phase of diel cycles. DimG imaging enabled accurate detection of diel leaf movement profiles in Arabidopsis circadian mutants, revealing genotype-specific phase differences under varying photoperiods. In lettuce, dimG pulses and continuous dimG enabled accurate quantification of diel leaf movement without affecting growth, stomatal opening, electron transport rate or chlorophyll content. Motion profiles under continuous dimG mirrored those under darkness. Our findings establish dim green illumination as a cost-effective solution for night-time imaging, simplifying phenotyping workflows with minimal impact on physiology.
Why it matches plant phenotyping methods植物の夜間画像取得用の低強度緑色照明を開発・生理影響評価し、葉運動の定量と統合的な画像解析ワークフローを実証しており、フェノタイピング手法が中心です。
abstractHere, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce).
Abstract Spatio-temporal fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, using Planet SuperDove satellite imagery which has 3 m spatial resolution and near-daily temporal resolution, and Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) data which has 0.05 m spatial resolution, downscaled to target resolution 0.5 m, and 1–4 week irregular temporal resolution. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms— Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (FitFC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)—within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous NDVI trajectories, from which growth metrics such as Vegetation Growth Metrics (VGM)85 and VGMmax were derived. The validation results indicated that ESTARFM achieved the highest Normalized Difference Vegetation Index (NDVI) performance among the evaluated algorithms, with an Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697, and CACAO further improved the results, with CA-ESTARFM providing the highest NDVI accuracy, with an RMSE of 0.108 and a UIQI of 0.740. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using VGM85 and VGMmax confirmed that CA-ESTARFM enhanced the reliability of crop growth evaluation compared to simple linear interpolation of UAV observations. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methods衛星・UAV画像の時空間融合アルゴリズムを比較検証し、NDVI軌跡から作物生育指標を抽出する方法が研究の中心であるため、植物フェノタイピング手法として採択する。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.
Why it matches plant phenotyping methodsナス幼苗の3D点群から形質を抽出する非破壊フェノタイピング手法を開発し、比較実験・アブレーションで技術性能を検証しているため、方法が中心的である。
abstractthis study proposes a non-destructive phenotyping method for eggplant seedlings
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Global changes in agricultural and environmental systems will necessitate new crop research methodologies in the future years to ensure more effective use of natural resources and food security. The progress in next-generation sequencing has led to the emergence of multi-omics techniques as successful crop improvement strategies. Multi-omics studies using high-throughput techniques have been critical in understanding growth, senescence, yield, and biotic and abiotic stress responses in an array of crops. When multi-omics provide a high-resolution map of the molecular frameworks governing stress responses, advanced deep phenotyping systems can utilize advanced sensors to quantify dynamic physiological and morphological traits non-destructively. The systematic integration of these multi-layered datasets through association mapping and machine learning frameworks allows for the identification of superior alleles and regulatory hubs. Currently, the non-invasive imaging methods have effectively incorporated computer vision, machine learning, and deep learning components of AI. The use of machine learning and deep learning have progressively increased the effectiveness of data gathering and analysis. The supervised, unsupervised, and deep learning architectures have become effective tools for overcoming the genotype-to-phenotype gap, enabling more accurate predictions of yield and stress tolerance. Despite challenges related to data dimensionality, high infrastructure costs, and the need for standardized protocols, the convergence of these fields offers a robust architecture for predictive breeding. By linking microscopic molecular shifts to macroscopic field performance, integrated strategies accelerate the discovery of adaptive traits and the delivery of high-yielding, climate-smart cultivars. This review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
Why it matches plant phenotyping methods深層フェノタイピングと非破壊センサー・画像解析を中心に、作物形態・生理形質の高スループット計測とマルチオミクス統合を論じる方法論的レビューである。
abstractThis review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
AppleField / plotFruitObject detectionGrowth / development / phenology
Introduction Deep learning-based apple maturity detection supports precise management in smart agriculture. However, deployment on resource-constrained edge devices requires minimizing network weights while ensuring accuracy, a challenge compounded by inclement weather and dense fruit clustering in orchard environments. Methods To address these challenges, we propose HRLN-YOLO, a lightweight high-efficiency apple maturity detection model that minimizes network weights while ensuring accuracy. Specifically, we design a lightweight backbone HGBackbone to enhance feature extraction and accelerate inference, construct an enhanced neck module RCF_Neck to improve multi-scale feature fusion under occlusion, develop a lightweight detection head LADH-Head to alleviate task conflicts with minimal computational cost, and introduce NWD-Loss to improve localization stability for small-scale targets. Results Experiments on the Orchard Apple Maturity Dataset demonstrate that HRLN-YOLO improves mAP@0.5 by 1.7% over the YOLO11n baseline while reducing parameters by 37.3% and computational complexity by 34.9%. Discussion The core contribution of this study lies in minimizing network weights while ensuring detection accuracy, providing a practical solution for edge deployment in smart orchard automated harvesting.
Why it matches plant phenotyping methodsリンゴ果実の成熟状態を推定する軽量画像認識モデルの開発と性能評価が中心であり、単なる収穫対象の位置検出ではなく、植物器官の状態を測定する方法である。
abstractwe propose HRLN-YOLO, a lightweight high-efficiency apple maturity detection model that minimizes network weights while ensuring accuracy.
Introduction Addressing the core bottleneck in traditional crop models-the disconnect between morphology and physiological function at the organ scale and their limited dynamic response to environmental changes-this study aimed to construct a multi-source data fusion maize growth model for simultaneous organ-scale simulation. Methods We developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture. By integrating environmental time-series data, RGB images, and 3D point clouds, we created a multimodal fusion model based on a gated attention network. This approach adaptively weights multi-source features and pioneers a bidirectional morphology-physiology feedback loop based on physiological development time (PDT) and NURBS surfaces. The WOFOST moisture response function was also improved. Results The model significantly enhanced the simulation accuracy of organ-scale growth, reducing the root mean square error (RMSE) for plant height by 74.6% through a morphology-physiology dynamic weighting mechanism. More fundamentally, it resolved the disconnect between morphological and physiological processes. The improved plant height prediction validates the model's effectiveness at the organ scale. Discussion The pioneering "physiology-morphology" parallel simulation architecture provides an interpretable theoretical model and robust quantitative tools for designing high-photosynthetic-efficiency plant architecture and enabling precision water-fertilizer management.
Why it matches plant phenotyping methodsRGB画像・3D点群・環境データを統合し、器官スケールの形態と生長をシミュレーションする手法を開発しており、植物形質（草丈など）の推定が中心的な技術貢献である。
abstractWe developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture.
Growth chamberGrowth / time-series analysisGrowth / development / phenology
Plant factories have evolved from automated cultivation facilities into data-driven crop production systems. Over the last decade, artificial intelligence has been applied to non-destructive crop monitoring, sensor correction, nutrient-solution diagnosis, growth prediction, environmental control, digital twins, and product-level inspection. This review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces. The main argument is that plant-factory AI should not be understood only as image-based phenotyping; practical systems require an integrated intelligence stack connecting visual perception, sensor calibration, nutrient modeling, control, remote operation, and industrial inspection. Remaining challenges include dataset scarcity, model generalization, sensor drift, explainability, energy-aware control, and closed-loop decision-making.
Why it matches plant phenotyping methods植物工場における機械視覚・センサー補正・作物モニタリングなどのフェノタイピング関連技術を中心に扱うレビューであり、方法論的役割が明確。
abstractThis review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces.
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 factories have evolved from automated cultivation facilities into data-driven crop production systems. Over the last decade, artificial intelligence has been applied to non-destructive crop monitoring, sensor correction, nutrient-solution diagnosis, growth prediction, environmental control, digital twins, and product-level inspection. This review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces. The main argument is that plant-factory AI should not be understood only as image-based phenotyping; practical systems require an integrated intelligence stack connecting visual perception, sensor calibration, nutrient modeling, control, remote operation, and industrial inspection. Remaining challenges include dataset scarcity, model generalization, sensor drift, explainability, energy-aware control, and closed-loop decision-making.
Why it matches plant phenotyping methods植物工場におけるAI技術レビューで、非破壊的な作物モニタリング、機械視覚、深層学習、センサー補正など、植物形質取得に関わる方法を中心的に扱っている。
abstractThis review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces.
Efficient gas and water exchange between plants and their environment largely depends on the number and distribution of stomata, cellular valves in leaf epidermis. Core genetic regulators of stomatal cell identity and pattern along with asymmetric stem-cell like divisions in stomatal precursors are hypothesized to customize stomatal production for optimal leaf performance. How these regulators work in concert and how division dynamics are modified and adjusted in different environments, however, are poorly understood. Here, we leveraged the variation in stomatal patterning in Arabidopsis thaliana accessions from diverse environments to define developmental rules and constraints in the stomatal lineage. The accessions subtle and quantitative variation enables us to identify which cellular parameters are flexible, revealing how developmental plasticity generates phenotypic plasticity. By developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins. Variation in final stomatal numbers is driven by differences in the relative contributions of stomatal initiation, cell size-based fate thresholds, general proliferative capacity, and coordination between sister and neighbor cell behaviors. Overall, diverse accessions converge toward two lineage regimes: one dominated by autonomous decisions with loose cell-cell coordination, the other by extensive cell-cell coordination. Challenging accessions with environmental fluctuations revealed regime-specific flexibility, with plasticity primarily mediated by a single division-related parameter. Our results show how cellular parameters integrate into alternative developmental strategies that shape environmental responsiveness.
Why it matches plant phenotyping methods葉の成長中の細胞挙動を追跡するライブセルイメージングツールを開発し、気孔密度の発生的起源を定量化しており、植物フェノタイピング手法が研究の中心である。
abstractBy developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins.
Plant disease is a serious threat to agricultural productivity and food security worldwide. Traditional diagnostic methods such as manual observation and laboratory testing are time-consuming, labor-intensive and error prone. The emergence of artificial intelligence (AI) and deep learning (DL) offer scalable solutions for precision agriculture in plant disease detection using advanced computational techniques to process large datasets. Hybrid deep learning architecture integrates Convolutional Neural Networks (CNNs) along with Artificial Neural Networks (ANNs) can leverage both visual and contextual data to improve detection performance. The hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics). The CNN module extracted spatial and textural features from plant images, while the ANN module processed environmental parameters. These outputs were fused into a unified feature vector for disease classification. A total of 15 plant species and their associated diseases were analyzed using 200-270 training samples and 150-190 testing samples for each disease across a total of 1000 images. The model was judged by metrics such as detection accuracy, AUC, sensitivity etc. Data augmentation, pre-trained architectures (e.g., ResNet50) and early stopping techniques were utilized to improvise model performance. The hybrid model saliently achieved detection accuracy consistently above 87% with majority of diseases surpassing 90%. Highperforming cases like Rice Blast (92.5%), Tomato Early Blight (93.8%), and Coffee Rust (93.0%), with AUC values of 0.93 or higher, sensitivity exceeding 94% and specifically above 90%. Diseases of Sugarcane Red Rot and Tea Blister Blight exhibited sensitivities of 92.4% and 92.1% and specificities of 91.1% and 90.5% respectively. Moderate accuracy for Coconut Bud Rot (87.5%) and Mustard Alternaria Blight (87.8%) was due to smaller training sample sizes.
Why it matches plant phenotyping methods植物画像から病害状態を分類するハイブリッドCNN-ANN手法を開発し、精度・AUC・感度などで評価しており、病害フェノタイピング手法が研究の中心である。
abstractThe hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics).
Reproduction assets foundThe paper's plant disease detection model was trained on public plant image datasets: the PlantSeg dataset (Zenodo record 13958858, DOI 10.5281/zenodo.13293891) and the UCI Machine Learning Repository Plants dataset. Both are cited in Materials and Methods as sources of the visual data used for the CNN module. No code,Dataset · publicebao (ZiranKexueBan)/Journal of
Huazhong University of Science and Technology
(Natural Science Edition). 2021;49(8).
37. Ma C, Mu X, Sha D. Multi-Layers Feature Fusion of
Convolutional Neural Network for Scene
Classification of Remote Sensing. IEEE Access.
2019;7.
38. Hämäläinen W.Plants Dataset[Internet]. 2024.
Available from:
https://archive.ics.uci.edu/dataset/180/plants
39. WeiT. PlantSeg: A Large-Scale In-the-wild Dataset
for Plant Disease Segmentation [Internet]. 2018.
Available from: https://zenodo.org/records/13958858Open asset ↗180pdf-raw-page:15 lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
RiceLeafClassificationDisease symptoms / severityGrowth / development / phenology
Paddy leaf diseases significantly affect rice yield and quality, making early detection and proper treatment essential for precision agriculture. This study proposes a deep learning-based decision support system for paddy leaf disease detection, growth-stage prediction, and stage-specific treatment recommendations. The dataset used in this study comprises paddy leaf images collected from multiple sources and categorized by growth stage and disease class. The dataset was divided into training (80%), validation (10%), and test (10%) sets to ensure proper model evaluation. For growth stage prediction, a lightweight Convolutional Neural Network (CNN) model was developed, while disease classification was performed using transfer learning models, including VGG16, ResNet50, InceptionV3, and MobileNetV2. An ensemble method based on average probability voting was used to improve classification performance. The models were evaluated using accuracy, precision, recall, and F1-score on an independent test set. The experimental results show that the ensemble model achieved higher accuracy compared to individual models, demonstrating improved robustness and generalization. The proposed system was implemented as a Streamlit web application that provides disease detection, growth-stage prediction, and treatment recommendations. The proposed integrated framework can support farmers and agricultural experts in making timely and accurate disease management decisions.
Why it matches plant phenotyping methods葉画像から病害状態と生育ステージを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が中心的です。治療推薦も含まれますが、画像による状態推定が中核です。
abstractThis study proposes a deep learning-based decision support system for paddy leaf disease detection, growth-stage prediction, and stage-specific treatment recommendations.
Crop phenology is a critical determinant for yield prediction and germplasm evaluation. However, precise phenological monitoring in large-scale rice breeding trials faces significant challenges due to the inherent phenological asynchrony among hundreds of cultivars and the trade-off between spatial resolution and temporal continuity in unmanned aerial vehicle (UAV) remote sensing. To address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement. We introduce a Missing Aware Gated Fusion (MAGF) mechanism to dynamically integrate multi-resolution features on non-aligned timelines, enabling robust modeling under irregular sampling conditions. Validated on a massive dataset covering approximately 500 rice cultivars and over 100,000 images across 2023 and 2024 growing seasons, the proposed method significantly outperformed single-temporal-scale baselines despite multiple growth stages coexisting within the same dates. The integration of multi-spatial-scale fusion with LSTM temporal modeling yielded superior performance considering efficiency, achieving an Overall Accuracy (OA) and F1-score of 0.873, with a Kappa coefficient of 0.84. A hybrid sampling strategy (daily MR image combined with weekly HR image) demonstrates that weekly flight time can be reduced from 28 h to approximately 6 h while maintaining high accuracy. Notably, even when HR acquisition was reduced to a once every 14 days frequency, the fusion performance remained significantly superior to that of daily MR monitoring alone. The model exhibited strong generalization capabilities. When directly applying the model trained on 2024 data to the 2023 dataset, it maintained an OA of 0.774 and an F1-score of 0.738 under a 3-day error tolerance, with recall for the maturity stage consistently exceeding 0.96. This framework offers a flexible, scalable, and cost-effective solution for high-throughput phenotyping in precision breeding.
Why it matches plant phenotyping methodsUAVリモートセンシング画像と深層学習によるイネの生育ステージ（フェノロジー）推定手法を開発・検証し、大規模育種データで性能評価しているため、フェノタイピング手法が中心である。
abstractTo address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement.
Reproduction assets foundThe article explicitly states that the authors' source code and test samples for the rice phenology identification framework are publicly available on GitHub, matching an allowed URL. No public dataset deposit is stated; additional data is only on request.Code · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/Rice-phenology-identification-by-UAV . Additional data can be made available upon reasonable request.Open asset ↗https://github.com/gfjiyue/Rice-phenology-identification-by-UAVlines:601-709Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Three-dimensional (3D) phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available Unmanned aerial vehicle (UAV) captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes.
Why it matches plant phenotyping methodsUAV画像から植物個体の高解像度3D形状を再構成する手法を開発しており、植物フェノタイピングの取得・抽出方法が研究の中心である。
abstractWe present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants.
Common beanAerial / UAVField / plotMultispectral / hyperspectralWhole plant / canopy / plot / fieldClassificationGrowth / development / phenology
Accurate and scalable prediction of physiological maturity (PM) in leguminous crops remains a key challenge due to indeterminate growth and canopy heterogeneity. Thus, causing difficulties for optimizing breeding decisions and field management. This study develops a novel UAV-based multispectral-index fusion framework for high-throughput maturity classification in dry bean. This is conducted by combining parametric and non-parametric machine learning (ML) classification models to capture non-linear maturity signatures. Multispectral imagery was collected over three growing seasons across multiple genotypes. Six spectral bands and five maturity-relevant vegetation indices capturing chlorophyll degradation, senescence, and canopy greenness were evaluated through 63 feature-set combinations and 10 parametric and non-parametric ML classifiers. Model performance was assessed using stratified five-fold cross-validation, composite z-score aggregation, and Friedman-Nemenyi statistical ranking to jointly evaluate accuracy and consistency. Among all the model-feature combinations, the Support Vector Classifier (SVC) paired with Band_All6 + MCARI feature-set achieved the highest test accuracy (>70%), with class-wise recall of 63.2%, 71.4% and 79.4% for early, medium and late maturity, respectively. Hybrid feature-sets integrating chlorophyll-sensitive indices with spectral bands consistently outperformed index-only or band-only sets, confirming the synergistic effect of bands and engineered index coupling. This research establishes a statistically validated pipeline linking UAV multispectral data, vegetation spectral-index and machine learning classification to quantify PM variability in dry bean with potential for validation in other legume crops. The proposed framework offers a generalizable approach for non-destructive maturity prediction, enabling breeders to accelerate genotype selection and harvest scheduling under field-scale conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習による乾燥豆の生理的成熟度推定パイプラインを開発・統計検証しており、植物状態の取得・抽出手法が研究の中心である。
abstractThis study develops a novel UAV-based multispectral-index fusion framework for high-throughput maturity classification in dry bean.
Field / plotWhole plant / canopy / plot / fieldMorphology / geometry measurementPhysiological trait estimationBiomass / plant weightGrowth / development / phenologyPlant / canopy heightWater status / transpiration
This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity.
Why it matches plant phenotyping methodsGNSS-IRによる作物構造・水分の推定手法が研究の中心であり、植生高やバイオマス密度などの植物形質を地上測定と比較して技術検証している。
abstractThis study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture.
Efficient phenotyping is essential for accelerating genetic improvement in turfgrass breeding, where manual measurements are labor-intensive. This study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes. HSI data (400-1000 nm) were processed through a user-in-the-loop hybrid segmentation pipeline integrating UMAP dimensionality reduction, DBSCAN clustering, Random Forest classification, and pseudo-RGB refinement. To independently assess vegetation classification performance, 10,000 manually annotated reference points from 50 pseudo-RGB images were compared with the automated module, yielding an overall accuracy of 0.9697, a precision of 0.8830, a recall of 0.9240, a specificity of 0.9779, an F1-score of 0.9030, and Cohen's kappa of 0.8851. A Combined Ranking Score (CRS) integrating five vegetation indices and vegetation pixel count was significantly associated with aerial shoot count ( r = -0.445, p r = -0.207, p < 0.001). The highest-ranked genotype showed a 9370.3-pixel increase in vegetation area between 6 and 16 weeks after transplanting, compared with 1417.7 pixels for the lowest-ranked genotype. Classification performance declined under high-coverage conditions, indicating increased mixed-pixel ambiguity in dense canopies. These results suggest that HSI-based CRS can support rapid, objective, and non-destructive relative ranking of density-related vegetative growth in turfgrass breeding. Because the study was conducted at a single location and season and correlations with manual traits were moderate, the framework is best interpreted as a screening and ranking tool rather than a direct predictive model.
Why it matches plant phenotyping methodsHSI画像と解析パイプラインを用いて、芝草の植生面積・密度関連成長形質を高スループットに推定・検証することが研究の中心であり、育種集団の表現型スクリーニング手法に該当する。
abstractThis study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes.
Land management and stewardship teams continue to lack the tools to capture 3D spatiotemporal insights of the ecosystems they oversee. For wildfire management at the wildland-urban interface, teams face challenges in capturing vegetation growth over time after a fuel reduction program and connecting seasonal changes to the vegetation distribution across the treated area. Current approaches rely on triangle meshes or point clouds generated from photogrammetry or LiDAR surveys on drones or hiked traverses. However, the difficulties in optimizing these meshes lead to large triangles that inadequately approximate the bulk vegetation shape, and the point cloud data is often too sparse for local plant-scale understanding. To address this gap, we extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals. We develop a new method to match the 3DGS reconstruction of these scenes across months, associating plant growth across seasons interactively in 3D. To achieve the centimeter-level matching, we adapt the Umeyama algorithm and the iterative closest point algorithm from point cloud maps to the 3DGS scene, leveraging the probabilistic interpretation of the 3D Gaussian data structure and robustly handling visual and geometric changes associated with vegetation phenology over time. We have applied our method to recent pile burns at Stanford’s Jasper Ridge ’Ootchamin ’Ooyakma Biological Preserve at monthly intervals. We demonstrate differences in ecological response where some piles featured the unexpected return of a rare and threatened bushmallow, and others remained more barren. This pile burn microcosm implicates the need for plant-level 3D spatiotemporal models to understand ecosystem recovery to fire mitigation practices. Please visit the project page for the spatiotemporal alignment video and more information: https://danineamati.github.io/burn-ecorecovery.github.io/ Please visit the Stanford Data Repository for the field data: https://doi.org/10.25740/ws901xs0162
Why it matches plant phenotyping methods植物の3D画像再構成と時系列位置合わせ手法を開発し、植物の個体・葉・草丈・成長を抽出することが中心であるため、植物フェノタイピング手法研究に該当します。
abstractwe extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals.
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Field / plotMultispectral / hyperspectralWhole plant / canopy / plot / fieldGrowth / time-series analysisGrowth / development / phenology
Accurate crop calendars are critical for understanding global agricultural phenology, mapping crop types, improving yield forecasts, and addressing food security challenges in the context of climate variability. This study presents significant advancements in crop calendar modeling, building upon the WorldCereal system by integrating Land Surface Phenology (LSP) metrics derived from MODIS AQUA NDVI time series with ERA5-Land climate parameters and machine learning algorithms, particularly XGBoost. Our new approach introduces key enhancements, including densification of training data in underrepresented regions, explicit modeling of dormancy periods for winter cereals, and the synergistic use of climate and phenological predictors to refine Start of Season (SOS) and End of Season (EOS) estimates across diverse agro-ecological zones for eleven crops types explicitly excluding rice, which falls outside the scope of the WorldCereal project. The model was validated against harmonized ground-truth data, remote sensing-based phenology retrievals, and expert assessments from agricultural monitoring institutions, achieving R 2 values of 0.94 (SOS) and 0.92 (EOS) for summer crops, with an RMSE of 18 and 22 days, respectively. For winter crops, the model reached R 2 values of 0.90 (SOS) and 0.92 (EOS), with RMSE values of 29 and 28 days. The inclusion of dormancy modeling significantly improved the representation of winter cereal cycles, reducing previous overestimations in high-latitude regions such as Canada, Northern Europe, and Central Asia. Additionally, enhanced spatial resolution addressed gaps in South America, Africa, and Asia, refining SOS and EOS timing in monsoon-driven cropping systems. Expert validation from institutions such as INPE (Brazil), NASA Harvest, and the Chinese Academy of Sciences further refined estimates, correcting anomalies in China and southern Brazil, ensuring alignment with observed agricultural cycles. The findings demonstrate the added value of combining remote sensing-derived phenology and climate data with XGBoost-based machine learning to improve crop calendar precision, optimize planting and harvesting schedules, and mitigate risks from climate variability. This study establishes a robust, data-driven tool for agricultural monitoring, supporting yield forecasting and global food security initiatives. • Enhanced WorldCereal global crop calendars using MODIS NDVI and ERA5-Land. • Modeled winter crop dormancy to improve phenological accuracy at scale. • Used XGBoost to fuse climate and phenology features for SOS/EOS prediction. • Validated against expert assessments and external geo-referenced datasets. • Achieved R 2 > 0.90 and RMSE <30 days across crop types and regions.
Why it matches plant phenotyping methodsMODIS NDVIと気候データをXGBoostで統合し、作物のSOS/EOS（生育季節）を推定する手法を開発・検証しており、植物フェノタイピングが研究の中心である。
abstractintegrating Land Surface Phenology (LSP) metrics derived from MODIS AQUA NDVI time series with ERA5-Land climate parameters and machine learning algorithms, particularly XGBoost
ABSTRACT Aims Natural woodland expansion into former agricultural land contributes to conservation and global reforestation goals. However, tree colonisation and woodland persistence depend on interactions among vegetation, land‐use history and suitable microclimatic conditions. Understanding these drivers is essential for anticipating woodland expansion outcomes over large spatial and temporal scales. Methods We combined airborne photogrammetry with field measurements to assess the long‐term success of Juniperus thurifera woodlands—a Natura 2000 priority habitat—across a 20‐km 2 region of central Spain. A canopy height model, calibrated with field biomass data, was used to map juniper biomass, while time series land‐cover maps estimated woodland age. This novel approach enabled evaluation of colonisation success using a space‐for‐time substitution along the expansion frontier. Results Over the past 34 years, land cover has changed markedly, with agricultural land declining by 75% and open woodland tripling. Juniper stands have expanded from steep slopes onto flatter terrain and into areas with lower irradiance. Increasing dwarf‐shrub density within stands reduced juniper biomass by up to 25% in the oldest woodlands. Higher solar exposure promoted faster biomass accumulation through time but limited biomass in younger stands. Contrarily, new stands under lower insolation showed greater biomass for their age, suggesting positive land‐use legacies where drought stress was reduced. Conclusion Overall, J. thurifera woodlands have expanded substantially over recent decades, yet growth constraints differ across the expansion front. With our innovative framework integrating high‐resolution photogrammetry and field‐based biomass models, our study underscores the interplay between local competition and abiotic factors in shaping woodland dynamics across space and time. This framework offers a valuable, scalable and transferable tool for monitoring and managing long‐term ecosystem dynamics across larger spatial and temporal scales and informing habitat restoration and conservation planning under changing environmental conditions.
Why it matches plant phenotyping methods航空写真測量とフィールド biomass データで樹木バイオマスを推定・地図化する手法が研究の中心であり、植物群落の形質を大規模に測定する実質的なフェノタイピング応用である。
abstractA canopy height model, calibrated with field biomass data, was used to map juniper biomass
Properly characterizing the stages of corn growth is critical to conducting successful experiments in maize genetics and breeding. Specifically, accurately identifying stages of growth is required to perform developmentally dependent sampling or data collection, to predict time to flowering and seed maturation, and to allow for comparisons between different lines and populations based on developmental time. In this protocol, we summarize previous knowledge about maize development and describe how to monitor these stages in the reference inbred line B73, a yellow dent corn.
Why it matches plant phenotyping methodsトウモロコシの発育段階という植物状態を、実験・育種で再現可能にモニタリングするプロトコルが中心であり、発育ステージ測定法として収録対象と判断します。
titleHow to Monitor Growth and Identify Developmental Stages of Maize ( Zea mays ).
Rice plant height is a key indicator of crop growth and phenology, yet continuous daily estimation remains challenging under limited field observations. This study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements. Daily plant height was estimated as a posterior-weighted ensemble of multiple LUT-derived heights, together with uncertainty reflecting ambiguity among plausible growth trajectories. Applied to rice paddies in Ryugasaki City, Japan, using Harmonized Landsat–Sentinel-2 data from the 2025 growing season, the method achieved R2=0.85 and RMSE = 7.08 cm on the validation dataset, outperforming simple baseline approaches. The estimated daily height time series also enabled evaluation of the timing at which plant height reached 70 cm, revealing clear spatial variability among fields and an associated uncertainty of approximately 10 days. Although this threshold was discussed with reference to previous studies on L-band SAR sensitivity, the present study relied solely on optical observations. Overall, the proposed framework provides a data-efficient and explainable approach for daily, spatially explicit rice growth monitoring, while current limitations include the single-region, single-year LUT construction and the simplified statistical assumptions used in the Bayesian weighting framework.
Why it matches plant phenotyping methods衛星由来時系列データからイネの草丈を推定する手法を開発し、検証データで性能評価しているため、植物フェノタイピング手法が中心です。
abstractThis study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements.
Rice (Oryza sativa) cultivation plays a critical role in food security across Asia, where smallholder farmers depend heavily on timely information about crop development and field conditions. Monitoring these changes using optical remote sensing is constrained by persistent cloud cover during monsoon-driven growing seasons, underscoring the necessity of Synthetic Aperture Radar (SAR) for continuous observation. This study evaluates the capability of Sentinel-1 C-band SAR for tracking rice phenology in two smallholder fields in Mayiladuthurai District, Tamil Nadu, during the Late Samba season (September to January). Field-scale analysis of VV, VH, and NDVI time series for 2023–2024 captured key phenological transitions, with polarization showing a strong correlation with NDVI (Farm 1: r = 0.75; Farm 2: r = 0.73). Radar Vegetation Indices (RVI, mRVI, and RVI4S1) were computed from multi-year Sentinel-1 data (2018–2023) and compared with MODIS NDVI. Although the radar indices showed high inter-correlation (r > 0.90), their relationship with NDVI remained weak (0.15–0.30). Machine learning experiments over a 1.5 × 1.5 km region (2018–2022) demonstrated that a Linear Regression model (6.92 × 10−5) outperformed Random Forest Regression (0.000258) in predicting RVI4S1 from VV and VH, indicating linear relationship between radar channels and the index. The study highlights the suitability of Sentinel-1 SAR-particularly VH polarization – for phenology tracking in smallholder contexts, especially where optical data are limited by cloud cover.
Why it matches plant phenotyping methodsSARセンシングとレーダー植生指数を用いてイネの生育・フェノロジーを推定し、光学指標との比較および機械学習による技術評価を行っており、表現型取得法が中心である。
abstractThis study evaluates the capability of Sentinel-1 C-band SAR for tracking rice phenology in two smallholder fields in Mayiladuthurai District, Tamil Nadu, during the Late Samba season (September to January).
The rapid advancement of the Internet of Things (IoT) has significantly transformed traditional methods of environmental monitoring by enabling intelligent, automated, and real-time data acquisition systems. In agriculture and plant care, continuous monitoring of temperature, humidity, and soil moisture is essential for ensuring optimal plant health. This paper presents a cost-effective IoT-based plant growth and health monitoring system using the NodeMCU ESP8266 platform, integrated with a DHT11 and soil moisture sensor. A machine learning model further classifies plant leaf conditions into four health categories: Healthy, Rust, Slug damage, and Powdery Mildew. Sensor data is processed and served through an embedded web server, enabling remote real-time monitoring via any standard web browser. Experimental results validate system accuracy, reliability, and suitability for smart agriculture applications
Why it matches plant phenotyping methods植物の生育・健康状態を継続取得するIoTシステムと、葉の状態を4分類する機械学習手法が研究の中心であり、植物の健康・病害状態を直接推定するため。
abstractThis paper presents a cost-effective IoT-based plant growth and health monitoring system using the NodeMCU ESP8266 platform, integrated with a DHT11 and soil moisture sensor.
Abstract. Effective crop monitoring is essential for optimizing agricultural practices and promoting sustainable production. This study explores the use of temporal Unmanned Aerial Vehicle (UAV) imagery to assess variations in crop growth dynamics across different developmental stages. UAV images were captured at five-day intervals, enabling the analysis of temporal changes in phenological parameters and plant health. Key vegetation indices and canopy height were derived at multiple time points and statistically evaluated to determine their effectiveness in monitoring crop development. The multi-temporal analysis identified the most informative vegetation indices and image processing techniques for assessing crop conditions. Results demonstrate that UAV-based temporal imaging offers valuable insights into crop growth patterns that are difficult to obtain through conventional monitoring approaches. The findings highlight the potential of UAV imagery as a practical tool for improving crop management by enabling timely and informed decision-making, ultimately contributing to enhanced yield and resource use efficiency.
Why it matches plant phenotyping methodsUAV画像を用いて作物の生育動態を時系列で評価し、植生指数と群落高を抽出・比較することが研究の中心であり、植物形質の取得手法として実質的です。
abstractUAV images were captured at five-day intervals, enabling the analysis of temporal changes in phenological parameters and plant health.
Climate change-induced weather variability poses a growing threat to global food security, yet plant resilience is still interpreted through static and reductionist models that treat stress as independent and transient. Here, we introduce a unified quantitative framework grounded in dynamical systems theory. We formalize three novel metrics: (1) the Phenological Weather Memory Index (PWMI), that quantifies exponentially decaying stress memory across developmental stages (with a decay constant α = 0.10 determined by cross‑validation); (2) the Treatment-Weather Resonance Coefficient (TWRC), which measures the alignment of agronomic interventions with favorable weather conditions; and (3) the Physiological State-Space Trajectory (PSST), which maps multi-trait plant physiology into low dimensional attractor basins. Analyzing 288 tomato plants across 24 cultivars under hot, sub-tropical conditions (mean VPD: 2.53 kPa, 65 heat days > 35 °C), we discovered that stress memory is strongly phase-dependent, remaining minimal during vegetative growth (PWMI = 0.009) but increasing sharply during reproductive phase (PWMI = 0.574). Despite the prolonged thermal stress, 97.9% of plants converged into a stable high-yield attractor basin, revealing a fundamental nonlinearity in plant performance. This convergence was driven by dynamic recovery, defined as the capacity of certain cultivars to rapidly forget the stress memory while maintaining internal physiological flexibility. Cultivars such as 'Pony Express', combined low PWMI with effective treatment-weather synchronization, enabling stable productivity under extreme conditions. Together, these results demonstrate that resilience is not a static trait of endurance, but an emergent property arising from temporal synchronization, rapid stress recovery and stable physiological organization. By quantifying stress "forgetting curves" and attractor dynamics, this framework provides a predictive, systems-based foundation for breeding and management strategies that prioritize dynamic recovery over stress tolerance alone.
Why it matches plant phenotyping methods植物のストレス記憶・回復・生理状態を定量化する新規指標と状態空間フレームワークを中心に提示しており、単なる生理測定ではなく表現型抽出・解析手法の開発に該当する。
abstractHere, we introduce a unified quantitative framework grounded in dynamical systems theory.
The article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies. The YOLO11x and YOLOx-seg models, pre-trained by transfer learning, are adapted to recognize and classify plants (plant class), leaves (leaf class), and a reference marker (ref_obj class) of a known size. Segmentation of strawberry leaves using the YOLO11x-seg model makes it possible to analyze the morphometric parameters of individual leaf plates (area, perimeter, roundness, aspect ratio). A set of RGB images (2000 pieces) obtained using a GoPro HERO11 camera under controlled laboratory conditions was formed and annotated, followed by augmentation to increase the model's resistance to variations in shooting conditions. The developed algorithm converts the coordinates of the bounding boxes and segmentation masks of recognized objects into metric units using calibration coefficients calculated from a marker of known size (100×100 mm). The software implemented using PyQt5, TensorFlow, Keras, and OpenCV libraries provides not only visualization of results but also data storage in a local SQLite database with the ability to export to JSON and Excel formats. Validation of the model showed high accuracy in detecting plant bounding boxes (mAP50 = 0.906) and leaf segmentation (mAP50 -mask = 0.625). The average processing speed was 20.3 ms/frame for detection and 34.5 ms/frame for segmentation. The measurement error was less than 3.5 % for the overall parameters of the plant and 5.2 % for the morphometric parameters of the leaves, confirming the effectiveness of the method for assessing the height, width and area of plants, as well as the analysis of the leaf apparatus. The research results show the promise of an approach for automating plant phenotyping in real time.
Why it matches plant phenotyping methods植物の成長・葉形態を画像から自動抽出するアルゴリズム、ソフトウェア、データセットを開発し、精度・処理速度・測定誤差を検証しているため、植物フェノタイピング手法が中心である。
abstractThe article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies.
Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R 2 ) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.
Why it matches plant phenotyping methods3D点群データセット、セグメンテーションネットワーク、形質抽出を開発・検証し、圃場キャベツの草高や冠幅を定量化する植物フェノタイピング手法が研究の中心である。
abstracta high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques.
Reproduction assets foundThe paper's authors publicly release their improved OneFormer3D point cloud segmentation code on GitHub; the cabbage 3D point cloud dataset is only available upon request.Code · publicThe code is available at https://github.com/PandaDalin/improve_oneformer3d. The data of this study are available from the corresponding author upon request.Open asset ↗PandaDalin/improve_oneformer3dhtml-lines:449-475Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
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.
This study aims to enhance the early warning and monitoring of frost damage in triticale (×Triticosecale Wittmack), as well as to identify frost-tolerant materials. To this end, this work focused on phenotyping the dynamics of triticale under different damage intensities using spectral indices. Sixteen triticale genotypes were planted under three sowing date (SD) treatments, with three sowing rate (SR) gradients set for each SD. The multispectral data of triticale under six frost damage intensities were acquired using an unmanned aerial vehicle (UAV) platform. A total of eight spectral indices (SIs) were extracted from samples under each intensity. In general, for each combination of SD and SR, all SIs decreased monotonically with increasing damage intensity. These indices are therefore suitable for monitoring frost damage in triticale under complex sowing scenarios. Under early frost damage, the relative decline rates (RDRs) of the SRI (Simple Ratio Vegetation Index), EVI2 (Enhanced Vegetation Index 2), NIRv (Near-Infrared Reflectance of Vegetation), and GLI (Green Leaf Index) were higher than those of other indices, indicating that they are more sensitive to early frost damage and thus more suitable for frost warning. Under frost stress, the RDRs of the indices were higher in early-sown samples than in late-sown samples. SD plays a more significant role than SR in determining the response of triticale indices to frost damage. Models were developed to detect triticale under varying damage intensities with SIs and classification algorithms—XGBoost, Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM). The SVM classifier demonstrated the best generalization performance (overall accuracy: 98.03%; F1-score: 0.98). The detection contributions of indices within the optimal model were evaluated by their respective SHAP (Shapley Additive Explanations) values. The GLI, NIRv, NDVI (Normalized Difference Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index) were identified as key indices, as they exhibit higher cumulative SHAP values. Identification models for triticale with different frost tolerance levels were established based on the time-series data of these key indices and the above four algorithms. The optimal model based on the SVM algorithm achieved an identification accuracy exceeding 90%. The average overwintering dynamics and frost damage responses of the key indices were analyzed for triticale with different frost tolerance levels under all treatments. Under frost stress, these indices and their RDRs in frost-tolerant triticale were generally higher and lower, respectively, than those in frost-sensitive triticale. These four key indices can thus assist in the identification of frost tolerance in triticale. This study aids in the early warning and monitoring of frost damage in triticale under complex planting scenarios and the evaluation of overwintering performance in triticale germplasm.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とスペクトル指標を用いて、コムギライムギの凍害強度・耐凍性を推定するモデルを開発・評価しており、植物表現型取得と解析手法が研究の中心である。
abstractThe multispectral data of triticale under six frost damage intensities were acquired using an unmanned aerial vehicle (UAV) platform.
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Roots play a pivotal role for plant performance, but they are difficult to access, which hampers quantitative measurements. Repeated imaging of rhizotrons, flat growth containers with a transparent side, has proven suitable to assess dynamics of root traits in indoor experiments. However, measuring hundreds of soil-grown plants with high temporal resolution remains a laborious challenge. We introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3. This platform was designed to image shoots and roots of individual plants simultaneously and derive digital proxy traits for biomass and growth. In addition, built-in weighing and watering stations deliver water use data for each rhizotron. To achieve the desired throughput (image all 896 plants once a day) a high degree of automatization and standardization was required. We realized a modular plant-to-sensor solution, using a fleet of automated guided vehicles (AGVs) to transport large rhizotrons (80x40x5 cm) to four measurement chambers for daily imaging, weighing, and watering. Simultaneous imaging of the root system with a high-resolution camera (116 μm per px) and the shoot from six different viewing angles allows to monitor plant growth with high spatial and temporal accuracy. First, we verified that moving plants to the measurement chambers did not significantly affect above- or belowground plant growth. Next, we measured phenotypic variation in root and shoot traits of 24 barley genotypes, parents of a nested association mapping population. Our analysis revealed that heritability of root traits such as root system depth and seminal root length was moderate to high (r 2 =0.52 and r 2 =0.93, respectively), enabling further assessment of increasing numbers of recombinant genotypes. The results demonstrate the suitability of GrowScreen-Rhizo 3 to phenotype a range of plant species characterized by various growth habits, including crop, niche, and wild plant species. We conclude that GrowScreen-Rhizo 3 will contribute significantly to the development of phenotyping pipelines for the identification of candidate genotypes with improved resource use efficiency and to pre-breeding processes of climate-resilient crops.
Why it matches plant phenotyping methods根とシュートを自動撮像し、バイオマス・成長などの形質を抽出する大規模フェノタイピング platform の開発・検証が中心である。
abstractWe introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3.
Field / plotRootGrowth / development / phenologyRoot system architecture
Understanding root growth and phenology is essential for improving the productivity, resilience, and sustainability of pasture-based systems. However, roots remain one of the most difficult components of plant systems to measure and monitor, particularly in managed, high-turnover pastures, such as those in New Zealand (NZ) dairy systems. As a result, root processes are often underrepresented in both experimental studies and pasture system models. This perspective paper identifies critical, but underdeveloped areas in root research, with particular focus on root phenology. Current studies are limited by insufficient temporal resolution, a lack of species- and cultivar-specific trait data in mixed swards, and weak integration of root dynamics into breeding programmes and farm system models. These constraints limit our ability to link root processes to pasture persistence, nutrient cycling, and climate resilience. To address this gap, we propose that root phenology should be treated as a dynamic functional trait that links plant responses to environmental and management drivers with ecosystem-level outcomes. This framing provides a conceptual foundation for integrating root dynamics into pasture research and modelling, particularly in systems subject to frequent defoliation and environmental variability. We further highlight opportunities arising from rapid advances in sensing technologies, automation, and data analytics, which enable continuous, high-resolution root monitoring systems at multiple scales. However, realising this potential requires integration of complementary measurement approaches and alignment with system-level research questions. In this context, NZ provides a unique platform for developing scalable, pasture-based root monitoring framework that integrates science, management and policy. We argue for a coordinated effort that bridges fundamental root biology with applied pasture management, supported by long-term datasets, methodological integration, and engagement with end users. Embedding root traits and phenological dynamics into the next generation of pasture models and decision-support tools will be critical for improving system performance and environmental outcomes. This perspective aims to stimulate a shift towards more integrated, temporally explicit approaches for studying root systems in pasture environments, with relevance to grazing system beyond NZ and across temperate regions.
Why it matches plant phenotyping methods根の成長・フェノロジーという植物形質の測定課題と、センシング・自動化・データ解析を統合した高頻度モニタリング手法を中心に論じる方法論的パースペクティブである。
abstractroots remain one of the most difficult components of plant systems to measure and monitor
The quality of forest reproductive material is crucial for successful reforestation and afforestation. While physical seed properties like mass are known indicators of quality, the potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration. This study investigates the relationship between the seed coat color of individual Pinus sylvestris seeds, quantified in RGB (Red, Green, Blue) space using a flatbed scanner, and their subsequent germination in container nurseries. The resulting images were processed using ImageJ software to measure the mean pixel intensity (0–255) for the Red (R), Green (G), and Blue (B) channels from the segmented seed area, following the «seed–culture» passport methodology [Forestry Engineering Journal 14 | 55 (2024), 37–60]. From a population of individually tracked seeds, we compared the RGB values of germinated (N = 942) and non-germinated (N = 258) seeds after 30 days. Results from the Kolmogorov-Smirnov test showed that non-germinated seeds had significantly lower individual mass (p = 0.0045) and significantly higher pixel brightness values in the R-, G-, and B-channels (p < 0.0001) compared to germinated seeds. Normalized RGB indices also showed significant differences between groups. Our findings demonstrate that seeds with a lighter, more reflective epidermis – indicative of higher RGB brightness – are statistically associated with a lower probability of successful germination under container nursery conditions. This non-destructive, low-cost method shows significant promise for the rapid pre-sorting of Scots pine seeds. It offers a practical tool to improve the efficiency and predictability of seedling production in forest nurseries by increasing the proportion of viable seeds in sowing batches.
Why it matches plant phenotyping methods個別種子のRGB画像から種皮色を定量抽出し、発芽予測・事前選別に用いる非破壊的な表現型計測法が研究の中心である。
abstractthe potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration
Reproduction assets foundThe paper openly deposits its three core phenotyping datasets in Mendeley Data: morphometric seed data (Dataset 1), the raw VIS/RGB scanner images of individual Pinus sylvestris seeds (Dataset 2), and germination outcome data (Dataset 3). All three DOIs are listed in the Data Availability statement and match allowed URDataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/8g258nbgmf.1Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:133-160Dataset · publicThe original VIS image data of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/dt78jhyw2j.2Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:133-160Dataset · publicThe original germination data—Dataset 3—are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:133-160Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
FruitClassificationGrowth / development / phenology
Introduction Cashew apple is a nutrient-rich fruit containing abundant minerals, vitamins, and energy. However, its fleshy texture and delicate skin significantly limit its storage life and market value. Accurate maturity grading is therefore essential for improving post-harvest management and transportation efficiency. Methods This study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher. The distillation framework integrates response-based soft-label supervision, attention transfer, and token-level feature regression to enhance representation learning under limited data conditions. Auxiliary lightweight architectures, including MobileNet, ConvNeXt, and EdgeNeXt, were trained independently to provide complementary predictions, and a weighted fusion strategy was employed for ensemble evaluation. Results The proposed ensemble ViT-KD with EdgeNeXt achieved 90% accuracy under the evaluated test split. To ensure statistical reliability and address potential partition bias, a stratified fivefold cross-validation was conducted on the dataset, yielding a mean accuracy of 86.89% ± 2.89% with consistent F1 scores and recall. The relatively low variance across the folds indicates stable internal generalization. Comparative experiments with conventional convolutional neural network (CNN) baselines and lightweight CNN baselines such as MobileViT-S and ShuffleNetV2 were performed, with the proposed ensemble framework achieving improved accuracy while maintaining computational efficiency. Computational analysis indicates that the stand-alone distilled ViT maintains a real-time inference capability of 8.79 ms per image, which supports suitability for edge-oriented agricultural applications. Discussion These results highlight the effectiveness of knowledge-distilled lightweight transformers for data-efficient maturity grading of cashew apples.
Why it matches plant phenotyping methodsカシューナッツ果実の成熟度という植物器官の状態を画像から推定する手法を開発し、交差検証・比較実験・推論速度評価まで行っており、フェノタイピング手法が中心である。
abstractThis study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher.
Reproduction assets foundThe paper's cashew apple maturity grading experiments use a public image dataset from IEEE Dataport (Sawant, 2025), explicitly linked in the data availability statement. No author code or models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieee-dataport.org/documents/goa-cashew-apple-maturity-grading .Open asset ↗goa-cashew-apple-maturity-gradinglines:837-851Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
ABSTRACT Aims Natural woodland expansion into former agricultural land contributes to conservation and global reforestation goals. However, tree colonisation and woodland persistence depend on interactions among vegetation, land‐use history and suitable microclimatic conditions. Understanding these drivers is essential for anticipating woodland expansion outcomes over large spatial and temporal scales. Methods We combined airborne photogrammetry with field measurements to assess the long‐term success of Juniperus thurifera woodlands—a Natura 2000 priority habitat—across a 20‐km 2 region of central Spain. A canopy height model, calibrated with field biomass data, was used to map juniper biomass, while time series land‐cover maps estimated woodland age. This novel approach enabled evaluation of colonisation success using a space‐for‐time substitution along the expansion frontier. Results Over the past 34 years, land cover has changed markedly, with agricultural land declining by 75% and open woodland tripling. Juniper stands have expanded from steep slopes onto flatter terrain and into areas with lower irradiance. Increasing dwarf‐shrub density within stands reduced juniper biomass by up to 25% in the oldest woodlands. Higher solar exposure promoted faster biomass accumulation through time but limited biomass in younger stands. Contrarily, new stands under lower insolation showed greater biomass for their age, suggesting positive land‐use legacies where drought stress was reduced. Conclusion Overall, J. thurifera woodlands have expanded substantially over recent decades, yet growth constraints differ across the expansion front. With our innovative framework integrating high‐resolution photogrammetry and field‐based biomass models, our study underscores the interplay between local competition and abiotic factors in shaping woodland dynamics across space and time. This framework offers a valuable, scalable and transferable tool for monitoring and managing long‐term ecosystem dynamics across larger spatial and temporal scales and informing habitat restoration and conservation planning under changing environmental conditions.
Why it matches plant phenotyping methods航空写真測量と現地 biomass データで樹冠高モデルを較正し、ジュニパー biomass を広域推定する手法が研究の中心であり、植物群落の形態・生長形質を測定する実質的なフェノタイピング手法に該当する。
abstractA canopy height model, calibrated with field biomass data, was used to map juniper biomass
This paper integrated multimodal remote sensing (RS) data with deep learning to develop a maize growth analysis and income prediction model based on CNN (Convolutional Neural Network)-Attention and Bi-LSTM (Bidirectional Long Short-Term Memory). Utilsing Landsat 8, Sentinel-2, and Sentinel-1 satellite data, the CNN-Attention network extracts vegetation features such as NDVI (Normalised Difference Vegetation Index), EVI (Enhanced Vegetation Index), and canopy density for corn growth stage identification and prediction. The study combined these features with meteorological, soil, and market data and fed them into a Bi-LSTM model for time series analysis to forecast corn income. The method achieved 98.3% accuracy in growth stage classification, with an RMSE (Root Mean Squared Error) of 0.04 and R² of 0.94 for canopy coverage prediction under 10-fold cross-validation, and an RMSE of 0.13 and R² of 0.94 for income prediction, showing high stability over time. Overall, this research supports data-driven precision agriculture through real-time monitoring and reliable economic forecasting.
Why it matches plant phenotyping methods衛星リモートセンシングと深層学習による作物生育段階・キャノピー密度の推定手法が中心的に開発・検証されており、植物状態の定量化を含むため。収益予測も扱うが、植物フェノタイプ推定部分が明示されている。
abstractintegrated multimodal remote sensing (RS) data with deep learning to develop a maize growth analysis and income prediction model
• Multisensor platform integrates RGB, depth, IR, and RTK-GPS data streams • Automated plant segmentation and 3D reconstruction extract plant traits in field conditions • System validation shows high correlation with manual and lab measurements • Public RGB-D lettuce dataset released to support reproducible AI research Accurate monitoring of leafy vegetable crops is essential to evaluate plant health, growth, yield, and quality, yet conventional methods based on manual measurements are labor-intensive and error-prone. This study proposes a data-driven framework for automated in-field monitoring of a lettuce crop based on multidimensional data acquired by a ground platform under various field conditions. Specifically, an advanced perception system is developed, including imaging and localization sensors to capture high-resolution visual, structural, and georeferenced information on the crop. An image processing pipeline is then proposed using zero-shot learning for plant segmentation, followed by 3D phenotyping techniques based upon computational geometry to automatically estimate plant biophysical traits, thus minimizing human input. An experimental trial conducted in a test field in Bari, Italy, between April and May 2025 validated the approach against manual and laboratory estimations. The results demonstrate strong correspondence between automated and reference measurements with a Pearson correlation coefficient r > 0.9 for key traits, confirming the potential of the framework. The influence of different nitrogen levels on the growing cycle is also evaluated, showing that the proposed system may provide a useful tool for decision support in lettuce crop monitoring and management.
Why it matches plant phenotyping methodsRGB・深度・IR等を統合したセンシング、植物セグメンテーション、3D形状解析による形質推定を開発し、手測定・実験室測定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。
abstractAn image processing pipeline is then proposed using zero-shot learning for plant segmentation, followed by 3D phenotyping techniques based upon computational geometry to automatically estimate plant biophysical traits
Abstract Monitoring spatial variations in plant growth and forecasting yield before harvest provides valuable insights for optimizing agronomic decision‐making in potato ( Solanum tuberosum L.) cultivation. Although unmanned aerial vehicle (UAV)‐based remote sensing has recently enabled the development of tuber fresh weight (TW) estimation models, their integration into practical yield‐forecasting systems remains limited. In this study, we developed machine learning models to estimate tuber weights at multiple preharvest time points using RGB and multispectral UAV imagery. Image‐derived features were extracted from the orthomosaic and digital surface model images for each plot, and a random forest regression model was trained for TW estimation. The estimated values were subsequently used to fit the Gompertz growth curves, which were then used to forecast the yield at the expected harvest time. The correlation between the estimated and observed values was strong in the UAV‐based TW estimation, with correlation coefficients exceeding 0.8 and coefficients of determination ( R 2 ) above 0.6 at all time points. Yield forecasts based on fitted growth curves achieved a correlation of 0.78 and an R 2 of −0.17 in 2023 and 0.70 and an R 2 of 0.47 in 2024. These results demonstrate that UAV‐based sampling combined with machine learning is a feasible approach for monitoring spatiotemporal variations in tuber growth and forecasting potato yield at the plot level prior to harvest.
Why it matches plant phenotyping methodsUAV画像からジャガイモ塊茎重量を推定し、機械学習と成長曲線で収量を予測する手法が研究の中心であり、推定精度も検証している。
abstractwe developed machine learning models to estimate tuber weights at multiple preharvest time points using RGB and multispectral UAV imagery.
This study aimed to train a Random Forest regression model using pollen germination rate and pollen tube length data obtained after 3 h of in vitro germination at 0.005, 0.05, and 0.5 mM concentrations of 24-epibrassinolide, methyl jasmonate, spermidine, spermine, and putrescine, and to evaluate the model’s accuracy in predicting responses at 0.025, 0.25, and 2.5 mM concentrations. Experimental data were compared with Random Forest Regression model predictions, and model performance was assessed using Absolute Error and Root Mean Square Error. Prediction accuracy was classified as good, moderate, or low based on Absolute Error thresholds applied to both pollen germination and pollen tube length (0-6, 6-15, ≥15), and Root Mean Square Error thresholds defined separately for pollen germination (0-10, 10-20, ≥20) and pollen tube length (0-20, 20-40, ≥40). Results indicated that the Random Forest Regression model provided reliable predictions at low and moderate plant growth regülatör concentrations, with 24-epibrassinolide and putrescine treatments aligning closely with experimental data. However, for methyl jasmonate, spermidine, and spermine at higher concentrations, the model exhibited overestimations, particularly in predicting pollen germination rates at inhibitory doses. The study highlights the potential of machine learning approaches in pollen biology research and demonstrates the necessity of optimizing model parameters for high-dose predictions. These findings contribute to the integration of data-driven decision-making in artificial pollination and plant growth regulators treatment strategies.
Why it matches plant phenotyping methodsランダムフォレストによる花粉発芽率と花粉管長の予測モデルを構築・評価しており、植物形質の推定とモデル性能検証が研究の中心である。
abstractThis study aimed to train a Random Forest regression model using pollen germination rate and pollen tube length data
In this study, we addressed agricultural labor shortages by developing a smart farming sensor module that integrated low-cost environmental sensors with a multipoint soil moisture sensor to predict broccoli growth, plant height ( PH ), and leaf count (L n ). Multivariable regression confirmed that integrated solar radiation ( S ) was the most dominant factor, although broccoli growth involved a complex interplay of solar radiation, optimal temperature, humidity, and soil moisture. More importantly, the analysis revealed that the middle layer soil moisture (u m ) exhibited the strongest positive contribution to PH . This finding indicated that water availability in the main root zone was essential for vertical growth and highlighted the indispensability of multipoint sensing over conventional single-depth measurements to accurately model the intricate relationship between soil moisture and crop development. Moving forward, we aim to leverage the superiority of multipoint data to construct a sophisticated growth prediction model, thereby contributing to the optimization of irrigation and temperature management in smart farming systems.
Why it matches plant phenotyping methods環境・土壌水分センサーを統合した測定モジュールを開発し、植物高や葉数などの作物形質を予測する手法が研究の中心である。
abstractdeveloping a smart farming sensor module that integrated low-cost environmental sensors with a multipoint soil moisture sensor to predict broccoli growth, plant height ( PH ), and leaf count (L n ).
Field / plotStress / disease detectionDisease symptoms / severityGrowth / development / phenology
Real-time and accurate monitoring of farmland environmental parameters and crop growth status is essential for precision agriculture and intelligent irrigation management. However, conventional agricultural monitoring approaches remain limited in spatial coverage, sensor adaptability, and intelligent data analysis. To address these limitations, this study proposes a multi-parameter collaborative monitoring system for precision agriculture that integrates flexible sensing, LoRa-based wireless communication, and deep learning-based data analysis. Specifically, a flexible capacitive humidity sensor based on graphene-PDMS composites was designed and fabricated for farmland environmental monitoring, and a distributed LoRa sensor network was developed to enable large-scale multi-parameter data acquisition and remote transmission. In addition, a convolutional neural network (CNN) was established for feature extraction and crop disease identification using multimodal sensor data. Experimental results showed that the flexible sensor exhibited a response time of 2.3 s and good mechanical stability, while the proposed model achieved an accuracy of 97.1% for crop disease identification on the test set. Field experiments conducted in 12 test fields across Hebei, Shandong, and Henan provinces showed that the proposed system achieved an average water-saving rate of 32.8% and an average crop yield increase of 10.6%. These results demonstrate that the proposed system can effectively improve farmland monitoring accuracy and support intelligent irrigation decision-making, highlighting its application potential in smart agriculture.
Why it matches plant phenotyping methods作物病害識別を含むマルチモーダルセンサ計測・CNN解析システムの開発と実証が中心で、植物の病害状態を推定するフェノタイピング手法に該当する。
abstracta multi-parameter collaborative monitoring system for precision agriculture that integrates flexible sensing, LoRa-based wireless communication, and deep learning-based data analysis
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Plant phenotyping relevance match · UnverifiedCrossref · checked 8 Sept 2026
Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches.
Why it matches plant phenotyping methodsSAR時系列から作物のフェノロジー指標を抽出・評価し、識別性能や転移性を検証することが研究の中心であるため、植物フェノタイピング手法として含める。
abstractThis study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems.
Plant-based infection models provide cost effective and biologically relevant systems for investigating bacterial pathogenesis and virulence in living hosts. The mung bean seedling model enables the study of bacterial biofilms on living surfaces by allowing attachment and biofilm development on plants, but its broader use has been limited by methodological complexity and variability in experimental outcomes. Here, we present a modified mung bean seedling biofilm infection model for assessing Pseudomonas aeruginosa virulence that improves both consistency and practicality. The assay incorporates a bleach-based seed sterilization protocol that effectively reduces surface associated contaminants while maintaining high seed germination percentages. Additional refinements, including dehulling germinated seedlings, a shortened bacterial inoculation period, and plate-based incubation of seedlings at 37 °C, minimize variability in plant health outcomes while supporting development of gnotobiotic plants. Plant mortality, cotyledon emergence, and root branching were identified as rapid and quantitative measures of biofilm associated disease. Using this modified assay, reproducible differences in virulence were detected among P. aeruginosa strains, including reduced pathogenicity in a pqsR quorum sensing mutant. This simplified mung bean seedling model provides an accessible platform for studying biofilm associated virulence and screening genes involved in biofilm-mediated pathogenicity on a biotic surface.
Why it matches plant phenotyping methodsムングビーン幼植物を用いた感染・疾患表現型測定系の改良と再現性評価が研究の中心であり、植物死亡、子葉出現、根分枝を定量的な病徴指標として開発・検証している。
abstractHere, we present a modified mung bean seedling biofilm infection model for assessing Pseudomonas aeruginosa virulence that improves both consistency and practicality.
Background and AimsClimate gradients influence seed morphology, emergence, and early life-history traits with cumulative impacts to individual fitness. For ex situ seed collections, which represent an invaluable repository of potential trait information for species management and conservation, climate data can guide preservation of adaptive variation and inform deployment strategies for restoration. Here we leverage a range-wide ex situ seed collection of critically endangered black ash seeds (Fraxinus nigra) to evaluate how climatic gradients shape variation in morphology and early life-history. MethodsTo test how climate of origin, seed morphology, and early life-history interact to impact first year fitness, high-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra. Following this, a subset of seeds were used to establish a common garden experiment and quantify variation in emergence, early life-history transitions, and their cumulative impact to first-year survival and growth. ResultsOn average, differences within-population explained [~]43% of the variability in seed morphology, while among-population differences explained [~]14%. This suggests that substantial genetic variation exists within populations for natural selection to act upon and differences have evolved among populations. Climate associations indicated warmer and drier environments predicted heavier seeds with faster developmental transitions and increased first-year height. Together, climate of origin, seed mass, and timing of developmental transitions best predicted cumulative fitness, with populations from more continental environments exhibiting greater survival and first-year height accumulation on average. ConclusionsOverall, these results highlight the importance of climate of origin, seed traits, and early developmental transitions to first-year fitness in a perennial tree species. This work demonstrates how ex situ collections can be used to identify climatically structured trait variation and guide conservation strategies aimed at maintaining adaptive potential under environmental change.
Why it matches plant phenotyping methods701系統の種子形態を高スループットX線画像とニューラルネットワーク分割で定量しており、形態表現型の取得・抽出が研究の主要な技術基盤である。
abstracthigh-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra
Rice ( Oryza sativa ) production underpins food security in many rice-consuming nations. As a critical developmental transition that directly determines yield and grain quality, flowering dates and timing are genetically complex and highly sensitive to environmental fluctuations. This complexity requires new methods to quantify diurnal floral characteristics, which are essential to hybrid breeding in cereals. Here, we present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice. After monitoring 172 rice accessions selected from the Chinese Rice Mini-Core Collection using cost-effective time-lapse imaging platforms for 16 days, we acquired over 530,000 accession-level images and established the Open Rice Flowering Training (ORFT) dataset, with over 39,000 panicles and 350,000 anthers annotated. Next, a two-stage customised DL model (i.e. YOLACT-Panicle for panicle segmentation and UNet-Anther for anther identification) was trained using the ORFT set, enabling ultra-high-frequency measures of anther extrusion at the minute level. Based on trait analysis, we further fitted curves to dynamically identify diurnal flowering patterns, including key timepoints such as the initial flowering timepoint ( T Ini. ), quickest flowering timepoint ( T Qck. ), and peak flowering time ( T Peak ), and novel traits such as the duration of rapid flowering phase ( P Rpd. ) and flowering density across key phases. After validating BloomSight-derived traits against manual observations, we classified the japonica and indica accessions into three patterns: Slow, Moderate, and Fast, all of which had distinct flowering windows. These analyses helped us integrate phenotypic variations into a genome-wide association study (GWAS), revealing many significant single nucleotide polymorphisms (SNPs) associated with known (e.g. EMF1 , OsMYB8 , and PME42 ) and several repeatedly identified unknown loci (one of these loci has been recently verified by other groups), demonstrating the value of the BloomSight framework. Taken together, we believe that BloomSight provides an ultra-high-frequency framework for diurnal flowering phenotyping, enabling the measurement of biological meaningful floral traits with minute-level resolution that can enable flowering-related developmental studies and hybrid-breeding applications in rice and more broadly benefit the plant and crop research community.
Why it matches plant phenotyping methodsイネの開花動態を高頻度画像と深層学習で抽出するフェノタイピング基盤を開発し、データセット構築と手動観測による検証も行っているため、方法が研究の中心である。
abstractwe present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice
Reproduction assets foundThe paper's Data and code availability statement explicitly provides public access to the ORFT annotated image dataset (BioStudies S-BSST2157), Python source code for floral trait analysis (GitHub The-Zhou-Lab/BloomSight), and trained DL models (GitHub releases). SRA accessions are molecular sequencing data, not phenotCode · publicPython-based source codes for automating floral trait analysis using the above data are accessible via our GitHub repository ( https://github.com/The-Zhou-Lab/BloomSight ).Open asset ↗The-Zhou-Lab/BloomSightlines:336-349Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R2 = 0.90) and weedy yield (MAE = 258, R2 = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からキャノピー形質を高スループットに抽出し、機械学習による予測性能も評価することが研究の中心であるため、植物フェノタイピング手法の実質的応用・検証に該当する。
abstractThis study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars
TomatoStem / branchMorphology / geometry measurementSegmentationGrowth / development / phenology
Introduction The industrial cultivation of tomato seedlings requires a high degree ofuniformity and consistency in grading. However, traditional grading methods based on phenotypictraits such as leaf area and canopy width are susceptible to environmental conditions, therebylimiting the accuracy and efficiency of grading. Since cumulative internode length is relativelystable and closely correlated with seedling vigor, this study aims to develop an accurate methodfor measuring and grading the cumulative internode length of tomato seedlings. Methods A tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework. First, deformable convolution was introducedinto the backbone to enhance feature extraction for curved stems and occluded regions. Second,the original loss function was replaced with SIoU to improve the alignment between predictedregions and the actual stem structure. Third, a Haar wavelet downsampling module was embeddedinto the backbone to preserve high-frequency detail information and reduce information loss underocclusion conditions. An intelligent grading system was further developed to verify the practicalapplicability of the proposed method. Results Experimental results showed that DSH_YOLO achieved a Precision of 96.1%, aRecall of 94.3%, and an mAP@0.5 of 92.1%. Compared with the baseline YOLOv8 model, theproposed method substantially improved segmentation performance for cumulative internoderegions in tomato seedlings. In prototype validation, the intelligent grading system achieved anaverage grading success rate of 87.50%, with an average cumulative internode length error of 8.0mm. Discussion The results indicate that DSH_YOLO and the grading system can meet therequirements for large-scale grading and detection of tomato seedlings, demonstrating highdetection accuracy and success rates. This approach can provide insights for grading other types ofseedlings during their growth stages and offer support for seedling production and thedevelopment of intelligent agricultural equipment.
Why it matches plant phenotyping methodsトマト苗の累積節間長という植物形態形質を画像分割・検出し、実用的な等級判定まで検証する手法開発が研究の中心であるため。
abstractA tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
As a core resource of traditional Chinese medicine (TCM), medicinal plants are conventionally monitored and assessed using high-cost, low-efficiency methods. Remote sensing offers an efficient technical alternative for large-scale and dynamic evaluation. This study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods, and specifically analyzed their applications in medicinal plant resource investigation, planting monitoring, stress monitoring, and TCM quality assessment. These studies mainly focus on resource surveys and quality analysis, targeting root and rhizome herbs. Integrated satellite-, UAV-, and ground-based remote sensing enables distribution mapping, growth retrieval, stress monitoring, and non-destructive quality evaluation in medicinal plants, achieving overall accuracies ranging from 80% to 100%. Currently, remote sensing applications in medicinal plants are evolving toward space–air–ground integration, multi-source data fusion, artificial intelligence empowerment, and multi-omics integration. However, they are constrained by complex wild habitats, difficulties in monitoring root herbs, spectral confusion, and limited model generalization. Future efforts should focus on establishing an integrated monitoring network, developing full-chain quality inversion models for geo-authentic herbs, building climate-adaptive cultivation systems, creating early pest–disease warning technologies, and deepening the integration of remote sensing and multi-omics to support the sustainable utilization and high-quality development of medicinal plant resources.
Why it matches plant phenotyping methods植物の成長・ストレス・品質などの状態を対象に、リモートセンシングのプラットフォーム、センサー、解析手法を体系的にレビューしており、植物フェノタイピング手法が中心です。
abstractThis study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods
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.
Growth chamberRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Abstract Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hair area from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for root area and 0.65 for root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm⁻³). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a rapid, scalable, and training-free method for integrated analysis of root architecture and root hairs under controlled physical conditions similar to soil, facilitating studies of root–soil interactions that require both spatial resolution and temporal continuity.
Why it matches plant phenotyping methods根系画像解析システムとRベースの画像分析ワークフローを開発し、根系構造と根毛面積の定量化およびアルゴリズム性能検証を中心に扱っているため、植物フェノタイピング手法として適格です。
abstractwe present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth.
Reproduction assets foundThe paper's authors state that the RootHairFinder C++ file, R script, and example rhizotron data will be available via a GitHub repository and Zenodo upload, and the preprint's supplementary files already include RootHairFinder.cpp and example rhizotron images (Supplimentaryfile4.tif, Supplimentaryfile5.tif). This is aCode · publicThe RootHairFinder cpp file, R script and example data files for rhizotron analysis will be made available through github https://github.com/TracyValentine/RootHairFinder and https://zenodo.org/uploads/19288922Open asset ↗TracyValentine/RootHairFinderlines:236-263Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
LeafStem / branchPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyWater status / transpiration
We report a scalable, moisture-powered in-planta sensor platform for the continuous monitoring of plant hydration and growth. The system integrates two components: a leaf-mounted tattoo sensor for estimating vapor pressure deficit (VPD) and a kirigami-inspired strain sensor for tracking radial stem growth. Uniquely, the tattoo sensor serves a dual function: measuring temperature and humidity beneath the leaf surface while simultaneously harvesting power from ambient moisture via a vanadium pentoxide (V 2 O 5 ) nanosheet membrane. This moist-electric-generator (MEG) configuration enables energy-autonomous operation, delivering a power density of 0.1114 μW/cm 2 . The V 2 O 5 -based sensor exhibits high sensitivity to humidity (4.2 mV/% RH) and temperature (1.02%/°C), enabling accurate VPD estimation for over 10 days until leaf senescence. The eutectogel-based kirigami strain sensor, wrapped around the stem, offers a gauge factor of 1.5 and immunity to unrelated mechanical disturbances, allowing for continuous growth tracking for more than 20 days. Both sensors are fabricated via cleanroom-free, roll-to-roll compatible methods, underscoring their potential for large-scale agricultural deployment to monitor abiotic stress and improve crop management.
Why it matches plant phenotyping methods植物の水分状態（VPD）と茎の成長を連続測定するセンサー基盤を開発・性能評価しており、植物表現型の取得が中心的な技術貢献である。
abstractWe report a scalable, moisture-powered in-planta sensor platform for the continuous monitoring of plant hydration and growth.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Multi-temporal remote sensing data in large-scale crop phenology identification and classification have become increasingly utilized, particularly for precision management in arid oasis agricultural regions with complex cropping systems. In this study, we developed a deep learning framework integrating Sentinel-2 multi-temporal imagery and normalized difference vegetation index (NDVI) time series for mapping cotton, winter jujube, and tiger nut crops in Tumushuke City, Xinjiang Uygur Autonomous Region, China. We employed the minimum redundancy maximum relevance (mRMR) algorithm for spectral and vegetation index feature selection, followed by Savitzky-Golay (S-G) filtering and double logistic function fitting, to automatically extract the key phenological parameters (start of season (SOS), peak of season (POS), and end of season (EOS)), significantly improving phenological feature extraction accuracy. By incorporating multi-temporal Sentinel-2 data and a multi-scale feature fusion approach, we could systematically compare five classification models (multi-layer perceptron (MLP), residual network-18 (ResNet-18), convolutional long short-term memory (ConvLSTM), Transformer, and random forest classifier (RFC)), demonstrating that high-resolution spatial details substantially enhance crop boundary delineation and classification consistency in complex environments. Further optimization of Transformer's spatial representation through multi-scale window analysis revealed that the use of 1×1+3×3+5×5 convolutional windows achieves an optimal balance between accuracy and computational efficiency. Independent validation on unseen areas confirmed robust model transferability, with F1 scores of 94.37%, 87.75%, and 86.35% for the three crops (winter jujube, cotton, and tiger nut), respectively. This study validates the high-precision identification potential of Sentinel-2 temporal data and deep neural networks for multi-crop environments, enabling the precise spatial mapping of crop distributions and providing methodological support for smart agricultural decision-making in arid oasis regions.
Why it matches plant phenotyping methodsSentinel-2時系列から植物の生育季節性（SOS、POS、EOS）を自動抽出し、その精度向上と独立地域での検証を行っており、作物分類を含む解析ワークフローにおける植物フェノタイプ抽出が中心的です。
abstractto automatically extract the key phenological parameters (start of season (SOS), peak of season (POS), and end of season (EOS)), significantly improving phenological feature extraction accuracy.
RGB / grayscaleFlowerClassificationGrowth / development / phenology
This study presents a comprehensive BDFlower growth stage dataset designed to support research in precision agriculture and floriculture. The dataset encompasses eight common flower species found in Bangladesh: Bush Allamanda, Red Hibiscus, Yellow Bell, Pinwheel Flower, Pink Periwinkle, White Madagascar Periwinkle, Marvel of Peru, and White Hibiscus. Each species is represented across three growth stages-Early, Mid, and Full-resulting in 24 distinct classes. A total of 23,334 colour images are included, comprising 3889 original photographs and 19,445 augmented samples generated with five augmentation techniques. Bush Allamanda contains 499 images, Red Hibiscus contains 489 images, Yellow Bell contains 483 images, Pinwheel Flower contains 497 images, Pink Periwinkle contains 452 images, White Madagascar Periwinkle contains 472 images, Marvel of Peru contains 468 images and White Hibiscus contains 529 images. Each image was collected using smartphone camera at three-time intervals per day, spaced eight hours apart, to capture natural variations in lighting and appearance. The dataset is further organized into training, validation, and testing splits, enabling direct application to machine learning workflows. This is a publicly available dataset specifically curated for flower growth stage classification. In addition to dataset collection, we also conducted a simple experiment using a CNN model to evaluate its performance on this dataset. It is intended to facilitate the development of robust computer vision models that can monitor flower development, with potential applications in automated plant phenotyping, crop monitoring, and digital floriculture systems.
Why it matches plant phenotyping methods花の生育段階を画像で分類する公開データセットを構築し、CNN評価も行っており、植物表現型取得・解析が研究の中心である。
abstractThis is a publicly available dataset specifically curated for flower growth stage classification.
Reproduction assets foundThe paper's own flower growth-stage image dataset (BDFlower) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, directly reproducing the paper's phenotyping (flower growth stage) image measurements. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicRepository name: Data Mendeley
Data identification number: 10.17632/m8g2wynwyr.2
Direct URL to data: https://data.mendeley.com/datasets/m8g2wynwyr/2Open asset ↗10.17632/m8g2wynwyr.2html-lines:94-129Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
RootMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
All plants rely on their roots for survival. Due to the natural plasticity of roots in response to different stimuli, breeders can investigate natural adaptation and uncover advantageous root features to increase plant yield in agricultural system. The plant's physiology, development, and ability to respond to different pressures are all influenced by the root system. Root system architecture (RSA)-related factors are very important for breeding selection. However, quantifying these traits is difficult, requires a lot of resources, and frequently produces a lot of variability. With the development of computer vision and machine learning (ML) technologies, which allow for effective trait extraction and evaluation, the use of RSA traits for genetic improvement to create more robust and resilient crop cultivars has attracted greater attention. Root phenotype is a crucial component of yield improvement which is regulated by the interaction of internal genetic factors and external environmental conditions. To meet the demands of population growth and climate change, significant increases in agricultural productivity are required. Enhancing crop root architecture has the potential to improve water and nutrient use efficiency; however, a major challenge remains in accurately characterizing the structure and function of the root phenome. Numerous advances have been made in recent years in the measurement and analysis of root system, including the development of 2D and 3D root phenotyping platforms. These platforms are high-throughput and non-invasive techniques for root phenotype characterization. These approaches involve the use of advanced imaging and analytical tools to collect data on root structure, growth, and function across a large number of plants, while enabling automated evaluation of multiple root traits. To phenotype root systems numerous imaging tools, software, and platforms have been developed. This study focuses on recent advancements in in-situ root phenotyping techniques that allow researchers and breeders to efficiently assess root characteristics and apply them to different breeding initiatives. In-situ root phenotyping techniques encompass a variety of 2D and 3D platforms for thorough and efficient root analysis. This review highlights current developments in in-situ root phenotyping and stresses their expanding potential to aid in the development of stress-resistant, high-yielding crops for sustainable agriculture.
Why it matches plant phenotyping methods根系表現型取得に関する2D・3D画像化、解析ツール、ソフトウェア、プラットフォームの進展を扱うレビューであり、植物フェノタイピング手法が中心である。
titleAdvances in In-situ Root Phenotyping: A Review
Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.
Why it matches plant phenotyping methodsUAV空撮とフォトグラメトリによる作物表面モデル（CSM）を用いたトウモロコシ生育差・収量変動の検出時期を検証しており、表現型取得法が研究の中心である。
abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.
Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.
Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動の検出時期を評価しており、植物表現型の取得方法の技術的適用と評価が中心である。
abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.
Double-cropping grape systems offer enhanced land productivity but face significant challenges from climate variability, particularly rain stress and pest outbreaks during critical phenological stages. Accurate phenological prediction is essential to synchronize management practices with crop development and improve ecological resilience. This study presents a novel deep learning framework that integrates MobileNet with an augmented version of Dream Optimizer [Augmented Dream Optimizer (ADO)] to model grape phenology using satellite-derived time series of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and rainfall from the CropHarvest dataset. The model transforms temporal data into pseudo-images for efficient spatiotemporal feature extraction, achieving 93% classification accuracy and a 6.1-day mean absolute error in stage prediction. ADO enhances convergence and generalization by optimizing key hyperparameters through a hybrid metaheuristic search. The system further identifies high-risk periods for rain damage and pest infestation, enabling proactive interventions. The model statistically significantly outperforms machine learning and deep learning baselines (p< 0.01) across three different agroecological zones [Mediterranean (California, USA, and southern Europe), subtropical (South Australia), and temperate (central Europe)] through spatially stratified fivefold cross-validation on 3,000 held-out test samples of the CropHarvest dataset. This work demonstrates the potential of optimized lightweight neural networks in sustainable viticulture, providing a scalable tool for precision management in climate-resilient double-cropping systems.
Why it matches plant phenotyping methods衛星時系列からブドウの生育ステージを推定する深層学習手法を開発し、交差検証とベースライン比較で性能を検証しており、植物フェノタイピング手法が中心である。
abstractThis study presents a novel deep learning framework that integrates MobileNet with an augmented version of Dream Optimizer [Augmented Dream Optimizer (ADO)] to model grape phenology using satellite-derived time series of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and rainfall from the CropHarvest dataset.
Introduction Accurate plant disease identification is of great importance for ensuring agricultural productivity and food security. However, complex illumination variations, leaf occlusion, and diverse disease spot scales throughout plant growth stages significantly increase the difficulty of real-time detection, leading to limited accuracy and robustness in existing approaches. Methods To address these challenges, we propose an improved RT-DETRv2-based plant disease detection model, termed AF-RT-DETR. A Bidirectional Cross Gate (BCG) module is introduced in the feature extraction stage to reduce channel redundancy and enhance discriminative feature representation through multi-level feature interactions. The original RepVGG structure is replaced with a Dynamic Channel Shift (DCS) module, effectively enlarging the receptive field and strengthening contextual feature fusion without additional computational overhead. Additionally, an improved Scale-aware Multi-level Loss (SML) emphasizes low-quality feature maps to improve detector robustness. Results The model achieves mAP50 and mAP50:95 of 93.6% and 67.2% on the Plant-Disease dataset, surpassing the baseline by 5.1% and 4.5%. Furthermore, the model was evaluated on multiple crops and growth stages under diverse field conditions, demonstrating robust performance and adaptability. Discussion These results indicate that AF-RT-DETR effectively enables real-time plant disease detection in complex field environments.
Why it matches plant phenotyping methods植物の病徴を画像から検出するモデルの開発と、複数作物・生育段階・圃場条件での性能評価が中心であり、植物病害状態の表現型計測手法に該当する。
Reproduction assets foundThe paper evaluates AF-RT-DETR on three public Roboflow plant-disease image datasets, each cited with an explicit public URL. No author analysis code or trained model release is mentioned. The Ultralytics YOLOv8 repository is a generic third-party library, not a paper-specific asset.Dataset · publicRoboflow Detecting rice crop diseases object detection dataset . Available online at: https://universe.roboflow.com/crop-diseases-l2qhk/detecting-rice-crop-diseases/dataset/21Open asset ↗lines:816-932Dataset · publicRoboflow Disease detection object detection dataset . Available online at: https://universe.roboflow.com/projects-h0apg/disease-detection-0slunOpen asset ↗lines:816-932Dataset · publicRoboflow Plant Disease v2 512×512 . Available online at: https://universe.roboflow.com/sangeeth-mathew-john-nl43i/plant-disease-czcfe/dataset/2Open asset ↗lines:933-1045Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Abstract (150 to 250 words) Spatial variability in barley maturation complicates preharvest classification for food-grade production and accurate conventional field-based assessments. Therefore, we classified barley growth into food-grade–oriented categories and developed a predictive framework integrating UAV-based multispectral imagery with machine learning. Grain quality metrics were first analyzed using principal component analysis and k-means clustering to define three physiologically distinct growth levels. Multi-temporal vegetation indices (NDVI, GNDVI, and NDRE) were extracted from UAV imagery, and key predictors were selected using Lasso regularization. Comparisons of Random Forest (RF), XGBoost, and Support Vector Machine models indicated that tree-based ensembles achieved high classification accuracy (>0.9), with late-season NDVI identified as the most influential predictor. Spatial mapping using the RF model revealed pronounced inter-field variability, identifying zones of incomplete maturation associated with lodging. Overall, integrating grain quality traits with UAV-based spectral monitoring allows accurate field-scale classification of food-grade barley growth and informs site-specific harvest management.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から大麦の成熟・生育状態を推定し、複数の機械学習手法を比較する予測フレームワークが研究の中心であるため、植物状態の実質的なフェノタイピング手法に該当する。
abstractwe classified barley growth into food-grade–oriented categories and developed a predictive framework integrating UAV-based multispectral imagery with machine learning.
The analysis of plant growth and development in controlled agricultural environments has become increasingly important to ensure sustainable and efficient food production. Traditionally, plant monitoring relied on manual observations and basic statistical approaches, which provided limited insights into the complex interactions between environmental factors and plant physiology. To address these challenges, this study proposes a machine learning–driven analytical framework designed for comprehensive plant development analysis. The system integrates data preprocessing, exploratory data analysis, classification, regression, and hybrid deep learning approaches within a unified pipeline. Classification algorithms such as Support Vector Classifier (SVC), Bernoulli Naive Bayes (BNC), and Multinomial Naive Bayes (MNC) are employed to identify plant growth stages. Regression models, including Decision Tree Regressor (DTR), Support Vector Regressor (SVR), and Ridge Regressor (RR), are utilized to estimate growth-related parameters. Furthermore, two hybrid models are introduced to enhance predictive performance. The Deep Feature Probabilistic Classifier (DFPC) combines Feed Forward Neural Networks (FFNN) with Gaussian Naive Bayes (GNB) for improved classification accuracy, while the Hybrid Deep Ridge Predictor (HDRP) integrates FFNN with Ridge Regressor to achieve precise regression outcomes. Experimental results demonstrate that the DFPC model attains an accuracy of 94.42%, whereas the HDRP model achieves an R² score of 0.999. These findings highlight the effectiveness of combining deep learning and machine learning techniques for accurate plant growth analysis and informed decision-making in controlled agricultural systems.
Why it matches plant phenotyping methods植物の成長段階と成長関連パラメータを推定する機械学習・深層学習パイプラインが研究の中心であり、表現型推定手法の開発に該当する。
abstractClassification algorithms such as Support Vector Classifier (SVC), Bernoulli Naive Bayes (BNC), and Multinomial Naive Bayes (MNC) are employed to identify plant growth stages.
ABSTRACT Oil palm production has increased rapidly since 2012, particularly in Guatemala, Malaysia, and Indonesia. Accurate Fresh Fruit Bunch classification is vital for oil yield and quality, but manual grading is inefficient and existing deep learning methods are computationally intensive. To overcome these challenges, this study proposes an optimized Oil Palm Fresh Fruit Bunch Ripeness classification framework based on a Mixed‐Order Relation‐Aware Recurrent Neural Network (OP‐FFBR‐MORA‐RNN). The proposed methodology integrates Confidence Partitioning Sampling Filtering (CPSF) for effective image localization, cropping, and resizing, followed by Revised Tunable Q‐Factor Wavelet Transform (RTQFWT) to extract discriminative features. These features are subsequently classified using a Mixed‐Order Relation‐Aware Recurrent Neural Network (MORA‐RNN), with its parameters optimized via the Fractional Pelican African Vulture Optimization (FPAVO) algorithm to enhance classification accuracy. The model categorizes FFBs into five ripeness classes: overripe, ripe, abnormal, empty fruit, and under‐ripe. Experimental evaluation on an oil palm dataset demonstrates that OP‐FFBR‐MORA‐RNN achieves 97.8% accuracy, 97.6% precision, and a low error rate of 2.4%, outperforming existing methods such as Oil Palm Fresh Fruit Bunch Ripeness categorization on mobile devices using Convolutional Neural Network (OP‐FFBR‐MD‐CNN), Machine Vision for maturity classification using Artificial Neural Network (MVM‐OP‐FFBR‐ANN), and Object Detection for Oil Palm Fruit Bunches using You‐Only‐Look‐Once (OB‐OPFBR‐YOLOv7). These results confirm the framework's effectiveness for reliable, scalable, and efficient ripeness classification, supporting improved agricultural monitoring and optimized crop management.
Why it matches plant phenotyping methods油ヤシ果房の熟度という植物器官の状態を画像から分類する手法を提案・評価しており、特徴抽出、分類モデル、精度比較が研究の中心である。
abstractthis study proposes an optimized Oil Palm Fresh Fruit Bunch Ripeness classification framework based on a Mixed‐Order Relation‐Aware Recurrent Neural Network (OP‐FFBR‐MORA‐RNN).
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Contemporary agrobiotechnology research increasingly relies on automated methods for capturing and interpreting morphophysiological and spectral plant characteristics - a field known as digital phenotyping. This approach aims to identify stable differences between genotypes cultivated under non-identical environmental conditions. We previously introduced StatFaRmer, an open-source tool that we further develop here for comprehensive analysis of temporal phenotypic datasets, with a primary focus on crops such as soybean (Glycine max). The tool implements automated data preprocessing procedures, including synchronization of timestamps across samples and removal of noise artifacts and outliers. These features are particularly relevant for multi-month experiments involving assessments of growth parameters, fluctuations in photosynthetic apparatus area, or other biometric indicators. Support for standardized data formats (XLSX, CSV) ensures compatibility with common phenotyping systems, simplifying cross-platform integration. Thus, the tool can integrate with widely used HTPP platforms (e. g., Traitmill, HyperAIxpert, Plant Accelerator), enabling data from diverse sources to be analyzed within a single pipeline. For soybean experiments, StatFaRmer provides customizable analysis of variance (ANOVA) with visualization of diagnostic parameters (normality of distribution, homogeneity of variances) and evaluation of effect significance between user-defined groups. An example application compares growth parameters across 20 soybean cultivars under controlled stress: the tool automatically aggregated data with uneven measurement frequencies (from 1 hour to 3 days), identified anomalies in hypocotyl elongation dynamics, and computed statistical significance between groups (p < 0.01).The tool has been tested on large-scale datasets (over 2,000 measurements per experiment). StatFaRmer is implemented as a Shiny-based web application, with step-by-step deployment guides for Windows and Linux. All processing stages - from raw data to final plots - are documented to ensure transparency and compliance with research reproducibility standards. Thus, StatFaRmer offers a specialized solution for statistical hypothesis testing in soybean digital phenotyping, reducing data preparation time and minimizing risks of error when handling non-stationary time series.
Why it matches plant phenotyping methods植物デジタルフェノタイピング用の時系列解析ツールを開発・拡張し、前処理、異常値除去、統計解析、再現可能なワークフローを提供しているため、フェノタイピング手法が中心である。
abstractWe previously introduced StatFaRmer, an open-source tool that we further develop here for comprehensive analysis of temporal phenotypic datasets
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Pepper / chilliTomatoGreenhouseFruitClassificationSegmentationGrowth / development / phenology
Accurate classification of fruit maturity is essential for automated grading and robotic manipulation in modern greenhouse cultivation. Most existing methods rely on crop-specific models, severely restricting their scalability in multi-crop scenarios. To overcome this limitation, this study presents SCEA-YOLO, a unified and efficient instance segmentation framework built on YOLOv11s-seg, for simultaneous maturity classification of tomatoes and sweet peppers. To boost feature discrimination, reduce computational redundancy, and alleviate class imbalance, SCEA-YOLO integrates spatial-channel reconstruction convolution and an efficient multi-scale attention mechanism, while replacing the original detection head with the proposed EA-Head. The model is evaluated on a hybrid dataset captured under diverse greenhouse conditions, including varying illumination, fruit occlusion, and overlapping canopies. Its robustness to different viewing angles and camera distances is further validated via deployment on an automated grading robot. Compared with the baseline, SCEA-YOLO enhances classification precision and mAP50–95 by 5.3% and 2.3% for tomatoes, and 1.2% and 1.4% for sweet peppers, respectively. With only 33.2 GFLOPs, the model satisfies real-time inference demands. Benefiting from its lightweight structure and real-time performance, SCEA-YOLO can be readily deployed on embedded systems and robotic platforms. It offers a practical, unified, and scalable solution for intelligent fruit maturity evaluation in multi-crop greenhouse production.
Why it matches plant phenotyping methodsトマトとピーマン果実の成熟度を画像から分類・評価するモデルを開発し、データセットおよびロボット上で性能検証しており、植物表現型取得手法が中心である。
abstractthis study presents SCEA-YOLO, a unified and efficient instance segmentation framework built on YOLOv11s-seg, for simultaneous maturity classification of tomatoes and sweet peppers.
Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.
Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動検出について、観測時期と技術性能を評価しており、植物表現型取得法が中心である。
abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.
Determining the optimal harvest time is among the most critical economic decisions for peanut ( Arachis hypogaea L.) growers, directly influencing yield, quality, and market value. Unlike many other crops, peanuts are indeterminate, continuing to flower and produce pods throughout their life cycle. As a result, pod development and maturation are asynchronous, making harvest timing particularly challenging. Conventional maturity estimation techniques, including the hull scrape method, pod blasting, and visual maturity profiling, are invasive, labor-intensive, time-consuming, and spatially limited. Moreover, differences in cultivar maturity rates and agroclimatic conditions exacerbate inconsistencies in maturity prediction. These challenges highlight the urgent need for scalable, objective, and data-driven methods to support growers in achieving optimal harvest outcomes. This review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation. It aims to identify the limitations of conventional techniques and explore the integration of advanced sensing technologies, artificial intelligence (AI), and geospatial analytics to enhance precision and scalability in peanut maturity assessment and harvest decision-making. This review examines traditional destructive techniques such as the hull scrape method and pod blasting, followed by emerging non-invasive methods employing proximal and remote sensing platforms. Applications of vegetation indices, multispectral and hyperspectral imaging, and AI-based data analytics are discussed in the context of maturity prediction. Additionally, the potential of multimodal remote sensing data fusion and digital frameworks integrating spatial big data analytics, centralized data management, and cloud-based graphical interfaces is explored as a pathway toward end-to-end decision-support systems. Recent advances in non-invasive sensing and AI-assisted modeling have demonstrated significant improvements in scalability, precision, and automation compared with traditional manual approaches. However, their effectiveness remains constrained by the limited inclusion of agroclimatic, phenological, and cultivar-specific variables. Furthermore, the translation of model outputs into actionable, field-level harvest decisions is still underdeveloped, underscoring the need for integrated, user-centric digital infrastructure. Achieving a robust and transferable digital peanut maturity estimation system will require comprehensive ground-truth data across cultivars, regions, and growing seasons. Multidisciplinary collaborations among agronomists, data scientists, growers, and technology providers will be essential for developing practical, field-ready solutions. Integrating AI, multimodal sensing, and geospatial analytics holds immense potential to transform peanut maturity estimation. Such innovations promise to enhance harvest precision, economic returns, and sustainability while reducing manual effort and uncertainty, ultimately improving the efficiency and quality of life for peanut producers worldwide.
Why it matches plant phenotyping methodsピーナッツ莢の成熟度という植物状態を対象に、従来法と非侵襲センシング、画像解析、AIによる推定手法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation.
Background: This review study examines the transformative potential of machine learning (ML) methods for real-time and continuous evaluation of legume crop development. It provides a structured and comprehensive synthesis of current ML applications, highlighting their potential to improve legume crop management in terms of accuracy, efficiency, scalability and sustainability. Unlike existing reviews, this study specifically emphasizes real-time monitoring frameworks that integrate multi-source data (satellite, UAV, IoT and sensors) for legume crops. Methods: Various machine learning methods, including supervised, unsupervised and deep learning paradigms, are reviewed with respect to their applications in crop health prediction, disease detection and yield estimation. The review further analyzes the integration of ML models with Internet of Things (IoT), edge computing and sensor-based systems to address challenges related to data quality, model interpretability, computational efficiency and real-time decision-making. Result: While challenges remain, such as data heterogeneity, limited model generalization and the integration of ML with traditional agronomic practices, recent technological advancements demonstrate promising solutions. Key trends include the development of robust and transferable models, improved human–machine interfaces and decision-support tools for farmers. These advances have the potential to enhance precision, resilience and sustainability in legume crop monitoring, thereby contributing to global food security and climate-smart agriculture.
Why it matches plant phenotyping methodsマメ科作物の生育・健康・病害・収量を対象に、機械学習と衛星・UAV・IoT・センサーを統合したモニタリング手法をレビューしており、植物形質・状態の取得および推定方法が中心である。
abstractThis review study examines the transformative potential of machine learning (ML) methods for real-time and continuous evaluation of legume crop development.
This study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns in single-staked white Guinea yams ( Dioscorea rotundata ). Multi-angle aerial images from nadir and oblique views were used to extract vegetation- and height-related indices that served as predictors in machine learning models. Support vector regression using combined-view imagery provided the highest prediction accuracy (R² = 0.79) and remained robust across growth stages, years, fertilizer treatments, and genotypes. Notably, the combined-view configuration outperformed single-view imaging, demonstrating the advantage of capturing complementary canopy-structure information in complex staked-vine canopies. Time-series biomass estimates enabled the fitting of genotype-specific Richards growth curves using Bayesian inference. Significant genotypic variations were observed in parameters associated with maximum biomass and early growth rate, whereas phenology-related parameters showed comparatively minimal differences. These parameter differences may reflect variation in canopy architecture and growth allocation among genotypes. Overall, this integrated workflow provides a scalable tool for nondestructive monitoring of yam growth dynamics and for summarizing biomass trajectories with interpretable parameters, supporting breeding efforts aimed at improving yam productivity and yield stability across diverse cultivation conditions.
Why it matches plant phenotyping methodsUAV画像からヤムのシュートバイオマスと生育を推定する画像解析・機械学習ワークフローが研究の中心であり、複数視点画像の比較検証と時系列形質推定を行っている。
abstractThis study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns
In the European framework for assessing the ecological effects of plant protection products on aquatic organisms, standard tests usually rely on constant laboratory exposure. This Tier 1 approach may lead to overly conservative assessments for short-term exposures lasting only a few hours under realistic environmental conditions. To address this, the European Food Safety Authority proposed a Tier 2C assessment to incorporate more realistic dynamic exposure profiles through refined tests and toxicokinetic/toxicodynamic (TKTD) modeling. Currently, the only accepted TKTD model for macrophyte growth inhibition relates to the duckweed Lemna sp. We hypothesize that this model can be adapted for other macrophytes including sediment rooted macrophytes. We propose a Modeling Approach for Growth inhibition of MAcrophytes (MAGMA), to simulate both constant and dynamic exposure tests under laboratory conditions. The model was calibrated using experimental data from Myriophyllum spicatum exposed to two herbicides with different modes of action. Validation against single and two-pulse exposure experiments showed good agreement between model predictions and observed data. The modeling approach, MAGMA can therefore serve as a valuable Tier 2C tool for predicting macrophyte growth rates under dynamic exposure conditions.
Why it matches plant phenotyping methodsマクロファイトの成長阻害を動的曝露下で予測するモデルを開発し、実験データで較正・検証しており、植物表現型（成長率）の推定手法が研究の中心である。
abstractWe propose a Modeling Approach for Growth inhibition of MAcrophytes (MAGMA), to simulate both constant and dynamic exposure tests under laboratory conditions.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Computer vision and Internet of Things (IoT) technologies offer robust solutions for plant phenotyping, but traditional mainstream segmentation methods often fail in high-density plantings with overlapping foliage. This study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions. While recent agricultural applications have shown limited success and required domain-specific adaptations, Segment Anything Model (SAM) and FastSAM were demonstrated to achieve exceptional zero-shot segmentation performance for individual lettuce plants in high-density arrangements without additional training. This capability effectively addresses the traditional challenges of species-specific parameter tuning and extensive training data requirements and fine-tuning. By mapping 2D segmentation masks to corresponding 3D point clouds, the system accurately extracted key phenotypic traits, namely plant height, length, and width, from which area and volume were subsequently estimated, showing strong correlations with manual measurements for Rex and Rouxai lettuce cultivars. This high-temporal, non-destructive monitoring provided unique insights into plant growth dynamics. The study highlights distinct growth patterns among these cultivars, underscoring the importance of tailored phenotyping approaches to optimise crop management strategies. By addressing the limitations of existing phenotyping methods, this work advances precision agriculture technologies, offering a cost-effective and efficient solution for monitoring dynamic crop growth with potential applications across various crops and growing conditions.
Why it matches plant phenotyping methodsRGB-D撮像、3D点群、セグメンテーションを統合した植物表現型取得システムを開発し、草丈・長さ・幅などを抽出して手測定と検証しているため、方法が中心的である。
abstractThis study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions.
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
Computer vision and Internet of Things (IoT) technologies offer robust solutions for plant phenotyping, but traditional mainstream segmentation methods often fail in high-density plantings with overlapping foliage. This study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions. While recent agricultural applications have shown limited success and required domain-specific adaptations, Segment Anything Model (SAM) and FastSAM were demonstrated to achieve exceptional zero-shot segmentation performance for individual lettuce plants in high-density arrangements without additional training. This capability effectively addresses the traditional challenges of species-specific parameter tuning and extensive training data requirements and fine-tuning. By mapping 2D segmentation masks to corresponding 3D point clouds, the system accurately extracted key phenotypic traits, namely plant height, length, and width, from which area and volume were subsequently estimated, showing strong correlations with manual measurements for Rex and Rouxai lettuce cultivars. This high-temporal, non-destructive monitoring provided unique insights into plant growth dynamics. The study highlights distinct growth patterns among these cultivars, underscoring the importance of tailored phenotyping approaches to optimise crop management strategies. By addressing the limitations of existing phenotyping methods, this work advances precision agriculture technologies, offering a cost-effective and efficient solution for monitoring dynamic crop growth with potential applications across various crops and growing conditions.
Why it matches plant phenotyping methodsRGB-D画像、3D点群、セグメンテーションを統合して植物形質を抽出するフェノタイピングシステムの開発・評価が中心であり、手動測定との相関検証も行っているため。
abstractThis study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions.
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining solar energy capture via photovoltaic panels with crop production. However, in-depth information on plant growth patterns within the spatially heterogenous microclimate created by the intermittent shading of APVs is largely missing. In the present study, we implement a customized robot-mounted 3D-multispectral imaging system to closely monitor the growth and spectral reflectance patterns of a conventional soybean cultivar “Eiko” (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in plant height, 3D leaf area, light penetration, and canopy volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would enable better crop management within such non-homogenous cultivation systems.
Why it matches plant phenotyping methodsロボット搭載の3D・マルチスペクトル画像システムを構築・適用し、作物の形態形質とストレス状態を高精度・高スループットに取得しており、表現型取得手法が研究の中心である。
abstractwe implement a customized robot-mounted 3D-multispectral imaging system to closely monitor the growth and spectral reflectance patterns
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Sugarcane ( Saccharum spp.), a C4 plant, is a vital renewable biofuel and sugar source for industries worldwide. However, synchronizing flowering between parental lines often poses challenges for breeders, hindering effective crossbreeding efforts. This study aimed to develop a high‐throughput phenotyping (HTP) strategy to evaluate flowering‐related traits using vegetation indices (VIs) and other metrics alongside artificial intelligence (AI)‐based prediction methods. A total of 154 genotypes were planted in an augmented block design at the IAC sugarcane breeding station in Serra Grande‐BA, Brazil. Raw RGB (Red, Green, Blue) images were captured using a DJI Mavic 3 Enterprise drone during the plant cane (PC) and first ratoon (FR) crop seasons. These images were processed to create orthomosaics and compute metrics/vegetation index; subsequently, machine learning (ML) and deep learning pipelines for systematic analysis were developed. A convolutional neural network (CNN) model achieved promising results, with an accuracy rate of up to 84% in the flowering detection task. Additionally, flower counts from the CNN model showed a moderate correlation with field data, evidenced by an R 2 value of 0.72 at the onset and an R 2 value of 0.29 at the conclusion of the flowering season for the PC. This resulted in an overall average regression R 2 of 0.46 with a root mean square error (RMSE) of 13.80. Furthermore, an artificial neural network classification model reached a notable accuracy of 0.87 in differentiating genotypes based on their flowering response (early‐flowering vs. late‐flowering), utilizing VIs and digital model‐based metrics as input parameters. The ML regression model demonstrated performance levels of R 2 = 0.51 and RMSE = 8.06 for days to flag leaf emergence in PC and R 2 = 0.52 and RMSE = 7.93 for days to flowering in FR. These results highlight the potential of HTP strategies, utilizing orthomosaics and AI, to accelerate data collection and analysis, offering significant insights for breeding programs in sugarcane.
Why it matches plant phenotyping methodsドローンRGB画像、オルソモザイク、植生指数、AIを組み合わせた開花形質のハイスループット取得・予測手法を開発し、精度検証まで実施しており、フェノタイピング手法が研究の中心である。
abstractThis study aimed to develop a high‐throughput phenotyping (HTP) strategy to evaluate flowering‐related traits using vegetation indices (VIs) and other metrics alongside artificial intelligence (AI)‐based prediction methods.
Introduction Accurate identification of rice seedling age is essential for guiding precise field management and optimizing agronomic practices. However, traditional identification methods mainly rely on manual experience or simple visual cues and often lack robustness under complex field conditions such as illumination variation, background interference, and subtle morphological differences between adjacent growth stages. Therefore, developing a reliable and automated method for fine-grained recognition of rice seedling stages is of great importance. Methods To address this problem, this study proposes two deep learning models for automatic recognition of 13 rice seedling stages. The first model, Lresnet50, enhances visual feature representation by improving the baseline Resnet50 with a Row-Prior Strip Attention (RPS) mechanism, a Feature Pyramid Network (FPN) for multi-scale feature extraction, and Dynamic Channel Pruning (DCP) to reduce redundant channels and improve computational efficiency. Based on this model, a multimodal framework named M-Lresnet50 is further developed by integrating image features with temporal environmental data through a Long Short-Term Memory (LSTM) network, enabling cross-modal feature fusion and improving recognition of continuous seedling growth stages. Results Experimental results demonstrate that the proposed models achieve high accuracy in recognizing 13 rice seedling stages. The Lresnet50 model achieves an average classification accuracy of 97.70%, outperforming several existing convolutional neural network architectures and showing strong performance in transitional growth stages where morphological differences are subtle. By integrating visual features with temporal environmental information, the multimodal M-Lresnet50 further improves the accuracy to 98.33%. The model contains 27.656 million parameters with a computational complexity of 13.965 GFLOPs, indicating a good balance between recognition accuracy and computational cost. Discussion The results confirm the effectiveness of the proposed improvements and multimodal fusion strategy. The Row-Prior Strip Attention (RPS) enhances the model's ability to focus on row-structured crop regions, while the Feature Pyramid Network (FPN) improves multi-scale feature representation. In addition, Dynamic Channel Pruning (DCP) reduces redundant channels and improves computational efficiency. The integration of temporal environmental information through the multimodal framework further enhances the robustness and consistency of seedling stage recognition. Overall, the proposed approach provides a practical solution for intelligent monitoring of rice seedling growth in greenhouse environments.
Why it matches plant phenotyping methods画像と環境時系列データからイネ幼苗の生育段階を自動認識する深層学習手法を開発し、複数モデルとの精度比較も行っているため、植物状態の取得・推定が中心である。
abstractthis study proposes two deep learning models for automatic recognition of 13 rice seedling stages.
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.
The aspect of crop monitoring takes into consideration the timely detection of crop stages, leaf disorders, and deficiencies to enhance crop yield and decrease losses in agriculture. However, most of the current methods are limited to either disease detection or nutrient evaluation and do not examine the conditions of crops at various stages of growth, even though several AI -IoT-based solutions have been suggested to be applied to crop health monitoring. In addition, the estimation of the severity of the diseases is neglected, and this restricts decision-making in favor of the farmers. To address these constraints, the paper presents a Parametrized Elliptical Cauchy Fuzzy Inference System (PECFIS) combined with a Deep Glorot Bessel Elliott Softplus Convolutional Neural Network (DGBESCNN), proposed as an AI-based solution for crop monitoring and IoT support. The IoT devices in the form of drones are used to get real-time field images, and they are preprocessed in terms of noise reduction, contrast enhancement by LHM-CLAHE, conversion to HSV color space, and feature discrimination by vegetation indexing, as well as C3MEK-Means. PECFIS is used to determine eight key stages of rice growth and the severity of leaf diseases, whereas DGBESCNN provides proper classification of leaf diseases and nutrient deficiencies at each growth stage. The evaluation of the proposed framework was conducted using publicly available datasets on rice leaf disease and nutrient deficiency. The results of the experiments show that the system achieves high classification performance, with an accuracy of 98.82, a precision of 98.65, a recall of 98.73, an F1-score of 98.59, and low error rates (MSE = 0.0135, RMSE = 0.116). The findings show that the developed AI-IoT system is superior to available approaches and can serve as a dependable, real-time, and scalable solution in precision agriculture and intelligent crop monitoring.
Why it matches plant phenotyping methodsドローン画像からイネの生育段階と葉病害の重症度を推定・分類するAI-IoT手法が研究の中心であり、植物状態の取得・抽出方法を技術的に評価している。
abstractThe IoT devices in the form of drones are used to get real-time field images
Reproduction assets foundThe paper evaluates its PECFIS-DGBESCNN crop monitoring framework on two publicly available Kaggle datasets (Nutrient Deficiency Symptoms in Rice, 1,156 images; Rice Leaf Diseases, 120 images), with explicit dataset links provided by the authors. No author code, models, or other paper-specific assets are shared.Dataset · publicn of the low-cost ground-based IoT and weather sensors and enhanced robustness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. SuOpen asset ↗Kaggle · guy007/nutrientdeficiencysymptomsinricepdf-raw-page:21 lines:1-50Dataset · publicstness in the
current unfavorable environmental conditions.
Future Enhancement
In the future, enhanced techniques will be developed to classify the numerous types of nutrient
deficiencies in rice crops for improved productivity in agriculture.
Dataset link: https://www.kaggle.com/datasets/guy007/nutrientdeficiencysymptomsinrice
https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases/data
References
[1] Aggarwal, M., Khullar, V., Goyal, N., Alammari, A., Albahar, M. A., & Singh, A. (2023).
Lightweight federated learning for rice leaf disease classification using non independent and
identically distributed images. Sustainability, 15(16), 12149.
https://doi.org/10.3390/su151612149
[2] AlfOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:21 lines:1-50Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
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.
Image / point-cloud registrationGrowth / development / phenology
The increasing complexity and volume of plant phenotypic data have driven the emergence of new computational and standardization frameworks to enable data integration, reproducibility, and reuse. This systematic literature review examines the current state of software tools, data models, and interoperability standards in plant phenomics, focusing on the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Using a structured PRISMA-based methodology, we analyze two major community driven initiatives MIAPPE and BrAPI as representative solutions for standardized data description and exchange. Furthermore, the study evaluates the role of High-Performance Computing (HPC) and deep learning in addressing computational challenges associated with large-scale datasets, including multi-sensor and 3D capture technologies. Special consideration is given to data governance, encompassing secure access, ethical use, and GDPR compliance within expanding phenomics ecosystems. The synthesis identifies persistent gaps in data harmonization and semantic alignment, proposing future research directions toward more integrated, secure, and scalable infrastructures. This review emphasizes that the success of plant phenomics depends on bridging the gap between standard definitions and their practical implementation within high-performance workflows.
Why it matches plant phenotyping methods植物フェノミクスのデータ標準、ソフトウェア、相互運用性を扱う方法論的レビューであり、MIAPPE・BrAPIや大規模フェノタイピング基盤が中心です。
abstractThis systematic literature review examines the current state of software tools, data models, and interoperability standards in plant phenomics
Abstract Image processing techniques for plant phenotypes are rapidly evolving, allowing faster, non-destructive and objective evaluation of plant growth parameters than conventional methods for plant physiological studies. The aim of this study was to evaluate the green area rate (GAR) per plant through smartphone images of magnolia, and to predict the differences in the growth parameters including root length from the differences in green area rate of ordinary saplings and ones grown using cerium chloride (CeCl3). An image analysis program developed using fuzzy C-means clustering algorithm from digital images was used to estimate GAR of magnolia saplings, and the correlation between green area rate and growth parameters was determined and regression model was constructed. Correlation coefficient of GAR was the highest with leaf length of 0.98 and lowest with above ground total dry mass of 0.87. Accuracy between the predicted values and measured values was estimated, and then difference of GAR between control saplings and ones grown using cerium chloride was calculated. Based on the changes in green area percentage, we predicted difference in growth parameters. No significant difference existed between the predicted and the measured values. The results obtained in present paper will play a significant role in predicting the growth parameters of saplings without affecting the growth and in detecting effectiveness of various growth promoters.
Why it matches plant phenotyping methodsスマートフォン画像とファジーC-meansによる緑色面積率推定を開発し、植物の成長形質を予測・検証しており、表現型取得・抽出法が研究の中心である。
abstractAn image analysis program developed using fuzzy C-means clustering algorithm from digital images was used to estimate GAR of magnolia saplings
CoffeeField / plotFruitObject detectionGrowth / development / phenology
Introduction Coffee cherry ripeness assessment is critical for harvesting efficiency and product quality, yet traditional manual inspection methods suffer from subjectivity and low efficiency. Methods To address the challenges of detecting small, occluded, and densely distributed coffee cherries in complex field environments, this study proposes an Occlusion and Density Aware Network (ODANet). Built upon the YOLOv8 framework, ODANet integrates three innovative modules: (1) Condition-Guided Windowed Attention (CGWA), which incorporates occlusion and density maps as auxiliary guidance signals for efficient feature enhancement; (2) Attention-guided Space-Preserving Convolution (ASPC), which employs space-to-depth transformation with cascaded attention to preserve spatial information during downsampling; and (3) Dual-Adaptive Dynamic Upsampling (DADU), which achieves content-adaptive feature reconstruction through dual-branch offset prediction with learnable fusion weights. Results Comprehensive evaluation on a publicly available dataset demonstrates that ODANet achieves state-of-the-art performance among 17 diverse detection architectures, attaining 76.7% mAP@0.5 with a 6.3 percentage point improvement over baseline YOLOv8, while maintaining computational efficiency (8.1 GFLOPs, 30.4M parameters) suitable for real-time deployment. Ablation studies validate the contributions of each module: ASPC improves performance by 2.2%, DADU by 0.6%, and CGWA by 3.5%. Discussion The model demonstrates robust performance across varying lighting conditions, occlusion levels, and growth stages, making it particularly suitable for practical agricultural deployment. This research provides an efficient solution for small object detection in precision agriculture.
Why it matches plant phenotyping methodsコーヒーチェリーの成熟度という植物器官の状態を画像から推定する検出手法を開発し、複数モデル比較・アブレーションで技術的に検証しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes an Occlusion and Density Aware Network (ODANet)
Reproduction assets foundThe paper analyzes a publicly available coffee cherry dataset hosted on Kaggle, explicitly linked in the data availability statement. This is the paper-specific image dataset used for its coffee cherry ripeness detection experiments. No author code or model checkpoints are stated as available.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/harisyunanda/dataset-coffee-cherry/data .Open asset ↗Kaggle · harisyunanda/dataset-coffee-cherrylines:682-758Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Eragrostis curvula serves as a valuable model for studying diplosporous apomixis due to its unique reproductive mode, wide ploidy range, and extensive genomic resources. A major limitation for reproductive studies in this species is the difficulty of isolating female tissues at precise developmental stages, for example, for transcriptomics studies, since different floral tissues can introduce expression noise from non-target tissues. To overcome this, we performed a detailed cytoembryological and morphometric characterization of male and female development in seven E. curvula genotypes with different ploidy levels (2X-7X) and reproductive modes (sexual, facultative apomictic, and obligate apomictic). Using differential interference contrast microscopy and methyl salicylate clarification, we described key cytological stages of male and female development. These stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars. Pistil length showed the strongest association with female developmental stage, particularly during the early phases of ovule development, enabling more precise staging. Synchrony between male and female development was also evaluated, revealing no consistent differences among reproductive modes or ploidy levels. This genotype-informed framework provides a practical tool for stage prediction and tissue selection, supporting future reproductive, developmental, and comparative studies in E. curvula and related grasses.
Why it matches plant phenotyping methods花器官の形態計測を細胞発生段階の推定に体系的に対応付け、遺伝子型別の発達カレンダーと再利用可能なステージ予測手法を構築しており、表現型取得が中心である。
abstractThese stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
Abstract Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Désirée) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers β-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth–defence trade-offs across platforms. Highlight High-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g
Why it matches plant phenotyping methods二つの独立施設で高スループット光学フェノタイピングを比較・検証し、画像由来形質と蛍光指標の再現性を評価しており、フェノタイピング手法が研究の中心である。
titleComparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development
Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1-0.2 mm. The OC's non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications.
Why it matches plant phenotyping methods植物の茎径を非接触・高精度に測定する光学デバイスを開発し、実験室および圃場で精度・再現性を検証しており、植物表現型取得法が研究の中心です。
abstractHere, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement.
Reproduction assets foundThe paper's measurement data (optical caliper, digital caliper, and micrometer readings on reference cylinders and tomato/cucumber stems) is openly deposited on the SURF data repository of The Hague University of Applied Sciences. No author analysis code or trained models are explicitly deposited; other allowed URLs (DDataset · publicThe data gathered during this study is openly available via https://hhs.data.surf.nl/s/nqnFYBf42KPA75P (accessed on 10 February 2026).Open asset ↗hhs.data.surf.nlhtml-lines:282-314Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 5 Sept 2026
Microbial transformations of nitrogen in soils strongly influence plant nutrition and ecosystem function, yet these processes remain difficult to monitor. Existing approaches rely largely on destructive soil sampling and laboratory analysis, limiting the ability to track nitrate dynamics in situ. Here, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate. The sensor uses a synthetic nitrate-responsive promoter coupled to a ratiometric luciferase reporter, enabling high-dynamic-range measurements. Sentinel plants exhibit a dose-dependent, reversible nitrate response with high specificity over alternative nitrogen sources. In agricultural soils from multiple California field sites, sensor output closely tracked analytically measured nitrate concentrations and resolved nitrate amendments without destructive extraction. Beyond environmental sensing, sentinel plants enabled screening of nitrogen-fixing microbial communities and the detection of microbially generated nitrate in both liquid culture and soil systems. Using this platform, we identified a minimal three-member microbial consortium capable of converting atmospheric nitrogen into nitrate via sequential nitrogen fixation and nitrification. This consortium increased tissue nitrate accumulation and plant fresh weight, demonstrating that sentinel plants can both monitor nitrate availability and identify microbial communities that enhance plant growth. Significance Statement Nitrogen availability in soils fluctuates across space and time, yet most measurements rely on extracting soil samples and analyzing them in the laboratory. Such measurements provide only snapshots of nitrogen status and do not necessarily reflect the nitrogen that plants themselves experience. Here, we engineer plants that act as living nitrate sensors by converting nitrate perception into a measurable optical signal. Because these sensors operate within intact plants, they report nitrate availability as integrated through plant uptake and physiology rather than through chemical extraction alone. Using this platform, we tracked nitrate levels in agricultural soils and identified a minimal microbial consortium capable of converting atmospheric nitrogen into plant-available nitrate. This plant-based sensing strategy enables direct monitoring of nitrogen dynamics in soils and microbial environments, providing a platform for identifying microbial communities that enhance nitrogen availability for crops.
Why it matches plant phenotyping methods植物を用いた遺伝子 encoded センサーを開発し、硝酸可給性を定量する光学的表現型取得法として検証・応用しているため、植物フェノタイピング手法が中心である。
abstractHere, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate.
Pesticides are widely used in agriculture to control weeds, insects, and diseases that threaten crop yields. However, their extensive use raises concerns about environmental impacts, particularly in aquatic ecosystems, which are vulnerable to contamination through runoff and leaching. To assess the toxicity of pesticides to aquatic plants, we applied an optimized automated duckweed (Wolffia globosa) frond-counting tool based on the StarDist technique. Using this method, we tested twenty-eight commonly used pesticides, including herbicides, fungicides, and insecticides effects on duckweed growth. The herbicide paraquat showed the strongest growth inhibition (IC 50 50 = 384.2 ppb). Simazine and pendimethalin exhibited moderate toxicity, while glyphosate, triclopyr, and glufosinate showed lower toxicity. Surprisingly, metamifop did not inhibit duckweed growth up to the highest tested concentration (10 6 ppb). Isoprothiolane was the only fungicide tested that exhibited significant toxic effects on duckweed (IC 50 ≈ 924.3 ppb). All others, including azoxystrobin, hexaconazole, difenoconazole, picoxystrobin, tebuconazole, and cyproconazole, only inhibited plant growth at unnaturally high concentrations. Interestingly, cyazofamid promoted duckweed growth under the test conditions. Among 12 insecticides tested, 8 exhibited relatively low toxicity to duckweed (IC 50 > 10 5 ppb). Cypermethrin, carbofuran, fenpropathrin, and nitenpyram showed very low toxicity, with IC 50 values exceeding 10 6 ppb. Our results both enhance understanding of agrochemical toxicity and demonstrate the utility of automated, high-throughput quantification of W. globosa growth, providing a rapid and effective approach for pesticide toxicity assessment in aquatic environments.
Why it matches plant phenotyping methodsStarDistによるウキクサ葉状体の自動カウントと成長定量が、農薬毒性評価のための中心的な方法として用いられているため。
abstractwe applied an optimized automated duckweed (Wolffia globosa) frond-counting tool based on the StarDist technique
MilletPeaField / plotGreenhouseNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldPose / keypoint estimation2D/3D reconstructionVisualization / data managementGrowth / development / phenology
Plant phenotyping in precision agriculture increasingly requires high-fidelity three-dimensional reconstruction and accessible visualization methods. This study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages. We collected multi-view imagery of finger millet, proso millet, mungbean, and field pea under controlled greenhouse conditions, aligning data acquisition with standardized BBCH phenological scales. Camera pose estimation was performed using GLOMAP, followed by reconstruction via both Nerfacto and G-Splat implementations. Quantitative evaluation using PSNR, SSIM, and LPIPS metrics revealed complementary strengths of the two approaches: G-Splat achieved superior structural fidelity, while NeRF provided enhanced perceptual realism. Both reconstruction methods were successfully integrated into an immersive VR greenhouse environment deployed on Meta Quest headsets, maintaining consistently high framerates. This framework establishes a practical foundation for incorporating neural reconstruction and immersive technologies into agricultural phenotyping workflows, supporting both research applications and educational engagement.
Why it matches plant phenotyping methods植物の多視点画像からNeRFと3D Gaussian Splattingで3D形状を再構成し、画質指標で比較評価する統合フェノタイピング基盤の開発・検証が中心である。
abstractThis study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages.
Existing tomato datasets often focus on short-term experiments or lack integrated environmental and agronomic data. We present Horti-M3-Tomato, a comprehensive three-year dataset collected in Northeast China's greenhouse, including high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices. Spanning three growing seasons (2023-2025), the dataset integrates temporal imaging, environmental monitoring, soil data, and manual phenotypic and yield records. Horti-M3-Tomato supports research on growth dynamics, genotype-environment interactions, and provides a benchmark for AI-based phenotyping and precision horticulture. The dataset is openly available for further research in controlled-environment agriculture.
Why it matches plant phenotyping methodsトマトの画像・環境センサーデータ・手動表現型記録を統合したデータセットであり、AIベースの表現型解析のベンチマークとして明示されているため、表現型データ基盤が中心です。
abstractincluding high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices.
With increasingly severe soil acidification, it is essential to screen and identify acid-tolerant crop varieties to safeguard agricultural production. Integration of hyperspectral imaging with machine learning models has been extensively used in high-throughput crop phenotyping. Here, we established a multi-indicator evaluation system for quantification of acidic tolerance in rapeseed based on hyperspectral data collected from 65 rapeseed varieties under pH = 5.2 (acidic) and pH = 6.4 (normal) soil conditions. After denoising and smoothing, 34 existing vegetation indices and band combination indices were derived from which eight growth-sensitive indices were selected based on their correlations with the actual growth scores. Six key spectral bands exhibiting significant changes under acidic stress were identified, with the feature importance outputs from three machine learning classification models. Comprehensive sensitivity coefficients (SC) were derived by integrating growth-sensitive indices and key band data using principal component analysis weighted-sum (PCA-WS). Hierarchical clustering classified the 65 tested varieties into strongly-tolerant (four varieties), moderately-tolerant (26 varieties), and weakly-tolerant (35 varieties). Physiological validation based on yield and branch number showed that the strongly-tolerant varieties had 10.03 % and 17.15 % higher relative yields and 13.24 % and 11.14 % higher relative branch numbers than moderately and weakly-tolerant varieties, respectively. These results have established a hyperspectral evaluation system that can accurately evaluate the acidic tolerance of rapeseed, providing a reliable basis for screening acid-tolerant rapeseed varieties.
Why it matches plant phenotyping methodsラペシードの酸性耐性をハイパースペクトル画像から定量評価する評価システムを構築し、特徴量選択・機械学習・検証まで行っており、植物表現型取得・抽出手法が中心である。
abstractIntegration of hyperspectral imaging with machine learning models has been extensively used in high-throughput crop phenotyping.
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 5 Sept 2026
Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable expertise. Within the scope of smart farming applications, this study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images. The proposed model determines whether a seed will germinate using RGB images and morphological traits automatically extracted from these images. The dataset used in this study comprises a total of 3,645 images belonging to three different seed types. For each seed type (okra, eggplant, and tomato), 405 seed images were acquired from three distinct imaging sources (digital microscope, camera, and scanner), labeled, and subsequently sown in seed trays. The germination status of each sown seed was systematically monitored and matched with its corresponding image data. The dataset was partitioned into 80% training and 20% testing subsets. Following 5-fold stratified cross-validation, the proposed model achieved an average weighted F1-score of 0.95 on the training set and 0.93 on the testing set for germination capacity prediction. The performance of the proposed model was further compared with widely used deep convolutional neural network architectures, including VGG19, ResNet50, and EfficientNetB5. Comparative results demonstrate that the proposed model provides competitive and robust performance for seed germination prediction. Overall, the findings indicate that the proposed approach can effectively be utilized for automated seed germination prediction. Future research should evaluate the generalizability of the model by conducting performance assessments on additional seed types.
Why it matches plant phenotyping methodsRGB画像から種子の形態形質を自動抽出し、発芽状態を予測する深層学習手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractthis study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images.
Lettuce ( Lactuca sativa ) is an important field crop, but our understanding of its phenotypic variation and underlying genetics under natural field conditions remains limited, posing challenges for identifying effective crop breeding targets. Longitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions. In this study, we used hyperspectral imaging to assess the phenotypic variation of almost 200 different field-grown lettuce varieties, following the same plants from just after seedling- to flowering-stage. With automated image processing, we extracted a wide range of spectral phenotypes related to metabolite content, growth efficiency, and environmental stress responses, creating a multi-dimensional time-resolved data set. Principal component analysis (PCA) revealed the major axes of spectral variation over time, and highlighted differences in spectral patterns among lettuce genotypes. Integrating on-site weather data, we modelled G×E interactions of reflectance, revealing regions of the lettuce vegetation spectrum that are primarily shaped by genotype and/or environment. We estimated phenotypic plasticity in response to time, temperature and rainfall using best linear unbiased predictions (BLUPs), capturing genotype-specific developmental trajectories and responses to the environment. We used genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs) of PC-based, single and BLUP-based phenotypes, disentangling the genetic architecture of spectral lettuce phenotypes from major axes of variation down to single wavelength spectral plasticity. These findings provide new insights into the genome-wide genetic regulation and dynamics of spectral phenotypes in field grown lettuce.
Why it matches plant phenotyping methods圃場レタスを対象に、縦断ハイパースペクトル画像と自動画像処理でスペクトル形質を抽出するフェノタイピング手法・データセットが研究の中心である。
abstractLongitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions.
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 · publicScripts used in this study can be found at https://github.com/SnoekLab/Hyperspec_Mehrem_etal_2025.Open asset ↗SnoekLab/Hyperspec_Mehrem_etal_2025pdf-page:9 lines:1-31Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
OzBarley is a comprehensive genotype-to-phenotype resource to support research and enhance barley breeding by integrating genotypic and phenotypic data for gene discovery. This publicly available dataset comprises genotypic data from historical and modern elite barley cultivars of significance to Australian barley breeding. The phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology. Users can leverage genome-wide association studies (GWAS) and genomic selection to identify genetic variants associated with agronomically important traits in the OzBarley datasets, thereby accelerating targeted breeding strategies. The dataset is accessible for download under CC-BY 4.0 license and users are invited to contribute new data when using OzBarley plant material in their research. Through its FAIR-compliant design (Findable, Accessible, Interoperable, Reusable), OzBarley represents a resource to protect genotypes of historical relevance, explore the genetic architecture of adaptation to dryland environments, and to enhance knowledge of the resilience, yield, and quality of barley cultivars under diverse environmental conditions, contributing to global food security and agricultural sustainability.
Why it matches plant phenotyping methods高スループット画像およびX線CTによる形質取得を含む、再利用可能な遺伝型・表現型データ資源であり、植物フェノタイピング手法とデータセットが中心です。
abstractThe phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology.
A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency.
Why it matches plant phenotyping methods自動化ハイスループット画像解析により根系形態・成長形質を抽出する表現型取得が研究の主要手段であり、根系構造の実質的な応用解析に該当する。
abstractWe evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15060935/s1 , Figure S1: Repeatability of image-derived shoot (a) and root traits (b) of the tested 65 maize inbred lines over time.Open asset ↗lines:68-215Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
ArabidopsisRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Predicting plant phenotypes from genomic data requires models that bridge molecular regulation and organ-scale morphogenesis. We introduce BioOS, a computational runtime in which plant behavior - cell division, differentiation, and elongation - emerges from the execution of a gene regulatory network rather than from hardcoded rules. The system is built on the Formal Cell abstraction: a minimal signal-processing unit analogous to the McCulloch-Pitts formal neuron, whose transfer function is gene expression. Each Formal Cell evaluates promoters, transcribes mRNA, translates proteins, and derives its entire behavioral repertoire from the resulting protein concentrations - without a single hardcoded rule in the simulator code. A multi-scale architecture with level-of-detail switching enables real-time simulation of Arabidopsis thaliana primary root development. On the current official five-case primary-root auxin benchmark, BioOS achieves 75.4% mean score, 5/5 qualitative matches, 5/5 cases passing all current gates, and Spearman severity correlation ρ = 0.70. The current root-auxin runtime is driven by a curated 35-gene registry with explicit promoter logic, kinetic parameters, and epigenetic state; for readability, this manuscript details a core 18-gene subnetwork that carries the main auxin benchmark logic. We describe the architecture, the gene expression runtime, the epigenetic memory model, the completed transition to post-hoc (non-causal) zone classification, and candidate benchmark extensions for persistent plasmodesmata and intracellular auxin compartmentalization within a broader six-suite, 63-case benchmark framework. Beyond the root-auxin slice, the current codebase also closes the official flowering (5/5), photosynthesis (7/7), and cytokinin (5/5) gates, while root-patterning remains a passing candidate panel.
Why it matches plant phenotyping methods植物の発生表現型を遺伝子制御モデルから予測する計算ランタイムの開発と、根の発生ベンチマークによる評価が中心であり、単なる生物学的測定ではない。
abstractPredicting plant phenotypes from genomic data requires models that bridge molecular regulation and organ-scale morphogenesis.
SoybeanRootSeed / grainMorphology / geometry measurementObject detectionGrowth / development / phenologyRoot system architectureStress response / tolerance
Introduction Nanoparticle-induced treatments can promote seed germination and improve germination potential under environmental stresses such as drought and salinity. This study aimed to investigate the effects of Zinc oxide nanoparticles (ZnONPs) on soybean seed germination and to develop a precise evaluation method. Methods We developed a full-time sequence crop growth vitality monitoring system. Using germination rate and root length as primary evaluation indicators, we conducted full-time sequence germination vitality monitoring experiments on soybean seeds treated with ZnONPs. A dataset was constructed from images documenting embryonic root growth. The developed detection model was used to evaluate image detection accuracy during germination. Germination index and embryonic root length were also calculated. Further tests were performed on seeds exposed to 600 mg/L ZnONPs dispersion, followed by treatment with different concentrations of NaCl and PEG6000 solutions. Results At a concentration of 600 mg/L ZnONPs dispersion, soybean seeds showed the highest germination rate (an increase of 28%) and the longest radicle length (an increase of 42%). Compared with deionized water, the 600 mg/L ZnONPs dispersion accelerated initial germination time, increased germination rate, and enhanced radicle length under low-concentration stress. Discussion The results indicate that, at certain concentrations, ZnONPs dispersion positively influences soybean seed germination under varying salinity and drought conditions. We examined morphological and physiological changes in ZnONPs-treated seeds under stress, establishing a preliminary foundation for evaluating crop and variety vitality. These findings provide new insights that may contribute to improving soybean germination under simulated stress conditions, serving as a preliminary theoretical reference for potential applications in arid and saline environments.
Why it matches plant phenotyping methods発芽中の画像から発芽率・幼根長を抽出する連続モニタリングシステムと検出モデルを開発し、精度評価とデータセット構築を行っており、表現型取得手法が中心である。
Monitoring crop conditions is crucial for effective crop management and provides valuable insights into soil-plant-atmosphere interactions. While some studies have used unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards, LiDAR-based time-series analysis to assess PAI variations with phenology throughout the growing season represents a significant gap in knowledge. Tracking PAI dynamics across phenological stages reflects canopy development and leaf expansion, which are directly linked to yield formation. Furthermore, the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point clouds is yet to be determined. This study aimed to demonstrate the potential of UAV-derived LiDAR time-series to monitor the PAI and tree vertical profiles at high spatial resolution throughout the growing season of a cherry orchard located in southeastern France. A time series of 14 point cloud acquisitions with a density of 3300 points/m² was collected between February and December 2022, with at least one acquisition per month, covering all phenological stages of the cherry orchard. Field measurements were collected on May 30, and October 6, to measure the PAI at twilight using an LAI-2200C Plant Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA), with 248 trees sampled. A voxel-based method was applied on the LiDAR point cloud data to create a three-dimensional grid within which PAI was estimated for each voxel. The results showed that a voxel size of at least 70 cm is required to retrieve reliable PAI estimates, while a voxel size of 100 cm produced the most accurate PAI estimates (RMSE = 0.5 m2.m-2, bias = 0.07, R2 = 0.59), when assessed against in-situ PAI measurements. The temporal variation of canopy PAI illustrated the progression of the phenological stages, including flowering, leaf development, ripening and senescence, and the response of the canopy to drought stress (reduction in PAI due to leaf rolling) during the summer. The maps of PAI successfully described the variations in leaf canopy density for different cherry varieties and allowed assessment of the vertical PAI profile at the individual tree level. The LiDAR-derived PAI maps and vertical profiles were able to detect trees exhibiting poor leaf development, which is an important health indicator for effective crop management in orchard settings. Future work should focus on applying UAV-derived observations to optimize crop models to enhancing decision-making tools for effective orchard management.
Why it matches plant phenotyping methodsUAV LiDARとボクセル解析により、樹木レベルのPAIおよび垂直プロファイルを推定し、実測値で精度検証しているため、植物表現型取得手法が研究の中心である。
abstractA voxel-based method was applied on the LiDAR point cloud data to create a three-dimensional grid within which PAI was estimated for each voxel.
Field / plotGrowth / time-series analysisGrowth / development / phenology
Phenology at the Deutscher Wetterdienst (DWD) involves the observation and analysis of recurring, seasonally driven development stages of plants, such as flowering, leaf unfolding, fruit ripening, and leaf fall. These phenological phases are closely linked to weather and climate conditions and therefore serve as biologically based indicators of climate variability and long-term change. The DWD phenological observations are used in multiple contexts, including climate monitoring and diagnostics, agrometeorological services, and scientific studies that analyse shifts in phase timing in relation to temperature trends. Phenological information also supports operational applications, for example in agricultural modelling and other environment-related services.A key element of DWD’s phenological activities is its long-running observation network, which is based on standardized and quality-controlled field observations. In recent years, DWD has started to complement this classical network with additional digital data sources in order to improve spatial coverage and temporal resolution. One important development is the crowdsourcing feature “Pflanzenmeldungen” in the DWD WarnWetter app, which allows citizens to submit geo-referenced reports of plant development stages via smartphone. These reports can help capture regional differences, extreme-year signals, and near-real-time vegetation responses, particularly in areas with fewer traditional observations. By integrating these app-based reports with established phenological datasets and expert workflows, DWD aims to extend and enrich its national phenology record while maintaining scientific usability.Further expansion of digital data sources is planned through a collaboration with Flora Incognita, a machine-learning-based plant identification platform. This cooperation aims to link phenological monitoring with scalable species recognition and user-driven reporting. The combination of supported plant identification and structured phenological input is expected to improve data consistency, encourage participation, and increase the volume and resolution of phenological information.Overall, DWD’s phenology programme is developing toward a hybrid monitoring system that integrates long-term standardized observations with crowdsourced app data and AI-supported plant identification. This approach enhances spatial and temporal coverage and strengthens the basis for monitoring climate impacts on vegetation and for providing robust phenology-based climate services.
Why it matches plant phenotyping methods植物の開花・葉展開・果実成熟・落葉などの表現型を、市民参加型アプリとAI植物同定を統合して取得・拡張する監視基盤が中心であり、単なる生物学的実験での routine 測定ではない。
abstractOne important development is the crowdsourcing feature “Pflanzenmeldungen” in the DWD WarnWetter app, which allows citizens to submit geo-referenced reports of plant development stages via smartphone.
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
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotMultimodalWhole plant / canopy / plot / fieldClassificationGrowth / development / phenology
Abstract Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.
Why it matches plant phenotyping methodsキウイフルーツの生育段階を対象とした注釈画像・地理参照動画のデータセットで、植物フェノロジー自動検出の訓練、検証、ベンチマークを目的とする方法論的成果である。
titleA Multi-Modal Dataset for Automated Phenological Stage Mapping in Actinidia chinensis
Reproduction assets foundThe paper is a Data Note describing the Multi-Modal Actinidia chinensis Phenology Dataset, which is explicitly stated to be publicly available on Zenodo with a DOI matching an allowed URL. The dataset contains the paper's own phenotyping assets: 1,665 annotated images with bounding-box phenological labels, georeferenedDataset · publicThe Multi-Modal Actinidia chinensis Phenology Dataset described in this Data Descriptor is publicly available
at Zenodo: https://doi.org/10.5281/zenodo.17371025. This dataset comprises two components: (1) 1 665
JPEG images (1 024 × 1 024 pixels) with corresponding Pascal VOC XML annotation files containing bounding
box coordinates and phenological class labels, and (2) 24 MP4 video files (3 840 × 2 160 pixels) with corresponding
GPX coordinate files and Excel validation files containing manual ground truth counts.Open asset ↗Zenodo · 10.5281/zenodo.17371025pdf-page:13 lines:1-62Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
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.
Spectral sensors have become an integral part of modern precision agriculture. It enables fast, non-destructive and map-based crop monitoring of key crop physiological parameters. The spectral resolution of a sensor plays an important role in determining its ability to detect subtle changes in the crop, nutrient status and canopy development. Sensor’s comparison based on spectral resolution remains limited particularly in the context of field-level agronomic monitoring. This study aims to address this gap by using three sensors UAV-based multispectral (MS), UAV-based hyperspectral (HS) and handheld Greenseeker (GS) NDVI measurements. Hyperspectral sensors provide continuous high-resolution data from visible to near infrared; MS use fewer broad bands; GS limits to two bands for quick NDVI field checks. The experimental study was conducted in the arid region of Uttar Pradesh, India. The experimental setup consisted of plots with same irrigation (100% ETc) and varying nitrogen dosage i.e. 150,120 and 90 kg/ha (Plot 1, Plot 2 and Plot 3, respectively) with three replications. Plots 4 and 5, representing farmer-field conditions with 120 kg ha⁻¹ nitrogen and no nitrogen respectively, followed regional irrigation practices, whereas Plot 6 (rainfed) was irrigated only once initially. A series of UAV-flights were conducted across critical phenological stages, and the reflectance was used to generate Normalized Difference Vegetation Index (NDVI) representing canopy density.The results showed that NDVI rapidly increased during early vegetative stage (61-75) DAS, saturated around (75-85) DAS, followed by a decline during (101-117) DAS. NDVI peaked around flowering stage for all the sensors. GS-NDVI varied between (0.46-0.78), MS-NDVI displayed (0.52-0.86), whereas HS- NDVI varied between (0.55-0.90). The mean NDVI values were (0.570 ± 0.085) for GS, (0.608 ± 0.075) for MS, and (0.664 ± 0.087) for the HS, with HS exceeding others by 16.5% (vs. GS) and 9.3% (vs. MS). Pearson correlation coefficients confirm strong inter-sensor agreement: Greenseeker-Hyperspectral r = 0.96, Multispectral-Hyperspectral r = 0.91, Greenseeker-Multispectral r = 0.87 (all p
Why it matches plant phenotyping methods複数のセンサーによる作物キャノピーNDVI測定を比較し、スペクトル分解能とセンサー間一致度を評価することが研究の中心であるため、植物フェノタイピング手法の技術比較・検証に該当する。
abstractThis study aims to address this gap by using three sensors UAV-based multispectral (MS), UAV-based hyperspectral (HS) and handheld Greenseeker (GS) NDVI measurements.
Precise monitoring of crop canopy cover (CC) is crucial for evaluating growth and health under diverse water and nutrient conditions. Although nano liquid urea has been promoted in India as an eco-friendly alternative to conventional nitrogen (N) fertilizers. Its effectiveness in potato cultivation, particularly for canopy development and yield, remains unclear. To address this gap, a field experiment was conducted during the 2024–25 winter season using three N treatments under a micro-irrigation system: T1-recommended granular urea (46% N), T2-without N, and T3-IFFCO nano liquid urea (4% w/v). Images were captured using a downward-looking smartphone camera positioned 2.5 meters above the crop near each treatment, serving as the primary input for estimating canopy cover. The images were processed using both a prompt-based segmentation model (SAM3) and an image-processing pipeline (Modified Excess Green Index + Otsu thresholding) to estimate CC. The SAM3 occasionally overestimated or failed to detect the potato canopy with the prompt “Green plants, leaves, vegetations, canopy cover”, whereas the image-processing approach consistently provided accurate CC estimates and was therefore used for subsequent analysis. The result revealed that the CC clearly showed differentiation among the treatments after 25 days after sowing (DAS). With this most of the treatment comparisons depict the CC peaking after 45 DAS, where the T1 recorded the highest canopy cover (~71%), indicating a healthy crop, while the pairwise comparisons showed CC values of ~55% (T1–T2), ~66% (T1–T3), ~33% (T2–T2), ~36% (T3–T3), and ~36% (T2–T3), depicting the nitrogen deficit. Similarly, the yield followed the same trend, with T1 producing the highest yield (26.78 t/ha), compared to 10.89 t/ha in T2 and 11.90 t/ha in T3. The results indicate that nano liquid urea does not supply sufficient nitrogen to support optimal potato canopy growth and productivity, resulting almost similar response to the no nitrogen application treatment. Increasing the nitrogen concentration in nano liquid urea formulations may improve their effectiveness. This study provides evidence to guide farmers in selecting appropriate nitrogen fertilizers for potato cultivation. In the future, such fertilizers should be evaluated across different crops to ensure their efficacy and to prevent farmers from adopting products that may not meet crop nutrient requirements.
Why it matches plant phenotyping methodsジャガイモのキャノピー被覆率という植物形質を、SAM3と画像処理パイプラインで推定し、両手法の性能を比較・評価しているため、施肥試験を含むがフェノタイピング手法が中心的です。
titleEvaluating SAM3 and Conventional Image Processing Method for Potato Canopy Cover Estimation as an Indicator of Crop Health
Field / plotRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architectureWater status / transpiration
Deep-rooted crops accessing water and nutrients from deep soil layers enhance the resource base for crop production. However, studying these roots in field conditions is labour-intensive, limiting research scope. We established a field root research facility with 48 plots for replicated experiments. The facility includes 144 6-metre-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis. We also attempted to install access tubes and customized ingrowth core production for less-invasive root activity determination. Our study revealed significant differences in deep root density among species, particularly at depths of 2.5 to 4.5 m, over 5 years. The less-invasive studies using ingrowth cores reached depths of 4.2 m. Nutrient tracer 15 N analysis showed marked differences in deep root activity among crop species. Time domain reflectometry sensors indicated varying water depletion in deeper soil layers, influenced by crop species and root growth patterns. We established a field facility for studying deep root growth and function, demonstrating its effectiveness in analysing diverse deep-rooted plant species. This facility provides an ideal platform for conducting meaningful research in deep soil layers, yielding statistically and biologically significant results for agricultural applications.
Why it matches plant phenotyping methods深根研究施設とAIによる根形質解析パイプラインの構築・有効性評価が中心的に記述されており、根密度などの植物形質を取得するフェノタイピング基盤に該当する。
abstractThe facility includes 144 6-metre-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis.
MultimodalFruitLeafClassificationObject detectionPhysiological trait estimationGrowth / development / phenologyFruit / seed / panicle traitsPlant / canopy temperature
ABSTRACT Plant‐wearable sensors are essential for real‐time, in situ monitoring in smart agriculture, yet their adoption is constrained by functional simplicity and inadequate mechanical compliance. Herein, we present a conformable plant‐attachable Janus electronic skin (SPM/HMA@P) for multimodal phenotyping. Featuring a scenario‐specific encapsulation strategy fabricated via a bottom‐up approach, the device enables non‐invasive monitoring of multiple physiological parameters. The sensor integrates a conductive base composed of biocompatible quaternized chitosan, multi‐walled carbon nanotubes, and silver nanowires, a gas‐sensing functional layer, and an adhesive polydimethylsiloxane encapsulation. In the fully encapsulated configuration, SPM/HMA@P exhibits a 210.9% fracture elongation and an adhesion force exceeding 0.3 N on leaves, alongside excellent biocompatibility. This configuration allows for simultaneous monitoring of leaf temperature and growth‐induced strain, maintaining stable signals over 5 days. Conversely, the semi‐open configuration demonstrates high ethylene sensitivity (0.5–100 ppm, theoretical detection limit of 0.12 ppm), with response and recovery times of 300 and 120 s, and a lifespan of over 10 days. Coupled with a convolutional neural network (CNN), it achieves 96.8% accuracy in classifying ethylene signals from fruits. This robust multimodal platform addresses key challenges in plant physiological monitoring, holding great potential for smart crop breeding, postharvest assessment, and phenotyping.
Why it matches plant phenotyping methods植物に装着するマルチモーダルセンサーを開発し、葉温度・成長誘導ひずみ・エチレンなどの生理状態を非侵襲的に測定するプラットフォームであり、植物フェノタイピング手法が中心的に扱われている。
titleConformable Plant‐Attachable Janus E‐Skin with Sandwich Architecture for Plant Multimodal Phenotyping
Abstract Soybean [ Glycine max (L.) Merr.] varieties are categorized into different relative maturity groups (MGs) that correspond to the approximate region that the variety is best adapted. Maturity is an important trait that growers consider when deciding which varieties to plant and for breeders as a covariate to compare genotypes. Accurate phenotyping of maturity is an important task during line development but is labor‐intensive. High‐throughput phenotyping (HTP) of soybean maturity using unmanned aerial systems can reduce the labor and error associated with manual maturity notes. An HTP program for maturity will provide higher quality maturity data that will improve breeders’ ability to evaluate the performance of breeding lines on a large scale. The objective of this study was to develop an intuitive, accessible, and precise HTP program to determine the maturity of soybean varieties in the field that can be deployed in soybean breeding programs. In this study, “Matti,” a QGIS plugin, was developed to track the average green leaf index (GLI) of soybean research plots during the senescence period. Piecewise and local polynomial regression models monitor the senescence curve and provide maturity estimates when the GLI values are near or below a user‐specified threshold. This algorithm resulted in moderate to high correlations ( r = 0.52–0.97) between the ground truth and estimated maturity of soybean lines in both early and late maturity MGs. Similar correlations ( r = 0.43–0.72) were found for early generation materials. Results indicate that Matti can be easily implemented by soybean breeding programs to provide timely estimates of relative maturity.
Why it matches plant phenotyping methodsUAV画像から大豆の成熟度を推定するHTPプログラムとQGISプラグインを開発し、地上真値との相関で検証しており、植物表現型取得・推定手法が中心である。
abstractThe objective of this study was to develop an intuitive, accessible, and precise HTP program to determine the maturity of soybean varieties in the field that can be deployed in soybean breeding programs.
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Accurate pre-harvest prediction of crop yield informs variety selection, optimizes management, and accelerates breeding. As potato is the world’s leading non-grain staple, here we evaluate a diverse panel of varieties in a three-year field trial across five European locations. Canopy development and environmental parameters are monitored throughout the growing season using drone-based imaging, in-field sensors and gene expression measurements, while tuber yield and quality traits are quantified at harvest. We show that these data enable the identification of climate-resilient, high-yielding genotypes and support the development of machine learning models that explain over 80% of yield variation in independent test sets. Strikingly, measurements collected within the first two months after planting achieve predictive performance comparable to models trained on full-season data. Model interrogation further shows that over 70% of yield variation can already be predicted based on a simple five-parameter linear equation. Our framework thus demonstrates the potential of integrative field phenotyping and data-driven modeling to improve variety selection across heterogeneous environments.
Why it matches plant phenotyping methodsドローン画像と圃場センサーによる作物表現型取得、および収量予測モデルの開発が研究の中心であり、単なる収量測定ではない。
abstractCanopy development and environmental parameters are monitored throughout the growing season using drone-based imaging, in-field sensors and gene expression measurements
ArabidopsisChlorophyll fluorescenceMicroscopyLiDAR / point cloudCell / cellular structureRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Embryogenesis in the model plant Arabidopsis thaliana provides a framework for understanding how cell polarity and patterning coordinate with hormonal signalling to establish the plant body plan. Following fertilisation, the zygote divides asymmetrically to generate apical and basal lineages, establishing the apical-basal axis that defines future shoot and root poles. Genetic and molecular analyses of classical mutants including gnom, monopteros (mp), bodenlos (bdl) and topless revealed that localised auxin biosynthesis, directional transport and downstream transcriptional responses are central to apical-basal axis establishment and organ initiation. The main components of this regulation are polarly localised PIN auxin transporters and downstream modules involving MONOPTEROS and WUSCHEL-RELATED HOMEOBOX transcription factors. Advances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics. Live ovule imaging setups with confocal laser scanning and multiphoton microscopes enable real-time observation of embryo development, while laser-assisted cell ablation can be used to probe cell-to-cell communication and fate plasticity. Together, these methodological breakthroughs position Arabidopsis embryos as a prime model for dissecting the chemical and biophysical cues that shape plant development.
Why it matches plant phenotyping methods胚発生を解析するための蛍光イメージング、三次元再構成、ライブ撮像などの方法論と定量的な細胞形態・分裂方向測定が中心であり、植物フェノタイピング手法に該当する。
abstractAdvances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics.
The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products—food, feed, fuel and fiber—will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, commonly known as duckweeds, are one family of plants that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab‐scale PHenotyping Apparatus, ALPHA, for high‐throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of six different clones of L. gibba .
Why it matches plant phenotyping methods水生植物の成長率をハイスループットに定量するフェノタイピング装置を開発・適用しており、表現型取得システムが研究の中心である。
abstractHerein we describe the Automated Lab‐scale PHenotyping Apparatus, ALPHA, for high‐throughput phenotyping of Lemnaceae.
• Modular RGB–D pipeline for real-time multi-attribute grading of strawberries. • ResNet-101 multi-head classifier predicted maturity, disease, and deformity jointly. • CycleGAN restored strawberry shape and texture from partially occluded views. • QH and MADM strategies converted multi-attribute predictions to grading decisions. • Field tests validated end-to-end perception-to-placement pipeline in farms. Strawberry grading was critical for meeting market standards, yet manual post-harvest sorting remained labor-intensive, damage-prone, and inconsistent, while most vision-based methods targeted single attributes and lacked integrated in-field operation. We presented a real-time RGB–D grading framework for tabletop-cultivated strawberries in greenhouse environments. The system combined instance segmentation (YOLOv11–Seg), occlusion-aware completion via CycleGAN, and a three-branch multi-task network based on ResNet-101 to simultaneously infer maturity, disease status (including asymptomatic fruit), and deformity. Pose normalization and depth alignment supported calibrated volume-to-mass regression for non-destructive weight estimation. To improve robustness to rare morphology, 500 deformed-fruit samples were synthesized with a Gemini-based generative model to mitigate class imbalance. Built upon this perception stack, we implemented two grading strategies: a deterministic Quality Hierarchy (QH) for real-time robotic routing and a weighted multi-attribute decision-making (MADM) scheme for flexible batch evaluation. The multi-attribute classifier achieved 93.65% overall accuracy (disease 94.09%, maturity 94.07%, shape 94.42%) with an inference latency of 33.65 ms (29.7 FPS). Depth-assisted mass estimation yielded average errors of 8.11% for complete fruits and 10.47% under occlusion after completion; in field deployment on marketable fruits routed to size grading, it achieved MAE/RMSE of 1.85/2.20 g with R 2 = 0.9384 (MAPE 8.48%) and a three-bin size-grade accuracy of 90.91% (100/110). In single-target harvesting mode on an RTX 4060 GPU (640x480, 30 fps), end-to-end latency was 87.56 ms per harvested target in complete mode and 197.56 ms when completion was invoked (CycleGAN 110.00 ms/instance). These results demonstrated a practical, non-destructive, occlusion-aware, and deployable multi-attribute grading solution for intelligent strawberry harvesting in real greenhouse scenarios.
Why it matches plant phenotyping methodsRGB-D画像解析でイチゴの成熟度、病徴、形状、重量を推定し、性能検証とロボット実装まで行う中心的なフェノタイピング手法研究。
abstractModular RGB–D pipeline for real-time multi-attribute grading of strawberries.
Field / plotRaman / spectroscopyLeafGrowth / time-series analysisGrowth / development / phenologyPigment / colour / senescenceWater status / transpiration
Mitigation of the ongoing biodiversity crisis requires thorough understanding of species dynamics across scales. However, monitoring plant species and their functional traits is time-consuming and challenging to implement across larger spatial scales and through time. Spectroscopy is emerging as a promising tool for monitoring plant functional and taxonomic diversity within and between ecosystems. This relies on the presence of a functional and taxonomic signal in leaf optical properties, which in turn depends on the spectral similarity of species, intra- and interspecific trait variation, and the timing of the measurements. In order to address these relations in natural and semi-natural ecosystems in Denmark, we are compiling a spectral library of plants. An important methodological aspect of this work is to assess how leaf degradation, and the phenological stage of the plant, affect leaf optical properties. This was assessed by measuring leaf spectra from 350 – 2500 nm of four plant species, representing different plant functional types, from the same site over five months. We measured leaves at the time of sampling, and repeatedly after detachment from the plant to test how sampling strategy, potential leaf degradation after detachment and phenological stage influence leaf optical properties. Furthermore, we collected spectral and functional trait data of dominant plant species in 100 vegetation plots across the Store Åmose nature area in July and August. We present results of the spectral and functional differences among species and taxonomic levels, and the significance of leaf degradation and phenology on measured spectra. Leaf water and chorophyll content are expected to be the major drivers of spectral variation over time. However, subtle spectral signals may reflect other biochemical traits or leaf biophysical changes during the growing season. These dynamics are expected to depend on plant ecology, functional types, and environmental conditions such as wet and dry habitats. Our results will demonstrate the potential (and challenges) of using spectroscopy for taxonomic and functional identification of plants. We will provide insights into the role of leaf sampling strategy and phenology on spectral signals of plant species, which can inform the planning of future remote sensing and field campaigns.
Why it matches plant phenotyping methods葉の分光計測を用いた植物機能形質・分類情報の取得を中心に、葉の劣化、採取方法、フェノロジーが光学特性へ与える影響を評価しており、表現型取得手法の方法論的検討が実質的に含まれる。
abstractSpectroscopy is emerging as a promising tool for monitoring plant functional and taxonomic diversity within and between ecosystems.
Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO2) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments.
Why it matches plant phenotyping methodsステレオ画像およびRGB-Dによる植物生長の非破壊定量と、データ統合型モニタリング基盤の実装が中心的に記述されており、植物フェノタイピング基盤として収載対象です。
abstractStereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth
LeafPhysiological trait estimationGrowth / development / phenology
Purpose This paper aims to explore the role of plant growth monitoring in precision agriculture, ecological environment protection and urban greening, this study analyzes the applications, challenges and future development directions of wearable flexible sensors for monitoring plant physiology and growth environments. Design/methodology/approach This review focuses on wearable flexible sensor technologies for plant physiology and environmental monitoring: optical sensors, flexible electronic sensors and chemical/biological sensors. The data were collected through Web of Science search, and then carefully manually organized, analyzed, and summarized. Findings Current challenges for wearable sensors in plant growth monitoring include insufficient long-term stability, environmental interference suppression and multi-sensor collaborative optimization. Originality/value Propose future development directions such as the development of new flexible materials, design of self-powered systems, solar energy harvesting sensors integrated with leaf veins to avoid shading critical photosynthetic tissues and AI-driven data intelligent analysis. These innovations, based on interdisciplinary integration, are expected to promote the development of precision agriculture and intelligent ecosystems.
Why it matches plant phenotyping methods植物生理・生育状態を測定するウェアラブル柔軟センサー技術を中心に整理したレビューであり、環境モニタリングも扱うが植物フェノタイピング手法のレビューとして中心性が明確。
abstractThis review focuses on wearable flexible sensor technologies for plant physiology and environmental monitoring: optical sensors, flexible electronic sensors and chemical/biological sensors.
Breeding rice varieties that are both salt-tolerant and high-yielding is essential for utilizing saline-alkaline lands and ensuring food security. However, However, high-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs. In this study, unmanned aerial vehicle (UAV) imaging was employed to screen salt-tolerant and high-yielding varieties among 60 rice varieties under saline-alkaline field conditions. Red-green-blue (RGB), multispectral, and thermal canopy images were acquired throughout the growing season by UAV, from which 41 phenotypic traits were extracted at each growth stage. These traits were categorized into early-stage (tillering and jointing), late-stage (booting, flowering, and maturity), and whole-growth-stage (from tillering to maturity) and subsequently used to screen salt-tolerant and high-yielding rice varieties. Results showed that: (1) An early high-throughput screening method for salt-tolerant rice varieties was developed based on the membership function and UAV phenotypes (MFuav), achieving high performance (Precision >0.8, OA > 0.7). MFuav demonstrated the highest accuracy at the early-stage, with Precision increasing by 0.29 and 0.43 compared to the late- and whole-stage models, respectively. (2) A machine learning based UAV phenotypes framework (MLuav) was developed to further improve salt-tolerance screening performance. Within this framework, the partial least squares regression (PLSR) was employed for early-stage salt-tolerance screening, which achieved a Precision of 0.97 and an OA of 0.78, outperforming the MFuav by 0.11 and 0.08, respectively. In addition, within the same MLuav framework, early-stage UAV phenotypes were further used for actual yield prediction using a Random Forest (RF) model. The model achieved a high Recall for high-yielding varieties (Recall = 1.00), ensuring that no potentially high-yielding germplasm was missed, although this was accompanied by a moderate Precision (0.51) and an overall accuracy of 0.70. (3) The MLuav consistently outperformed the MFuav in screening salt-tolerant and high-yielding varieties across all 60 rice varieties. Among the five referenced salt-tolerant and high-yielding rice varieties, the MLuav correctly screened four using early-stage phenotypes, whereas the MFuav only screened three. Overall, the proposed method enables early screening of salt-tolerant and high-yielding rice varieties, offering an efficient tool for the screening and utilization of elite stress-resilient germplasm.
Why it matches plant phenotyping methodsUAV画像から多数のイネ表現型形質を抽出し、塩耐性・収量性を早期スクリーニングする方法と機械学習フレームワークを開発・評価しており、表現型取得・解析手法が研究の中心である。
abstracthigh-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs
Rice early tillering characteristics are key indicators for high-yield breeding, with tiller number and tillering rate as core parameters. High-throughput, temporal, and precise monitoring of tiller numbers via drone digital imagery provides quantitative support for tillering trait screening in breeding, serving as an important auxiliary tool for smart breeding. However, during the early tillering stage, complex backgrounds (e.g., water bodies, soil) and small, dense breeding plots pose challenges to high-throughput rice plant extraction and accurate tiller number estimation. To address this, this study proposes a rice tiller number estimation method based on an improved Swin-UNet model and multi-feature fusion. A PSO-optimized XGBoost model was constructed for tiller number estimation by integrating selected features. Experimental results show that the improved Swin-UNet model achieved a segmentation accuracy of 92.5% (7.2% higher than U-Net), and the PSO-XGBoost model, using 12 features (10 morphological and 2 color), yielded R²=0.85 and RMSE = 0.35. Application verification on 576 untrained breeding plots generated tiller number thematic maps, providing data support for germplasm tillering trait identification and advancing smart breeding.
Why it matches plant phenotyping methodsドローン画像からイネの分げつ数を抽出・推定する画像解析手法を開発し、セグメンテーション精度と推定性能を検証しているため、植物表現型計測が研究の中心である。
abstractthis study proposes a rice tiller number estimation method based on an improved Swin-UNet model and multi-feature fusion.
Plant pathogens cause yield losses worldwide, threatening food security and livelihoods. Because early infection is difficult to diagnose, management often relies on prophylactic pesticide use, increasing costs and environmental impact. Here we present PSNet, a multimodal framework that fuses hyperspectral imaging with RGB information for presymptomatic plant disease detection, together with a low-cost hyperspectral camera incorporating a 3D-printed housing, costing under £500. We validate PSNet using Arabidopsis thaliana infected with the oomycete Albugo candida . Imaging at 2 and 4 days post inoculation, prior to visible symptoms, revealed spectral signatures that distinguished infected from healthy plants, while imaging at 6 days post inoculation captured the transition toward early symptom emergence. Discriminative spectral regions overlapped wavelengths associated with plant responses to biotic stress, supporting the biological plausibility of these signatures. Performance was evaluated using strict plant-level partitioning, ensuring samples from the same plant were confined to a single split. On a four-class task (healthy, 2 dpi, 4 dpi, 6 dpi), PSNet achieved 90.00% accuracy and 97.50% accuracy for binary classification. Together, these results demonstrate that presymptomatic detection is feasible under controlled conditions using low-cost hardware and multimodal learning, underscoring the potential of scalable multimodal systems for early disease monitoring.
Why it matches plant phenotyping methods低コストのハイパースペクトル・RGB融合による植物病害状態の非破壊推定手法を開発し、植物単位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。
abstractHere we present PSNet, a multimodal framework that fuses hyperspectral imaging with RGB information for presymptomatic plant disease detection, together with a low-cost hyperspectral camera incorporating a 3D-printed housing, costing under £500.
Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop's growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.
Why it matches plant phenotyping methods時系列点群から作物器官をセグメンテーションし、葉追跡、形態形質、成長量、光合成関連指標を抽出する手法が研究の中心であるため。
abstractA skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' public URLs for a subset of the analysis code (GitHub) and the complete time-series 3D crop point cloud dataset (Baidu pan), both directly supporting this paper's phenotyping measurements and analysis.Code · publicA subset of the code and dataset used in this study is publicly available on our GitHub repository: https://github.com/JiarenZhou/LTPCDCCM .Open asset ↗JiarenZhou/LTPCDCCMlines:578-686Dataset · publicThe complete time-series 3D crop point cloud dataset can be downloaded from https://pan.baidu.com/s/1mNSDz4F0ZjOwmqzMuXozSQ?pwd=1234 .Open asset ↗lines:578-686Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Modeling plant growth dynamics plays a central role in modern agricultural research. However, learning robust predictors from multi-view plant imagery remains challenging due to strong viewpoint redundancy and viewpoint-dependent appearance changes. We propose a level-aware vision language framework that jointly predicts plant age and leaf count using a single multi-task model built on CLIP embeddings. Our method aggregates rotational views into angle-invariant representations and conditions visual features on lightweight text priors encoding viewpoint level for stable prediction under incomplete or unordered inputs. On the GroMo25 benchmark, our approach reduces mean age MAE from 7.74 to 3.91 and mean leaf-count MAE from 5.52 to 3.08 compared to the GroMo baseline, corresponding to improvements of 49.5% and 44.2%, respectively. The unified formulation simplifies the pipeline by replacing the conventional dual-model setup while improving robustness to missing views. The models and code is available at: https://github.com/SimonWarmers/CLIP-MVP
Why it matches plant phenotyping methods植物画像から葉数・植物齢を推定するマルチビュー表現学習手法を開発し、ベンチマークで性能評価しており、表現型取得・推定が研究の中心である。
abstractWe propose a level-aware vision language framework that jointly predicts plant age and leaf count using a single multi-task model built on CLIP embeddings.
Reproduction assets foundThe paper explicitly states that the model and code are publicly available at the authors' GitHub repository, which qualifies as a paper-specific public code asset.Code · publicm 7.74 to 3.91 and mean leaf-count MAE from 5.52 to 3.08 compared to the GroMo baseline, corresponding to improvements of 49.5% and 44.2%, respectively. The unified formulation simplifies the pipeline by replacing the conventional dual-model setup while improving robustness to missing views. The modela and code is available at: https://github.com/SimonWarmers/CLIP-MVP
Index Terms:
Plant phenotyping, Multi-view learning, Multi-task regression, Precision agriculture
† † address: † Computer Vision Lab, CAIDAS, IFI, University of Würzburg, Germany
‡ Technological University Dublin, Ireland
1 Introduction
Plant phenotyping from multiview imagery is crucial for precision agriculture, enabling non-Open asset ↗SimonWarmers/CLIP-MVPlines:1-53