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:1-44Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Grain yield is a central target in wheat breeding, yet accurately predicting it remains challenging because it depends on many genes and responds strongly to environmental variation. Genomic selection (GS) has improved breeding efficiency by enabling genome-based prediction of genetic merit, but predictability (PA) for grain yield is often limited under stress environments. At the same time, advances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance and may complement genomic information. In this study, we evaluated genomic and phenomic models for predicting grain yield in elite bread wheat lines across irrigated, drought, and heat-stress environments. Using a sparse phenotyping framework, we compared parametric and non-parametric models. PA was evaluated within environments and under cross-environment sparse phenotyping scenarios. Genomic models provided a stable baseline and enabled effective information sharing across environments when phenotypic data were incomplete. Phenomics-only models captured environment-specific plant responses but were more sensitive to environmental context. Multiomics models that integrated genomic and phenomic information consistently achieved the highest PA, with the largest gains observed under stress conditions. Overall, our results demonstrate that integrating genomics and UAV-based phenomics within sparse phenotyping designs offers a practical and scalable approach to improve grain yield prediction in wheat.
Why it matches plant phenotyping methodsUAV由来のフェノミクスを用いた疎な表現型取得と予測モデルを中心に、環境横断で評価しており、収量という植物形質の推定手法が主要な貢献である。
abstractadvances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance
Reproduction assets foundThe paper's grain yield BLUEs, spectral wavelength BLUEs, and genotypic data are publicly deposited in the CIMMYT data repository (https://doi.org/10.71682/10549399), directly reproducing this paper's phenotyping measurements. No author analysis code with a public URL is stated; other URLs are generic tools/services.Dataset · publicok.com.
Paolo Vitale, Email: p.vitale@cgiar.org.
DATA AVAILABILITY STATEMENT
The datasets generated and analyzed during this study, including best linear unbiased estimates (BLUEs) for grain yield and spectral wavelengths, as well as the corresponding genotypic information, are publicly available in the CIMMYT data repository ( https://doi.org/10.71682/10549399 ).
REFERENCES
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(2018). Translating high‐throughput phenotyping into genetic gain. Trends in Plant Science, 23(5), 451–466. 10.1016/j.tplants.2018.02.001
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Bertrand, Y.Open asset ↗CIMMYT data repository · 10.71682/10549399lines:280-433Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.
Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。
abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract. In the face of escalating global food demand and increasing climate variability, precise and granular crop yield monitoring is indispensable for maintaining regional agricultural stability. However, current deep learning approaches for yield estimation are severely constrained by their heavy reliance on massive in situ labeled data, which limits their application in data-scarce regions. Furthermore, these models often overlook the essential temporal evolution logic of yield formation and lack a systematic discussion regarding the contribution patterns of different feature dimensions, resulting in a black-box nature of the underlying model mechanisms. To address these challenges, this study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning. The framework's core strength lies in a physiologically complete simulation database, using the WOFOST model to exhaustively cover 30 years of climate variability and habitat combinations across Northeast China (1.24 × 106 km2). A Gated Recurrent Unit (GRU) network was then introduced for end-to-end modeling, accurately capturing the energy accumulation trajectory from vegetative to reproductive growth. Validation against 458 independent ground points (2022–2024) demonstrated robust generalization with an R2 of 0.69, an RMSE of 1.21 t ha−1, and an RRMSE of 13.73 %, despite using no ground data for training. Our analysis revealed that integrating photosynthetic intensity (LAImean), duration (LAD) and peak features (LAImax) across growth stages is critical for accuracy, while omitting early-stage features significantly impairs the model's ability to capture cumulative growth effects. Furthermore, the model successfully captured the spatiotemporal yield anomalies caused by the 2023 typhoon and flooding events. Ultimately, this study generated a 10 m resolution maize yield dataset (2019–2024) for Northeast China. The dataset exhibits consistent interannual stability, with the RRMSE ranging from 7.98 % to 12.92 % and the R2 remaining above 0.44 at the city level. By deeply coupling mechanistic simulation with data mining, this dataset provides detailed support for optimizing agricultural production and guiding farming practices. The Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).
Why it matches plant phenotyping methodsトウモロコシ収量という植物・作物群落の形質を推定する計算フレームワークを開発し、独立地点で性能検証したうえで再利用可能な10 m解像度データセットを生成しており、単なる農業実験の routine measurement ではない。
abstractthis study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning.
Reproduction assets foundThe paper's core output, the NortheastChinaMaizeYield10m maize yield dataset (2019–2024) with accompanying uncertainty layers, is openly deposited on Zenodo with an explicit availability statement and DOI. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).Open asset ↗Zenodo · 10.5281/zenodo.19547014lines:158-191Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.
Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。
abstractThis project develops a workable solution.
Reproduction assets foundThe paper builds its multimodal wheat phenotyping analysis on several explicitly cited public data assets: the Kaggle Wheat Plant Diseases image dataset (used for disease classification, Tables 2 and 9), the Global Wheat Head Detection dataset (used for head detection, Tables 1 and 6), FAOSTAT and India Open GovernmentDataset · publicAvailable online at: https://www.fao.org/faostat/ . FAOSTAT statistical database.Open asset ↗lines:1110-1162Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus .
Why it matches plant phenotyping methodsスパーステスト設計とゲノム予測を用いて、複数環境での植物形質予測と表現型測定コスト削減を評価しており、表現型取得・予測手法が研究の中心である。
abstractSparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations.
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit (DOI 10.6084/m9.figshare.31796794) containing the datasets analyzed in this Miscanthus sparse-testing genomic prediction study, including the phenotypic and genotypic data used for the models.Dataset · publicThe datasets analyzed for this study can be found in the figshare repository at https://doi.org/10.6084/m9.figshare.31796794 .Open asset ↗figshare · 10.6084/m9.figshare.31796794lines:603-621Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Why it matches plant phenotyping methodsロボット3D画像とカスタムガス交換による非破壊的な植物形態・光合成表現型測定系を開発し、従来形質との比較検証も行っており、方法が研究の中心である。
abstractwe developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract Blanket-rate agrochemical scheduling — a practice wherein the same quantity of fertilizer or pesticide is spread uniformly across an entire field irrespective of spatial or temporal crop need — persists as the dominant farm management paradigm across rural India and large parts of South Asia. This approach generates cascading inefficiencies: excess nitrogen drains into waterways, off-target pesticide deposits devastate pollinators, input costs erode thin profit margins, and wide-scale greenhouse gas release from soil microbial activity accelerates climate change. The study documented here addresses this challenge through a purpose-built, four-layer intelligent field management platform. The platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack. A fine-tuned YOLOv8-L network performs real-time pest and foliar disease localisation; a ResNet-50 backbone quantifies canopy health across five stress gradients; a two-layer stacked LSTM projects short-horizon yield trajectories; and a Deep Q-Network autonomously plans drone spray routes weighted by field-specific prescription maps. Field validation spanned two consecutive growing seasons (Rabi 2022–23 and Kharif 2023–24) across six georeferenced plots covering 4.8 ha at Baramati, Maharashtra. Outcome metrics recorded during head-to-head comparison with conventional practice included a disease detection score of 95.6% mAP, a 47.3% reduction in total nitrogen applied, a 38.1% decrease in pesticide volume, and a 22.4% uplift in harvested grain weight. Together, these field-verified numbers substantiate the operational readiness of integrated AI precision agriculture for smallholder deployment.
Why it matches plant phenotyping methodsマルチスペクトル画像・深層学習による病害局在化とキャノピー健康状態の定量化を中核機能とする統合プラットフォームであり、植物の病害状態・生育状態を直接推定して現地検証している。
abstractThe platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicData Availability The annotated image dataset (14,300 images, 23 classes), trained YOLOv8-L and ResNet-50 weights, LSTM model files, DQN policy checkpoint, and all analysis scripts are archived at https://github.com/precision-agri-ai (Zenodo DOI: 10.5281/zenodo.XXXXXXX).Open asset ↗precision-agri-ailines:161-182Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
0. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3’s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R 2 = 0.935, n = 74) under simple-background conditions and (R 2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica . Object counts matched manual counts almost exactly for well-separated insects in an insect box (R 2 = 0.9997, n = 12). By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Why it matches plant phenotyping methods画像から花の面積など植物形態形質を抽出する汎用システムを開発し、手動測定との一致で検証しており、植物フェノタイピング手法が中心である。
abstractWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts.
Reproduction assets foundThe paper's Data and code availability statement provides a public Zenodo deposit containing the validation datasets and code used for the EcoMorph phenotyping measurements (floral area, insect morphometrics, counts), plus a public web deployment of the EcoMorph software itself.Code · publicValidation datasets and code are available here on Zenodo
https://zenodo.org/records/20980236.Open asset ↗Zenodo · 20980236pdf-page:2 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sweet potato is a nutritionally valuable crop that contributes to food security, owing to its storage roots rich in starch, sugars, and antioxidants, while requiring minimal cultivation inputs. Estimating its yield based on visible above-ground traits remains challenging due to weak and inconsistent correlations between shoot biomass and storage root development. Therefore, direct assessment of underground biomass is essential. In this study, we demonstrate the field application of ground penetrating radar (GPR) for non-destructive detection and yield estimation of sweet potato. GPR is a geophysical technique that typically transmits ultra high frequency radio waves into the soil and records reflections from subsurface objects. Electromagnetic wave simulations within the soil-root system revealed GPR signals that strongly correlate with root length, forming the basis for yield quantification. We developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data. By integrating detection and quantification approaches, a linear regression model predicting sweet potato yield from GPR signals achieved moderate accuracy ( R 2 = 0.567, normalized RMSE 0.190). We established a non-destructive and low-labor approach for monitoring root systems, providing a foundation for rapid, scalable, and field-ready yield estimation in sweet potato and other root and tuber crops.
Why it matches plant phenotyping methodsGPRによる地下貯蔵根の検出・定量化と収量推定を中心に、信号処理および画像処理パイプラインを開発・評価しているため、植物フェノタイピング手法として収載する。
abstractWe developed an image-processing pipeline comprising static correction, gain adjustment, noise filtering, and hyperbola segmentation via the Hough transform to enable semi-automated storage root detection from GPR data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe datasets analyzed during the current study consist of GPR line scans of the field at ALRC and list of sweet potato storage root weights. These data are available together with the analysis scripts on GitHub under open access. All data and scripts are the property of NARO and are distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). The repository can be accessed at: https://github.com/mtei1/GPRScript.Open asset ↗mtei1/GPRScripthtml-lines:240-264Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Mangroves play a critical role in coastal ecosystem services, particularly through their capacity to sequester large amounts of atmospheric carbon, contributing to climate change mitigation. Developing accurate mangrove carbon models is therefore essential for monitoring ecosystem condition and carbon stocks at relevant scales. This study aimed to estimate mangrove Above-Ground Carbon (AGC) in Baluran National Park by integrating field measurements and remote sensing data within a Machine Learning (ML) framework. The study utilised an extensive field data collection programme of 60 sampling plots of girth at breast height, canopy cover, tree height, and tree density. Mangrove AGC was estimated using allometric equations. AGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE). The RF model, using field-measured tree height, Ratio Vegetation Index (RVI), and Transformed Soil-Adjusted Vegetation Index (TSAVI), achieved the best performance ( R² training = 0.93, R² testing = 0.84, 5-fold CV RMSE = 12.20 Mg C ha⁻¹). Predicted AGC ranged from 5.39 to 57.18 Mg C ha⁻¹ (mean ± Standard Deviation (SD) = 30.43 ± 16.09 Mg C ha⁻¹) and showed improved accuracy compared to the global mangrove biomass dataset of (Simard et al., 2019). A key contribution of this study is the integration of field-measured tree height within a satellite-based ML framework, which enhances the accuracy and ecological relevance of AGC estimation compared to approaches relying solely on spectral predictors or remotely sensed canopy height products, offering a practical and cost-effective alternative for sites where UAV or LiDAR data are unavailable. This approach provides a practical method for regional mangrove carbon monitoring, national carbon accounting and supports climate change mitigation efforts.
Why it matches plant phenotyping methodsマングローブの樹高・樹冠情報と衛星データを統合し、機械学習で個体・プロットレベルの地上部炭素量という植物状態を推定する手法を開発・交差検証しており、単なる生態系測定ではなく表現型取得手法が中心である。
abstractAGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE).
Reproduction assets foundThe authors state that all analysis code (model development, hyperparameter configuration, diagnostics, accuracy assessment) is publicly available in their GitHub repository Mangroves-AGC-Baluran, which reproduces this paper's mangrove AGC machine-learning analysis.Code · publicThe Python codes were available on
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Mangroves-AGC-Baluran.git.Open asset ↗Mangroves-AGC-Baluran.git · Mangroves-AGC-Baluran.gitpdf-raw-page:17 lines:1-379Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
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:76-146Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield ( r = 0.79-0.82, p r = 0.72 in hard spring, r = 0.53 in soft spring). These conditions also improved discrimination between varieties. Although heritability patterns were not clearly differentiated by stress clusters, environments with higher genetic control of yield also tended to show stronger NDVI heritability. Overall, NDVI reliably captured wheat grain yield, which is governed by the genotype × environment driven variability, with its predictive value strongest in stress-prone conditions. These findings underline NDVI's usability as a practical digital trait for improving variety testing and guiding breeding decisions in challenging environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを抽出し、複数環境・品種で収量との関係、予測性、遺伝率を評価しており、デジタル植物形質の測定・検証が中心である。
abstractThis study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicTrial data, including grain yield, variety, and market class information, were obtained from the Washington State University Extension Cereal Variety Selection and Testing Program ( https://smallgrains.wsu.edu/variety/ ).Open asset ↗lines:38-48Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Manual quantification of specialty crop traits, such as flowers and fruits, is often labor-intensive, time-consuming, and inconsistent, limiting scalability and precision. We present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images. PhenoSnap bridges the gap between advanced computer vision technologies and practical agricultural applications by eliminating the need for programming expertise. This ready-to-use solution can enable growers, breeders, and Extension faculty to accelerate field work and enhance decision-making related to strawberry and tomato yield estimation for breeding selections and strawberry runner management. Written by Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, and Jessica Chitwood-Brown, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2026.
Why it matches plant phenotyping methods画像から花・果実などの植物形質を自動抽出するAIウェブアプリケーションの開発・提供が中心であり、植物フェノタイピング手法およびソフトウェアとして適格。
abstractWe present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images.
Reproduction assets foundThe article describes PhenoSnap, a publicly accessible AI web application for specialty crop trait extraction, and cites a publicly released Dryad imagery dataset (Zhou et al. 2025b) that is a subset of the training data for the Strawberry Runner model. Both are paper-specific, public, and actionable. No author code orDataset · publicDataset preparation
and the training process are detailed in Zhou et al. (2025a),
and a subset of the dataset has been publicly released on
Dryad (Zhou et al. 2025b).Open asset ↗Dryadpdf-raw-page:5 lines:1-55Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Why it matches plant phenotyping methodsUAS画像からキャノピー高、穂形質、植生指数を抽出し、機械学習でソルガムのプロット収量を推定するパイプラインが研究の中心であり、形質取得・推定手法の評価も行っている。
abstractthis study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Spatially accurate estimates of forest above-ground biomass (AGB) are indispensable for carbon-stock accounting and sustainable silviculture. Existing mapping approaches face challenges in densely vegetated Coastal Plain forests because of seasonal optical variability, radar–optical saturation, and limited wall-to-wall structural information. We aimed to (i) develop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping, (ii) quantify the added value of seasonal optical data and photogrammetric canopy-height profiles, and (iii) interpret model drivers using explainable artificial intelligence (AI) to relate predictors to forest structure and composition. We mapped AGB across ~ 10,500 km 2 in southeastern North Carolina using wall-to-wall predictors from optical, radar, and photogrammetric sources. Forest Inventory and Analysis plot data (n = 305) were used to train and evaluate an ensemble of gradient-boosted tree models (CatBoost, LightGBM, XGBoost) and a neural network (RealMLP) via cross-validation. Model behavior was interpreted using feature importance and partial dependence analysis. Expanding Sentinel-2 temporal coverage from summer-only to four-season composites improved normalized RMSE by 8.7%. Incorporating canopy-height profiles from NAIP produced the largest accuracy gain, lowering nRMSE by 15.9–18.0% relative to the multisensor baseline, which underscores the critical value of structural information for AGB prediction. Three key predictors illustrated complementary ecological dimensions: the 10th percentile canopy height captured canopy openness, L-band polarimetric alpha indicated volume-scattering regime, and spring red-edge reflectance captured vegetation biochemistry. These findings show that fusing structure, polarimetry, and spectral phenology yields robust AGB maps and improves generalizability across heterogeneous landscapes. This transferable, broadly accessible framework integrating structural, polarimetric, and spectral phenology data enables landscape-scale AGB monitoring and supports targeted conservation planning, restoration tracking, and adaptive management for carbon sequestration. The incorporation of high-resolution wall-to-wall structural data is particularly valuable for improving the accuracy and usability of forest AGB maps, thereby informing more responsive decision-making.
Why it matches plant phenotyping methods森林の地上部バイオマスという植物群落形質を対象に、光学・レーダー・写真測量データを融合した推定フレームワークを開発・評価しており、形質推定手法が研究の中心である。
abstractdevelop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping
Reproduction assets foundThe authors explicitly state that the code reproducing all figures and analyses is archived in a GitHub repository and permanently preserved via Zenodo (doi 10.5281/zenodo.18688899). The GEDI-derived CHM25 product (Zenodo 11176727) is a cited prior-work dataset from Wang et al. (2025), not this paper's own asset, and FCode · publicGEDI data products are distributed by NASA’s Land Processes
Distributed Active Archive Center and are accessible through
Google Earth Engine. The code used to reproduce all figures
and analyses has been archived in a GitHub repository (https://
github.com/ChaoEcohydroRS/NC_SoutheastBiomass) and
permanently preserved via Zenodo (https://doi.org/10.5281/zenodo.18688899, submitted on 20 February 2026).
Declarations
Conflict of interest The authors declare no competing inter-
ests.
Disclaimer The findings and conclusions in this publication
are those of the author(s) and should not be construed to rep-
resent any official USDA or U.S. Government determination or
policy.
Open Access This articleOpen asset ↗Zenodo · 10.5281/zenodo.18688899pdf-raw-page:23 lines:1-89Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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
Abstract Purpose The primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix, and to assess whether integrating spectral, texture, soil, and fertilization information could improve prediction performance for precision sugarcane management. Methods Jilin-1 satellite imagery acquired at four growth stages, from seedling to maturity, was used to derive vegetation indices (VIs) and texture indices (TIs), including the normalized difference texture index (NDTI), enhanced vegetation texture index (EVTI), and double-difference ratio texture index (DDRTI). Soil chemical properties (SCPs) and fertilization information (FI) were further incorporated with the remotely sensed variables. Machine learning models were developed for plot-level prediction of sugarcane traits across plant cane and first ratoon cane, and texture window size was optimized to improve TI extraction and model performance. Results For yield, the combination of VIs and TIs outperformed VIs alone at the tillering stage (R 2 CV = 0.65, RMSECV = 15.06 t/ha, RPDCV = 1.68). Adding SCPs and FI further improved yield prediction across plant cane and first ratoon cane (R 2 CV = 0.70, RMSECV = 13.84 t/ha, RPDCV = 1.83). Millable stalk population was best predicted at the maturation stage by VIs and Tis, achieving the best performance (R 2 CV = 0.63, RMSECV = 6602 stalks/ha, RPDCV = 1.66). The best Brix model integrated VIs, TIs, SCPs, and FI at the maturation stage (R 2 CV = 0.44, RMSECV = 0.53 °Bx, RPDCV = 1.33). SHAP analysis identified VIs as the dominant features for sugarcane traits prediction. And, DDRTI contributed more than NDTI and EVTI in yield and Brix prediction. Conclusion It is concluded that integrating spectral, texture, soil, and fertilization information from high spatial resolution Jilin-1 imagery is a promising approach for improving plot-level prediction of key sugarcane traits.
Why it matches plant phenotyping methods衛星画像からサトウキビの収量、可販茎数、Brixを plot レベルで推定し、テクスチャ特徴抽出の最適化と機械学習性能評価を行っており、表現型取得・推定手法が中心である。
abstractThe primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the authors' model-training code and test data for the sugarcane trait prediction analysis. No public phenotype dataset or imagery deposit is stated; additional data are only available on request.Code · publicPart of the code and test data for model training are available at https://github.com/guangtaoxu08-dev/SPT_JL .Open asset ↗guangtaoxu08-dev/SPT_JLlines:228-248Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Phenotyping plant traits using UAV-based multispectral imaging offers a robust and unbiased approach to assessing crop status. With approximately 70% of smallholder farmers in East and Southern Africa cultivating common beans as a key source of food and income, there is a critical need for accurate and timely measurements of crop health and yield to support data-driven management decisions and disease mitigation. Traditional phenotyping methods are labor-intensive, and existing remote sensing and machine learning approaches remain limited. This study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery. Data collected over three growing seasons (2022–2024) were used to extract canopy variables and vegetation indices (VIs) across phenological stages. For yield prediction, traditional machine learning models achieved a root mean squared error (RMSE) of 242.33 kg ha⁻¹ and an R² of 0.66 using an Extra Trees Regressor. A novel BY-GRU architecture improved performance, achieving an RMSE of 242.40 kg ha⁻¹ and an R² of 0.79. The analysis also identified 45–60 days after sowing as the optimal window for prediction. To address limitations in conventional plant health assessments, this study introduces a novel Health Index. Comparative analysis demonstrated its robustness across genotypes and stronger correlation with yield. Machine learning and deep learning models, including MaxViT, were applied to estimate the Health Index, achieving improved predictive performance. Overall, this work integrates UAV sensing and modelling to provide scalable tools for phenomics, crop management, and breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像から作物の健康状態、収量、キャノピー形質を推定するセンシング・機械学習フレームワークが研究の中心であり、植物フェノタイピング手法として適格です。
abstractThis study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery.
Reproduction assets foundThe preprint's DATA AVAILABILITY section states that all processed data required to reproduce the results are publicly available in a Google Drive repository, which qualifies as a paper-specific public phenotype dataset asset. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicCommon Bean Breeding Program for
facilitating field trials. We also thank the Phenomics team for their valuable assistance with UAV-based
data collection.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY
The datasets generated and/or analyzed during the current study are publicly available at:
https://drive.google.com/drive/folders/1fN3Q9n3bK_YoXFK8VFKZ3uEb13y9iRWj?usp=sharing. This
repository includes all processed data required to reproduce the results presented in this study.
SUPPLEMENTAL MATERIAL
Supp. Figure 1. Drone-based field view of the bean trial site at CIAT Palmira Research Station: A) RGB
image and B) NDVI image.
Supp. Figure 2. Drone Features Open asset ↗pdf-raw-page:40 lines:1-46Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Effective high-throughput phenotyping is crucial for modern plant breeding, yet the optimal image acquisition parameters for UAV-based systems in forage crops remain poorly defined. We optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height. Machine learning algorithms and mixed model analyses were applied to evaluate predictive power and heritability. Pixel count and Haralick's entropy showed strong correlations with conventional yield measurements, particularly in Environment 2, while most vegetative indices were poor predictors. Integrating machine learning substantially enhanced predictive power for green and dry matter yield (r > 0.80). For canopy height, machine learning models achieved correlations of 0.71 with ground truth measurements despite weak pairwise correlations. Mixed model analysis revealed high broad-sense heritability (0.7 < H 2 < 0.87) for yield traits, pixel count, and entropy, while vegetative indices and canopy height showed greater environmental susceptibility. Moderate GSD resolutions (0.5–1.0 cm) consistently outperformed both very high (0.27 cm) and very low (1.5 cm) resolutions. Coincidence index analysis demonstrated 80% correspondence between top genotypes ranked by pixel count and conventionally measured dry matter yield. This study provides an optimized framework for UAV-based phenotyping in M. maximus , demonstrating that combining advanced digital traits with machine learning accurately predicts key agronomic traits and significantly enhances genotype selection efficiency in forage breeding programs.
Why it matches plant phenotyping methodsUAV画像取得条件、RGBデジタル形質、機械学習による収量・草高推定を最適化・検証する研究であり、植物表現型取得法が中心的です。
abstractWe optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height.
Reproduction assets foundThe paper's data availability statement points to a public Mendeley Data repository containing the study's UAV-derived digital phenotyping and conventional trait datasets. No author analysis code repository is explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://data.mendeley.com/datasets/jrrb76x82h/1 .Open asset ↗jrrb76x82h/1lines:435-487Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像を用いた高スループット圃場フェノタイピング枠組みを導入・検証し、植生被覆、草丈、NDVIから複数の植物形質・状態を抽出しており、フェノタイピング手法が中心的です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analysed (UAV-derived phenotyping measurements) in a public Zenodo repository with a DOI matching an allowed URL.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:197-235Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
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Value of the Data
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The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Developing climate-resilient wheat varieties requires combining high yield with stability across diverse environments, especially under increasingly variable precipitation and rising temperatures. This study evaluated 64 post-Green Revolution durum wheat cultivars under irrigated and rainfed conditions at two contrasting Mediterranean sites in Spain. A classification framework was developed to support genotype selection based on yield and yield stability, estimated using linear mixed models and yield slopes across environments. Genotypes were classified by interquartile thresholds, and those showing either low yield or low stability were considered undesirable for selection. High-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data. VIs and TIR at anthesis and grain filling, and their differences (senescence proxies), were used to train Random Forests for yield and stability estimation including sequential feature selection. Environmental covariates (water input, reference evapotranspiration) were integrated in yield models, with strong outcomes (R 2 > 0.74; MAPE <23.6%). Stability predictions were based on VI stability and, though moderate (R 2 up to 0.56; MAPE <17.75%), outperformed previous studies. Selected features were used to evaluate seasonal reflectance phenotypes: “keep” genotypes (intermediate/high yield or/and stability) exhibited early-vigor but lower green retention by the end of grain filling, while “discard” genotypes (low yield or/and stability) showed reduced early vigor and “stay-green” behavior. This study highlights early-vigor and earlier senescence over “stay-green” for wheat selection, offering a cost-effective approach shifting the breeding focus from yield maximization to joint yield-stability evaluation, promoting sustainability.
Why it matches plant phenotyping methods高スループットの地上・UAVセンサーによる表現型取得と、機械学習による収量・安定性推定が研究の中心であり、育種選抜に用いる手法を実質的に評価・適用している。
abstractHigh-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data.
Reproduction assets foundThe authors explicitly state that the datasets and analysis scripts for all analyses (yield/stability modeling, VI extraction, Random Forest workflows) are publicly available in their Zenodo repository (DOI 10.5281/zenodo.17435708), referenced both in the statistical analysis section and the Data Availability statementCode · publicThe datasets and scripts for all the analyses conducted are available in our repository ( https://doi.org/10.5281/zenodo.17435708 ).Open asset ↗zenodo · 10.5281/zenodo.17435708lines:222-237Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Quantitative characterization of complete canopy architecture is essential for accurate evaluation of crop photosynthesis and yield potential, thereby supporting crop ideotype design. Although various sensing technologies enable three-dimensional (3D) reconstruction of individual plants and canopies, they often fail to describe canopy architecture accurately because of severe occlusion in dense populations. To address this limitation, we developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model. A complete point cloud generation pipeline was first established to enable automated training data annotation, allowing discrimination between surface points and occluded points within the canopy. The proposed crop population point cloud completion network (CP-PCN) integrates a multi-resolution dynamic graph convolutional encoder, a point pyramid decoder, a dynamic graph convolutional feature extractor, and a generative adversarial network-based loss function to predict occluded canopy points. CP-PCN achieved chamfer distance values of 3.35 to 4.51 cm across four growth stages, outperforming the state-of-the-art transformer-based method PoinTr. Ablation analyses confirmed that each of the four modules contributes to overall model accuracy. In addition, validation experiments showed that the improved architectural completeness achieved by CP-PCN resulted in more accurate yield estimation compared with incomplete and PoinTr-completed point clouds. CP-PCN also demonstrated strong cross-crop generalizability by successfully reconstructing mature rice canopies. Overall, this framework provides a scalable approach for quantitative analysis of complex canopy architectures in field-grown crops.
Why it matches plant phenotyping methodsUAVマルチビュー画像から遮蔽点を補完し、作物群落の3Dキャノピー構造を再構成する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model
Reproduction assets foundThe paper's Data and code availability statement explicitly deposits all source code and test data for the CP-PCN phenotyping pipeline on a public GitHub repository, matching an allowed URL.Code · publicAll source code and test data used in this study are publicly available on GitHub ( https://github.com/Ziyue-Guo/CP-PCN.git ).Open asset ↗https://github.com/Ziyue-Guo/CP-PCN.git · CP-PCNlines:133-158Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
To address the inefficiency and high cost of manual counting of tobacco leaves, this study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext network. Although UAV imagery has been applied to crop phenotyping, most existing UAV-based leaf-counting methods still rely on 2D images or hand-crafted features and rarely exploit 3D point clouds with dedicated leaf-level segmentation, which limits accuracy and robustness under leaf overlap, variable viewing angles, and complex field backgrounds. In this work, oblique UAV photogrammetry is used to reconstruct individual plants into 3D point clouds, and a segmentation network, SRW-PointNext, is developed by integrating an SCSA attention mechanism and a Residual-SegHead to enhance feature extraction and segmentation performance, while a re-weighted loss alleviates class imbalance. Leaf point clouds are then clustered using MeanShift to obtain leaf counts. Experiments on field-grown tobacco demonstrate that the proposed method achieves a point-cloud segmentation precision of 92.09%, a MIoU of 76.13%. Compared with the original PointNext baseline, SRW-PointNext increased MIoU and overall precision by 3.34% and 2.42% respectively. The final accuracy rate of leaf counting was 92.61%, effectively achieving accurate and stable leaf counting under actual field conditions, and providing technical support for digital management, yield estimation and seedling breeding in tobacco production.
Why it matches plant phenotyping methodsUAV三次元画像と改良セグメンテーション手法により圃場タバコの葉数を推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractthis study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext
Reproduction assets foundThe paper reports a UAV-based tobacco leaf counting method with an annotated 1000-plant point cloud dataset and SRW-PointNext code, both explicitly declared publicly available at author-provided Zenodo and GitHub URLs matching the allowed list.Dataset · publicData supporting the reported results can be found at: https://zenodo.org/records/15130271 .Open asset ↗zenodo · 15130271lines:531-564Code · publicThe code used in this study is available at: https://github.com/Nan20377/SRW-Pointnext.git .Open asset ↗github · Nan20377/SRW-Pointnextlines:531-564Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Agricultural research increasingly relies on data-driven approaches for crop yield prediction that complement more established crop growth models, including machine learning techniques. However, these approaches rely on large training datasets. Here, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England on which a variety of crops are grown. In addition, the data also contains satellite-derived remote sensing data, weather data, and data on soil type, all aligned at a grid resolution of 10 km. Weather data is available at a daily temporal resolution, satellite data at 5-day resolution, while crop yield data is available at yearly resolution. This effort has been made possible through careful anonymisation of the yield data while preserving the alignment with remote sensing, weather, and soil data. This data will be useful both to train machine learning models of yield prediction as well as to parameterize mechanistic crop growth models. Furthermore, the anonymisation procedure itself will be of interest to the research community, as it represents a solution to a common problem on the interface of agricultural research and farming practice.
Why it matches plant phenotyping methods圃場単位の作物収量という植物形質を、衛星・気象・土壌情報と整合した再利用可能な大規模データセットとして構築しており、収量予測モデルの訓練・評価用データ基盤が中心です。
abstractHere, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England
Reproduction assets foundThe paper's authors provide public R code for merging/aligning climate, soil, and Sentinel-1 data and anonymising yield data in a GitHub repository. The CYCLeSS dataset itself is on figshare, but that URL is not in the allowed list, so only the code asset is reported.Code · publicnts of this repository. Researchers who are further interested in the underlying data should contact the authors affiliated with UKCEH.
Code availability
R code used to merge and align available UK climate, soil, and Sentinel-1 synthetic aperture radar data to the same 1 km 2 grid is provided in the following GitHub repository: https://github.com/alan-turing-institute/CYCLeSS-dataset-code . Dummy data and code needed to replicate the final process of merging climate, soil, and satellite data with UKCEH precision yield data and anonymisation of field locations is contained within the ‘CLYCESS_anonymisation.zip’ folder shared as part of this repository. R version 4.2.3 was used for the creatioOpen asset ↗https://github.com/alan-turing-institute/CYCLeSS-dataset-codelines:200-271Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP 25 of 84.55%, outperforming mainstream algorithms.
Why it matches plant phenotyping methods米の3D形質取得・再構成・分割を中核とする手法開発であり、データセット／ベンチマークも提供しているため、植物フェノタイピング手法文献に該当する。
abstractwe propose a novel method named Multi-Scale NeRF(MSNeRF)
Reproduction assets foundThe paper's authors explicitly state that the source code for MSNeRF and VRKGNet is publicly available on GitHub with testing scripts and test cases to reproduce the main results. The MMR dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publicof Hefei Artificial Intelligence Breeding Accelerator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
•
MSNeRF : https://github.com/qfwysw/MSNeRF.git
•
VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial iOpen asset ↗https://github.com/qfwysw/MSNeRF.gitlines:620-663Code · publicator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
•
MSNeRF : https://github.com/qfwysw/MSNeRF.git
•
VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could haveOpen asset ↗https://github.com/qfwysw/VRKGNet.gitlines:620-663Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
High-dimensional time series phenotypic data is becoming increasingly common within plant breeding programmes. However, analysing and integrating such data for genetic analysis and genomic prediction remains difficult. Here we show how factor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction. We use a subset of Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) elite yield wheat trial of 2014-2015, consisting of 1,033 genotypes. These were measured across three irrigation treatments at several timepoints during the season, using manned airplane flights with hyperspectral sensors capturing 62 bands in the spectrum of 385-850 nm. We perform multivariate genomic prediction using latent variables to improve within-trial genomic predictive ability (PA) of wheat grain yield within three distinct watering treatments. By integrating latent variables of the hyperspectral data in a multivariate genomic prediction model, we are able to achieve an absolute gain of .1 to .3 (on the correlation scale) in PA compared to univariate genomic prediction. Furthermore, we show which timepoints within a trial are important and how these relate to plant growth stages. This paper showcases how domain knowledge and data-driven approaches can be combined to increase PA and gain new insights from sensor data of high-throughput phenotyping platforms.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトルセンサーによる植物表現型時系列データから潜在特徴を抽出し、収量予測に統合する解析手法が研究の中心であるため。
abstractfactor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction
Reproduction assets foundThe paper's Data and code statement provides public GitHub repositories containing the authors' analysis scripts for the hyperspectral latent-factor/Procrustes workflow and the glfBLUP R package implementing the genomic prediction methodology. The hyperspectral phenotype dataset itself is only available upon request, iCode · publicy of secondary trait data and successful integration in multivariate genomic prediction.
As such, this method can contribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning mOpen asset ↗KunstJF/glfBLUP-Procrusteslines:388-492Code · publicntribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning methods for genomic prediction .
Springer , Cham, Switzerland .
External Links: ISBN 978-3-030-89009-4 978-3-030-8901Open asset ↗KillianMelsen/glfBLUPlines:388-492Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the datasets generated and analyzed (phenotype/trait and vegetation index data from the metribuzin tolerance phenotyping experiments) in the Washington State University Research Exchange repository with a public DOI. No author analysis code repository is URLDataset · public20-
67037-30671, 2022-67013-36426, and 2022-68013-36439.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L I B I L I T Y S TAT E M E N T
The datasets generated and analyzed for this study are avail-
able in the Washington State University Research Exchange
repository (https://doi.org/10.7273/000007507).O RC I D
Melinda Zubrod https://orcid.org/0000-0001-7024-8421
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Ahmadi, Z., Mehrabadi, M., Fazli, M., Khalesro, S., Abedi, R., &
Mokhtassi-Bidgoli, A. (2025). Enhancing tolerance of wheat culti-
vars to meOpen asset ↗Washington State University Research Exchange · 10.7273/000007507pdf-raw-page:12 lines:1-81Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Potato yield is a key indicator for optimizing cultivation practices in agriculture. Potato yield can be estimated on harvesters using RGB-D cameras, which capture three-dimensional (3D) information of individual tubers moving along the conveyor belt. However, point clouds reconstructed from RGB-D images are incomplete due to self-occlusion, leading to systematic underestimation of tuber weight. To address this, we introduce PointRAFT, a high-throughput point cloud regression network that directly predicts continuous 3D shape properties, such as tuber weight, from partial point clouds. Rather than reconstructing full 3D geometry, PointRAFT infers target values directly from raw 3D data. Its key architectural novelty is an object height embedding that incorporates tuber height as an additional geometric cue, improving weight prediction under practical harvesting conditions. PointRAFT was trained and evaluated on 26,688 partial point clouds collected from 859 potato tubers across four cultivars and three growing seasons on an operational harvester in Japan. On a test set of 5,254 point clouds from 172 tubers, PointRAFT achieved a mean absolute error of 12.0 g and a root mean squared error of 17.2 g, substantially outperforming a linear regression baseline and a standard PointNet++ regression network. With an average inference time of 6.3 ms per point cloud, PointRAFT supports processing rates of up to 150 tubers per second, meeting the high-throughput requirements of commercial potato harvesters. Beyond potato weight estimation, PointRAFT provides a versatile regression network applicable to a wide range of 3D phenotyping and robotic perception tasks. The code, network weights, and a subset of the dataset are publicly available at https://github.com/pieterblok/pointraft.git.
Why it matches plant phenotyping methods部分点群からジャガイモ塊茎重量を推定する3D深層学習手法を開発・評価しており、植物形質取得が研究の中心である。
abstractwe introduce PointRAFT, a high-throughput point cloud regression network that directly predicts continuous 3D shape properties, such as tuber weight, from partial point clouds.
Reproduction assets foundThe paper publicly releases its authors' analysis code and trained network weights on GitHub, and a subset of its potato tuber partial point cloud dataset (with ground truth weights) on Hugging Face. Both are paper-specific, public, and actionable.Code · publicThe code, network weights, and a subset of the dataset are publicly available at https://github.com/pieterblok/pointraft.git .Open asset ↗pieterblok/pointraftlines:1-93Dataset · publicA subset of the datasets generated and/or analyzed during this study is publicly available at: https://huggingface.co/datasets/UTokyo-FieldPhenomics-Lab/3DPotatoTwinOpen asset ↗UTokyo-FieldPhenomics-Lab/3DPotatoTwinlines:447-463Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely and accurate in-season estimation of aboveground biomass (AGB) and yield in winter wheat is crucial for optimizing resources and ensuring food security. Light use efficiency (LUE) models have proven effective in estimating crop gross primary productivity and yield across sites and years due to their strong physiological and ecological mechanisms. However, existing studies are limited to satellite applications and have not utilized unmanned aerial vehicle (UAV) imagery. This study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments. Subsequently, the scalability of the LUE-ML yield prediction approach was assessed in farmer's fields from five counties of Jiangsu Province, China. The results demonstrated that while the AGB for the heading stage was estimated by combining the retrieved LAI and 20-day accumulated meteorological features, the AGB during the post-heading period could be estimated accurately using the stage-skipping or stage-progressive strategy, with the latter ( R val 2 = 0.93) outperforming the former ( R val 2 = 0.84). The combination of one spectral index, LUE-derived AGB, and three 20-day accumulated relative meteorological features (Comb. #6) performed the best ( R cal 2 = 0.89; R val 2 ≥ 0.79) for yield prediction among all combinations. When extended to farmer-field yield prediction across the province, Comb. #6 also achieved acceptable performance. This study suggests the use of LUE-ML models represents a significant step forward towards mechanistic estimation of AGB and yield for cereal crops from UAV imagery.
Why it matches plant phenotyping methodsUAV画像から冬コムギの地上部バイオマスと収量を推定するLUE-ML手法を開発・評価し、圃場で性能検証しているため、植物形質取得・推定が研究の中心である。
abstractThis study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments.
Reproduction assets foundThe paper's Data Availability statement explicitly hosts the core code for the two UAV-LUE AGB estimation strategies and related test data in a public GitHub repository; other data are only available upon request.Code · publicThe core code for the two strategies and related test data in the UAV-LUE method for estimating wheat AGB are hosted in a public repository: https://github.com/qtaocheng/agb-estimation-uav-lue-two-strategies . Other data that support the findings of this study are available from the corresponding author (T.C.) upon reasonable request.Open asset ↗qtaocheng/agb-estimation-uav-lue-two-strategieslines:507-519Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Sustainable improvement of crop performance requires integrative approaches that link genomic variation to phenotypic expression through intermediate molecular pathways. Here, we present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction. Using a panel of 198 soybean accessions grown under well-watered and drought conditions, we first evaluated four direction-specific prediction models (genome → microbiome, genome → metabolome, metabolome → microbiome, and microbiome → metabolome) to estimate the predictability of individual omics features. We evaluated whether subsets of features with high cross-omics predictability improved phenotype prediction. These cross-layer models identify features that play physiologically meaningful roles within multi-omics systems, enabling the prioritization of variables that capture coherent biological signals enriched with phenotype-relevant information. Consequently, metabolome features were highly predictable from microbiome data, whereas microbiome predictability from metabolomic data was weaker and more environmentally dependent, revealing an asymmetric relationship between these layers. In the subsequent phenotype prediction analysis, the model incorporating predictability-based feature selection substantially outperformed models using randomly selected features and achieved prediction accuracies comparable to those of the full-feature model. Under drought conditions, the phenotype prediction models based on metabolomic or microbiomic kernels (MetBLUP or MicroBLUP) outperformed the genomic baseline (GBLUP) for several biomass-related traits, indicating that the environment-responsive omics layers captured phenotypic variations that were not explained by additive genetic effects. Our results highlight the hierarchical interactions among genomic, metabolic, and microbial systems, with the metabolome functioning as an integrative mediator linking the genotype, environment, and microbiome composition. The Reciprocal BLUP framework provides a biologically interpretable and practical approach for integrating multi-omics data, improving phenotype prediction, and guiding omics-based feature selection in plant breeding.
Why it matches plant phenotyping methods植物形質予測のための新しい多層オミクス統合フレームワークを提案し、予測モデル比較と性能評価を行っているため、計算的フェノタイピング手法が中心である。
abstractwe present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits all source codes and data in a public GitHub repository (Yoska393/ReciprocalBLUP), which contains the authors' analysis code and data for the soybean multi-omics phenotype prediction study. The NARO Genebank URL is only the source of plant accessions, not a phCode · publicAll source codes and data are available from the repository in GitHub: https://github.com/Yoska393/ReciprocalBLUP (accessed on 20 November 2025).Open asset ↗Yoska393/ReciprocalBLUPlines:285-308Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published19 Nov 2025ISPRS Journal of Photogrammetry and Remote SensingCited by 3 · OpenAlex ↗
Accurate fruit shape reconstruction under real-world field conditions is essential for high-throughput phenotyping, sensor-based yield estimation, and orchard management. Existing approaches based on 2D imaging or explicit 3D reconstruction often suffer from occlusions, sparse views, and complex scene dynamics as a result of the plant geometries. This paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards. The proposed method integrates (1) Grounded-SAM2 for multi-object tracking and segmentation (MOTS), (2) photogrammetric structure-from-motion for 3D scene reconstruction, and (3) DeepSDF, an implicit neural representation, for completing occluded fruit geometries with a neural network. We furthermore propose a new MOTS evaluation protocol to assess tracking performance without requiring ground truth annotations. Experiments conducted in both controlled laboratory conditions and an operational apple orchard demonstrate the accuracy of our 3D fruit reconstruction at the centimeter level. The Chamfer distance error of the proposed shape completion method using the DeepSDF shape prior reduces this to the millimeter level, and outperforms the traditional method, while Grounded-SAM2 enables robust fruit tracking across challenging viewpoints. The approach is highly scalable and applicable to real-world agricultural scenarios, offering a promising solution to reconstruct complete fruits with visibility higher than 10% for precise 3D fruit phenotyping at a large scale under occluded conditions.
Why it matches plant phenotyping methods果実形状を対象とするUAV画像・3D再構成・形状補完法を開発し、実験で精度評価しており、植物表現型取得が研究の中心である。
abstractThis paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards.
Reproduction assets foundThe paper's authors publicly release their UAV orchard video data, lab 3D apple scans, and analysis code via a GitHub repository explicitly stated in the text. A Zenodo deposit (10.5281/zenodo.15635994) is also mentioned for the data, but its URL is not among the allowed URLs, so only the GitHub asset is reported.Code · publicing in orchard environments,(2) to propose a novel method to evaluate
MOTS without any annotations, and (3) to provide a highly accurate
3D apple dataset collected in a laboratory environment, along with
UAV-captured high-resolution videos in the field. The dataset and codes
for this research are publicly available at: https://github.com/Kaiwen-Robotics/Mono3DOrchard.2. Study area and materials
This study contains two data collection areas: field data collection
and laboratory data collection.
2.1. Field data collection
2.1.1. Study area
The field data collection was conducted within an apple orchard
located in Randwijk, Overbetuwe, the Netherlands (51.9376, 5.703057
in WGS84 UTM 31U), as shOpen asset ↗Kaiwen-Robotics/Mono3DOrchard.2pdf-raw-page:2 lines:75-128Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.
Why it matches plant phenotyping methods植物圃場の衛星画像を処理し、NDVI等からバイオマスを推定するオープンソース基盤の設計・実装・検証が中心であり、植物形質推定ワークフローとして収録対象。
titleAn Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' complete source code, documentation, and example datasets for the Sentinel-2 phenotyping/analysis platform on a public GitHub repository under MIT license. Sentinel-2 imagery is from the public Copernicus browser, but that is a generic data sourceCode · publicresearch received no external funding
Institutional Review Board Statement: Not applicable. This study did not involve humans or animals.
Informed Consent Statement: Not applicable. This study did not involve humans.
Data Availability Statement: Complete source code, documentation, and example datasets are publicly available
at https://github.com/rexionmars/icev-remote-sensing under MIT license. The platform can be deployed locally
or accessed via hosted instance for testing purposes. Sentinel-2 satellite imagery used in this study was obtained
from the Copernicus Open Access Hub (https://browser.dataspace.copernicus.eu/) and is freely available.
Acknowledgments: The authors thank the iCEV IOpen asset ↗https://github.com/rexionmars/icev-remote-sensing · icev-remote-sensingpdf-layout-page:11 lines:1-70Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Unoccupied aerial vehicle (UAV)‐based high‐throughput phenotyping provides scalable and cost‐effective access to phenotypic information for crop improvement, yet its application in minor crops such as lentil ( Lens culinaris Medik.) remains limited. This study applied UAV‐derived canopy traits and crop growth regression modeling to a nested association mapping population developed from CDC Redberry crossed with 32 diverse founder lines. UAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points. Crop growth regression models were fitted to derive crop growth parameters, maximum canopy size, growth rate, and cumulative growth anchored to phenological stages. These static and time‐series traits were evaluated for seed yield prediction using partial least squares regression with a 70:20:10 data split and 10‐fold cross‐validation. Static traits such as maximum crop volume and maximum crop area were consistently associated with yield. Dynamic trait‐based models improved prediction accuracy and identified the swollen pod stage (R5–R6) as the most informative forecasting window. External validation using an independent trial confirmed the generalizability of the approach. This study presents a UAV phenotyping framework that supports crop growth dissection and early yield prediction and downstream trait discovery in lentil.
Why it matches plant phenotyping methodsUAV画像から作物の形態・成長形質を抽出し、時系列成長モデルと収量予測を検証するフレームワークが研究の中心であるため。
abstractUAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points.
Reproduction assets foundThe paper's data availability statement points to a public KnowPulse experiment page hosting the study's UAV-derived phenotyping and yield data; no author analysis code repository is stated.Dataset · publicof S).
We thank Dr. Ana Vargas at the Crop Development Center, U
of S for generously providing yield data from the independent
field trial.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data supporting this study are available at:
https://knowpulse.usask.ca/experiment/AGILE-NAM-UAV-growth-modelling or from the authors upon request.
O RC I D
SandeshNeupane https://orcid.org/0000-0003-3679-1046
KirstinE. Bett https://orcid.org/0000-0001-7959-6959
SteveJ. Shirtliffe https://orcid.org/0000-0002-3603-7417
R E F E R E N C E S
Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., & Cairns, J.Open asset ↗KnowPulse · AGILE-NAM-UAV-growth-modellingpdf-raw-page:15 lines:1-90Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches—such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration—provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics and cross-theme predictive integration to guide climate adaptation.
Why it matches plant phenotyping methods作物の生産性、フェノロジー、環境ストレスをリモートセンシングで測定・推定する方法論レビューであり、植物形質・状態の取得手法が中心である。
abstractThis review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring
Reproduction assets foundThis methodological review includes a case study (Figure 2) using MODIS NDVI composites, SILO gridded climate data, and ABARES historical winter crop yield data. The authors explicitly state the case-study datasets are publicly accessible via official portals; the SILO and ABARES portals are paper-specific public data-Dataset · publiclies, and observed productivity. Note: This figure is derived from
the authors’ ongoing study. The monthly NDVI composites (MOD13C2) were generated post-season, which limits their
utility for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available onlOpen asset ↗SILOpdf-layout-page:8 lines:1-53Dataset · publiclity for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available only after crop harvest, limiting its usefulness for in-
season yield forecasting or early drought warning. In other words, detailed phenological curves and
productivity metrics can onlyOpen asset ↗ABARESpdf-layout-page:8 lines:1-53Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.
Why it matches plant phenotyping methods衛星データとAI/MLを用いて水稲の生育状態（phenology）と収量を圃場レベルで推定する手法を開発・評価しており、植物形質の取得・推定が研究の中心である。
abstractdeveloped several crop-based indicators to assess crop phenology
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.29858924) containing the data supporting the study's rice monitoring findings, which qualifies as a paper-specific public asset.Dataset · publicuddin Shaik: Software, Visualization, Writing –
original draft
Suman Saraswathibatla: Investigation, Project admin-
istration, Supervision
Mukund Patil: Validation, Writing – review & editing
Data Availability Statement
The data that support the findings of this study can be found
at https://figshare.com/s/b611c04368825e6a028b
(https://doi.org/10.6084/m9.figshare.29858924).Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Mandapati, R., Gumma, M. K., Metuku, D. R., Bel-lam, P. K.,
Panjala, P., Maitra, S., Maila, N. Crop yield assessment using
field-based data and crop models at the village level: A case
study on a homogeneous rice area in Telangana, India.
AgOpen asset ↗figshare · 10.6084/m9.figshare.29858924pdf-raw-page:21 lines:1-101Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and dynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors. A temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (classification accuracy ≥0.97) even when relying solely on predictors from the early drought response phase. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage emerged as key classification features. A temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Importantly, prediction accuracy for these traits remained high (R 2 ≥ 0.84) even when restricted to early developmental phase data, including the stem elongation stage. Models trained on pooled drought and control data outperformed single-treatment models and maintained high predictive power across treatments. Together, these findings highlight the value of integrating high-throughput phenotyping with temporal modeling to enable earlier, more cost-effective selection of drought-resilient genotypes and demonstrate the broader potential of phenomics-driven strategies for accelerating crop improvement under stress-prone environments.
Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる時系列表現型取得と、収穫形質予測モデルの構築・評価が研究の中心であるため。
abstractdynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors
Reproduction assets foundThe authors explicitly state that the data and analysis pipeline code for this barley phenotyping study is publicly available on GitHub at https://github.com/hatiez/barley-TPP-pipeline. This is a paper-specific computational asset (the temporal phenomic classification/prediction pipeline) with an authors' public URL. DCode · publicThe data and analysis pipeline code is available on https://github.com/hatiez/barley-TPP-pipeline .Open asset ↗https://github.com/hatiez/barley-TPP-pipelinelines:390-415Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurately predicting yield during the growing season enables improved crop management and better resource allocation for both breeders and growers. Existing yield prediction models for an entire field or individual plots are based on satellite-derived vegetation indices (VIs) and widely used machine learning-based feature extraction models, including principal component analysis (PCA) and autoencoders (AE). Here, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery. Our approach uses a dataset of approximately 4,000 satellite images collected from replicated plots of 84 hybrid maize varieties grown at five distinct locations across the U.S. Corn Belt. By deploying the CAE model, we improve the separation of genotype and environment effects, enabling more accurate incorporation of genotype-by-environment (GxE) interactions for downstream prediction tasks. Results show that the CAE-based features improve early-stage yield predictions by up to 10% compared to traditional autoencoder-based features and outperform vegetation indices (VIs) by 9% across various growth stages. The CAE model also excels in separating environmental factors, achieving a high silhouette score of 0.919, indicating effective clustering of environmental features. Moreover, the CAE consistently outperforms standard models in unseen environments and unseen genotypes yield predictions, demonstrating strong generalizability. This study demonstrates the value of disentangling G and E effects for providing more accurate and early yield predictions that support informed decision-making in precision agriculture and plant breeding.
Why it matches plant phenotyping methods作物プロットの収量という植物形質を、衛星画像から推定する深層学習特徴抽出法を開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractHere, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery.
Reproduction assets foundThe paper's data availability statement provides public access to both the authors' analysis code (Bitbucket repository) and the paper-specific satellite plot-level images with ground-truth yield data (Dryad DOI deposit), directly reproducing this study's phenotyping measurements and analysis.Code · publicAll code is available at bitbucket at https://bitbucket.org/ JS has equity interests in Data2Bio, LLC, and Dryland GeneticsOpen asset ↗pdf-page:14 lines:1-66Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Accurate yield prediction before maize harvest is crucial for advancing agricultural management and ensuring food security. Unlike conventional approaches that rely on phenotypes from a single growth stage, this study models multiple traits across different developmental stages, all targeting final yield, thereby uncovering their dynamic and cumulative contributions. We introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data 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 consistently outperforms traditional machine learning and graph-based models in both trait completion and yield prediction. 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多段階の植物形質を補完・統合し、収量という植物形質を予測するグラフ手法を開発・ベンチマークしており、形質取得・推定ワークフローが中心です。
abstractWe introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data for yield prediction.
Reproduction assets foundThe paper's authors publicly release their Yield-Graph analysis code on GitHub; the phenotype datasets themselves are only available on request.Code · publicthe
manuscript. All authors read and approved the final manuscript.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable
request.
Code availability
The code developed to generate the results and analysis in this article is available at https://github.com/wjhhh2928/Yield-GraphOpen asset ↗https://github.com/wjhhh2928/Yield-Graphpdf-raw-page:14 lines:1-38Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction Monitoring the growth status and aboveground biomass of wild and cultivated medicinal herbs remains a persistent challenge in precision agriculture. Methods In this study, we developed machine learning and deep learning models to estimate SPAD values and biomass of Lamiophlomis rotata (Benth.). The models used hyperspectral data and time-series phenotypic traits from 508 samples collected across different altitudes. Regions of interest (ROIs) were manually defined from plant contours. The corresponding mean spectral profiles were then preprocessed. To improve feature selection, we proposed a Dynamic Reptile Search Algorithm-enhanced CARS (DRSA-CARS) method. This method integrates a dynamic behavioral strategy into the CARS framework to identify informative spectral bands. Vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM)-based texture parameters were extracted and combined with spectral features to construct the PLSR, SVR, FNN, and CNN models. Results Compared to CARS, the DRSA-CARS method reduced feature dimensionality by up to 75.7% for SPAD and 29.2% for biomass, while improving prediction accuracy ( R ²) by 24.4% and 34.7%, respectively. Among all models, the FNN achieved the highest performance, with R ² values of 0.7732 (training) and 0.7502 (testing) for SPAD and 0.8260 and 0.7933 for biomass. Feature fusion further improved predictive accuracy by 11% for SPAD and 30% for biomass compared to models based on individual feature types. Discussion These results demonstrate that coupling DRSA-CARS-based feature selection with deep learning provides a robust, non-destructive approach for evaluating plant growth status. This framework highlights the potential of hyperspectral imaging as a rapid, reliable, non-invasive tool for precision cultivation of medicinal herbs.
Why it matches plant phenotyping methodsハイパースペクトル画像からSPAD値とバイオマスという植物形質を非破壊推定し、特徴選択法と深層学習モデルを開発・評価しており、フェノタイピング手法が中心である。
abstractwe developed machine learning and deep learning models to estimate SPAD values and biomass of Lamiophlomis rotata (Benth.).
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2
Hyperparameters and settings of PLSR, SVR, FNN, and CNN models for above-ground biomass prediction.Open asset ↗lines:760-839Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像から植被率・草高・NDVIなどの植物形質を取得する高スループット表現型解析フレームワークを導入・検証しており、フェノタイピング手法が研究の中心です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe preprint's data availability statement deposits the datasets generated and analysed in the study (UAV-derived phenotypic measurements and field data) on Zenodo with a DOI that appears verbatim in the allowed URL list. No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:135-161Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions.
Why it matches plant phenotyping methods衛星画像から圃場レベルのイネ作付面積・収量を推定する分類および回帰手法が研究の中心であり、精度評価も実施しているため、植物形質推定の方法論として適格。
abstractThis research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that all data and code to reproduce the rice area and yield maps are publicly available in the authors' GitHub repository (ozdogan15/srilanka), which directly reproduces this paper's rice mapping and yield estimation analysis.Code · publicbrella Facility for Trade trust fund
(financed by the governments of the Netherlands, Norway, Sweden, Switzerland, and the United
Kingdom) and the World Bank’s Research Support Budget for financial support.
Data Availability Statement: All data and code to reproduce rice and yield maps are publicly
available at this repository: https://github.com/ozdogan15/srilanka#.
Acknowledgments: The authors acknowledge funding from the World Bank Whole of Economy
Program. We also thank the reviewers. The findings, interpretations, and conclusions expressed in
this paper are solely those of the authors and do not necessarily represent the views of the World
Bank, its affiliated organizations, or the ExOpen asset ↗ozdogan15/srilankapdf-layout-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Multispectral leaf canopy reflectance as measured by unmanned aerial vehicles is the result of genetic and environmental interactions driving plant physiochemical processes. These measures can then be used to construct relationship matrices for modeling genetic main effects. This type of phenotypic prediction is particularly relevant for trials with many entries, such as those used in early generation potato (Solanum tuberosum) breeding. We compared three methods for making predictions in our potato breeding program: first, using multispectral-derived relationship matrices; second, using the traditional approach based on genomic derived relationships; and third, using a combination of both. Multispectral bands were collected at five different time points for two market classes of potato: chipping and fresh market. We modeled genetic main effects for yield and quality traits at each time point and all stages combined. Models with multispectral relationship matrices exhibited better prediction accuracy for yield and roundness than genomic only models and models featuring spectra plus genomic kernels outperformed both single-kernel predictions in terms of accuracy for most traits. Time points were variably informative depending on the trait measured, however, for all traits combining across time points performed as well or better than single time point models. Similarly, using feature selection to limit our models to important variables did not improve prediction accuracy significantly. This work highlights two potential uses for spectral data in genomic prediction: first, as an alternative to genetic data and second, in combination with genetic data to increase precision of selection.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングを用いたキャノピー反射データをゲノム予測に組み込み、複数手法と予測精度を比較しており、植物表現型取得・推定ワークフローが研究の中心です。
abstractMultispectral leaf canopy reflectance as measured by unmanned aerial vehicles is the result of genetic and environmental interactions driving plant physiochemical processes.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing all data and scripts used for the multispectral genomic prediction analysis.Code · publicAll data and scripts used for this work are available at: https://github.com/shannonlabumn/GS_multispectra_analysis.git .Open asset ↗shannonlabumn/GS_multispectra_analysislines:375-518Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The accessibility of flying drones (Unoccupied Aerial Vehicles) presents scientists and managers with reproducible and cost-effective methods to monitor submerged aquatic vegetation. In particular, drone-borne topobathymetric LiDAR provides high-resolution (cm-scale), three-dimensional information about the geometry and structure of surveyed areas, allowing for quantification of vegetation volume in addition to bathymetry. For habitat-forming submerged and intertidal vegetation like seagrass, this information can advance research regarding the structure and patchiness of canopies in relation to biodiversity, blue carbon storage, and hydrodynamic processes. Here, we report how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in south-eastern Norway. After classifying LiDAR points using a Random Forest model, we created a Digital Terrain Model of the sea floor and a Digital Surface Model of the eelgrass canopy. From these models, we estimated eelgrass canopy volume to range between 862 and 1099 m3 across the small study area. From the volume, we estimated above-ground carbon storage in living eelgrass tissue to range between 96 and 122 kg. To our knowledge, this is the first study to utilise drone-borne LiDAR to quantify the volume and carbon-storage potential of a marine habitat-forming species like eelgrass, thereby demonstrating the potential of drone-borne LiDAR as an efficient tool to provide reproducible and high-resolution data for submerged aquatic habitats, including seagrass meadows.
Why it matches plant phenotyping methodsドローン搭載LiDARを用いて eelgrass のキャノピー体積という植物形態形質を推定する方法が研究の中心であり、分類、地形・表面モデル作成、再現可能な高解像度測定手法として記述されているため。
abstractHere, we report how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in south-eastern Norway.
Reproduction assets foundThe paper's R analysis code (point cloud cleaning, Random Forest classification, DTM/DSM/canopy height and biomass/carbon computations) is publicly available on the corresponding author's GitHub repository. The underlying LiDAR/field data are only available upon request, so no public data asset qualifies.Code · publicPre-print 15
Code for the present analysis is available at the corresponding author’s GitHub
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(https://github.com/charles-patrick-lavin/NIVA-SeaBee-LiDAR), while the data
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analysed are available upon request.
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Acknowledgements
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This work was funded by the Research Council of Norway and is a product of SeaBee
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(Norwegian Infrastructure for drone- based research, mapping and monitoring in the
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coastal zone, RCN project ID #296478). Additional funding was received frOpen asset ↗charles-patrick-lavin/NIVA-SeaBee-LiDARpdf-raw-page:15 lines:1-32Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Wheat spike morphology plays a critical role in determining grain yield and has garnered significant interest in genetics and breeding research. However, traditional measurement methods are limited to simple traits and fail to capture complex spike phenotypes with high precision, thus limiting progress in yield-related trait analysis. In this study, a deep learning pipeline, called Speakerphone, for acquiring precise wheat spike phenotypes was developed. Our pipeline achieved a mean intersection over union (mIoU) of 0.948 in spike segmentation. Additionally, the spike traits measured by our method strongly agreed with the manually measured values, with Pearson correlation coefficients of 0.9865 for spike length, 0.9753 for the number of spikelets per spike, and 0.9635 for fertile spikelets. Using experimental data of 221 wheat cultivars from various regions of Zhao County, Hebei Province, China, our pipeline extracted 45 phenotypes and analyzed their correlations with thousand-grain weight (TGW) and spike yield. Our findings indicate that precise measurements of spike area, spikelet area, and other phenotypic traits clarify the correlation between spike morphology and wheat yield. Through hierarchical clustering on the basis of spike morphology, we categorized wheat spikes into six classes and identified the phenotypic differences among these classes and their effects on TGW and yield. Furthermore, phenotypic differences among wheat cultivars from different geographical regions and over decades were revealed in this study, with an increase in the number of large-spike cultivars over time, especially in southern China. This research may help breeders understand the relationship between wheat spike morphology and yield, thus providing an important basis for future wheat breeding efforts.
Why it matches plant phenotyping methods小麦穂の画像から形態形質を抽出する深層学習パイプラインを開発し、セグメンテーション性能と手動測定との一致を検証しているため、フェノタイピング手法が研究の中心です。
abstracta deep learning pipeline, called Speakerphone, for acquiring precise wheat spike phenotypes was developed.
Reproduction assets foundThe paper's SpikePheno phenotyping pipeline (deep learning segmentation and trait extraction for wheat spikes) is explicitly stated to be publicly available on GitHub. No public dataset of the 2198 spike images or annotations is stated; the labelme link is a generic third-party tool, not a paper-specific asset.Code · publicThe full implementation of the spikePheno pipeline is available in GitHub at the following URL: https://github.com/Jiang-Phenomics-Lab/spikePheno .Open asset ↗Jiang-Phenomics-Lab/spikePhenolines:210-330Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems.
Why it matches plant phenotyping methods植物バイオマスを連続測定する低コストのロードセル式センサーシステムを開発し、手動収穫データで検証しており、植物フェノタイピング手法が中心である。
abstractThis work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications.
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 following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/s25154770/s1 , Data: sensor data test experiment; Data: sensor data validation experiment.Open asset ↗lines:115-311Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Common bean ( Phaseolus vulgaris L.) can fix atmospheric nitrogen (N) through symbiosis with Rhizobia species. This trait is often underutilized by growers and overlooked by breeders due to the laborious and costly evaluation techniques involved. There is a critical need for the development of new screening tools to enhance nitrogen fixation efficiency. Remote sensing techniques utilizing unmanned aerial systems offer a potential solution to this challenge, providing a high‐throughput phenotyping method for trait evaluation. In this study, we investigated the use of vegetation indices and machine learning (ML) methods in estimating symbiotic nitrogen fixation (SNF). Forty‐two black bean breeding lines from the Dry Bean Breeding Program at Michigan State University were grown and compared under both high and low N conditions. A random forest model developed to predict percent nitrogen derived from the atmosphere (%Ndfa) using remote sensing (RS) data resulted in an average accuracy of R 2 = 0.86. A 3‐year evaluation of these trials in Michigan demonstrated how seed yield under unfertilized conditions could be used as an indirect indicator of SNF ability. Two accurate prediction models for yield were developed using stepwise general linear modeling (StepwiseGLM) and Bayesian regularized artificial neural network (BRNeural Network) (stepwise general linear model r = 0.64; Bayesian regularized neural network r = 0.65). These results suggest that seed yield and RS data coupled with ML offer a promising tool to efficiently implement indirect selection for SNF in common bean.
Why it matches plant phenotyping methodsUASリモートセンシングと機械学習により、共生窒素固定という植物形質を推定するスクリーニング手法を開発・評価しており、表現型取得と予測モデルが研究の中心である。
abstractRemote sensing techniques utilizing unmanned aerial systems offer a potential solution to this challenge, providing a high‐throughput phenotyping method for trait evaluation.
Reproduction assets foundThe paper's data availability statement explicitly says the code and methodologies used in this study are available in the authors' public GitHub repository (msudrybeanbreeding). No phenotype dataset, imagery, or model checkpoint deposit is stated in the supplied blocks.Code · publicte helpful conversations and comments from J.D.
Kelly, which improved the quality of our final manuscript.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Code and methodologies used in this study are available in the
GitHub repository: https://github.com/msudrybeanbreeding
O RC I D
MasonJackson https://orcid.org/0009-0004-7635-0418
LeonardoVolpato https://orcid.org/0000-0003-1119-0615
EvanM. Wright https://orcid.org/0009-0003-7512-0963
ValerioHoyos-Villegas https://orcid.org/0000-0003-1080-9148
FranciscoE. Gomez https://orcid.org/0000-0002-2862-7118
R E F E R E N C E S
Ahamed, T., Tian, L., ZhangOpen asset ↗msudrybeanbreedingpdf-raw-page:13 lines:1-83Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R2
Why it matches plant phenotyping methodsUAV LiDARとフィールド分光法を用いて、植生残渣量（RDM）という植物状態を推定するセンサー手法の評価が研究の中心であり、単なる農業実験での routine measurement ではない。
titleEvaluating UAV LiDAR and Field Spectroscopy for Estimating Residual Dry Matter Across Conservation Grazing Lands
Reproduction assets foundThe paper's UAV LiDAR data (used to derive canopy height models for RDM estimation) is explicitly stated to be publicly available in the OpenTopography Community Dataspace. The KNB deposit containing RDM weights, field spectra, and analysis data is also mentioned, but its DOI URL is not among the allowed URLs, so only Dataset · publicAll the LiDAR data used in this study are publicly available in the
Open Topography Community Dataspace: https://doi.org/10.5069/G9S180QVOpen asset ↗10.5069/G9S180QVpdf-page:17 lines:1-33Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Phenotypic variation results from the combination of genotype, the environment, and their interaction. The ability to quantify the relative contributions of genetic and environmental factors to complex traits can help in breeding crops with superior adaptability for growth in varied environments. Here, we developed and extensively evaluated the performance of an explainable machine-learning framework named explainable genotype-by-environment interactions prediction (EXGEP) to accurately predict the grain yield in crops. To assess the performance of EXGEP, we applied it to a dataset comprising 70 693 phenotypic records of grain yield traits for 3793 hybrids (also including both genotype and environmental condition data). When used with four different combinations of genotypes and environmental data, EXGEP exceeded the yield prediction performance of the classic model Bayesian ridge regression model by 17.37%-42.35%. Moreover, EXGEP incorporates SHapley Additive exPlanations values that can uncover complex nonlinear relationships between genotype and environment and identify key features, and their interactions, that provide the main contributions to model performance, thus enhancing our understanding of genotype-by-environment interactions. Additionally, data from a series of tests support that EXGEP exhibits superior performance in terms of prediction accuracy and explainability. Our development of EXGEP and comparisons of it against alternative models provides valuable insights into methods for accurately predicting complex traits in multiple environments.
Why it matches plant phenotyping methods作物の穀粒収量という植物形質を予測する説明可能な機械学習フレームワークを開発し、他モデルとの性能比較・評価を行っており、表現型推定手法が研究の中心である。
abstractHere, we developed and extensively evaluated the performance of an explainable machine-learning framework named explainable genotype-by-environment interactions prediction (EXGEP) to accurately predict the grain yield in crops.
Reproduction assets foundThe paper's raw G2F maize genotype/phenotype/environment data are publicly deposited (Zenodo DOI 10.25739/tq5e-ak26) and the authors' EXGEP analysis code is on GitHub (AIBreeding/EXGEP), with an accompanying web server.Dataset · publicThese raw data are available from: https://doi.org/10.25739/tq5e-ak26 .Open asset ↗doi.org · 10.25739/tq5e-ak26lines:744-907Code · publicThe codes for the EXGEP framework used in this project are available on GitHub: https://github.com/AIBreeding/EXGEP .Open asset ↗github.com/AIBreeding/EXGEPlines:744-907Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The aboveground biomass (AGB) of crops is an essential metric for monitoring crop growth, making timely and accurate AGB forecasting critical for effective agricultural management. The introduction of Unmanned Aerial Vehicles (UAVs) and advanced sensor technologies has revolutionized traditional AGB prediction techniques. Currently, machine learning (ML) combined with UAV data are commonly utilized, along with the Vegetation Index Weighted Canopy Volume Model (CVM VI ) for AGB prediction. Nevertheless, there is limited investigation into how these methods perform across different agricultural conditions. This study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments. We utilized LiDAR, multispectral (MS), thermal infrared (TIR), along with measured AGB and Leaf Area Index (LAI) data from various growth stages to develop a stacking ensemble learning model. This model effectively integrates data from multiple sources, resulting in a strong prediction performance with R 2 of 0.86, Mean Absolute Error (MAE) of 1.54 t/ha, and Root Mean Square Error (RMSE) of 2.06 t/ha. Meanwhile, the analysis of the accuracy of CVM VI revealed its efficacy during the early-stage when corn is short, with its predictive capability diminishing as AGB increases. Consequently, we recommend the CVM VI for early-stage AGB prediction, which can streamline data collection and computational efforts. In contrast, the ML approach, which benefits from data fusion, is more appropriate for predicting AGB during the mid to late growth stages. This study enhances AGB prediction accuracy and speed, providing critical understanding of regional AGB dynamics and supporting better agricultural decision-making.
Why it matches plant phenotyping methodsUAVのLiDAR・マルチスペクトル・熱赤外データを統合し、トウモロコシの地上部バイオマスを推定するモデルを開発・比較・評価しており、植物形質取得手法が中心である。
abstractThis study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits authors' model training code and test data at a public GitHub repository, which qualifies as a paper-specific public code asset for the AGB prediction analysis.Code · publicCode and test data for model training are available at https://github.com/Joker1xuan/model_training .Open asset ↗Joker1xuan/model_traininglines:323-356Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Multispectral optical data significantly enhances cereal crop monitoring by enabling precise tracking of growth stages, early detection of germination issues, and assessment of plant health. This study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring. The aim was to determine the optimal UAV imaging timing that aligns with proximal sensor measurements to improve growth stage assessments. Experiments were conducted on 41 cereal genotypes, including ancient and modern varieties, under two nitrogen top-dress dosages across 130 plots. The top ten performing genotypes were analyzed to identify resilient varieties adaptable to climate change and evolving field conditions. Our results demonstrate that vegetation indices during booting and spike emergence stages consistently predict yield potential, offering a robust framework for early-stage yield estimation. Additionally, we provide a comparative analysis of UAV and handheld sensor data, highlighting their respective strengths and limitations. Three vegetation indices, GRDVI, NDVI and SAVI demonstrated a very strong average positive correlation: 0.957, 0.954 and 0.944 across the selected genotypes from different performance levels. The combined dataset supports improved fertilization strategies, optimized seeding cycles, and identification of genotypes with stable agronomic traits. This study underscores the synergistic potential of aerial and proximal sensing technologies for next-generation cereal crop management and precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と近接センサーを統合し、画像取得時期、センサーデータの比較、植物生育段階・収量予測を評価しており、植物形質取得手法が研究の中心である。
abstractThis study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's dataset (UAV multispectral and Plant-O-Meter phenotyping measurements) on Zenodo with a public DOI, matching an allowed URL. No separate analysis code repository is stated.Dataset · publicWe have made the dataset publicly available, and it can be accessed through the following reference: Grbović Ž, Ivošević B, Buden M, Waqar R, Pajević N, Ljubičić N, et al. (2025) Integrating UAV multispectral imaging and proximal sensing for high-precision cereal crop monitoring [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15133473 .Open asset ↗Zenodo · 10.5281/zenodo.15133473lines:281-306Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring, essential for crop-growth management. However, the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application. This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS) aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information. Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical imaging. A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was proposed, and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information. Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest (RF) achieved better results in predicting leaf area index (LAI) and above-ground biomass (AGB) for wheat and rice. For wheat, the R 2 and root mean square error (RMSE) values for the LAI and AGB prediction models were 0.81 and 0.85, and 0.682 and 1.127 t/ha, respectively. For rice, the R 2 and RMSE values for the LAI and AGB prediction models were 0.89 and 0.93, and 0.818 and 0.866 t/ha, respectively. Overall, SMICGS provides a reliable foundational tool for real-time, non-destructive monitoring of field crop growth information, offering significant potential for the precise management of agricultural production.
Why it matches plant phenotyping methods作物生育情報を定量化するUAVマルチスペクトルセンサー、補正法、処理システムを開発し、LAIと地上部バイオマス推定を検証しており、植物フェノタイピング手法が中心である。
abstractThis paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS)
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe figures, tables and data mentioned in the article can be downloaded from https://github.com/ikjkj2/Plant-Phenomics .Open asset ↗ikjkj2/Plant-Phenomicslines:395-413Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis can be useful for assessing crop health and predicting yield. Instead of expensive equipment, smartphones are considered an accessible and low-cost alternative. The objectives of this study were to evaluate whether fresh weight in green and red lettuce could be predicted by leaf color (intensity of green color measured by RGB) under different fertilizer treatments using RGB imaging from two widely used smartphone models (Samsung Galaxy and Apple iPhone). The two smartphones showed similar longitudinal patterns of RGB data (the intensity and dark green proportion), but the absolute difference in the RGB data was significantly different. Therefore, the averaged results were used for the analyses. Color intensity and dark green proportion were associated with the fresh lettuce weight (p = 0.005, 0.003, 0.014 and p < 0.001, respectively). This study suggests that farmers and practitioners can use these economic devices as a non-destructive method to diagnose and monitor the nutritional status and predict lettuce yield.
Why it matches plant phenotyping methodsスマートフォンRGB画像から葉色を抽出し、レタスの生体重・栄養状態を非破壊推定する手法が研究の中心であり、植物表現型取得への実質的な応用に該当する。
abstractsmartphones are considered an accessible and low-cost alternative
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 separated GIF files were uploaded to the image analysis program freely available at http://mkwak.org/imgarea .Open asset ↗lines:321-349Code / dataset availability confirmedOpenAlex · Europe PMC · checked 13 Sept 2026
Community-based forest restoration has the potential to sequester large amounts of atmospheric carbon, avoid forest degradation, and support sustainable development. However, if partnered with international funders, such projects often require robust and transparent aboveground carbon measurements to secure payments, and current monitoring approaches are not necessarily appropriate due to costs, scale, and complexity. The use of consumer-grade drones in combination with open source structure-from-motion photogrammetry may provide a solution. In this study, we tested the suitability of a simplified drone-based method for measuring aboveground carbon density in heavily degraded tropical forests at a 2 ha restoration site in Sabah, Malaysia, comparing our results against established field-based methods. We used structure-from-motion photogrammetry to generate canopy height models from drone imagery, and applied multiple pre-published plot-aggregate allometric equations to examine the importance of utilising regionally calibrated allometric equations. Our results suggest that this simplified method can produce aboveground carbon density measurements of a similar magnitude to field-based methods, quickly and only with a single input metric. However, there are greater levels of uncertainty in carbon density measurements due to errors associated with canopy height measurements from drones. Our findings also highlight the importance of selecting regionally calibrated allometric equations for this approach. At scales between 1 and 100 ha, drone-based methods provide an appealing option for data acquisition and carbon measurement, balancing trade-offs between accuracy, simplicity, and cost effectiveness and coinciding well with the needs of community-scale aboveground carbon measurement. Of importance, we also discuss considerations relating to the accessibility of this method for community use, beyond purchasing a drone, that must not be overlooked. Nevertheless, the method presented here lays the foundations for a simple workflow for measuring aboveground carbon density at a community scale that can be refined in future studies.
Why it matches plant phenotyping methodsドローン画像とSfMから森林キャノピー高モデルを生成し、地上部炭素密度を推定する測定ワークフローを開発・検証しており、植物群落の形態・状態の取得が研究の中心である。
abstractWe used structure-from-motion photogrammetry to generate canopy height models from drone imagery
Reproduction assets foundThe authors state that all drone images and field data underlying this study's aboveground carbon density measurements are publicly available in the CEDA Archive with a catalogue record and DOI. This is a paper-specific, public, directly actionable dataset. Other URLs (OpenDroneMap, LAStools, QGIS, PyCrown) are genericDataset · publicAll drone images and field data are publicly available from the CEDA Archive, a NERC repository for earth observation data. The dataset can be accessed via the following catalogue record link: https://catalogue.ceda.ac.uk/uuid/98692ec457ee431cacc4027820e46411/ (DOI: https://doi.org/10.5285/98692ec457ee431cacc4027820e46411 ).Open asset ↗CEDA Archive · 10.5285/98692ec457ee431cacc4027820e46411lines:151-177Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Alfalfa is a deep-rooted perennial forage crop with diverse drought-tolerant traits. This study evaluated 250 alfalfa half-sib populations over three growing seasons (2021–2023) under irrigated and rainfed conditions in the Mediterranean drought-prone region of Central Chile (Cauquenes), aiming to identify high-yielding, drought-tolerant populations using remote sensing. Specifically, we assessed RGB-derived indices and canopy temperature difference (CTD; Tc − Ta) as proxies for forage yield (FY). The results showed considerable variation in FY across populations. Under rainfed conditions, winter FY ranged from 1.4 to 6.1 Mg ha−1 and total FY from 3.7 to 14.7 Mg ha−1. Under irrigation, winter FY reached up to 8.2 Mg ha−1 and total FY up to 25.1 Mg ha−1. The AlfaL4-5 (SARDI7), AlfaL57-7 (WL903), and AlfaL62-9 (Baldrich350) populations consistently produced the highest yields across regimes. RGB indices such as hue, saturation, b*, v*, GA, and GGA positively correlated with FY, while intensity, lightness, a*, and u* correlated negatively. CTD showed a significant negative correlation with FY across all seasons and water regimes. These findings highlight the potential of RGB imaging and CTD as effective, high-throughput field phenotyping tools for selecting drought-resilient alfalfa genotypes in Mediterranean environments.
Why it matches plant phenotyping methodsRGB画像指標と冠層温度差を用いた高スループット表現型解析を、アルファルファ集団の収量・干ばつ耐性選抜に実質的に適用しており、表現型取得手法が中心的である。
titleSelecting High Forage-Yielding Alfalfa Populations in a Mediterranean Drought-Prone Environment Using High-Throughput Phenotyping
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicthe Mosaic tool software and Cereal-Scanner plugin,
developed by Shawn Kefauver from the University of Barcelona, were utilized for further
analysis (available at https://gitlab.com/sckefauver/cerealscanner (accessed on 6 March
2025)).Open asset ↗gitlab.com/sckefauver/cerealscannerpdf-page:7 lines:1-55Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R2 values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems.
Why it matches plant phenotyping methods画像・VIS-NIRセンシング、画像セグメンテーション、特徴抽出、機械学習による牧草バイオマスおよび品質形質の推定が研究の中心であり、植物フェノタイピング手法の開発・応用に該当する。
abstractThis study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies.
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 · publicSupplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/agriengineering7040111/s1. Database S1: Database of experiment.Open asset ↗Database S1pdf-page:27 lines:1-54Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The unmanned aerial vehicle (UAV) platform has emerged as a powerful tool in soybean (Glycine max (L.) Merr.) breeding phenotype research due to its high throughput and adaptability. However, previous studies have predominantly relied on statistical features like vegetation indices and textures, overlooking the crucial structural information embedded in the data. Feature fusion has often been confined to a one-dimensional exponential form, which can decouple spatial and spectral information and neglect their interactions at the data level. In this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies. Newly point cloud deep learning models SoyNet and SoyNet-Res were further created with two novel data-level fusion that integrate spatial structure and color information. Our results reveal that incorporating RGB color and vegetation index (VI) spectral information with spatial structure information, leads to a significant reduction in root mean square error (RMSE) for yield estimation (22.55 kg ha -1 ) and an improvement in F1-score for five-class lodging discrimination (0.06) at S7 growth stage. The SoyNet-Res model employing multi-task learning exhibits better accuracy in both yield estimation (RMSE: 349.45 kg ha -1 ) when compared to the H2O-AutoML. Furthermore, our findings indicate that multi-task deep learning outperforms single-task learning in lodging discrimination, achieving an accuracy top-2 of 0.87 and accuracy top-3 of 0.97 for five-class. In conclusion, the point cloud deep learning method exhibits tremendous potential in learning multi-phenotype tasks, laying the foundation for optimizing soybean breeding programs.
Why it matches plant phenotyping methodsUAV・SfM-MVSによるダイズ群落の3D構造再構成と、収量推定・倒伏判別のための専用深層学習モデル開発が研究の中心であり、再利用可能な表現型取得・推定手法に該当する。
abstractIn this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies.
Reproduction assets foundThe article's Data availability statement explicitly says the code and data used in the study (soybean UAV point cloud phenotyping, SoyNet/SoyNet-Res models, yield/lodging analysis) are publicly downloadable from the authors' GitLab repository.Code · publicData availability
The code and data mentioned in the article can be downloaded from https://gitlab.com/zlyzly28/plant-phenomics .Open asset ↗gitlab.com/zlyzly28/plant-phenomicslines:588-659Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Leaf rust and yellow rust are globally significant fungal diseases that severely impact wheat production, causing yield losses of up to 60% in highly susceptible cultivars. Early and accurate detection is crucial for integrating precision crop protection strategies to mitigate these losses. This study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity. Results showed that grain yield decreased by 10–50% depending on cultivar susceptibility, with the durum wheat cultivar ‘Kiko Nick’ and bread wheat ‘Califa’ exhibiting the most severe reductions (~50–60%). While plant height and biomass remained relatively unaffected, LiDAR-derived intensity values strongly correlated with disease severity (R2 = 0.62–0.81, depending on the cultivar and infection stage). These findings demonstrate that LiDAR can serve as a non-destructive, high-throughput tool for early rust detection and biomass estimation, highlighting its potential for integration into precision agriculture workflows to enhance disease monitoring and improve wheat yield forecasting. To promote transparency and reproducibility, the dataset used in this study is openly available on Zenodo, and all processing code is accessible via GitHub, cited at the end of this manuscript.
Why it matches plant phenotyping methodsLiDARによる小麦の病害状態・バイオマス等の非破壊推定を中心に評価しており、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。
abstractThis study investigates the potential of 3D LiDAR technology for monitoring rust-induced physiological changes in wheat by analyzing variations in plant height, biomass, and light reflectance intensity.
Reproduction assets foundThe paper's LiDAR-derived wheat rust phenotyping dataset is openly available on Zenodo (DOI 10.5281/zenodo.14889285), and the authors' point-cloud processing and parameter-extraction code is publicly available on GitHub (eapolo/agrolidarwheatrust). Both are explicitly stated in the Data Availability Statement.Dataset · publicData Availability Statement: The dataset used in this study has been published on the Zenodo
platform under the DOI: https://doi.org/10.5281/zenodo.14889285Open asset ↗Zenodo · 10.5281/zenodo.14889285pdf-page:21 lines:1-60Code · publicalong with the code, which is
available in the GitHub repository at https://github.com/eapolo/agrolidarwheatrust, accessed on
10 March 2025.Open asset ↗github.com/eapolo/agrolidarwheatrustpdf-page:21 lines:1-60Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
In the early stages of selection, many plant breeding programmes still rely on visual evaluations of traits by experienced breeders. While this approach has proven to be effective, it requires considerable time, labour and expertise. Moreover, its subjective nature makes it difficult to reproduce and compare evaluations. The field of automated high-throughput phenotyping aims to resolve these issues. A widely adopted strategy uses drone images processed by machine learning algorithms to characterise phenotypes. This approach was used in the present study to assess the dry matter yield of tall fescue and its accuracy was compared to that of the breeder's evaluations, using field measurements as ground truth. RGB images of tall fescue individuals were processed by two types of predictive models: a random forest and convolutional neural network. In addition to computing dry matter yield, the two methods were applied to identify the top 10% highest-yielding plants and predict the breeder's score. The convolutional neural network outperformed the random forest method and exceeded the predictive power of the breeder's eye. It predicted dry matter yield with an R² of 0.62, which surpassed the accuracy of the breeder's score by 8 percentage points. Additionally, the algorithm demonstrated strong performance in identifying top-performing plants and estimating the breeder's score, achieving balanced accuracies of 0.81 and 0.74, respectively. These findings indicate that the tested automated phenotyping approach could not only offer improvements in cost, time efficiency and objectivity, but also enhance selection accuracy. As a result, this technique has the potential to increase overall breeding efficiency, accelerate genetic progress, and shorten the time to market. To conclude, phenotyping by means of RGB-based machine learning models provides a reliable alternative or addition to the visual evaluation of selection candidates in a tall fescue breeding programme.
Why it matches plant phenotyping methodsRGBドローン画像と機械学習により乾物収量などの植物形質を推定し、育種家評価および実測値と比較検証しており、フェノタイピング手法が中心である。
abstractThe field of automated high-throughput phenotyping aims to resolve these issues.
Reproduction assets foundThe paper's data availability statement explicitly deposits the study's datasets (RGB image-derived phenotyping data for tall fescue yield prediction) on Zenodo and all analysis scripts on a public GitHub repository, both with URLs matching allowed_urls.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/14289667 .Open asset ↗zenodo · 14289667lines:530-564Code · publicAll scripts used are provided in the following GitHub repository: https://github.com/SarahGhysels/Estimation-of-individual-plant-performance-in-tall-fescue-through-RGB-image-analysis .Open asset ↗github · SarahGhysels/Estimation-of-individual-plant-performance-in-tall-fescue-through-RGB-image-analysislines:530-564Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions. The most comprehensive parametric model tested, which included SNPs, HSI, and environmental covariates data, consistently achieved the best results, closely followed by machine learning (ML) approaches when considering the same omics data. For example, the most comprehensive model (M9), under the forward prediction cross-validation scheme, predicted the GY of the 2023 growing season using data from 2021 and 2022 for a correlation between predicted and observed values of 0.53. This model demonstrated superior performance compared to less complex models, emphasizing the advantage of integrating numerous data sources and their interactive effects. Furthermore, when comparing the top 25% of the predicted lines versus the corresponding observed lines with the highest GY, the M9 model returned a coincide index (CI) of 55% (i.e., in both sets, 55% of the top 25% values were common), whereas for the highest performing ML model (gradient boosting regression), the CI was of 46%. This study highlights the potential of multi-data source approaches to accelerate the selection of heat-tolerant wheat genotypes.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像を用いた小麦収量形質の推定を、ゲノム・環境データとの統合モデルで検証しており、形質予測性能の比較が研究の中心である。
abstractIntegrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits.
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2件を確認しました。Dataset · publicThe datasets used in this study can be found at http://datadryad.org/stash/share/t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDg and http://datadryad.org/stash/share/UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpk .Open asset ↗Dryad · t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDglines:343-470Dataset · publicThe datasets used in this study can be found at http://datadryad.org/stash/share/t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDg and http://datadryad.org/stash/share/UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpk .Open asset ↗Dryad · UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpklines:343-470Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Monitoring plant growth is crucial for effective crop management, and using color and depth (RGBD) cameras to model lettuce has emerged as one of the most convenient and non-invasive methods. In recent years, deep learning techniques, particularly neural networks, have become popular for estimating lettuce fresh weight. However, these models are typically specific to particular datasets, lack domain adaptation, and are often limited by the availability of open-access datasets. In this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce. This new approach was compared to existing methods that reconstruct surfaces from point clouds, such as Ball Pivoting and Alpha Shapes. The proposed method creates a tight hull around the plant's point cloud, preserving high detail of the rosette structure while filling in surface holes in areas not visible to 3D cameras. Using a linear regression model, we estimated fresh weight for this dataset, achieving a root mean square error (RMSE) of 18.2 g when using only the estimated plant volume, and 17.3 g when both volume and geometric features were included. Additionally, we introduced new geometric features that characterize leaf density, which could be useful for breeding applications. A dataset of 402 point clouds of lettuce plants, captured before harvest, was compiled using one top-down and three side-view 3D cameras.
Why it matches plant phenotyping methodsRGB-D画像からレタスの構造・体積・葉密度を抽出し、生体重推定を検証する手法開発が研究の中心であり、データセットも構築している。
abstractIn this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce.
Reproduction assets foundThe paper's own lettuce 3D point cloud dataset (Pii, 402 point clouds with fresh weight references) is deposited on Zenodo, and the vacuum-package surface reconstruction code plus data processing scripts are publicly available on the authors' GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicData used in this study and developed models are available on Zenodo storage service https://zenodo.org/records/8410252 .Open asset ↗Zenodo · 8410252lines:158-220Code · publicThe code used at this study is available at https://github.com/VicB18/LettuceFW (accessed on 1 November 2024).Open asset ↗GitHub · VicB18/LettuceFWlines:158-220Code · publicThe code for the vacuum package method, along with the data processing scripts used in this study, are available in the Supplementary Information.Open asset ↗lines:98-114Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The primary challenge facing the agricultural sector, which is essential for ensuring global food security, is enhancing crop productivity while effectively addressing the challenges posed by plant diseases. Advanced technologies have the potential to completely transform agricultural methods, especially in the areas of computer vision and machine learning. This study uses meteorological as well as fruit and vegetables image datasets to create an integrated agricultural decision support system for crop yield estimation and disease prediction. By enabling early plant disease detection and precise crop yield estimates, the system seeks to improve precision agriculture techniques. To analyze and classify the images and predict the possibility of crop disease harming fruits and vegetables, a Convolutional Neural Network (CNN) deep learning model is used. The Multilayer Perceptron algorithm is used to train the model using a large dataset that contains historical meteorological data, allowing it to identify patterns and connections between environmental conditions. Finally, farmers receive an SMS notice with prediction specifics.
Why it matches plant phenotyping methods植物画像から病害状態を推定し、気象・画像データから収量を推定するCNN/MLP統合手法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractThis study uses meteorological as well as fruit and vegetables image datasets to create an integrated agricultural decision support system for crop yield estimation and disease prediction.
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 first dataset used in the study is a collection of fruit and vegetable images obtained
from Kaggle (https://www.kaggle.com/datasets/muhammad0subhan/fruit-and-vegetable-Open asset ↗Kaggle · muhammad0subhan/fruit-and-vegetable-pdf-page:5 lines:1-28Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including plant science, animal science, breeding, statistics, computational biology, and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 focused on genetics. Other high-performing teams' methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics, weather, and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.
Why it matches plant phenotyping methods遺伝・環境情報からトウモロコシ収量という植物形質を予測するモデルを競争形式で比較・評価しており、計算的形質推定とベンチマークが中心である。
titleGlobal genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates.
Reproduction assets foundThe paper is the G2F maize G×E prediction competition report. Its curated phenotype/genotype/weather/EC dataset is public (DOI 10.25739/tq5e-ak26), but that DOI is not among the allowed URLs, so it cannot be listed. However, the authors explicitly state that code from all participating teams is publicly available, and Code · publicour abilities to
solve critical, and technically challenging, problems.
Data availability
All data used in this manuscript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EOpen asset ↗ksegaba/Genomes2Field_Competitionpdf-raw-page:13 lines:1-91Code · publicta availability
All data used in this manuscript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartyboiOpen asset ↗pdf-raw-page:13 lines:1-91Code · publicript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartybois: https://github.com/Thyra/g2f-maize-challenge-202Open asset ↗mwylerCH/GxEcompetitionpdf-raw-page:13 lines:1-91Code · public0.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartybois: https://github.com/Thyra/g2f-maize-challenge-2022
Kernel of Truth: https://github.com/robertkhu/mOpen asset ↗alenxav/Lecturespdf-raw-page:13 lines:1-91Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Mungbean is an important sub‐tropical legume crop grown across Asia, Africa, and Australia. Yield improvement is crucial for expanding production, but phenotyping important traits across diverse environments using current approaches is challenging, limiting the scale and complexity of information captured. High‐throughput phenotyping platforms offer a solution by rapidly screening traits at scale. This study deploys an unmanned aerial vehicle (UAV) platform to determine the potential of phenotyping a range of agronomic and physiological traits within a diverse mungbean population evaluated across three field trials. Three predictive data‐driven modeling approaches were undertaken to evaluate performance accuracy in predicting these traits: linear regression, stepwise regression, and partial least squares regression. Results show that using the geometric trait “coverage” as a proxy is most suitable for screening visual traits like early vigor. For functional traits (i.e., aboveground biomass), predictive data‐driven models demonstrate high accuracy during early‐ and mid‐canopy development stages ( R 2 0.79, root mean square error [RMSE] 4.08 and R 2 0.8, RMSE 26.92, respectively), but accuracy declines in late‐canopy development ( R 2 0.33 and RMSE 43.15). Prediction accuracy can be optimized by using different modeling approaches at different stages during the transition from early‐ to mid‐canopy development as well as canopy closure. Similar findings were observed when examining the prediction models for the physiological trait, stomatal conductance ( R 2 0.69 and RMSE 0.10). These approaches are expected to enable breeders and researchers to incorporate UAV‐based phenotyping systems into mungbean improvement programs. Such approaches might be most efficiently used at scale if applied as part of a “real‐time” calibration approach.
Why it matches plant phenotyping methodsUAVプラットフォームと予測モデルにより、マングビーンの農業・生理形質を推定し、精度を評価することが研究の中心である。
abstractThis study deploys an unmanned aerial vehicle (UAV) platform to determine the potential of phenotyping a range of agronomic and physiological traits within a diverse mungbean population evaluated across three field trials.
Reproduction assets foundThe article's Data Availability Statement points to a public UQ eSpace deposit (DOI 10.48610/20cffed.O) containing the paper's UAV phenotyping dataset. No author analysis code or trained model checkpoints are explicitly deposited; the Supporting Information is only generically referenced.Dataset · publicy The University of
Queensland, as part of the Wiley - The University of Queens-
land agreement via the Council of Australian University
Librarians.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
This dataset is available at UQ eSpace: https://doi.org/10.48610/20cffed.O RC I D
ShaniceVanHaeften https://orcid.org/0000-0003-0412-3457
Daniel Smith https://orcid.org/0000-0002-5867-9613
HannahRobinson https://orcid.org/0000-0002-8303-8076
CaitlinDudley https://orcid.org/0000-0001-5297-7487
YichenKang https://orcid.org/0000-0002-3613-7426
LeeT. Hickey https://orcid.org/0000-0001-6909-7101
Andries PoOpen asset ↗UQ eSpace · 10.48610/20cffed.Opdf-raw-page:15 lines:1-87Code / dataset availability confirmedOpenAlex · arXiv · checked 6 Sept 2026
Yield is one of the core goals of crop breeding. By predicting the potential yield of different breeding materials, breeders can screen these materials at various growth stages to select the best performing. Based on unmanned aerial vehicle remote sensing technology, high-throughput crop phenotyping data in breeding areas is collected to provide data support for the breeding decisions of breeders. However, the accuracy of current yield predictions still requires improvement, and the usability and user-friendliness of yield forecasting tools remain suboptimal. To address these challenges, this study introduces a hybrid method and tool for crop yield prediction, designed to allow breeders to interactively and accurately predict wheat yield by chatting with a large language model (LLM). First, the newly designed data assimilation algorithm is used to assimilate the leaf area index into the WOFOST model. Then, selected outputs from the assimilation process, along with remote sensing inversion results, are used to drive the time-series temporal fusion transformer model for wheat yield prediction. Finally, based on this hybrid method and leveraging an LLM with retrieval augmented generation technology, we developed an interactive yield prediction Web tool that is user-friendly and supports sustainable data updates. This tool integrates multi-source data to assist breeding decision-making. This study aims to accelerate the identification of high-yield materials in the breeding process, enhance breeding efficiency, and enable more scientific and smart breeding decisions.
Why it matches plant phenotyping methodsUAVリモートセンシングによる作物フェノタイピングデータを基盤に、収量推定手法と対話型Webツールを開発しており、植物形質（小麦収量）の取得・推定が研究の中心である。
abstractBased on unmanned aerial vehicle remote sensing technology, high-throughput crop phenotyping data in breeding areas is collected to provide data support for the breeding decisions of breeders.
Reproduction assets foundThe article states that all study data (UAV remote sensing, LAI/CH phenotyping, yield, meteorological and soil data) are publicly available via a Zenodo deposit, which directly reproduces this paper's plant-phenotyping measurements. No author analysis code or trained model checkpoint URL is explicitly provided; other LDataset · publicAll data in this study are publicly available (https://doi.org/10.5281/zenodo.14376799).Open asset ↗zenodo · 10.5281/zenodo.14376799pdf-page:6 lines:1-52Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Global climate change has driven breeding programs to develop abiotic stress‐resilient plant varieties. Traditionally, assessing drought resilience involves labor‐intensive and time‐consuming processes. This study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle. We grew transgenic maize hybrids in two trials, one irrigated and another subjected to drought stress, and used a drone equipped with red–green–blue (RGB) and multispectral sensors to capture images of the plots over time. Machine learning models and various prediction scenarios revealed significant correlations between vegetation indices over time. Interestingly, the RGB sensor outperformed the multispectral sensor in trait prediction. Prediction accuracy across scenarios with untested genotypes and environments ranged from 0.40 to 0.70 for grain yield, 0.43 to 0.69 for days to anthesis, 0.51 to 0.67 for days to silking, and 0.35 to 0.57 for plant height. Ridge and random forest models consistently delivered the most accurate predictions across traits and environments. The vegetation indices normalized green–red difference index, VARI, and RCC also effectively predicted and captured the plant response to drought. This study highlights the value of UAS phenotyping as a practical tool for assessing abiotic stress due to its straightforward implementation.
Why it matches plant phenotyping methodsUASによるRGB・マルチスペクトル画像と機械学習で、作物形質および干ばつ応答を予測するフェノタイピング手法を、複数環境・遺伝子型で検証しているため。
abstractThis study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle.
Reproduction assets foundThe paper's data availability statement says all codes and datasets (phenomic prediction scripts, folder 'Phenomic prediction', and described datasets) are publicly available at the authors' GCCRC publications page and on Dryad (doi:10.5061/dryad.0zpc8677b).Code · public14 of 16 PEREIRA ET AL.
in this work to perform phenomic prediction for all the eight
models and the four cross-validation scenarios were given as
examples in the folder “Phenomic prediction.” All the codes
and the datasets described are available at https://www.gccrc.unicamp.br/publications/ and https://doi.org/10.5061/dryad.0zpc8677b.O RC I D
HelcioDuartePereira https://orcid.org/0000-0002-2837-9396
Juliana Vieira Almeida Nonato https://orcid.org/0000-0003-4448-4652
Rafaela CarolineRangni MoltocaroDuarte https://orcid.org/0000-0003-2622-3758
Isabel Rodrigues Gerhardt https://orcid.org/0000-0003-1397-0199
RicardoAuOpen asset ↗GCCRCpdf-raw-page:14 lines:1-75Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In plant breeding and genetics, predictive models traditionally rely on compact representations of high-dimensional data, often using methods like Principal Component Analysis (PCA) and, more recently, Autoencoders (AE). However, these methods do not separate genotype-specific and environment-specific features, limiting their ability to accurately predict traits influenced by both genetic and environmental factors. We hypothesize that disentangling these representations into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employed a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrated superior modeling of environmental influences and out-performs PCA (principal component analysis), PLSR (Partial Least square regression) and vanilla autoencoders by 7 times for 'Days to Pollen' trait and 10 times improved predictive performance for 'Yield'. By disentangling latent features, the CAE provided a powerful tool for precision breeding and genetic research. This work has significantly enhanced trait prediction models, advancing agricultural and biological sciences.
Why it matches plant phenotyping methods植物形質（開花日数・収量）の予測を目的とする新規オートエンコーダを開発し、既存手法と比較評価しており、計算的な形質推定手法が研究の中心である。
abstractwe developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features.
Reproduction assets foundThe paper's data availability statement explicitly deposits the hyperspectral reflectance dataset and trained model weights on Figshare, and the authors' analysis code on a public Bitbucket repository (baskargroup/cae_hyperspectral). Both are paper-specific, public, and actionable.Dataset · publicte the technical advantages of disentanglement, it is not immediately clear how to connect these disentangled features to biological insights.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/articles/dataset/Hyperspectral_reflectance_data_molecular_and_weights_for_trained_model/24808491/4 ; https://bitbucket.org/baskargroup/cae_hyperspectral/src/main/ .
Author contributions
AP: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. TJ: Conceptualization, Open asset ↗figshare · 24808491lines:460-495Code / dataset availability confirmedOpenAlex · Crossref · checked 6 Sept 2026
Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and quality. On the other hand, high-yielding crops bring in high profits per acre of planting. Harvesting and quantifying the yield for each blueberry breeding accession are labor-intensive and impractical. Instead, visual ratings as an estimation of yield are often used as a faster way to quantify the yield, which is categorical and subjective. In this study, we developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield, overcoming the labor constraints of obtaining high-frequency data. We aim to facilitate further research in computer vision and precision agriculture by publishing the labeled image dataset and the trained model. In this research, true-color images of blueberry bushes were collected, annotated, and used to train a deep neural network object detection model [You Only Look Once (YOLOv11)] to detect mature and immature berries. Different versions of YOLOv11 were used, including nano, small, and medium, which had similar performance, while the medium version had slightly higher metrics. The YOLOv11m model shows strong performance for the mature berry class, with a precision of 0.90 and an F1 score of 0.90. The precision and recall for detecting immature berries were 0.81 and 0.79. The model was tested on 10 blueberry bushes by hand harvesting and weighing blueberries. The results showed that the model detects approximately 25% of the berries on the bushes, and the correlation coefficients between model-detected and hand-harvested traits were 0.66, 0.86, and 0.72 for mature fruit count, immature fruit count, and mature ratio, respectively. The model applied to 91 blueberry advance selections and categorized them into groups with diverse levels of maturity and productivity using principal component analysis (PCA). These results inform the harvest window and yield of these breeding lines with precision and objectivity through berry classification and quantification. This model will be helpful for blueberry breeders, enabling more efficient selection, and for growers, helping them accurately estimate optimal harvest windows. This open-source tool can potentially enhance research capabilities and agricultural productivity.
Why it matches plant phenotyping methodsブルーベリーの成熟度・収量 proxy を画像とニューラルネットワークで推定する高スループット表現型計測法を開発・検証し、モデルとラベル付きデータセットを共有しているため、方法が研究の中心である。
abstractwe developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield
Reproduction assets foundThe paper publishes its labeled blueberry image dataset on Zenodo (record 14014858) and its trained YOLOv11-based blueberry fruit counting model/code on GitHub (jeromemaleski/blueberry), both directly supporting the paper's phenotyping measurements and analysis.Dataset · public32. Zhang, J. Blueberry Images and Labels for YOLO Model Training. Zenodo. 2024. Available online: https://zenodo.org/records/Open asset ↗zenodopdf-page:14 lines:1-36Code / dataset availability confirmedCrossref · OpenAlex · checked 15 Sept 2026
Cover crops (CC) immobilize mineral soil N in their biomass, preventing N losses during crop rotation intervals. As the CC biomass is incorporated into the soil and decomposes, N is released for the following main crop. The efficiency of CC N uptake and release depends on CC quantity and quality, which can be enhanced in mixtures. Traditional N uptake measurements are labour-intensive and limited in capturing spatial variability. We calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures. This innovative approach combined vegetation indices, textural features, and a photogrammetry-derived canopy surface model to predict CC traits. Linear models were trained for biomass, N uptake, and C:N predictions, while a K-Nearest-Neighbour model was trained for N concentration. When evaluated on the test set, the calibrated remote sensing models accurately predicted CC aboveground biomass (R 2 : 0.71, RMSE: 287.1 kg/ha, NRMSE: 11.74 %), N concentration (R 2 : 0.80, RMSE: 1.77 gN /kg, NRMSE: 6.96 %), N uptake (R 2 : 0.56, RMSE: 9.38 kgN /ha, NRMSE: 15.08 %), and C:N ratio (R 2 : 0.62, RMSE: 1.86, NRMSE: 10.98 %). The field experiment included monocultures, bi-, and tri-species mixtures of common vetch ( Vicia sativa ), black oat ( Avena strigosa ), and fodder radish ( Raphanus sativus ). N uptake was similar between treatments, yet the CC species differed in strategies, producing high biomass with low N concentration or vice versa. This study provides a basis for spatially predicting key CC traits using UAV optical data.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、テクスチャ特徴、フォトグラメトリ由来モデルを用いて、植物のバイオマス、窒素濃度、窒素吸収量、C:N比を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractWe calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures.
Reproduction assets foundThe paper's Data availability statement explicitly states the authors' R code for image processing, model training, and figure production is publicly available on the authors' WUR GitLab repository (uav4covercroptraits). No phenotype dataset or image deposit is stated separately.Code · publictal for the
UAV data acquisition.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.atech.2024.100608.
Data availability
The R code generated during this study to process the images, train
the models and produce the figures, is publicly available at https://git.wur.nl/dall002/uav4covercroptraits.References
[1] C. Aita, S.J. Giacomini, Crop residue decomposition and nitrogen release in singleOpen asset ↗git.wur.nl/dall002/uav4covercroptraitspdf-raw-page:10 lines:1-89Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Timeseries data captured by unoccupied aircraft systems (UASs) are increasingly used for agricultural applications requiring accurate prediction of plant phenotypes from remotely sensed imagery. However, prediction models often fail to generalize well from one year to the next or to new environments. Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). Manifold learning on raw imagery was better suited for the prediction of phenological traits due to the preservation of local structure in image embeddings at some time points. Together, these results highlight the competitiveness of classical ML approaches for UAS image analysis alongside computationally expensive deep learning models. Along with a new benchmark dataset for rice, our results help extend the toolkit for UAS image analysis, contributing to improved phenotype prediction in plant breeding and precision agriculture applications.
Why it matches plant phenotyping methodsUASマルチスペクトル時系列画像から収量・生育期形質を予測する機械学習手法を比較・評価し、米のベンチマークデータセットも提供しており、表現型取得・推定手法が研究の中心である。
abstractprediction models often fail to generalize well from one year to the next or to new environments.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw and processed UAS imagery, extracted features, and agronomic data on Dryad, and the authors' analysis code on GitHub. Both are paper-specific, public, and actionable.Dataset · publicts complied with the
current laws of the United States, the country in which they
were performed.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
Emily S. Bellis is a full time employee of Avalo, Inc., a crop
improvement company.
DATA AVA I L A B I L I T Y S TAT E M E N T
Raw and processed UAS images are available on Dryad
(https://doi.org/10.5061/dryad.v41ns1s4z) along with
extracted features and agronomic data for the 2021 and 2022
field seasons. Code to reproduce the analyses are available at
https://github.com/FareedFarag/TPPJ-Modeling-Code.O RC I D
FaredFarag https://orcid.org/0000-0002-4659-6781
Trevis D. Huggins https://orcid.org/0000-0002-1937-6687
JeremyD. Edwards https://orcidOpen asset ↗Dryad · 10.5061/dryad.v41ns1s4zpdf-raw-page:16 lines:1-86Code · public61/dryad.v41ns1s4z) along with
approaches for rice trait prediction using UAS imagery as extracted features and agronomic data for the 2021 and 2022
the primary data source. While showcasing the potential of field seasons. Code to reproduce the analyses are available at
various modeling approaches, it also emphasizes the trade- https://github.com/FareedFarag/TPPJ-Modeling-Code.
offs between performance and interpretability for applications
in precision agriculture and plant breeding. Looking for- ORCID
ward, extending the study over multiple years, extending to Fared Farag https://orcid.org/0000-0002-4659-6781
hyperspectral sensors, and exploring additional remotely Trevis D. Huggins https:/Open asset ↗GitHub · FareedFarag/TPPJ-Modeling-Codepdf-layout-page:16 lines:1-54Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Plant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red-green-blue (RGB) indices collected with standard settings in non-standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R² = 0.554, root mean square error [RMSE] = 219.29 kg ha⁻¹), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R² = 0.573, RMSE = 3.5 g kg⁻¹) and carbon:nitrogen ratio (R² = 0.574, RMSE = 1.29 g g⁻¹). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.
Why it matches plant phenotyping methods低コストRGB画像と画素分類を用いて、植物被覆、バイオマス、窒素含量、C:N比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す。
abstractUsing low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management.
Reproduction assets foundThe paper's authors state that all referenced analysis scripts for the RGB vegetation index processing, thresholding, and canopy cover estimation are publicly available on GitHub. The phenotype/trait data (images, biomass, N, C:N measurements) are deposited at a U of M repository (hdl.handle.net/11299/263900), but thatCode · publicreferenced scripts are available at https://github.com/RTGS- of nitrogen dictated by biomass and nitrogen content, and theOpen asset ↗pdf-page:4 lines:1-49Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multispectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11 to 0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multispectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数を用いてアルファルファのバイオマス蓄積を推定・モデル化し、遺伝パラメータや生育安定性を評価することが研究の中心であるため。
abstractMultispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests.
Reproduction assets foundThe paper's authors publicly deposited the R analysis code and input data for the random regression growth-curve modeling and stability analysis in a GitHub repository, explicitly stated in the Data availability section. Phenotype/imagery data themselves are only available upon request (request_only), and Pix4D is a第三方Code · publicAll data is available upon request. R Code and input data are available in the github: https://github.com/rthapa1/FFAR_RandomRegressionModel_growthcurve_modelling_stabilityanalysis_alfalfa .Open asset ↗rthapa1/FFAR_RandomRegressionModel_growthcurve_modelling_stabilityanalysis_alfalfalines:145-180Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.
Why it matches plant phenotyping methods中国白菜の表現型を3D点群から抽出する測定法を開発し、従来法との精度比較・検証および高速化を行っており、植物表現型測定が研究の中心である。
abstractThis study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology
Reproduction assets foundThe paper's phenotyping analysis code is explicitly deposited on a public GitHub repository with an authors' URL. The phenotype/trait measurement data themselves are only available upon request, so they do not qualify as a public asset.Code · publicapproach significantly streamlines the process, saving time and
enhancing efficiency by automating tasks which previously required extensive manual ef-
fort, thereby ensuring a more systematic and reliable method of phenotypic information
detection. The code used in this study can be accessed at the following GitHub repository:
https://github.com/chongchong123123/code (accessed on 18 October 2024).
2.4. Accuracy Analysis of Agronomic Parameter Measurements
In the course of agronomic trait measurement research, we utilized point cloud tech-
nology to measure key agronomic traits, including the plant height, plant spread, various
leaf dimensions (leaf length and leaf width), the width and thicOpen asset ↗chongchong123123/codepdf-raw-page:8 lines:1-62Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.
Why it matches plant phenotyping methodsUAV・衛星リモートセンシングと機械学習により森林の地上部バイオマス（炭素蓄積量）を推定し、現地測定と精度検証しているため、植物群落レベルの形質推定手法が中心です。
abstractWe used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest
Reproduction assets foundThe paper's Data Availability Statement points to an openly available Zenodo deposit containing the original study data (field carbon stock measurements, UAV-derived datasets, and Sentinel-2 based analysis inputs). No separate author analysis code or trained model repository is mentioned.Dataset · publicData Availability Statement: The original data presented in the study are openly available here:
https://doi.org/10.5281/zenodo.11657557, accessed on 5 June 2024.Open asset ↗zenodo · 10.5281/zenodo.11657557pdf-page:17 lines:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とYOLOv9、二重チャネルNMSを用いてコーヒー植物の検出・セグメンテーション・個体数推定手法を開発し、精度評価しているため、植物フェノタイピング手法が中心である。
titleA Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicWe will publish all the codes and datasets in this study after the article is accepted
https://github.com/legend2588/Coffee-plant-counting.gitOpen asset ↗https://github.com/legend2588/Coffee-plant-counting.gitpdf-page:19 lines:1-58Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2003 to 2015 improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art techniques, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R 2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.
Why it matches plant phenotyping methods作物収量という植物形質を遠隔センシング画像から推定する前処理法を開発し、CNN・LSTM・ViTとの比較で性能を検証しており、フェノタイピング手法が中心です。
abstractThis paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers.
Reproduction assets foundThe paper's declarations section explicitly states that the code for data collection, processing, and analysis is openly available on GitHub, and that a sample dataset used in the study is available via a Google Drive link. Both are paper-specific, public, and actionable.Code · publicThe code used in this study is openly available on GitHub at
https://github.com/mananthakkar24/RemoteSensingBlobDetection. This reposi-
tory contains all the necessary code for data collection, processing, and analysis as
described in this paper.Open asset ↗mananthakkar24/RemoteSensingBlobDetectionpdf-page:3 lines:1-48Dataset · publicA sample dataset used in this study is available at:
https://drive.google.com/drive/folders/18Z3hcqRf0nnE5vjqDat99qh3o-Open asset ↗pdf-page:3 lines:1-48Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
To address the underestimation of rape yield by traditional gramineous crop yield simulation methods based on crop models, this study used the WOFOST crop model to estimate rape yield in the main producing areas of southern Hunan based on 2 years of field-measured data, with consideration given to the photosynthesis of siliques, which are non-foliar green organs. First, the total photosynthetic area index (TPAI), which considers the photosynthesis of siliques, was proposed as a substitute for the leaf area index (LAI) as the calibration variable in the model. Two parameter calibration methods were subsequently proposed, both of which consider photosynthesis by siliques: the TPAI-SPA method, which is based on the TPAI coupled with a specific pod area, and the TPAI-Curve method, which is based on the TPAI and curve fitting. Finally, the 2 proposed parameter calibration methods were validated via 2 years of observed rape data. The results indicate that compared with traditional LAI-based crop model calibration methods, the TPAI-SPA and TPAI-Curve methods can improve the accuracy of rape yield estimation. The estimation accuracy ( R 2 ) for the total weight of storage organs (TWSO) and above-ground biomass (TAGP) increased by 9.68% and 49.86%, respectively, for the TPAI-SPA method and by 14.04% and 42.94%, respectively, for the TPAI-Curve method. Thus, the 2 calibration methods proposed in this study are of important practical importance for improving the accuracy of rape yield simulations. This study provides a novel technical approach for utilizing crop growth models in the yield estimation of oilseed crops.
Why it matches plant phenotyping methods非葉部器官の光合成を組み込んだTPAIを新たな校正変数として提案し、作物モデルによる収量・バイオマス推定法を開発・検証しているため、表現型推定手法が中心である。
abstractthe total photosynthetic area index (TPAI), which considers the photosynthesis of siliques, was proposed as a substitute for the leaf area index (LAI) as the calibration variable in the model.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' TPAI-SPA and TPAI-Curve calibration code/training scripts for the WOFOST rape yield estimation on GitHub at a public URL, which is an allowed URL and matches the paper's computational analysis.Code · publicThe code and training script of TPAI-SPA and TPAI-Curve has been hosted to GitHub and is available at https://github.com/rsw1998/TPAI-SPA-Curve-for-WOFOST .Open asset ↗rsw1998/TPAI-SPA-Curve-for-WOFOSTlines:675-747Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops. Specifically for sorghum ( Sorghum bicolor L.), rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle. In this context, the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time. Therefore, this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions. A preharvest benchmark dataset was collected at field scale (2023 season, Kansas, USA), with 648 images of sorghum panicles retrieved via smartphone device, and grain number counted. Each sorghum panicle image was manually labeled, and the images were augmented. Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation, with an average precision of 75% and 89%, respectively. For the grain number, 3 models were trained: MCNN (multiscale convolutional neural network), TCNN-Seed (two-column CNN-Seed), and Sorghum-Net (developed in this study). The Sorghum-Net model showed a mean absolute percentage error of 17%, surpassing the other models. Lastly, a simple equation was presented to relate the count from the model (using images from only one side of the panicle) to the field-derived observed number of grains per sorghum panicle. The resulting framework obtained an estimation of grain number with a 17% error. The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level.
Why it matches plant phenotyping methodsスマートフォン画像からソルガム穂の検出・分割と粒数推定を開発・検証しており、植物形質取得手法が研究の中心である。
abstractthis study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions.
Reproduction assets foundThe paper's authors explicitly state that the code used to train, test, and analyze the data is publicly available on GitHub. The phenotype image datasets are only available upon request.Code · publicThe code used to train, test, and analyze the data is available at https://github.com/GustavoSantiago113/Sorghum_Grain_Counter .Open asset ↗GustavoSantiago113/Sorghum_Grain_Counterlines:169-296Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early prediction of crop production by remote sensing (RS) may help to plan the harvest and ensure food security. This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering. A parametric linear model using all hyperspectral normalized difference spectral indices (NDSI) and two non-parametric models (artificial neural network and random forest) were used to assess their estimation ability combining NDSIs and other RS indicators. The feasibility of using freely available multispectral satellite was tested by applying the same methodology but using Sentinel-1 and Sentinel-2 bands. Yield estimation obtained the highest R² value, showing that the visible and short-wave infrared region (VSWIR) had similar accuracy to the hyperspectral and Sentinel-2 imagery (R² ≈ 0.84). The SWIR bands were important in the GPC estimation with both sensors, whereas N output was better estimated using red-edge-based NDSIs, obtaining satisfactory results with the hyperspectral sensor (R² = 0.74) and with the Sentinel-2 (R² = 0.62). When including the Sentinel-2 SWIR index, the NDSI (B11, B3) improved the estimation of N output (R² = 0.71). Ensemble models based on Sentinel were found to be as reliable as those based on hyperspectral imagery, and including SWIR information improved the quantification of N-related traits.
Why it matches plant phenotyping methods航空ハイパースペクトル画像とSentinel画像、複数の推定モデルを用いて小麦の収量・タンパク質濃度・窒素出力を定量化し、センサー間の性能を比較しているため、表現型取得・推定法が研究の中心である。
abstractThis study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery.
Reproduction assets foundThe paper's Data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.21865410.v1) containing the data supporting the study's winter wheat trait estimations from airborne hyperspectral and Sentinel imagery. This is a paper-specific, publicly accessible dataset with an authors' URL. No作者分析Dataset · publicatory work,
and QuantaLab-IAS-CSIC staff members A. Hornero, A. Vera, D. Notario, and R. Romero for airborne and
laboratory assistance.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Data availability The data that support the findings presented in this study are available online at https://doi.org/10.6084/m9.figshare.21865410.v1.Declarations
Conflict of interest The authors declare no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the oOpen asset ↗figshare · 10.6084/m9.figshare.21865410.v1pdf-raw-page:20 lines:1-46Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimate the three-dimensional (3D) volume of individual potato tubers. A challenge, however, is that the 3D shape derived from RGB-D images is only partially completed, underestimating the actual volume. To address this issue, we developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images. CoRe++ is a deep learning network that consists of a convolutional encoder and a decoder. The encoder compresses RGB-D images into latent vectors that are used by the decoder to complete the 3D shape using the deep signed distance field network (DeepSDF). To evaluate our CoRe++ network, we collected partial and complete 3D point clouds of 339 potato tubers on an operational harvester in Japan. On the 1425 RGB-D images in the test set (representing 51 unique potato tubers), our network achieved a completion accuracy of 2.8 mm on average. For volumetric estimation, the root mean squared error (RMSE) was 22.6 ml, and this was better than the RMSE of the linear regression (31.1 ml) and the base model (36.9 ml). We found that the RMSE can be further reduced to 18.2 ml when performing the 3D shape completion in the center of the RGB-D image. With an average 3D shape completion time of 10 milliseconds per tuber, we can conclude that CoRe++ is both fast and accurate enough to be implemented on an operational harvester for high-throughput potato yield estimation. CoRe++'s high-throughput and accurate processing allows it to be applied to other tuber, fruit and vegetable crops, thereby enabling versatile, accurate and real-time yield monitoring in precision agriculture. Our code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git.
Why it matches plant phenotyping methodsRGB-D画像からジャガイモ塊茎の3D形状を補完し、体積・収量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手法が研究の中心である。
abstractwe developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images.
Reproduction assets foundThe paper's abstract explicitly states that the authors' code, network weights, and the potato tuber RGB-D/3D point cloud dataset are publicly available at the authors' GitHub repository (UTokyo-FieldPhenomics-Lab/corepp), which is a paper-specific, public, actionable asset for the CoRe++ phenotyping analysis.Code · publicOur code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git .Open asset ↗UTokyo-FieldPhenomics-Lab/corepplines:1-93Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Unoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools that can provide transformational insights into biological and agricultural research, but practical and scientific questions remain. The utility of dense versus sparse temporal collections (e.g., daily, weekly, and monthly flights) has important implications for experimental design, resource allocation, and the scope of scientific questions investigated through UAS. UAS‐derived image data were collected on over 1500 maize hybrid yield trial plots with a temporal (longitudinal, 4D) sampling density of 2.8 days on average between 43 flights throughout the growing season. Correlations of vegetation index (VI) phenomic features between flight dates were generally high between flights separated by only 1 or 2 days but dropped when 3, 4, or more days separated the flights. These varied depending on specific dates and the VI used. Correlations between flights were lower around flowering time than during other parts of the season indicating the phenotypic uniqueness of this developmental period. The cross‐validation accuracy of end of season yields prediction models on untested genotypes from the UAS data (0.59 and 0.62) far exceeded genomic prediction accuracy (0.24) for the same test set hybrids regardless of whether all flight dates were used for prediction or only dates before flowering. Phenomic prediction accuracy marginally increased as additional flight dates were added throughout the season.
Why it matches plant phenotyping methodsUAS画像を用いた高頻度植物フェノタイピングの時間分解能と予測性能を評価しており、取得・解析方法の技術的検証が中心です。
abstractUnoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools
Reproduction assets foundThe paper's data availability statement explicitly provides the authors' analysis/figure-generation scripts on GitHub and both the scripts and phenotypic tabular data on Zenodo, directly enabling reproduction of this paper's UAS phenomic prediction analyses.Code · publicd by USDA award # 2022-
70412-38454 Agriculture Genome to Phenome Initiative
(AG2PI) seed grant.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The scripts used in the analyses and figure generation
for this manuscript are available at https://github.com/JacobWashburn-USDA/dense_UAV. Both the scripts and
phenotypic tabular data needed to recreate the analyses are
available at https://doi.org/10.5281/zenodo.11085557.O RC I D
JacobD. Washburn https://orcid.org/0000-0003-0185-7105
Alper Adak https://orcid.org/0000-0002-2737-8041
AaronJ. DeSalvio https://orcid.org/0000-0003-1818-4699
R E F E R E N C E SOpen asset ↗JacobWashburn-USDA/dense_UAVpdf-raw-page:10 lines:1-332Dataset · publicE N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The scripts used in the analyses and figure generation
for this manuscript are available at https://github.com/JacobWashburn-USDA/dense_UAV. Both the scripts and
phenotypic tabular data needed to recreate the analyses are
available at https://doi.org/10.5281/zenodo.11085557.O RC I D
JacobD. Washburn https://orcid.org/0000-0003-0185-7105
Alper Adak https://orcid.org/0000-0002-2737-8041
AaronJ. DeSalvio https://orcid.org/0000-0003-1818-4699
R E F E R E N C E S
Adak, A., Anderson, S. L., & Murray, S. C. (2023). Pedigree-
management-flight interaction for temporal phenotype analysis and
temporal phenomOpen asset ↗10.5281/zenodo.11085557pdf-raw-page:10 lines:1-332Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p
Why it matches plant phenotyping methods光学・SARセンサーデータを統合してライムギの地上部バイオマスを推定し、回帰モデルを訓練・検証しているため、植物形質の取得・推定手法が研究の中心です。
abstractThis study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets.
Reproduction assets foundThe paper's Sentinel-1 SAR processing workflow is publicly shared as a Google Earth Engine JavaScript script with an explicit availability statement and URL. The field AGB measurements and PlanetScope data are not public (available only on reasonable request from the corresponding author).Code · publicData Availability Statement: The Google Earth Engine script to process Sentinel-1 data is available at
[https://code.earthengine.google.com/219fc3c05b8ae8132ae2d758ccba3d1e?noload=true] (accessed
on 12 July 2024).Open asset ↗pdf-page:17 lines:1-59Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.
Why it matches plant phenotyping methodsドローン画像由来の時系列植生指数をFPCAで要約し、穀粒収量・草丈予測へ統合するフェノタイピング手法と予測モデルを複数年・環境で評価しており、表現型取得・抽出と技術的評価が中心である。
titleField-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize
Reproduction assets foundThe authors deposited a public figshare archive ('Data.zip') containing the paper's phenomic FPCA result files, plant height and grain yield BLUEs, genomic numerical files, and the R prediction/FPCA code needed to reproduce the analysis.Dataset · publicData are available at figshare: https://doi.org/10.25387/g3.24657666 .Open asset ↗figshare · 10.25387/g3.24657666lines:273-291Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Precision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.
Why it matches plant phenotyping methodsUAV画像から植物高と成長率を抽出し、時系列形質による圃場内収量変動予測を評価しており、植物表現型の取得・解析手法が研究の中心です。
abstractTemporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield
Reproduction assets foundThe paper explicitly states that all analysis scripts are on GitHub and all UAV-derived phenotypic data (plot heights, vegetative indices, orthomosaics, DEMs, plot boundaries, masks, manual heights, yield, weather) are deposited in DRUM with a DOI.Code · publicAll of the scripts and files used to generate and analyze data are available on GitHub at
https://github.com/HirschLabUMN/Production_Drone_Height.git.Open asset ↗HirschLabUMN/Production_Drone_Heightpdf-page:17 lines:1-56Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.
Why it matches plant phenotyping methods高スループット表現型プラットフォームによる時系列の植物形質取得が研究の中心的データ基盤であり、複数形質の縦断測定と予測への応用を評価している。
abstractwe combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons
Reproduction assets foundThe paper's Data Availability Statement points to a public figshare deposit containing all phenotypic data (temporal HTP traits: plant height, LAI, spectral vegetation indices), genomic data, and GWAS results for this sesame study. No author analysis code repository is explicitly deposited.Dataset · publicfor longitudinal traits derived from single time points (green) and random regression (orange) analysis.
Figure S3 . Phenotypic (green) and genetic (orange) correlations between longitudinal traits at each time point and seed‐yield.
Data Availability Statement
All phenotypic data, genomic data, and GWAS results can be found at https://doi.org/10.6084/m9.figshare.24961491 .Open asset ↗figshare · 10.6084/m9.figshare.24961491lines:1055-1061Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as “pot binding”, particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants.
Why it matches plant phenotyping methodsPlantEyeを用いたデジタルフェノタイピングの適用と精度評価が研究上の主要要素であり、植物のキャノピー温度や成長状態を測定し、植物サイズによる測定精度低下も検討している。
abstractPlants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this potato pot-binding phenotyping study.Dataset · publicThe datasets generated and analysed for this study can be found in the Zendo repository at https://doi.org/10.5281/zenodo.10707587 .Open asset ↗Zenodo · 10.5281/zenodo.10707587lines:845-856Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published23 May 2024International Journal of Applied Earth Observation and GeoinformationCited by 9 · OpenAlex ↗
In smallholder areas, the abandonment of orchards is a recent phenomenon with socioeconomic and environmental consequences. Biomass estimation and monitoring of these areas is essential to analyze their influence on the CO2 balance and to quantify carbon pools. In the current context of energy supply uncertainties and considering the demanding use of alternative energy sources, the quantification of fruit tree biomass in abandoned areas is a question of great interest. In this study, the above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique. From these data, a canopy height model was calculated and used to apply a developed crown contour detection algorithm. Using this information and 3D points, the tree parameters crown area, crown diameter, crown length, maximum tree height, minimum tree height, mean and standard deviation of crown point heights were calculated for a set of 36 felled and weighted orange trees. Stepwise regression was used to estimate the above biomass values. All previously reported variables were included. The crown area parameter produced the most accurate model with R2, RMSE and RMSE % values of 0.85, 10.165 kg and 19.56 %, respectively. These results demonstrate the potential of UAV-SfM-derived 3D point clouds to estimate the above-ground biomass of abandoned fruit trees, relevant information for environmental analysis and biofuel energy production.
Why it matches plant phenotyping methodsUAV-SfMによる3D画像から樹冠形状・樹高などの植物形質を抽出し、樹冠検出アルゴリズムと回帰モデルで個体バイオマスを推定する方法が中心である。
abstractthe above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique
Reproduction assets foundThe article's 'Research data' section states that the point cloud, crown delineation source code, reference data, and tree parameters are publicly available in a Mendeley Data repository (doi:10.17632/j3k2mctbnv.1). This is a paper-specific, public, actionable asset. Note: the Mendeley DOI itself is not in the allowed-Dataset · publicThe dataset containing point cloud, source code for crown delinea
tion, reference data, and parameters of abandoned orange groves, is
available for download from Mendeley data repository doi: 10
.17632/j3k2mctbnv.1.Open asset ↗Mendeley data repositorypdf-raw-page:9 lines:1-76Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.
Why it matches plant phenotyping methods作物の遺伝型・表現型データから収量を予測する移植学習フレームワークを開発し、複数作物と既存手法で精度比較しているため、植物表現型推定手法が中心である。
abstractWe therefore developed TrG2P, a transfer-learning-based framework.
Reproduction assets foundThe paper's authors publicly released the Python implementation of TrG2P (the transfer-learning G2P analysis tool used to produce the paper's yield-prediction results) on GitHub, with an explicit availability statement, plus a web-based tool. The phenotype/genotype datasets themselves are from previously published, citCode · publicThe Python implementation of TrG2P along with the demo files is available at https://github.com/lijinlong1991/TrG2P . The web-based tool is available at http://trg2p.ebreed.cn:81 .Open asset ↗https://github.com/lijinlong1991/TrG2Plines:224-294Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F 2 whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024-genomic vs. 0.612 ± 0.045-phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034-genomic vs. 0.617 ± 0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.
Why it matches plant phenotyping methodsトウモロコシ粒の高スループットNIRS測定と回帰モデルを用いて収量・千粒重を予測し、ゲノム予測との比較検証を行っており、表現型取得・推定法が研究の中心である。
titleNear-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the annotated R analysis code and all files needed to reproduce the NIRS phenomic and genomic prediction results. The NIRS/phenotype data themselves are from prior works (Lane et al. 2020, Farfan et al. 2015) and no separate public,Code · publicAn annotated script of the R code used in this research can be accessed via GitHub (DeSalvio, 2023) [https://github.com/ajdesalvio/Maize‐NIRS‐GBS.git]. All files needed to reproduce the results are provided in the GitHub repository for users to access.Open asset ↗ajdesalvio/Maize‐NIRS‐GBShtml-lines:240-391Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Breeding for improved, reliable cultivars despite growing environmental irregularity can be challenging. Unoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology that has been shown to help interpret the mechanisms associated with crop productivity and environmental response, creating potential for improved breeding strategies. Spectral reflectance indices (SRIs), encompassing both vegetation and water indices like normalized difference vegetation index (NDVI), normalized difference red‐edge index, and normalized water index, were employed to assess 4094 winter wheat genotypes across 11,593 breeding plots at Washington State University from 2019 through 2022. SRIs were then used with genomic data in univariate models as covariates and multivariate models as secondary response variables for predictions of grain yield. The prediction accuracy of models was evaluated using a leave‐one‐year‐out validation strategy against a base genomic prediction method. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but require high‐performance computational resources that could limit feasibility. In univariate models, when test year NDVI data were available and used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limited reliability of SRI use in genomic prediction of untested environments and locations. However, a significant application for the technology can be found in early‐season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.
Why it matches plant phenotyping methodsUASによる大規模なスペクトル形質取得を用い、SRIの予測性能を年次交差検証しており、植物フェノタイピング手法の実質的な適用・評価が中心である。
abstractUnoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology
Reproduction assets foundThe paper's data availability statement explicitly deposits all code and data (including UAS-derived SRI/NDVI phenotyping data and genomic prediction analysis) in a public GitHub repository whose URL matches an allowed URL.Code · publicNational Institute of Food and Agriculture, Hatch project
1014919, and the O.A. Vogel Research Endowment at
Washington State University.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
All code and data used in the study can be found
at https://github.com/AW-Herr/Large-scale-breeding-applications-of-UAS-enabled-genomic-prediction.O RC I D
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Appels, R., Eversole, K., Stein, N., Feuillet, C., Keller, B., Rogers, J.,
Pozniak, C. J., Choulet, F., Distelfeld, A., Poland, J., Ronen, G.Open asset ↗AW-Herr/Large-scale-breeding-applications-of-UAS-enabled-genomic-predictionpdf-raw-page:10 lines:1-85Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Projected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the coming decades, especially for developing nations. By providing real-time and low-cost observations, satellite remote sensing has been widely employed to estimate crop yield across various scales. Most such efforts are based on statistical approaches that require large amounts of ground measurements for model training/calibration, which may be challenging to obtain on a large scale in developing countries that are most food-insecure and climate-vulnerable. In this paper, we develop a generalizable framework that is mechanism-guided and practically parsimonious for crop yield estimation. We then apply this framework to estimate crop yield for two crops (corn and wheat) in two contrasting regions, the US Corn Belt US-CB, and India’s Indo–Gangetic plain Wheat Belt IGP-WB, respectively. This framework is based on the mechanistic light reactions (MLR) model utilizing remotely sensed solar-induced chlorophyll fluorescence (SIF) as a major input. We compared the performance of MLR to two commonly used machine learning (ML) algorithms: artificial neural network and random forest. We found that MLR-SIF has comparable performance to ML algorithms in US-CB, where abundant and high-quality ground measurements of crop yield are routinely available (for model calibration). In IGP-WB, MLR-SIF significantly outperforms ML algorithms. These results demonstrate the potential advantage of MLR-SIF for yield estimation in developing countries where ground truth data is limited in quantity and quality. In addition, high-resolution and crop-specific satellite SIF is crucial for accurate yield estimation. Therefore, harnessing the mechanism-guided MLR-SIF and rapidly growing satellite SIF measurements (with high resolution and crop-specificity) hold promise to enhance food security in developing countries towards more effective responses to food crises, agricultural policies, and more efficient commodity pricing.
Why it matches plant phenotyping methods衛星SIFを用いて作物収量という植物形質を推定する機構ガイド型フレームワークを開発し、複数地域・機械学習手法と比較検証しており、形質取得・推定法が中心である。
titleA scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)
Reproduction assets foundThe paper's yield estimation analysis relies on publicly available datasets explicitly named in the data availability statement: the OCO-2 SIF product (SIF_oco2_005) at ORNL DAAC, USDA NASS QuickStats corn yields, and ICRISAT DLD wheat yields. No author code, models, or paper-specific image/annotation assets are sharedDataset · public2 SIF available
from previous work (details below). Yield estimation
in US-CB was conducted for five years from 2015
to 2020 (when corn-specific OCO-2 SIF is available)
except 2017 (when OCO-2 had an instrument fail-
ure in August). The district-level wheat yield in IGP-
WB came from the District Level Database (DLD)
for India (http://data.icrisat.org/dld/), including 55
districts for the states of Bihar, Uttar Pradesh, and
Haryana. Yield estimation in IGP-WB was carried out
from 2015 to 2017 (the maximum overlap between
OCO-2 SIF and yield data).
2.2. The MLR-SIF yield estimation framework
The MLR-SIF based framework for yield estimation
consists of three steps. First, it estimaOpen asset ↗pdf-raw-page:4 lines:1-131Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Estimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a user-specified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 Ml models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d > 0.80) with higher accuracy when super learner (stacked ensemble) was used (R 2 = 0.51, d = 0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R 2 = 0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21% and 5% under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher uncertainty under the highest emission scenario (SSP5-8.5). The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.
Why it matches plant phenotyping methods圃場レベルの小麦収量という植物形質を、リモートセンシング等とAutoMLで推定する手法を開発・検証しており、収量取得・予測ワークフローが中心である。
titleDeveloping automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset
Reproduction assets foundThe paper's authors developed an H2O AutoML workflow in R for wheat yield prediction and explicitly state the full script is publicly hosted on GitHub. This is a paper-specific, publicly available analysis code asset. The data availability statement only says data are within the article/supplements, so no separate phenCode · publicweb GUI, H2O AutoML is also accessible in Python, R,
Java, and Scala. The technique is entirely automated, but many of the settings are made available to the user as
parameters so that some parts of the modelling phases can be changed. In our case, we developed H2OAutoML
in R language and the full script is hosted on GitHub at https://github.com/DrAhmedKheir/H2O_
AutoML.git.
2.3.1. Dataset preprocessing and AutoML training
Currently, all H2O supervised learning algorithms offer the same kind of automatic data-preprocessing as H2O
AutoML. Categorical data can be handled natively because H2O tree-based models (Gradient Boosting
Machines, Random Forests) provide group-splits on categoricalOpen asset ↗DrAhmedKheir/H2O_pdf-layout-page:6 lines:1-35Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.
Why it matches plant phenotyping methods携帯型および自動クロロフィル蛍光フェノタイピングによる光合成効率の反復測定と、収量予測への技術適用が研究の中心であるため。
abstractUnder fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping.
Reproduction assets foundThe paper's field MultispeQ chlorophyll fluorescence phenotyping data (Fq'/Fm' with PPFR and environmental covariates for the Dar18B, Dar19B, and Pal19D trials) are publicly available on the PhotosynQ platform via three author-provided project URLs. Glasshouse ChlF/biomass data are only in supplementary files without aDataset · publicThe MultispeQ data are also available on the PhotosynQ data base after creating an account (Darién 2018: https://photosynq.org/projects/climbers-in-darien-2018Open asset ↗PhotosynQ · climbers-in-darien-2018lines:374-422Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published9 Jan 2024The Plant journal : for cell and molecular biologyCited by 3 · OpenAlex ↗
CucumberGreenhouseRootSegmentationYield / biomass estimationRoot system architectureYield / yield components
The root system is important for the absorption of water and nutrients by plants. Cultivating and selecting a root system architecture (RSA) with good adaptability and ultrahigh productivity have become the primary goals of agricultural improvement. Exploring the correlation between the RSA and crop yield is important for cultivating crop varieties with high-stress resistance and productivity. In this study, 277 cucumber varieties were collected for root system image analysis and yield using germination plates and greenhouse cultivation. Deep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images. The results showed that U-Net can automatically extract cucumber root systems with high quality (F1_score ≥ 0.95), and the trained ResNet50 can predict cucumber yield grade through seedling root system image, with the highest F1_score reaching 0.86 using 10-day-old seedlings. The root angle had the strongest correlation with yield, and the shallow- and steep-angle frequencies had significant positive and negative correlations with yield, respectively. RSA and nutrient absorption jointly affected the production capacity of cucumber plants. The germination plate planting method and automated root system segmentation model used in this study are convenient for high-throughput phenotypic (HTP) research on root systems. Moreover, using seedling root system images to predict yield grade provides a new method for rapidly breeding high-yield RSA in crops such as cucumbers.
Why it matches plant phenotyping methods根系画像の自動セグメンテーションと収量予測モデルを開発・評価し、高スループット表現型解析への適用を中心に扱うため。
abstractDeep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images.
Reproduction assets foundThe paper reports cucumber root-image phenotyping (U-Net segmentation, ResNet50 yield-grade classification) and states that the segmentation and classification model code was uploaded to a public GitHub repository under the author's account. No public phenotype/image dataset deposit is stated in the supplied blocks.Code · publicsis of variance was used to compare trait differences
between the different yield grades. Deep learning model train-
ing and testing were conducted using the PyTorch framework,
mainly running on a cloud platform (https://www.autodl.com).The codes for the segmentation and classification models used
in this study were uploaded to https://github.com/zhucuifang/.AUTHOR CONTRIBUTIONS
Cuifang Zhu: Investigation, Data collection and analysis;
Writing – original draft; Hongjun Yu: Investigation, Data
collection, Funding acquisition; Tao Lu and Yang Li: Super-
vision; Weijei Jiang: Methodology, Guidance, Funding
acquisition; Qiang Li: Review and editing, Guidance.
Ó 2024 Society for Experimental BOpen asset ↗zhucuifangpdf-raw-page:19 lines:112-171Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation.
Why it matches plant phenotyping methodsコーヒーとカシューナッツの画像データセットを構築し、開花強度、成熟段階、収量などの植物形質・状態推定に利用する方法基盤を提供しており、表現型取得用データセットが研究の中心である。
abstractWe present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons.
Reproduction assets foundThe paper's own UAV coffee and cashew image datasets with YOLO annotations are publicly deposited on Mendeley Data (DOI 10.17632/r46c6bpfpf.1), directly reproducing the paper's phenotyping measurements. Annotation tools (Makesense AI, VGG Image Annotator) are generic third-party tools, not paper-specific assets.Dataset · publicre of f/1.7 and focus range of 1 m to ∞, shutter speed of 2-1/8000s and ISO range of 100-6400 (Auto and Manual)
Data source location
Institution: Makerere University
City: Kampala
Country: Uganda
Data accessibility
Repository name: Mendely Data
Data identification number: http://doi.org/10.17632/r46c6bpfpf.1
Direct URL to data:
https://data.mendeley.com/datasets/r46c6bpfpf/1
1.
Value of the Data
•
Flowering intensity estimation. Flowering represents an important stage in coffee and cashew farming since it affects crop yield. It has a significant impact on yield in that flowering intensity is positively correlated with the amount of crop yield. Therefore, flowering intensity could be an imporOpen asset ↗10.17632/r46c6bpfpf.1lines:1-51Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R 2 ) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R 2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.
Why it matches plant phenotyping methodsUAV画像から個体特徴を自動抽出し、収穫重量という植物形質を予測する手法が研究の中心であるため。
abstractwe propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub with explicit availability language. The UAV imagery (RGB/multispectral orthomosaics and point cloud data) is only available upon reasonable request from the corresponding author, so it does not qualify as a public asset.Code · publicAll code associated with the current study is available at: https://github.com/anaguilarar/CC_Weight_Prediction .Open asset ↗anaguilarar/CC_Weight_Predictionlines:155-233Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
Why it matches plant phenotyping methodsトウモロコシの収量・開花形質に関する大規模な再利用可能データセットを構築し、農業モデリングのベンチマークとして提示しており、表現型データセットが研究の中心である。
abstractThe resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids.
Reproduction assets foundThe paper's curated maize G×E dataset (phenotypes, SNP genotypes, environmental covariates) is publicly deposited on Figshare, and the authors' analysis scripts are publicly available on GitHub (MAIZE-HUB). Both are paper-specific, public, and actionable.Dataset · publicThe aggregated curated data set (including the SNP genotypes, phenotypes, and ECs) is available in the Figshare repository [ https://doi.org/10.6084/m9.figshare.22776806 ] 58 .Open asset ↗Figshare · 10.6084/m9.figshare.22776806lines:207-229Code · publicThe scripts used to implement all the analyses described in this study are provided in the GitHub repository [ https://github.com/QuantGen/MAIZE-HUB ].Open asset ↗GitHub · QuantGen/MAIZE-HUBlines:207-229Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.
Why it matches plant phenotyping methodsTLSによる個体樹木の体積という植物形質の推定手法を、破壊的実測データと比較検証しており、測定・推定法が研究の中心である。
abstractTerrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees
Reproduction assets foundThe paper's Data availability statement points to a public EnviDat deposit (DOI 10.16904/envidat.403) containing the data associated with this TLS-based tree volume estimation study, including the TLS point clouds and destructive reference measurements collected in the SwissBiomass project. No author analysis code URL,Dataset · publicvolume by integrating them into new allometric models.
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Data associated with this article is available at http://dx.doi.org/10.16904/envidat.403.Acknowledgements
The authors would like to thank everyone involved in the WSL
project ‘‘SwissBiomass’’, in the course of which the data used in this
study were collected. In particular, we thank Marina Beck for project
coordination, as well as all those who helped with the field and lab-
oratory measurements. We also thank DanielOpen asset ↗envidat · 10.16904/envidat.403pdf-raw-page:11 lines:1-93Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Detailed and precise knowledge of production parameters (yield, quality, health status, etc.) in agriculture is the basis for analyzing the effect of any agricultural practice. Fine mapping of production parameters makes it possible to identify the origin of observed variability, whether associated with environmental factors or with agricultural practices. In viticulture, in real commercial context, these data are rare because monitoring systems embedded on harvesting machines for grape yield and quality are not yet available. As a result, they are costly and/or cumbersome to acquire manually. As an alternative, a research project has been proposed to test low-cost methods using GNSS tracking devices for yield and harvest quality mapping in viticulture. The data set was acquired as part of this research. The methodology was applied on a commercial vineyard of 30 ha during the whole 2022 harvest season. The method has identified harvest sectors (HS) associated to measured production parameters (grape mass and harvest quality parameters: sugar content, total acidity, pH, yeast assimilable nitrogen, organic nitrogen) and calculated production parameters (potential alcohol of grapes, yield, yield per plant, percentage of unproductive plants) over the entire vineyard. The grape mass was measured at the vineyard cellar or at the wine-growing cooperative by calibrated scales. The harvest quality parameters were measured from samples on grape must at a commercial laboratory specialized in oenological analysis (Institut Coopératif du Vin, Montpellier, France) with standardized protocols. The percentage of unproductive plants of a harvest sector was calculated from the manually geolocation of each unproductive plants (dead plants + missing plants) over the entire vineyard, the plantation density of blocks, and the geolocalization of the harvest sector. The mean area of these harvest sectors is 0.3 ha. The data set is supplemented by climatic data from a weather station deployed in the center of the vineyard. It provided three climatic parameters (relative humidity, rainfall, air temperature) every 15 min, for the 2020, 2021 and 2022 years. It was also supplemented by a complete description of the vineyard blocks (grape variety, plantation year, area, inter-row distance and vine distance). The proposed data set constitutes a unique and interesting resource for research in agronomy, vine ecophysiology and remote sensing. It can be used for any research in vine ecophysiology aimed at identifying potential relationships between yield and harvest quality parameters for different grape varieties. The data set only covers one year, which is a limitation for studying inter-annual variability of the parameters measured. Another limitation of the method concerns the footprint (0.3 ha on average) of the parameters measured.
Why it matches plant phenotyping methodsGNSSを用いた低コストのブドウ収量・収穫品質マッピング手法と、その大規模データセットが研究の中心であり、収量や不生産株割合などの植物・圃場形質を抽出している。
abstracta research project has been proposed to test low-cost methods using GNSS tracking devices for yield and harvest quality mapping in viticulture.
Reproduction assets foundThe article is a Data in Brief describing the authors' own public Zenodo deposit containing the vineyard phenotyping measurements (block, agronomic/harvest-sector, and weather data as .shp and .csv files), plus an example analysis script (Yield_vs_variety.py) and yield map, all hosted at the stated Zenodo DOI.Dataset · publicce), as well as the geolocation of unproductive wines were obtained from the vineyard Farm Management Information System.
Data source location
Institution: Institut Agro Montpellier
City: Montpellier
Country: France
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.8328384
Direct URL to data: https://doi.org/10.5281/zenodo.8328384
Related research article
J-P. Gras, S. Moinard, T. Crestey and B. Tisseyre. Mapping grape yield with low-cost vehicle tracking devices, In Precision agriculture’23 , Wageningen Academic Publishers. (2023) 555-561. https://doi.org/10.3920/978-90-8686-947-3_70
1.
Value of the Data
The dataset presented in this paper is a particulOpen asset ↗Zenodo · 10.5281/zenodo.8328384lines:32-61Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R 2= 0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs
Why it matches plant phenotyping methodsRGB画像から種子形態形質を抽出し、機械学習で種子重量を予測する画像ベース表現型解析が研究の中心である。
abstractThe present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean.
Reproduction assets foundThe paper's image-based seed architectural trait (i-trait) measurements and hundred-seed weight data for 164 soybean accessions are stated to be included in the article's Supplementary Material (e.g., Supplementary Table 1 of genotypes and trait data), publicly available at the Frontiers supplementary-material URL. No专Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1206357/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
References
Abdelhakim L. O. A., Rosenqvist E., Wollenweber B., Spyroglou I., Ottosen C. O., Panzarová K. (2021). Investigating combined drought-and heat stress effects in wheat under controlled conditions Open asset ↗lines:434-465Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract This research presents an innovative approach called Tenacious Fish Swarm Optimization based Hidden Markov Model (TFSO-HMM) for augmented accurate cotton leaf disease identification and yield prediction. Cotton leaf diseases significantly threaten crop productivity, requiring timely detection and precise prediction for effective disease management. The proposed TFSO-HMM framework combines the strengths of Tenacious Fish Swarm Optimization (TFSO) and the Hidden Markov Model (HMM) to address the challenges associated with disease identification and yield prediction in cotton plants. TFSO, a nature-inspired optimization algorithm, optimizes the classification process, enhancing the accuracy of disease identification. By harnessing the collective intelligence of fish swarms, TFSO intelligently explores the search space to identify the optimal solution. The selected information is then incorporated into the HMM framework, which captures the temporal dependencies in disease progression and yield prediction. HMM's sequential modelling approach facilitates understanding the dynamic behaviour of cotton leaf diseases over time, leading to more accurate predictions. Experimental results on a comprehensive dataset demonstrate the superior performance of the TFSO-HMM method over existing approaches in terms of accuracy and predictive capability. The augmented accuracy achieved through TFSO-HMM enables early detection and precise prediction of cotton leaf diseases, enabling timely interventions for disease management and maximizing crop yield.
Why it matches plant phenotyping methods綿花葉の病害状態を対象に、TFSO-HMMという計算手法を開発・評価して病害識別を行っており、植物の病害表現型の抽出が中心的です。
abstractThis research presents an innovative approach called Tenacious Fish Swarm Optimization based Hidden Markov Model (TFSO-HMM) for augmented accurate cotton leaf disease identification and yield prediction.
Reproduction assets foundThe paper uses the public Kaggle 'Cotton Plant Disease Dataset' as its phenotyping image dataset, with an explicit public URL. The authors' analysis code is only available on request, so it does not qualify as a public asset.Dataset · publicThe “Cotton Plant Disease Dataset” available at
https://www.kaggle.com/datasets/dhamur/cotton-plant-diseaseOpen asset ↗kaggle.com · dhamur/cotton-plant-diseasepdf-page:44 lines:1-24Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Rice ( Oryza sativa L.) is one of the most important cereals, which provides 20% of the world's food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t·ha -1 across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel -1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting.
Why it matches plant phenotyping methodsRGB画像と深層学習によりイネ収量を推定する手法を開発・検証しており、植物表現型の取得・抽出が研究の中心である。
abstractHere, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images.
Reproduction assets foundThe paper explicitly states that the code to run the developed rice-yield CNN model is publicly available on the authors' GitHub repository. The image/yield database itself is not stated to be publicly deposited, so only the code qualifies as a paper-specific public asset.Code · publicThe code to run the developed CNN model is available at https://github.com/r1wtn/rice_yield_CNN .Open asset ↗r1wtn/rice_yield_CNNlines:57-71Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact. Computer vision-based algorithms in particular, are commonly used for fruit segmentation, enabling in-depth analysis of the harvest quality and accurate yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes. When evaluated against other competitive methods, our model demonstrates state-of-the-art (SOTA) performance, even in challenging environments with highly occluded fruits. Additionally, we introduce Tomatopia, a new, large and challenging dataset of greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and pixel-level annotations of the fruits.
Why it matches plant phenotyping methods植物上のトマト果実を画像からセグメンテーションする手法を開発・比較し、RGB-D画像と画素アノテーションのデータセットも提供しており、植物器官の状態・位置推定に関わる方法が中心である。
abstractwe propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes
Reproduction assets foundThe paper introduces TomatoDIFF and the Tomatopia dataset, with explicit public availability of source code and dataset at the authors' GitHub repository. It also trains/evaluates on the public Kaggle 'Tomato dataset' (andrewmvd/tomato-detection), which is a paper-specific public image dataset used directly in the phenCode · publicThe source code of TomatoDIFF and Tomatopia are available at https://github.com/MIvanovska/TomatoDIFF .Open asset ↗MIvanovska/TomatoDIFFlines:1-44Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Crop yield is determined by potential harvest organ size, source organ photosynthesis and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain-filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high-yielding rice cultivars, each yielding approximately 21 t ha−1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain-filling rate could lead to a potential yield increase of 30–40% in an elite rice cultivar. Notably, the instantaneous grain-filling rates around 15- and 38-day post-flowering significantly influence grain yield; and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict the maximum dried brown rice yield (Y, t ha−1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha−1): Y = 0.74 × Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
Why it matches plant phenotyping methods全植物の炭素・窒素動態と収量形成を推定する速度論モデルを開発し、耳の呼吸速度による粒充填速度の定量化手法も導入しているため、表現型取得・推定が中心的です。
abstractHere, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics.
Reproduction assets foundThe paper's authors publicly released the WACNI model source code (the computational framework used for all simulations and analysis) on GitHub, with explicit availability language in the MODEL AND DATA AVAILABILITY section. Supplementary Data 2 contains literature-extracted experimental data but no separate public URLCode · publicip
help improve model parameterization.
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MODEL AND DATA AVAILABILITY
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Experimental data extracted from literature, used in model-data comparison, are tabulated in
Supplementary Data 2.
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The source code used for this study, along with the operational commands and user guide, is
freely available for non-commercial use at https://github.com/rootchang/WACNI-rice.git.
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28Open asset ↗rootchang/WACNI-ricepdf-layout-page:28 lines:1-44Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
Why it matches plant phenotyping methodsドローン画像から植物高・葉面積・分げつ密度を推定し、これらを用いたフェノミック選抜モデルを評価しており、形質取得と解析ワークフローが研究の中心である。
abstractThe dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's datasets (phenotypic/trait data from drone-based wheat phenotyping) in the ETH Research Collection and the phenomics data processing source code in a public ETH GitLab repository. Both are paper-specific, public, and actionable.Dataset · publicThe datasets generated and analyzed during the current study are openly available in the ETH Research Collection repository, http://doi.org/10.3929/ethz-b-000566864 .Open asset ↗ETH Research Collection · 10.3929/ethz-b-000566864lines:210-223Code · publicSource code for the phenomics data processing methods used in this study are openly available in the ETH gitlab repository, https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing .Open asset ↗ETH gitlab · crop_phenotyping/htfp_data_processinglines:210-223Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
Why it matches plant phenotyping methodsRGB・LiDAR・VNIRリモートセンシングによる作物形質抽出について、行選択とプロットトリミングを反復性・収量予測で評価しており、フェノタイピング手法の技術評価が中心である。
abstractThe objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data.
Reproduction assets foundThe paper states that remote sensing data, yield data, and the authors' R analysis code are publicly deposited in the Purdue University Research Repository under DOI 10.4231/PF9S-4G38. This is a paper-specific, publicly actionable asset covering both the phenotyping measurements (RGB/LiDAR/VNIR remote sensing traits, 4Dataset · publicRemote sensing data, yield data, and R code used for this study are available at the Purdue University Research Repository (10.4231/PF9S-4G38).Open asset ↗Purdue University Research Repository · 10.4231/PF9S-4G38lines:404-413Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Motivation Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed. Results We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties. Availability and implementation Available at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).
Why it matches plant phenotyping methodsUAS由来の植物表現型データを用いて収量を推定する深層学習手法を開発・評価しており、表現型の取得・統合・推定が研究の中心である。
abstractWe propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems.
Reproduction assets foundThe paper's availability statement explicitly links a public GitHub repository with the authors' analysis code (PheGeMIL) and a Dryad DOI deposit containing the paper's phenotyping data (UAV multispectral/thermal images, DEMs, genotypes, yield). Both are paper-specific, public, and actionable.Dataset · publicAvailable at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).Open asset ↗doi:10.5061/dryad.kprr4xh5plines:49-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Predicting phenotypes accurately from genomic, environment, and management factors is key to accelerating the development of novel cultivars with desirable traits. Inclusion of management and environmental factors enables in silico studies to predict the effect of specific management interventions or future climates. Despite the value such models would confer, much work remains to improve the accuracy of phenotypic predictions. Rather than advocate for a single specific modeling strategy, here we demonstrate within large multi-environment and multi-genotype maize trials that combining predictions from disparate models using simple ensemble approaches most often results in better accuracy than using any one of the models on their own. We investigated various ensemble combinations of different model types, model numbers, and model weighting schemes to determine the accuracy of each. We find that ensembling generally improves performance even when combining only two models. The number and type of models included alter accuracy with improvements diminishing as the number of models included increases. Using a genetic algorithm to optimize ensemble composition reveals that, when weighted by the inverse of each model’s expected error, using combinations of best linear unbiased predictors, linear fixed effects models, deep learning models, and select machine learning models perform best on our datasets.
Why it matches plant phenotyping methodsトウモロコシ収量という植物形質を対象に、BLUP・機械学習・深層学習のアンサンブル予測を比較・検証し、予測精度を評価することが中心であるため。
abstractPredicting phenotypes accurately from genomic, environment, and management factors is key to accelerating the development of novel cultivars with desirable traits.
Reproduction assets foundThe paper's maize yield, weather, soil, management, and genomic data come from the Genomes to Fields initiative's public releases (2014-2019), accessible via the G2F resources page. The authors also deposit cleaned data (Zenodo 10.5281/zenodo.6916775) and analysis scripts/model predictions (Zenodo 10.5281/zenodo.769738Dataset · publicinversely proportionate to expected model error was
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phenotypic measurements. Weather data was supplemented with data from Daymet (Thornton
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et al. 2020). Genomic, environmental, and management data was quality controlled using
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cuOpen asset ↗Genomes to Fieldspdf-raw-page:6 lines:1-81Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
As human population continue to increase, our food production system is challenged. With tomatoes as the main indoor produced fruit, the selection of adapter varieties to each specific condition and higher yields is an imperative task if we wish to supply the growing demand of coming years. To help farmers and researchers in the task of phenotyping, we here present a study case of the A gro s cope ph en otyping tool (ASPEN) in tomato under indoor conditions. We prove that using the ASPEN pipeline it is possible to obtain real time in situ yield estimation not only in a commercial-like greenhouse level but also within growing line. To discuss our results, we analyse the two main steps of the pipeline in a desktop computer: object detection and tracking, and yield prediction. Thanks to the use of YOLOv5, we reach a mean average precision for all categories of 0.85 at interception over union 0.5 with an inference time of 8 ms, who together with the best multiple object tracking (MOT) tested allows to reach a 0.97 correlation value compared with the real harvest number of tomatoes and a 0.91 correlation when considering yield thanks to the usage of a SLAM algorithm. Moreover, the ASPEN pipeline demonstrated to predict also the sub following harvests. Confidently, our results demonstrate in situ size and quality estimation per fruit, which could be beneficial for multiple users. To increase accessibility and usage of new technologies, we make publicly available the required hardware material and software to reproduce this pipeline, which include a dataset of more than 850 relabelled images for the task of tomato object detection and the trained YOLOv5 model[1] [1]https://github.com/camilochiang/aspen
Why it matches plant phenotyping methodsASPENはトマト果実の検出・追跡から収量、果実サイズ、品質を推定する画像ベースの表現型解析パイプラインであり、手法の評価と再現可能なソフトウェア・データセット提供が中心です。
abstractwe here present a study case of the A gro s cope ph en otyping tool (ASPEN) in tomato under indoor conditions.
Reproduction assets foundThe authors explicitly make publicly available the ASPEN pipeline software/hardware materials, a dataset of 850+ relabelled tomato images, and the trained YOLOv5 model via their GitHub repository.Dataset · publicThe dataset supporting the conclusions of this article is available in the github repository (https://github.com/camilochiang/aspen).Open asset ↗github.com/camilochiang/aspenlines:119-149Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
In most of the countries, grapes are considered as a cash crop. Currently huge research is going on in development of automated grape harvesting systems. Speedy and reliable grape bunch detection is prime need for various deep learning based automated systems which deals with object detection and object segmentation tasks. But currently very few datasets are available on grape bunches in vineyard, because of which there is restriction to the research in this area. In comparison to the vineyard in outside countries, Indian vineyard structure is more complex, so it becomes hard to work in real-time. To overcome these problems and to make vineyard dataset for suitable for Indian vineyard scenarios, this paper proposed four different datasets on grape bunches in vineyard. For creating all datasets in GrapesNet, natural environmental conditions have been considered. GrapesNet includes total 11000+ images of grape bunches. Necessary data for weight prediction of grape cluster is also provided with dataset like height, width and real weight of cluster present in image. Proposed datasets can be used for prime tasks like grape bunch detection, grape bunch segmentation, and grape bunch weight estimation etc. of future generation automated vineyard harvesting technologies.
Why it matches plant phenotyping methodsブドウ果房画像データセットを構築し、果房の検出・セグメンテーションに加えて重量推定用の寸法と実重量を提供することが中心で、再利用可能な植物表現型データセットに該当する。
abstractthis paper proposed four different datasets on grape bunches in vineyard.
Reproduction assets foundThe paper is a data descriptor for GrapesNet, a public Mendeley Data repository of Indian vineyard RGB/RGB-D grape bunch image datasets with ground-truth cluster height, width, and weight measurements used for phenotyping tasks (detection, segmentation, weight estimation). The dataset is the paper's core asset and is aDataset · publicRepository name: GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets
Data identification number (DOI): 10.17632/mhzmzd5cwx.1
Direct URL to data: https://data.mendeley.com/datasets/mhzmzd5cwx/1Open asset ↗10.17632/mhzmzd5cwx.1lines:1-95Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Remote-sensing data has become essential for site-specific farming methods. It is also a powerful tool for monitoring the agroecosystem services offered by integrating cover crops (CC) into crop rotations. This study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs). Images captured with red–green–blue cameras mounted on UAVs in two missions varying in ground sample distances were used as input for generating three-dimensional point clouds using the structure-from-motion approach. These point clouds were then compared to manual ground measurements. The results showed that the agreement between the methods was closest when CC presented dense and smooth canopies. However, stands with rough canopies or gaps showed substantial differences between the UAV method and ground measurements. We conclude that the UAV method is substantially more precise and accurate in determining CH than measurements taken with a ruler since the UAV introduces additional dimensions with greatly increased resolution. CH can be a reliable indicator of biomass yield, but no differences between the investigated methods were found, probably due to allometric variations of different CC species. We propose the presented UAV method as a promising tool to include site-specific information on CC in crop production strategies.
Why it matches plant phenotyping methodsUAV画像とSfM点群を用いて被覆作物の群落高を推定する手法を提示し、地上測定と比較検証しており、植物形質取得法が研究の中心である。
abstractThis study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs).
Reproduction assets foundThe paper's canopy height and biomass measurements (UAV-derived CH, ruler measurements, DMY) are openly available as a Zenodo dataset, explicitly stated in the Data Availability Statement. The Metashape scripts GitHub link and CRAN raster package are generic third-party tools, not authors' analysis code.Dataset · publicData Availability Statement: The data presented in this study are openly available in the Zenodo
archive at the following DOI: https://doi.org/10.5281/zenodo.7713341 (Kümmerer, 2023).Open asset ↗Zenodo · 10.5281/zenodo.7713341pdf-page:15 lines:1-51Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Recent studies indicate that food demand will increase by 35-56% over the period 2010-2050 due to population increase, economic development, and urbanization. Greenhouse systems allow for the sustainable intensification of food production with demonstrated high crop production per cultivation area. Breakthroughs in resource-efficient fresh food production merging horticultural and AI expertise take place with the international competition "Autonomous Greenhouse Challenge". This paper describes and analyzes the results of the third edition of this competition. The competition's goal is the realization of the highest net profit in fully autonomous lettuce production. Two cultivation cycles were conducted in six high-tech greenhouse compartments with operational greenhouse decision-making realized at a distance and individually by algorithms of international participating teams. Algorithms were developed based on time series sensor data of the greenhouse climate and crop images. High crop yield and quality, short growing cycles, and low use of resources such as energy for heating, electricity for artificial light, and CO 2 were decisive in realizing the competition's goal. The results highlight the importance of plant spacing and the moment of harvest decisions in promoting high crop growth rates while optimizing greenhouse occupation and resource use. In this paper, images taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest. The resulting plant height and coverage could be accurately estimated with an R 2 of 0.976, and a mIoU of 98.2, respectively. These two traits were used to develop a light loss and harvest indicator to support remote decision-making. The light loss indicator could be used as a decision tool for timely spacing. Several traits were combined for the harvest indicator, ultimately resulting in a fresh weight estimation with a mean absolute error of 22 g. The proposed non-invasively estimated indicators presented in this article are promising traits to be used towards full autonomation of a dynamic commercial lettuce growing environment. Computer vision algorithms act as a catalyst in remote and non-invasive sensing of crop parameters, decisive for automated, objective, standardized, and data-driven decision making. However, spectral indexes describing lettuces growth and larger datasets than the currently accessible are crucial to address existing shortcomings between academic and industrial production systems that have been encountered in this work.
Why it matches plant phenotyping methods深度カメラ画像とコンピュータビジョンによりレタスの草丈・被覆率・収量関連形質を推定し、精度評価と自動意思決定指標への応用を行っており、植物表現型取得法が中心である。
abstractimages taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest.
Reproduction assets foundThe paper's complete challenge dataset (climate time-series and annotated lettuce crop images used for the computer vision phenotyping) is published open access on 4TU.ResearchData, cited both in the Data Availability Statement and in reference 56.Dataset · public3rd Autonomous Greenhouse Challenge-Real Challenge Data Climate and Images Dataset: 4TU.ResearchData 2023 Available online: https://data.4tu.nl/articles/dataset/3rd_Autonomous_Greenhouse_Challenge_Online_Challenge_Lettuce_Images/15023088Open asset ↗4TU.ResearchData · 15023088lines:853-968Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The use of drones for vegetation monitoring allows the acquisition of large amounts of high spatial resolution data in a simple and fast way. In this study, we evaluated the accuracy of vegetation cover estimation by drones in Mediterranean semi-arid shrublands (Sierra de Filabres; Almería; southern Spain) after prescribed burns (2 years). We compared drone-based vegetation cover estimates with those based on traditional vegetation sampling in ninety-six 1 m2 plots. We explored how this accuracy varies in different types of coverage (low-, moderate- and high-cover shrublands, and high-cover alfa grass steppe); as well as with diversity, plant richness, and topographic slope. The coverage estimated using a drone was strongly correlated with that obtained by vegetation sampling (R2 = 0.81). This estimate varied between cover classes, with the error rate being higher in low-cover shrublands, and lower in high-cover alfa grass steppe (normalized RMSE 33% vs. 9%). Diversity and slope did not affect the accuracy of the cover estimates, while errors were larger in plots with greater richness. These results suggest that in semi-arid environments, the drone might underestimate vegetation cover in low-cover shrublands.
Why it matches plant phenotyping methodsドローン画像による植生被覆率という植物群落形質の推定を、従来の植生サンプリングと比較して精度検証しており、フェノタイピング手法が研究の中心である。
abstractwe evaluated the accuracy of vegetation cover estimation by drones
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 · publicSupplementary Materials: All codes used are deposited at https://serpam.github.io/rpasveg_alcon‐
tar/.Open asset ↗pdf-page:11 lines:1-58Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Context Mixing cultivars is an agroecological practice of crop diversification, increasingly used for cereals. The yield of such cereal mixtures is higher on average than the mean yield of their components in pure stands, but with a large variance. The drivers of this variance are plant- plant interactions leading to different plant phenotypes in pure and mixed stands, i.e phenotypic plasticity. Objectives The objectives were (i) to quantify the magnitude of phenotypic plasticity for yield in pure versus mixed stands, (ii) to identify the yield components that contribute the most to yield plasticity, and (iii) to link such plasticities to differences in functional traits, i.e. plant height and flowering earliness. Methods A new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth. Eight commercial cultivars of Triticum aestivum L. were grown in pure and mixed stands in field plots repeated for two years (2019-2020, 2020-2021) with contrasted climatic conditions and with nitrogen fertilization, fungicide and weed removal management strategies. Two quaternary mixtures were assembled with cultivars contrasted either for height or earliness. Results Compared to the average of cultivars in pure stands, the height mixture strongly underyielded over both years (-29%) while the earliness mixture overyielded the second year (+11%) and underyielded the first year (-8%). The second year, the magnitude of cultivar’s grain weight plasticity, measured as the difference between pure and mixed stands, was significantly and positively associated with their relative yield differences in pure stands (R 2 =0.51). When grain weight plasticity, measured as the log ratio of pure over mixed stands, was partitioned as the sum of plasticities in each yield component, its strongest contributor was the plasticity in spike number per plant (∼56% of the sum), driven by even stronger but opposed underlying plasticities in both tiller emission and regression. For both years, the plasticity in tiller emission was significantly, positively associated with the height differentials between cultivars in mixture (R 2 =0.43 in 2019-2020 and 0.17 in 2020-2021). Conclusions Plasticity in the early recognition of potential resource competitors is a major component of cultivar strategies in mixtures, as shown here for tillering dynamics. Our results also highlighted a link between plasticity in tiller emission and height differential in mixture. Both height and tillering dynamics displayed plasticities typical of the shade avoidance syndrome. Implications Both the new experimental design and decomposition of plasticities developed in this study open avenues to better study plant-plant interactions in agronomically-realistic conditions. This study also contributed a unique, plant-level data set allowing the calibration of process-based plant models to explore the space of all possible mixtures.
Why it matches plant phenotyping methods精密播種による個体レベルの生育形質フェノタイピング実験デザインと、形質可塑性の分解手法が明示的な技術的貢献であり、単なる収量測定にとどまらない。
abstractA new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth.
Reproduction assets foundThe paper's phenotyping data, supplementary material, and analysis code are openly deposited on Recherche Data Gouv at https://doi.org/10.57745/LZS8SU, as stated in the Reproducibility section. This is a paper-specific, public, actionable asset directly reproducing the wheat pure/mixed-stand phenotyping measurements (eDataset · publicSupplementary information, data
and code that support the findings of this study are openly available at the INRAE
space of the Recherche Data Gouv repository https://doi.org/10.57745/LZS8SU.Open asset ↗Recherche Data Gouv · 10.57745/LZS8SUpdf-page:15 lines:1-40Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R 2 , root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R 2 , RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R 2 , RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.
Why it matches plant phenotyping methods消費者向けUAVマルチスペクトル画像を用いて小麦の収量および窒素利用効率を推定・検証する方法が研究の中心であり、植物形質の取得と予測性能を評価している。
abstractThis study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat
Reproduction assets foundThe article's data availability statement points to a public figshare deposit containing the paper's Supplementary Material and Appendix (including e.g. Supplementary Table S1 with VARI-threshold background-removal accuracy results). No author analysis code, raw imagery, or trained models are explicitly deposited; DJI/Supplement · publicapplications should be thoroughly explored.
Data availability statement
The original contributions presented in the study are included in the article/
Supplementary Materials
. Further inquiries can be directed to the corresponding author. The Supplementary material and Appendix document for this article can be found online at: https://figshare.com/s/fa258c55dd6bd9fc69b9 named as Supplementary Material.zip.
Author contributions
XL, JL, and YZ designed and developed the research idea. YZ, XC, and XT conducted the field data collection. JL and YZ performed the data analysis. JL wrote the manuscript. JL, YZ, XT, XC, and XL contributed to the results and data interpretation, discussion, and reviOpen asset ↗figsharelines:861-886Code / dataset availability confirmedarXiv · OpenAlex · checked 14 Sept 2026
We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).
Why it matches plant phenotyping methods植物の多視点画像取得、SfM再構成、質量推定を中核とするロボット型フェノタイピング手法の開発・実証であり、単なる生物学的測定ではない。
abstractWe describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each.
Precise and site-specific nitrogen (N) fertilizer management of vegetables is essential to improve the N use efficiency considering temporal and spatial fertility variations among fields, while the current N fertilizer recommendation methods are proved to be time- and labor-consuming. To establish a site-specific N topdressing algorithm for bok choy ( Brassica rapa subsp. chinensis ), using a hand-held GreenSeeker canopy sensor, we conducted field experiments in the years 2014, 2017, and 2020. Two planting densities, viz, high (123,000 plants ha -1 ) in Year I and low (57,000 plants ha -1 ) in Year II, whereas, combined densities in Year III were used to evaluate the effect of five N application rates (0, 45, 109, 157, and 205 kg N ha -1 ). A robust relationship was observed between the sensor-based normalized difference vegetation index (NDVI), the ratio vegetation index (RVI), and the yield potential without topdressing (YP 0 ) at the rosette stage, and 81-84% of the variability at high density and 76-79% of that at low density could be explained. By combining the densities and years, the R 2 value increased to 0.90. Additionally, the rosette stage was identified as the earliest stage for reliably predicting the response index at harvest (RI Harvest ), based on the response index derived from NDVI (RI NDVI ) and RVI (RI RVI ), with R 2 values of 0.59-0.67 at high density and 0.53-0.65 at low density. When using the combined results, the RI RVI performed 6.12% better than the RI NDVI , and 52% of the variability could be explained. This study demonstrates the good potential of establishing a sensor-based N topdressing algorithm for bok choy, which could contribute to the sustainable development of vegetable production.
Why it matches plant phenotyping methods携帯型キャノピーセンサーのNDVI/RVIから収量ポテンシャルと施肥応答を推定するアルゴリズムを開発・検証しており、植物形質推定手法が研究の中心です。
abstractTo establish a site-specific N topdressing algorithm for bok choy ( Brassica rapa subsp. chinensis ), using a hand-held GreenSeeker canopy sensor, we conducted field experiments in the years 2014, 2017, and 2020.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2 ), the empirical exponential model was used to determine the relationship between YP 0 and the sensor-based vegetation indices (NDVI and RVI) for bok choy across growth stages ( Table 3 ).Open asset ↗lines:391-483Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
Fresh weight is a widely used growth indicator for quantifying crop growth. Traditional fresh weight measurement methods are time-consuming, laborious, and destructive. Non-destructive measurement of crop fresh weight is urgently needed in plant factories with high environment controllability. In this study, we proposed a multi-modal fusion based deep learning model for automatic estimation of lettuce shoot fresh weight by utilizing RGB-D images. The model combined geometric traits from empirical feature extraction and deep neural features from CNN. A lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits. A multi-branch regression network was performed to estimate fresh weight by fusing color, depth, and geometric features. The leaf segmentation model reported a reliable performance with a mIoU of 0.982 and an accuracy of 0.998. A total of 10 geometric traits were defined to describe the structure of the lettuce canopy from segmented images. The fresh weight estimation results showed that the proposed multi-modal fusion model significantly improved the accuracy of lettuce shoot fresh weight in different growth periods compared with baseline models. The model yielded a root mean square error (RMSE) of 25.3 g and a coefficient of determination ( R 2 ) of 0.938 over the entire lettuce growth period. The experiment results demonstrated that the multi-modal fusion method could improve the fresh weight estimation performance by leveraging the advantages of empirical geometric traits and deep neural features simultaneously.
Why it matches plant phenotyping methodsRGB-D画像からレタスの生体重を非破壊推定する画像解析・深層学習手法の開発が研究の中心であり、植物表現型取得法に該当する。
abstractA lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits.
Reproduction assets foundThe paper's phenotyping inputs (top-view RGB and aligned depth images of 388 lettuces with destructively measured traits) come from the publicly available 3rd Autonomous Greenhouse Challenge Online Challenge Lettuce Images dataset, with an explicit public URL in the data availability statement. No author analysis code,Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.4tu.nl/articles/dataset/3rd_Autonomous_Greenhouse_Challenge_Online_Challenge_Lettuce_Images/15023088 .Open asset ↗data.4tu.nl · 15023088lines:657-691Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Yield estimation (YE) of the crop is one of the main tasks in fruit management and marketing. Based on the results of YE, the farmers can make a better decision on the harvesting period, prevention strategies for crop disease, subsequent follow-up for cultivation practice, etc. In the current scenario, crop YE is performed manually, which has many limitations such as the requirement of experts for the bigger fields, subjective decisions and a more time-consuming process. To overcome these issues, an intelligent YE system was proposed which detects, localizes and counts the number of tomatoes in the field using SegNet with VGG19 (a deep learning-based semantic segmentation architecture). The dataset of 672 images was given as an input to the SegNet with VGG19 architecture for training. It extracts features corresponding to the tomato in each layer and detection was performed based on the feature score. The results were compared against the other semantic segmentation architectures such as U-Net and SegNet with VGG16. The proposed method performed better and unveiled reasonable results. For testing the trained model, a case study was conducted in the real tomato field at Manapparai village, Trichy, India. The proposed method portrayed the test precision, recall and F1-score values of 89.7%, 72.55% and 80.22%, respectively along with reasonable localization capability for tomatoes.
Why it matches plant phenotyping methodsトマト果実の検出・局在化・計数による収量推定手法を開発し、複数モデルと比較・実地検証しており、植物フェノタイピング手法が中心である。
abstractan intelligent YE system was proposed which detects, localizes and counts the number of tomatoes in the field using SegNet with VGG19
Reproduction assets foundThe paper's phenotyping input images come from a publicly available annotated tomato image dataset (Rob2Pheno, 123 RGB images) hosted on 4TU under CC BY 4.0, which the authors explicitly used for training their SegNet-VGG19 yield estimation model. No author analysis code or trained model is reported as publicly shared.Dataset · publicThe dataset of 123 RGB images 18 used for this work is acquired from the publicly available dataset under a creative common license. ( https://data.4tu.nl/articles/dataset/Rob2Pheno_Annotated_Tomato_Image_Dataset/13173422 ), ( https://creativecommons.org/licenses/by/4.0/ ).Open asset ↗data.4tu.nl · Rob2Pheno_Annotated_Tomato_Image_Dataset/13173422lines:74-89Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield – those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
Why it matches plant phenotyping methods遺伝子型・環境・管理データからトウモロコシ収量という植物形質を予測する深層学習手法を開発・比較しており、表現型推定が研究の中心である。
abstractHere we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models
Reproduction assets foundThe paper uses publicly available Genomes to Fields (G2F) maize phenotype/weather/soil data (2014-2019) and provides authors' custom Python processing/analysis scripts on two public Bitbucket repositories. A Zenodo deposit (10.5281/zenodo.6916775) with PCA eigenvectors is mentioned but its URL is not among the allowed,Dataset · publicrformance, while avoiding
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overfitting the model to any location
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Materials and Methods
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Data Preparation
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We used data from the Genomes to Fields (G2F) initiative for years 2014-2019
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(McFarland et al. 2020), focusing on the sites within the continental United States. Each year’s
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data are publicly available (https://www.genomes2fields.org/resources/), including weather and
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soil data for field sites, genomic data, management schedules (e.g., application of fertilizer,
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herbicides, irrigation) and yield (in addition to other phenotypic variables). We augmented this
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through additional genomic and weather data. Weather data retrieved from Daymet (Thornton et
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alOpen asset ↗Genomes to Fieldspdf-raw-page:7 lines:1-53Code · publicthe eigenvectors
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resulting from the principal components analysis are provided to enable transformation of
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provided genomes. Additional weather measurements were retrieved from Daymet (Thornton et
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al. 2020). Custom python scripts for downloading, aggregating and processing these data are
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available on bitbucket (https://bitbucket.org/washjake/maizemodel and
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https://bitbucket.org/daniel_kick/maizemodel/ ) in the notebooks directory (files with the prefix
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0.0 to 0.5).
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and is also made available for use under a CC0 license.
was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105Open asset ↗washjake/maizemodelpdf-raw-page:17 lines:1-46Code · publicscripts were used to
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aggregate and standardize terminology across years. Rather than itemizing each operation, we
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restrict ourselves to those which are likely to be of interest to those working with similar data
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sets. The scripts used are available through Bitbucket
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(https://bitbucket.org/washjake/maizemodel and https://bitbucket.org/daniel_kick/maizemodel/ ).
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Scripts were written in Python (Van Rossum and Drake 2009 p. 3) and rely on scientific and
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common general libraries (Seabold and Perktold 2010; Pedregosa et al. 2011; fuzzywuzzy
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2017; Virtanen et al. 2020; team 2020; Harris et al. 2020; Da Costa-Luis et al. 2022) along with
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plotting libraries for exploraOpen asset ↗daniel_kick/maizemodelpdf-raw-page:8 lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed 'TPanicle-RCNN,' and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.
Why it matches plant phenotyping methodsイネの収量関連形質を高スループットに取得する統合計測・画像解析システムを開発し、検出精度と測定効率を検証しているため、植物フェノタイピング手法が中心である。
abstracta novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy.
Reproduction assets foundThe article explicitly states that all threshed panicle training and testing data (1,072 threshed panicle images with PASCAL VOC annotations, augmented to 3,432 training and 856 testing images) are publicly available via a Baidu Netdisk link with an extraction code for non-commercial research. This is a paper-specific,Dataset · publicAll the training and testing data are available at https://pan.baidu.com/s/1-XawHGseIc5bboVOP48Fkw?pwd=153w with the extraction code ‘153w’ for non-commercial research purposes.Open asset ↗pan.baidu.com · 1-XawHGseIc5bboVOP48Fkw?pwd=153wlines:330-340Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.
Why it matches plant phenotyping methods衛星リモートセンシングによるLAI・NDVIという植物キャノピー形質の推定を、APSIM制約のためのエミュレーションおよびベイズ最適化ワークフローとして技術的に評価しており、形質取得・抽出法が中心的です。
abstractWe leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest.
Reproduction assets foundThe paper uses a publicly available maize yield dataset from Beck's Hybrids covering 332 locations (2014-2019) with management, soil, and weather information as site-level inputs for APSIM simulations and yield validation. No author analysis code, trained models, or data deposit is disclosed (Data Availability and AcknDataset · public4 of 25
Figure 1. 2 Figures side by side
million ha from 2014-2019 [31]. To perform APSIM simulations at a series of randomly 143
selected locations, site-level information was acquired from the publicly available maize 144
yield dataset maintained by Beck’s Hybrids (https://www.beckshybrids.com/Research/ 145
Yield-Data). The dataset included information on management operations (i.e., planting 146
date, harvesting date, plant population, row spacing, and previous crop planted for residue 147
type), soil, and weather for 332 locations from 2014 to 2019 (Figure 2(a)). Information on 148
soil texture and soil organic carbon (SOC)Open asset ↗pdf-raw-page:4 lines:1-19Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract. Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) Rcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (Rcv2=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (Rcv2=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step.
Why it matches plant phenotyping methodsUASマルチスペクトルセンサーと機械学習を用いて、草地の乾物バイオマスおよび植物窒素濃度という植物形質を推定し、センサー・アルゴリズム・予測変数を比較検証しているため、フェノタイピング手法が中心である。
abstractOur study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration.
Reproduction assets foundThe paper's field dataset (in situ DM, plant N concentration, canopy height, and corresponding UAS multispectral measurements from 10 grassland sites) is publicly deposited in PANGAEA. The authors' analysis code is only available upon request, so it does not qualify as a public asset.Dataset · publicThe field data set used in this study is
available in the PANGAEA repository at https://doi.org/10.1594/PANGAEA.920600 (Schucknecht et al., 2020b).Open asset ↗PANGAEA · 10.1594/PANGAEA.920600lines:1344-1401Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Measurement of variation in plant biomass is essential for answering many ecological and evolutionary questions. Quantitative estimates require plant destruction for laboratory analyses, while field studies use allometric approaches based on simple measurement of plant dimensions.We estimated the biomass of individual shrub-sized plants, using a low-cost unmanned aerial system (drone), enabling rapid data collection and non-destructive sampling. We compared volume measurement (a surrogate for biomass) and sampling time, from the simple dimension measurements and drone, to accurate laboratory-derived biomass weights. We focused on three Australian plant species which are ecologically important to their terrestrial and floodplain ecosystems: porcupine grass Triodia scariosa , Queensland bluebush Chenopodium auricomum , and lignum Duma florulenta .Estimated volume from the drone was more accurate than simple dimension measurements for porcupine grass and Queensland bluebush, compared to estimates from laboratory analyses but, not for lignum. The latter had a sparse canopy, with thin branches, few vestigial leaves and a similar color to the ground. Data collection and analysis consistently required more time for the drone method than the simple dimension measurements, but this would improve with automation.The drone method promises considerable potential for some plant species, allowing data to be collected over large spatial scales and, in time series, increasing opportunities to answer complex ecological and evolutionary questions and monitor the state of ecosystems and plant populations.
Why it matches plant phenotyping methodsドローン画像から個体の体積を推定し、植物バイオマスの代理指標として実測値と比較検証する手法開発が研究の中心である。
abstractWe estimated the biomass of individual shrub-sized plants, using a low-cost unmanned aerial system (drone), enabling rapid data collection and non-destructive sampling.
Reproduction assets foundThe article's Data Availability Statement points to a public Dryad deposit containing the paper's biomass/volume measurement data. Software tools cited (Pix4D, CloudCompare, rLiDAR) are generic third-party libraries, not authors' analysis code, and no author code or image repository is disclosed.Dataset · publicDATA AVAILABILITY STATEMENT
Data are available on the Dryad Digital Repository at: https://doi.org/10.5061/dryad.xwdbrv1g1 .Open asset ↗Dryad Digital Repository · 10.5061/dryad.xwdbrv1g1lines:209-279Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R 2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha -1 . Compared to county-level statistics during 2007-2015, the ranges of R 2 , RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha -1 , and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.
Why it matches plant phenotyping methods冬小麦の地上部バイオマスという明示的な植物形質を、作物モデルとMODIS LAIデータ同化で広域推定し、地上測定および統計値で検証したデータセット研究であり、形質推定手法と検証が中心です。
abstractwe assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015.
Reproduction assets foundThe paper's winter wheat AGB analysis code is publicly available on the authors' GitHub repository, and the MODIS LAI product assimilated into WOFOST is publicly accessible via the Land-Atmosphere Interaction Research Group website. The generated AGB dataset itself is on figshare (10.6084/m9.figshare.16680784.v3), but那Code · publicHuang and Jianxi Huang designed the research, performed the analysis, and wrote the paper; Xuecao Li, Wen Zhuo, Yantong Wu, Quandi Niu, Wei Su, and Wenping Yuan edited and revised the manuscript.
Code availability
Python scripts that implement model calibration, data assimilation, dataset generation, and mapping are available ( https://github.com/paperoses/CHN_Winter_Wheat_AGB ). Further questions can be directed towards Hai Huang (haihuang@cau.edu.cn).
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
The onOpen asset ↗paperoses/CHN_Winter_Wheat_AGBlines:117-143Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Herbaceous aboveground biomass (HAB) is a key indicator of grassland vegetation and indirect estimation tools, such as remote sensing imagery, increase the potential for covering larger areas in a timely and cost-efficient way. Structure from Motion (SfM) is an image analysis process that can create a variety of 3D spatial models as well as 2D orthomosaics from a set of images. Computed from Unmanned Aerial Vehicle (UAV) and ground camera measurements, the SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study. Both UAV and ground camera recordings were used at three different scales: temporal, landscape, and national (across Senegal). All images were processed using PIX4D software (photogrammetry software) and were used to extract vegetation indices and heights. A random forest algorithm was used to estimate the HAB and the average estimation errors were around 150 g m - ² for fresh mass (20% relative error) and 60 g m - ² for dry mass (around 25% error). A comparison between different datasets revealed that the estimates based on camera data were slightly more accurate than those from UAV data. It was also found that combining datasets across scales for the same type of tool (UAV or camera) could be a useful option for monitoring HAB in Sahelian rangelands or in other grassy ecosystems.
Why it matches plant phenotyping methodsSfM画像解析とUAV・地上カメラを用いて、植生高や指数から草本地上部バイオマスを推定し、異なるデータセット・スケールで精度比較を行っており、植物形質推定手法が中心です。
abstractthe SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study.
Reproduction assets foundThe paper's UAV and ground-camera SfM phenotyping datasets (National, Landscape, and Temporal UAV datasets) are explicitly deposited on Zenodo with DOIs given in the Open Research Badges and Data Availability sections, making them public, paper-specific, and directly actionable.Dataset · publicWriting – review & editing (equal). Emile Faye: Conceptualization (equal); Methodology (equal).
OPEN RESEARCH BADGES
This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5281/zenodo.6421543 , https://doi.org/10.5281/zenodo.5148337 and https://doi.org/10.5281/zenodo.5145395 .
Supporting information
Appendix S1
Click here for additional data file.
ACKNOWLEDGMENTS
This research was financed by the Carbon Sequestration and Green‐house Gas Emissions in (Agro) Sylvopastoral Ecosystems in the Sahelian CILSS States (CaSSECS) project, supported by the European UnOpen asset ↗zenodo · 10.5281/zenodo.5148337lines:571-608Dataset · publicaye: Conceptualization (equal); Methodology (equal).
OPEN RESEARCH BADGES
This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5281/zenodo.6421543 , https://doi.org/10.5281/zenodo.5148337 and https://doi.org/10.5281/zenodo.5145395 .
Supporting information
Appendix S1
Click here for additional data file.
ACKNOWLEDGMENTS
This research was financed by the Carbon Sequestration and Green‐house Gas Emissions in (Agro) Sylvopastoral Ecosystems in the Sahelian CILSS States (CaSSECS) project, supported by the European Union under the Development Smart InnovationOpen asset ↗zenodo · 10.5281/zenodo.5145395lines:571-608Dataset · publicview & editing (equal). Rasmus Fensholt: Writing – review & editing (equal). Emile Faye: Conceptualization (equal); Methodology (equal).
OPEN RESEARCH BADGES
This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5281/zenodo.6421543 , https://doi.org/10.5281/zenodo.5148337 and https://doi.org/10.5281/zenodo.5145395 .
Supporting information
Appendix S1
Click here for additional data file.
ACKNOWLEDGMENTS
This research was financed by the Carbon Sequestration and Green‐house Gas Emissions in (Agro) Sylvopastoral Ecosystems in the Sahelian CILSS States (CaSSEOpen asset ↗zenodo · 10.5281/zenodo.6421543lines:571-608Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Estimation of cotton yield before harvest offers many benefits to breeding programs, researchers and producers. Remote sensing enables efficient and consistent estimation of cotton yields, as opposed to traditional field measurements and surveys. The overall goal of this study was to develop a data processing pipeline to perform fast and accurate pre-harvest yield predictions of cotton breeding fields from aerial imagery using machine learning techniques. By using only a single plot image extracted from an orthomosaic map, a Support Vector Machine (SVM) classifier with four selected features was trained to identify the cotton pixels present in each plot image. The SVM classifier achieved an accuracy of 89%, a precision of 86%, a recall of 75%, and an F1-score of 80% at recognizing cotton pixels. After performing morphological image processing operations and applying a connected components algorithm, the classified cotton pixels were clustered to predict the number of cotton bolls at the plot level. Our model fitted the ground truth counts with an R 2 value of 0.93, a normalized root mean squared error of 0.07, and a mean absolute percentage error of 13.7%. This study demonstrates that aerial imagery with machine learning techniques can be a reliable, efficient, and effective tool for pre-harvest cotton yield prediction.
Why it matches plant phenotyping methods航空画像と機械学習による綿花の収量・果球数推定パイプラインを開発し、画素分類と地上計数で性能検証しており、植物表現型の取得・抽出が研究の中心である。
abstractThe overall goal of this study was to develop a data processing pipeline to perform fast and accurate pre-harvest yield predictions of cotton breeding fields from aerial imagery using machine learning techniques.
Reproduction assets foundThe paper's cotton boll classification/counting pipeline is publicly available: a Dockerized web app on Docker Hub and code with sample test images on GitHub, both explicitly stated by the authors. Raw aerial imagery/ground truth data are only available on request.Code · publicAdditionally, we will provide the code and some sample images for testing at https://github.com/Javi-RS/Cotton_Yield_Estimation .Open asset ↗Javi-RS/Cotton_Yield_Estimationlines:394-495Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available.
Why it matches plant phenotyping methods衛星データによる作物収量推定について、7種類の圃場測定法を比較し、収量推定モデルの適合度・精度への影響を検証している。収量という植物形質の取得・推定法が研究の中心である。
abstractTo evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019.
Reproduction assets foundThe paper's field-measured maize yield dataset (seven sampling methods plus full-field harvest for 196 Ethiopian fields) is openly available via a Zenodo DOI in the Data Availability Statement. No code availability is stated.Dataset · publicData Availability Statement: The data presented in this study are openly available here:
https://doi.org/10.5281/zenodo.6471977.Open asset ↗zenodo · 10.5281/zenodo.6471977pdf-page:11 lines:1-58Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
The wheat grain three-dimensional (3D) phenotypic characters are of great significance for final yield and variety breeding, and the ventral sulcus traits are the important factors to the wheat flour yield. The wheat grain trait measurements are necessary; however, the traditional measurement method is still manual, which is inefficient, subjective, and labor intensive; moreover, the ventral sulcus traits can only be obtained by destructive measurement. In this paper, an intelligent analysis method based on the structured light imaging has been proposed to extract the 3D wheat grain phenotypes and ventral sulcus traits. First, the 3D point cloud data of wheat grain were obtained by the structured light scanner, and then, the specified point cloud processing algorithms including single grain segmentation and ventral sulcus location have been designed; finally, 28 wheat grain 3D phenotypic characters and 4 ventral sulcus traits have been extracted. To evaluate the best experimental conditions, three-level orthogonal experiments, which include rotation angle, scanning angle, and stage color factors, were carried out on 125 grains of 5 wheat varieties, and the results demonstrated that optimum conditions of rotation angle, scanning angle, and stage color were 30°, 37°, black color individually. Additionally, the results also proved that the mean absolute percentage errors (MAPEs) of wheat grain length, width, thickness, and ventral sulcus depth were 1.83, 1.86, 2.19, and 4.81%. Moreover, the 500 wheat grains of five varieties were used to construct and validate the wheat grain weight model by 32 phenotypic traits, and the cross-validation results showed that the R 2 of the models ranged from 0.77 to 0.83. Finally, the wheat grain phenotype extraction and grain weight prediction were integrated into the specialized software. Therefore, this method was demonstrated to be an efficient and effective way for wheat breeding research.
Why it matches plant phenotyping methods構造化光画像から小麦粒の3D表現型と腹溝形質を抽出する手法を開発し、精度評価・条件最適化・ソフトウェア統合まで行っており、表現型取得が中心である。
abstractIn this paper, an intelligent analysis method based on the structured light imaging has been proposed to extract the 3D wheat grain phenotypes and ventral sulcus traits.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2
The original data of structured light imaging, X-ray CT, and manual measurements of 125 wheat grains.Open asset ↗lines:608-666Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 8 Sept 2026
Plant breeders, scientists, and commercial producers commonly use growth rate as an integrated signal of crop productivity and stress. Plant growth monitoring is often done destructively via growth rate estimation by harvesting plants at different growth stages and simply weighing each individual plant. Within plant breeding and research applications, and more recently in commercial applications, non-destructive growth monitoring is done using computer vision to segment plants in images from the background, either in 2D or 3D, and relating these image-based features to destructive biomass measurements. Recent advancements in machine learning have improved image-based localization and detection of plants, but such techniques are not well suited to make biomass predictions when there is significant self-occlusion or occlusion from neighboring plants, such as those encountered under leafy green production in controlled environment agriculture. To enable prediction of plant biomass under occluded growing conditions, we develop an end-to-end deep learning approach that directly predicts lettuce plant biomass from color and depth image data as provided by a low cost and commercially available sensor. We test the performance of the proposed deep neural network for lettuce production, observing a mean prediction error of 7.3% on a comprehensive test dataset of 864 individuals and substantially outperforming previous work on plant biomass estimation. The modeling approach is robust to the busy and occluded scenes often found in commercial leafy green production and requires only measured mass values for training. We then demonstrate that this level of prediction accuracy allows for rapid, non-destructive detection of changes in biomass accumulation due to experimentally induced stress induction in as little as 2 days. Using this method growers may observe and react to changes in plant-environment interactions in near real time. Moreover, we expect that such a sensitive technique for non-destructive biomass estimation will enable novel research and breeding of improved productivity and yield in response to stress.
Why it matches plant phenotyping methodsRGB-D画像と深層学習を用いて、遮蔽下のレタス個体バイオマスを非破壊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す。
abstractwe develop an end-to-end deep learning approach that directly predicts lettuce plant biomass from color and depth image data as provided by a low cost and commercially available sensor.
Reproduction assets foundThe article provides an authors' public GitHub repository containing the analysis code for the RGB-D deep learning biomass estimation pipeline. The raw image/biomass dataset is only available on request (no public deposit).Code · publicCode available at https://github.com/NicoBux/Plant-Biomass-Monitoring .Open asset ↗NicoBux/Plant-Biomass-Monitoringlines:466-524Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m -2 , and per-PFT RMSE ranged from 17.14-164.3 g m -2 . Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.
Why it matches plant phenotyping methodsUAV画像とSfMから植物機能タイプの被覆、草丈、地上部バイオマスを推定し、現地推定値との比較で精度評価を行うことが研究の中心であるため。
abstractWe assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types.
Reproduction assets foundThe paper's plant-phenotyping data (UAV-derived PFT cover, canopy height, biomass, and field validation measurements) are explicitly stated to be publicly archived at the NSF Arctic Data Center under DOI 10.18739/A2R785Q5B. No author analysis code or trained model checkpoints are described with a public deposit.Dataset · publicns Attribution 4.0 International License (CC BY 4.0),
which permits unrestricted use, distribution, and reproduc-
tion in any medium, provided the original author(s) and
source are credited.
Data availability
Data supporting the results in this paper are publicly
archived at the National Science Foundation Arctic Data Cen-
ter: https://doi.org/10.18739/A2R785Q5B.Author information
Author ORCIDs
Kathleen M. Orndahlhttps://orcid.org/0000-0002-4873-4375
Author contributions
KMO and SJG conceived the ideas; KMO, LPWE, and JDH de-
signed the methodology; KMO, LPWE, JDH, and REP collected
the data; KMO, LPWE, and REP processed and curated the
data; KMO analyzed the data with input from MH; KMO ledOpen asset ↗10.18739/A2R785Q5Bpdf-raw-page:14 lines:1-99Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からイネの収量を推定する深層学習ワークフローを比較・評価しており、植物形質の取得・推定手法が研究の中心である。
abstractHere we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas.
Reproduction assets foundThe paper's authors explicitly state that the Python and R code used to process the UAV imagery data, train and evaluate the CNN models, and recreate Figures 2–5 is publicly available on GitHub. No separate public phenotype dataset or trained model checkpoint deposit is stated in the supplied blocks; other URLs are forCode · publicPython and R code used to process data, train and evaluate models, and recreate Figures 2 – 5 , is available at https://github.com/em-bellis/XASU_rice .Open asset ↗em-bellis/XASU_ricelines:521-535Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.
Why it matches plant phenotyping methodsSARデータとGPRモデルにより作物のPAI・VWC・湿重量バイオマスを推定する手法を開発・評価しており、植物形質取得が研究の中心である。
abstractThe purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the authors' GPR analysis code for crop biophysical parameter retrieval from RADARSAT-2 data. The in situ SMAPVEX16-MB measurements and RADARSAT-2 imagery themselves are not stated as publicly released by the authors.Code · publicData Availability Statement: The code for the present work is available at: https://github.com/
Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2, accessed 15 February 2022.Open asset ↗Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2pdf-page:24 lines:1-60Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から画像特徴を抽出して二次作物形質を構築し、育種選抜性能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研究に該当する。
abstractA UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons.
Reproduction assets foundThe paper explicitly states that the LASSO model code and the UAV imagery datasets are publicly available in the authors' GitHub repository. The raw data availability statement only offers data on request, but the code/imagery asset has an explicit public URL.Code · publicThe code for the LASSO model and the imagery datasets can be found at: https://github.com/Heyphil/Soybean-variety-selection.git .Open asset ↗https://github.com/Heyphil/Soybean-variety-selection.gitlines:320-328Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract High‐throughput phenotyping (HTP) has the potential to revolutionize plant breeding by providing scientists with exponentially more data than was available through traditional observations. Even though data collection is rapidly increasing, the optimum use of this data and implementation in the breeding program has not been thoroughly explored. In an effort to apply HTP to the earliest stages of a plant breeding program, we extended field‐based HTP pipelines to evaluate and extract data from spaced single plants. Using a panel of 340 winter wheat (Triticum aestivum L.) lines planted in full plots and grid‐spaced single plants for two growing seasons, we evaluated relationships between single plants and full plot yields. Normalized difference vegetation index (NDVI) was collected multiple times through the growing season using an unoccupied aerial vehicle. NDVI measurements during grain filling stage from both single plants and full plots were typically positively associated with their respective grain yield with correlation ranging from ‐0.22 to 0.74. The relationship between single plant NDVI and full plot yield, however, was variable between seasons ranging from ‐0.40 to 0.06. A genome wide association analysis (GWAS) identified the same marker trait associations in both full plots and single plants, but also displayed variability between growing seasons. Strong genotype by environment interactions could impede selection on quantitative traits, yet these methods could provide an effective tool for plant breeding programs to quickly screen early‐generation germplasm. Efficient use of early‐generation, affordable HTP data could improve overall genetic gain in plant breeding.
Why it matches plant phenotyping methods単一個体向けに圃場HTPパイプラインを拡張し、UAVによるNDVI取得・抽出と全区画収量との関係を評価しており、表現型取得法の応用・検証が中心です。
abstractwe extended field‐based HTP pipelines to evaluate and extract data from spaced single plants
Reproduction assets foundThe paper's data availability statement explicitly deposits phenotypic data, raw images, and analysis scripts in a public Zenodo repository (DOI 10.5281/zenodo.6515042), which directly reproduces this paper's plant-phenotyping measurements and computational analysis. The NCBI BioProject (PRJNA764168) contains DNA/genoyDataset · publicilized in this work should
be applicable to a range of different crops and plant breeding
programs and allow the development of crops that can meet
the world’s food, fiber, and fuel needs.
DATA AVA I L A B I L I T Y S TAT E M E N T
Phenotypic data, including raw images and analysis scripts
are available in the Zenodo Repository https://doi.org/10.5281/zenodo.6515042. DNA sequence data from genotypes
used in this study is available in NCBI Sequence Read
Archive (SRA) (https://www.ncbi.nlm.nih.gov/bioproject/) as
BioProject accession number PRJNA764168.
AC K N OW L E D G M E N T S
We thank Shuangye Wu and Ethan Faryna for assistance
in genotyping. This publication is supported by the EArly-Open asset ↗Zenodo · 10.5281/zenodo.6515042pdf-raw-page:12 lines:1-85Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Why it matches plant phenotyping methods植物のLAI・バイオマス等の状態観測をリモートセンシングとデータ同化で作物モデルへ統合し、収量推定性能を評価する計算・計測ワークフローが研究の中心であるため、植物表現型計測手法として収載する。
abstractthe assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Reproduction assets foundThe paper's field validation dataset (wheat/soil state observations from the 2018/19 Cora Lynn experiment) is openly available on the authors' PRISM (Monash) site, and the authors' APSIM-EnKF data assimilation source code is explicitly stated to be publicly available on GitHub. Weather data sources (BoM, Weather UndergDataset · publicThe field validation data
presented in this study are openly available in the P-band Radiometer Inferred Soil Moisture (PRISIM)
website at https://www.prism.monash.edu/index.htmlOpen asset ↗pdf-page:19 lines:1-59Code · publicThe APSIM-EnKF data assimilation framework used in this study was the version
developed and described by Zhang [28] (source code available on https://github.com/Open asset ↗pdf-page:3 lines:1-53Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic‐functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic‐functional community composition of vegetation. We examined the relationships between the image‐derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.
Why it matches plant phenotyping methods航空機イメージング分光法により植物バイオマス、緑色被覆、機能形質、群集組成を抽出し、地下プロセスとの関係を評価しており、植物形質取得が研究の中心的手法である。
abstractImaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic‐functional community composition of vegetation.
Reproduction assets foundThe paper's Open Research statement deposits its data and novel code (Cavender-Bares et al. 2021) on DRUM (University of Minnesota) under a Creative Commons license, and Cedar Creek LTER data are available at the Cedar Creek data repository. These are paper-specific, public, actionable assets covering the phenotyping/遥Dataset · publicData and novel code (Cavender‐Bares et al. 2021 ) are accessible through a Creative Commons license for non‐commercial use on DRUM, the Data Repository of the University of Minnesota, at https://conservancy.umn.edu/handle/11299/220311Open asset ↗DRUM · 11299/220311lines:294-333Dataset · publicand at the Cedar Creek Ecosystem Science Reserve Long‐Term Ecological Research data repository: https://www.cedarcreek.umn.edu/research/dataOpen asset ↗Cedar Creek Ecosystem Science Reserve Long‐Term Ecological Research data repositorylines:294-333Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
A primary selection target for wheat ( Triticum aestivum ) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0-0.6), while the correlation between grain yield and secondary traits ranged from -0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58-0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.
Why it matches plant phenotyping methods小麦育種試験で近接センシングによりNDVI・群落温度を取得し、統計モデルで収量を予測するワークフローを5年間検証しており、形質取得と予測手法が研究の中心である。
abstractThe objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh.
Reproduction assets foundThe paper's data availability statement explicitly deposits all phenotypic data (NDVI, CT, agronomic traits) and analysis code in the Dryad Digital Repository with a public DOI, making it a paper-specific, publicly actionable asset.Dataset · publicAll phenotypic data and code for analysis have been placed in the Dryad Digital Repository available at: https://doi.org/10.5061/dryad.vdncjsxrz .Open asset ↗Dryad Digital Repository · 10.5061/dryad.vdncjsxrzlines:724-739Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Kenaf ( Hibiscus cannabinus L.) is an industrial crop used as a raw material in various fields and is cultivated worldwide. Compared to high potential for its utilization, breeding sector is not vigorous partially due to laborous breeding procedure. Thus, efficient breeding methods are required for varieties that can adapt to various environments and obtain optimal production. For that, identifying kenaf’s characteristics is very important during the breeding process. Here, we investigated if RGB based vegetative index (VI) could be associated with traits for biomass. We used 20 varieties and germplasm of kenaf and RGB images taken with unmanned aerial vehicles (UAVs) for field selection in early and late growth stage. In addition, measuring the stem diameter and the number of nodes confirmed whether the vegetative index value obtained from the RGB image could infer the actual plant biomass. Based on the results, it was confirmed that the individual surface area and estimated plant height, which were identified from the RGB image, had positive correlations with the stem diameter and node number, which are actual growth indicators of the rate of growth further, biomass could also be estimated based on this. Moreover, it is suggested that VIs have a high correlation with actual growth indicators; thus, the biomass of kenaf could be predicted. Interstingly, those traits showing high correlation in the late stage had very low correlations in the early stage. To sum up, the results in the current study suggest a more efficient breeding method by reducing labor and resources required for breeding selection by the use of RGB image analysis obtained by UAV. This means that considerable high-quality research could be performed even with a tight budget. Furthermore, this method could be applied to crop management, which is done with other vegetative indices using a multispectral camera.
Why it matches plant phenotyping methodsUAVのRGB画像から植生指数、表面積、推定草丈を抽出し、茎径・節数・バイオマスを推定する画像ベース表現型計測法を評価しており、方法の適用と妥当性確認が中心です。
abstractHere, we investigated if RGB based vegetative index (VI) could be associated with traits for biomass.
Reproduction assets foundThe article states that all relevant data (kenaf phenotyping measurements and UAV RGB-derived traits/VIs) are available on the Open Science Framework (osf.io/tfamn), which is a paper-specific public deposit. However, the only permitted URL in this audit is the FAOSTAT statistics page, which is a generic external data源,Dataset · publicAll relevant data are available on the Open Science Framework ( osf.io/tfamn ).Open asset ↗lines:584-612Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.
Why it matches plant phenotyping methods大豆地上部バイオマスを時系列のRGB・マルチスペクトル画像から予測するハイスループット表現型解析法を開発し、予測精度も評価しているため、表現型取得・推定法が中心的です。
abstractOur main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) previously reported in the literature to correlate with crop biomass ( Babar et al.Open asset ↗lines:305-313Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Environmental factors might influence the carbon balance and sugar content in grapevine. In this two-year research, the STELLA software was employed to predict dry matter accumulation in Sangiovese vines, comparing the traditional vertical shoot positioning (VSP) and the single high wire (SHW) trellis systems. Every week, vegetative, eco-physiological and grape quality parameters were collected for 15 tagged vines per trellis system to set up the software. Significant differences in photosynthesis were recorded in 2014, with higher values in VSP (23-25% more). Shoot growth was significantly higher in VSP (20-25% more), whereas higher dry matter (30%) and yield (9-11% more) were detected for SHW. At harvest, berry composition suggested a slower ripening in SHW compared to VSP, which was linked to the shading of clusters in SHW. Finally, for the first time, linear regressions were found between measured berry sugar content and STELLA-estimated dry matter (R 2 = 0.96 in VSP; R 2 = 0.95 in SHW). This latter evidence allowed the estimation of berry sugar content, showing this software to be a practical tool to support winegrowers in decision making. Other studies are already underway to calibrate and validate the model for other varieties, training systems and environments.
Why it matches plant phenotyping methodsSTELLAモデルによるブドウの乾物蓄積・果実糖含量の推定と、実測値との回帰による検証が研究の中心であり、植物形質の計算推定手法として扱える。
abstractthe STELLA software was employed to predict dry matter accumulation in Sangiovese vines
Reproduction assets foundThe paper's phenotyping measurements (gas exchange, dry matter, berry composition) are reported only within the article itself ('Data is contained within the article'), with no public dataset deposit. However, the authors provide a public supplement containing paper-specific assets: Figure S1 (experimental site images)Supplement · publicbut, above all, herself for the tenacity in being able to finally publish the results of her master’s thesis. Another chapter is closed or not?
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Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10081675/s1 , Figure S1: Experimental site pictures, Figure S2: simplified model structure of STELLA software.
Click here for additional data file.
Author Contributions
Conceptualization, G.B.M. and L.S.; methodology and software validation, L.S. and E.C.; formal analysis, investigation and data curation, L.S., E.C., S.S., F.Open asset ↗lines:74-114Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha -1 . Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.
Why it matches plant phenotyping methodsUAV画像からキャノピー高を推定し、地上部バイオマスを予測するフォトグラメトリ手法の開発・検証・標準化が研究の中心であるため。
abstractHere we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's UAV aerial images, marker/plot coordinates, and harvested dry biomass weights at the NERC Environmental Information Data Centre, and the photogrammetric processing and statistical analysis code at Zenodo. Both are paper-specific, public, and have作者Dataset · publicThe data collected for this publication, including aerial images, marker and plot coordinates and dry sample weights, as well as site and survey metadata, are available from the NERC Environmental Information Data Centre < https://doi.org/10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424 >.Open asset ↗NERC Environmental Information Data Centre · 10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424lines:222-246Code · publicCode for photogrammetric processing and statistical analysis is available at Zenodo < https://doi.org/10.5281/zenodo.4783021 >Open asset ↗Zenodo · 10.5281/zenodo.4783021lines:222-246Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Solving many phenotyping problems involves not only automatic detection of objects in an image, but also counting the number of parts per object. We propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster. The suggested network incorporates object detection, object resizing, and part counting as modules in a single deep network, with several variants tested. The detection module is based on a Retina-Net architecture, whereas for the counting modules, two different architectures are examined: the first based on direct regression of the predicted count, and the other on explicit parts detection and counting. The results are promising, with the mean relative deviation between estimated and visible part count in the range of 9.2% to 11.5%. Further inference of count-based yield related statistics is considered. For banana bunches, the actual banana count (including occluded bananas) is inferred from the count of visible bananas. For spikelets-per-wheat-spike, robust estimation methods are employed to get the average spikelet count across the field, which is an effective yield estimator.
Why it matches plant phenotyping methods植物器官の可視パーツ数を画像から検出・計数する深層学習手法を開発し、複数作物データセットで評価しているため、表現型取得・推定法が中心です。
abstractWe propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster.
Reproduction assets foundThe paper's grape experiments use the public Embrapa WGISD dataset, extended by the authors with berry dot annotations that they state were made publicly available as part of that dataset extension. The banana and wheat datasets (Israel Phenomics Consortium) and the authors' code/models have no stated public release,; Dataset · publicThe dot annotations were made
publicly available as part of Embrapa WGISD dataset extension.Open asset ↗Embrapa WGISDpdf-page:5 lines:1-59Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.
Why it matches plant phenotyping methodsイネの地上部バイオマスという植物形質を、航空マルチスペクトル画像から抽出・推定する特徴抽出手法を開発し、比較検証しているため、フェノタイピング手法が中心です。
abstractThis work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery.
Reproduction assets foundThe paper's multispectral UAV imagery and ground-truth biomass measurements are deposited on OSF (via a view-only link, which is nonetheless publicly reachable with the URL), and the crop-monitoring protocol is on protocols.io. No author analysis code repository is stated.Dataset · publicDatasets supporting the experimental results presented in Figure 4 , Figure 5 and Figure 6 are available at the Open Science Framework: https://osf.io/cde6h/?view_only=1c4e5e03b9a34d3b96736ad8ab1b2774 folder Raw Data—MDPI Sensors.Open asset ↗Open Science Frameworklines:197-212Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Recent advances in unmanned aerial vehicle (UAV) remote sensing and image analysis provide large amounts of plant canopy data, but there is no method to integrate the large imagery datasets with the much smaller manually collected datasets. A simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets. It has three steps: developing a model for predicting sample values from UAV imagery, field gridding and trait value prediction, and statistical testing of predicted values. A field cultivation experiment was conducted to examine the effectiveness of the GAUSS framework, using a soybean-wheat crop rotation as the model system Fourteen soybean cultivars and subsequently a single wheat cultivar were grown in the same field. The crop rotation benefits of the soybeans for wheat yield were examined using GAUSS. Combining manually sampled data ( n = 143) and pixel-based UAV imagery indices produced a large amount of high-spatial-resolution predicted wheat yields ( n = 8,756). Significant differences were detected among soybean cultivars in their effects on wheat yield, and soybean plant traits were associated with the increases. This is the first reported study that links traits of legume plants with rotational benefits to the subsequent crop. Although some limitations and challenges remain, the GAUSS approach can be applied to many types of field-based plant experimentation, and has potential for extensive use in future studies.
Why it matches plant phenotyping methodsUAV画像と手作業データを統合し、植物形質・収量を高解像度で推定するGAUSS解析フレームワークを開発・実証しており、フェノタイピング手法が研究の中心である。
abstractA simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2 ) with different plant types and yield potentials were used ( Kaga et al.Open asset ↗lines:317-335Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
Why it matches plant phenotyping methodsUAS画像から植生指数と草冠高を抽出し、機械学習で収量・開花期を推定する高スループット表現型解析が研究の中心です。
abstractUnoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials
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 · publicand PLSR regression. All R codes are available in Github repository (https://github.com/Open asset ↗pdf-page:8 lines:1-175Code / dataset availability confirmedOpenAlex · checked 8 Sept 2026
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
Why it matches plant phenotyping methodsUAV時系列画像から作物形質を抽出し、成長動態と収穫期バイオマスを予測するHTP手法を、サンプリング頻度や予測性能とともに技術的に評価しており、フェノタイピング手法が中心である。
abstractUnmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops
Reproduction assets foundThe paper's UAV-derived sorghum phenotyping datasets (imagery features, AGB measurements) are deposited in the Illinois Databank with a public DOI listed in the Data Availability Statement. No author analysis code or trained models are explicitly shared.Dataset · publicData Availability Statement: The datasets used and analyzed during the current study are available
from the corresponding author via Illinois Databank at https://doi.org/10.13012/B2IDB-5649852_V2.Open asset ↗Illinois Databank · B2IDB-5649852_V2pdf-page:14 lines:1-59Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R 2 p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs. CORE IDEAS A low-cost, handheld near-infrared spectrometer was tested for phenotyping of cassava roots Plant breeding-relevant cross-validation schemes were used for predictions High prediction accuracies were achieved for cassava root dry matter content A spectral region predictive of plant water content was identified as important
Why it matches plant phenotyping methodsカッサバ根の乾物含量という植物形質を対象に、低コスト携帯型NIR分光計と予測モデルを開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava.
Reproduction assets foundThe paper's raw RDMC and NIR spectral data (21,044 SCiO scans paired with oven-dry RDMC from 10 Nigerian field trials) are publicly deposited on Cyverse under the GoreLab shared directory, as stated in the Data Availability section. The analysis R code on GitHub (GoreLab/CassavaNIRS) is paper-specific but its URL is anDataset · publicRaw RDMC and spectral data are available for download on Cyverse atOpen asset ↗Cyversepdf-page:20 lines:1-54Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data.
Why it matches plant phenotyping methods米収量という植物・作物群の形質を対象に、Sentinel-2データ用の3D CNNを開発し、データベース構築と既存手法との性能比較・検証を行っており、収量推定手法が中心である。
abstractwe build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicData Availability Statement: The codes related to this work will be released for reproducible research
at https://github.com/rufernan/RicePAL (accessed on 3 April 2021).Open asset ↗rufernan/RicePALpdf-page:23 lines:1-60Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Site-specific nitrogen (N) management in precision agriculture is used to improve nitrogen use efficiency (NUE) at the field scale. The objective of this study has been (i) to better understand the relationship between data derived from an unmanned aerial vehicle (UAV) platform and the crop temporal and spatial variability in small fields of about 2 ha, and (ii) to increase knowledge on how such data can support variable application of N fertilizer in winter wheat (Triticum aestivum). Multi-spectral images acquired with a commercially available UAV platform and soil available mineral N content (Nmin) sampled in the field were used to evaluate the in-field variability of the N-status of the crop. A plot-based field experiment was designed to compare uniform standard rate (ST) to variable rate (VR) N application. Non-fertilized (NF) and N-rich (NR) plots were placed as positive and negative N-status references and were used to calculate various indicators related to NUE. The crop was monitored throughout the season to support three split fertilizations. The data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits. Grain yield was mostly in the expected range and inconsistently higher in VR compared to ST. In contrast, N fertilizer application was reduced in the VR treatments between 5 and 40% depending on the field heterogeneity. The study showed that the methods used provided a good base to implement variable rate fertilizer application in small to medium scale agricultural systems. In the majority of the case studies, NUE was improved around 10% by redistributing and reducing the amount of N fertilizer applied. However, the prediction of the N-mineralisation in the soil and related N-uptake by the plants remains to be better understood to further optimize in-season N-fertilization.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とスペクトル植生指数を用いて作物バイオマスおよびN状態を推定し、その感度を2作期で検証しており、植物形質取得・検証が実質的な構成要素である。
abstractThe data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits.
Reproduction assets foundThe article includes an explicit data availability statement depositing the plant and spectral data supporting the study's phenotyping measurements in the public ETH Research Collection repository, making it a paper-specific, publicly actionable asset. Supplementary XLSX files also exist but the repository deposit is aDataset · publicThe plant and spectral data that support the findings of this study, as well as the supplementary material, are available in the online repository with the identifier, https://doi.org/10.3929/ethz-b-000380508 . At https://www.research-collection.ethz.ch/handle/20.500.11850/380508 last accessed [09/06/2020].Open asset ↗10.3929/ethz-b-000380508lines:160-271Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Crop mixtures are often beneficial in crop rotations to enhance resource utilization and yield stability. While targeted management, dependent on the local species composition, has the potential to increase the crop value, it comes at a higher expense in terms of field surveys. As fine-grained species distribution mapping of within-field variation is typically unfeasible, the potential of targeted management remains an open research area. In this work, we propose a new method for determining the biomass species composition from high resolution color images using a DeepLabv3+ based convolutional neural network. Data collection has been performed at four separate experimental plot trial sites over three growing seasons. The method is thoroughly evaluated by predicting the biomass composition of different grass clover mixtures using only an image of the canopy. With a relative biomass clover content prediction of R2 = 0.91, we present new state-of-the-art results across the largely varying sites. Combining the algorithm with an all terrain vehicle (ATV)-mounted image acquisition system, we demonstrate a feasible method for robust coverage and species distribution mapping of 225 ha of mixed crops at a median capacity of 17 ha per hour at 173 images per hectare.
Why it matches plant phenotyping methods高解像度カラー画像とCNNにより作物群落の種構成・バイオマス組成を推定する手法を開発し、複数地点・年で評価しているため、植物表現型取得・推定が中心である。
abstractwe propose a new method for determining the biomass species composition from high resolution color images using a DeepLabv3+ based convolutional neural network.
Reproduction assets foundThe paper's phenotyping analysis (canopy segmentation and biomass clover-content prediction) is trained and evaluated on the publicly available GrassClover dataset, explicitly linked in the Data Availability Statement. The DeepLab GitHub repository is a generic third-party model library, not a paper-specific asset, andDataset · publicPublicly available datasets were analyzed in this study. This data [ 16 ] can be found here: https://vision.eng.au.dk/grass-clover-dataset .Open asset ↗lines:627-638Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.
Why it matches plant phenotyping methods高速・高スループットな植物カウント手法を開発し、複数作物で検証した植物フェノタイピング手法の中心的研究。
abstractThe goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery.
Reproduction assets foundThe paper's authors publicly released their TasselNetV2+ PyTorch implementation (the paper's plant counting/phenotyping analysis code) online. The three plant counting datasets used are cited prior datasets, not paper-specific deposits.Code · publicTo encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plusOpen asset ↗TasselNetV2pluslines:225-304Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Premise Measuring plant productivity is critical to understanding complex community interactions. Many traditional methods for estimating productivity, such as direct measurements of biomass and cover, are resource intensive, and remote sensing techniques are emerging as viable alternatives. Methods We explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity. We apply these various productivity measures to trace the evolution of plant productivity and the traits underlying it. Results The correlation between remote sensing data and direct measurements of productivity varies depending on vegetation diversity, but the volume of vegetation estimated from drone-based photogrammetry is among the best predictors of biomass and cover regardless of community composition. The commonly used normalized difference vegetation index (NDVI) is a less accurate predictor of biomass and cover than other equally accessible vegetation indices. We found that the traits most strongly correlated with productivity have lower phylogenetic signal, reflecting the fact that high productivity is convergent across the phylogeny of prairie species. This history of trait convergence connects phylogenetic diversity to plant community assembly and succession. Discussion Our study demonstrates (1) the importance of considering phylogenetic diversity when setting management goals in a threatened North American grassland ecosystem and (2) the utility of remote sensing as a complement to ground measurements of grassland productivity for both applied and fundamental questions.
Why it matches plant phenotyping methodsドローン遠隔センシングとフォトグラメトリで植物群落の生産性・バイオマス・被覆を推定し、地上測定との予測性能を比較検証しており、植物形質取得手法が中心的です。
abstractWe explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity.
Reproduction assets foundThe authors publicly deposited the scripts and data used in this drone-based prairie phenotyping study (biomass, cover, vegetation index measurements, trait data) on GitHub and archived on Zenodo, with explicit availability statements and URLs in the article.Dataset · publicraw measurements can be found in the data sets provided in GitHub ( https://github.com/lanescher/prairie-remote-sensing-2020/tree/master/DATA )Open asset ↗https://github.com/lanescher/prairie-remote-sensing-2020/tree/master/DATAlines:88-96Code · publicScripts and data used in these analyses, as well as all supplements referenced in this article, are available on GitHub ( https://github.com/lanescher/prairie‐remote‐sensing‐2020 ) and on Zenodo ( https://doi.org/10.5281/zenodo.3981500 ; Scher et al., 2020 ).Open asset ↗https://github.com/lanescher/prairie‐remote‐sensing‐2020lines:956-978Dataset · publicScripts and data used in these analyses, as well as all supplements referenced in this article, are available on GitHub ( https://github.com/lanescher/prairie‐remote‐sensing‐2020 ) and on Zenodo ( https://doi.org/10.5281/zenodo.3981500 ; Scher et al., 2020 ).Open asset ↗https://doi.org/10.5281/zenodo.3981500 · 10.5281/zenodo.3981500lines:956-978Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.
Why it matches plant phenotyping methodsNDVIおよび炭素同位体組成を用いたバイオマス・干ばつ適応形質の推定性能と測定時期の影響を評価しており、表現型取得法の応用・技術評価が中心である。
titleCarbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection
Reproduction assets foundThe paper's Supplementary Materials (hosted at the MDPI URL listed in allowed_urls) contain paper-specific phenotyping assets: Table S1 (flowering time/phenological stage data for the 248 accessions), Tables S2–S3 (linear regressions of biomass vs. NDVI and biomass vs. δ13C), and Figure S1 (image of the field trial at Supplement · publicAgronomy 2020, 10, 1679 16 of 19
these traits are evaluated and (2) genotypic variability in intrinsic characteristics, such as phenology,
is accounted for.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4395/10/11/1679/s1,
Table S1: Number of accessions at different flowering times and phenological stages, Table S2: Linear regression
of the relationship between the biomass at mid grain filling and the NDVI, Table S3: Linear regression of the
relationship between the biomass and δ13C in the flag leaf dry matter sampled before imposition of stressOpen asset ↗mdpi.compdf-raw-page:16 lines:1-55Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
This study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret variations to nutrient uptake in a model mixed-pasture system in response to factorial low and high nitrogen (N) and phosphorus (P) application. HTP has not previously been used to examine pasture species in mixture. We used red-green-blue (RGB) imaging to obtain smoothed projected shoot area (sPSA) to predict absolute growth (AG) up to 70 days after planting (sPSA, DAP 70), to identify variation in relative growth rates (RGR, DAP 35-70) and detect overyielding (an increase in yield in mixture compared with monoculture, indicating facilitation) in a grass-legume model pasture. Finally, using principal components analysis we interpreted between species changes to HTP-derived temporal growth dynamics and nutrient uptake in mixtures and monocultures. Overyielding was detected in all treatments and was driven by both grass and legume. Our data supported expectations of more rapid grass growth and augmented nutrient uptake in the presence of a legume. Legumes grew more slowly in mixture and where growth became more reliant on soil P. Relative growth rate in grass was strongly associated with shoot N concentration, whereas legume RGR was not strongly associated with shoot nutrients. High throughput, image-based phenotyping was a useful tool to quantify growth trait variation between contrasting species and to this end is highly useful in understanding nutrient-yield relationships in mixed pasture cultivations.
Why it matches plant phenotyping methodsRGB画像による高スループット表現型計測を用いて、投影シュート面積から成長形質を抽出・定量する手法の実質的な適用研究であり、表現型取得が中心的です。
abstractThis study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret variations to nutrient uptake in a model mixed-pasture system
Reproduction assets foundThe authors deposited the manuscript's underlying phenotype data (sPSA/growth trait measurements from the HTP experiment) in Figshare, with a provisional DOI (10.25909/12895121) and an access link. This is a paper-specific public data asset. The R packages cited (dae, growthPheno, asremlPlus) are generic third-party CRDataset · publicthe associated data for the manuscript has now been placed in a repository and the provisional DOI is 10.25909/12895121, which once published will be linked to the article. Until the article is published, the data can be viewed via this private link: https://figshare.com/s/99e05c190be6cb416164Open asset ↗figshare · 10.25909/12895121lines:657-683Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, totaling 330 plots. Two regression models based on Convolutional Neural Networks (CNNs) named AlexNet and ResNet18 were evaluated, and compared to VGGNet—adopted in previous work in the same thematic for other grass species. The predictions returned by the models reached a correlation of 0.88 and a mean absolute error of 12.98% using AlexNet considering pre-training and data augmentation. This proposal may contribute to forage biomass estimation in breeding populations and livestock areas, as well as to reduce the labor in the field.
Why it matches plant phenotyping methods飼料作物のバイオマス収量をUAV画像とCNNで推定する手法を提案・評価しており、植物形質取得が研究の中心である。
abstractTo promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq.
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 · publicFigure 2 shows the plot’s definition procedure. We developed a python script tool (https://github.Open asset ↗pdf-page:3 lines:1-53Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Increasing dry matter yield is the most important objective in perennial ryegrass breeding program. Current yield assessment methods are time-consuming and subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based dry matter yield (DMY) yield estimation of individual plants through normalised difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing fifty advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable high-throughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-correlation between the predicted and measured DMY across three seasons with R² between 0.19- 0.81 and root mean square errors (RMSE) values ranging from 0.09-0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.
Why it matches plant phenotyping methods植物バイオマス収量を推定する画像・センサー・計算ワークフローの開発と検証が研究の中心であり、高スループット表現型解析手法として明確に該当する。
abstractThis study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass
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 · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:407-424Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of Cytisus scoparius homogenizes the landscape negatively affecting local plant diversity. Detecting structural change (e.g., biomass) is essential for assessing negative impacts of encroachment. Hence, exploring new monitoring tools to achieve this task is important for effectively capturing change and evaluating management activities.This study combines traditional field-based measurements with novel Light Detection and Ranging (LiDAR) observations from an Unmanned Aircraft System (UAS). We investigate the accuracy of mapping C. scoparius in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh-density point cloud data (>1,000 pts/m 2 ). Presence-absence of 12 shrub or tree genera was recorded across a 6.7 ha seminatural grassland area in Denmark. Furthermore, 10 individuals of C. scoparius were harvested for biomass measurements. With a UAS LiDAR system, we collected ultrahigh-density spatial data across the area in October 2017 (leaf-on) and April 2018 (leaf-off). We utilized a 3D point-based classification to distinguish shrub genera based on their structural appearance (i.e., density, light penetration, and surface roughness).From the identified C. scoparius individuals, we related different volume metrics (mean, max, and range) to measured biomass and quantified spatial variation in biomass change from 2017 to 2018. We obtained overall classification accuracies above 86% from point clouds of both seasons. Maximum volume explained 77.4% of the variation in biomass.The spatial patterns revealed landscape-scale variation in biomass change between autumn 2017 and spring 2018, with a notable decrease in some areas. Further studies are needed to disentangle the causes of the observed decrease, for example, recent winter grazing and/or frost events. Synthesis and applications: We present a workflow for processing ultrahigh-density spatial data obtained from a UAS LiDAR system to detect change in C. scoparius . We demonstrate that UAS LiDAR is a promising tool to map and monitor grassland shrub dynamics at the landscape scale with the accuracy needed for effective nature management. It is a new tool for standardized and nonbiased evaluation of management activities initiated to prevent shrub encroachment.
Why it matches plant phenotyping methodsUAS LiDARによる植物群落・低木の3D構造とバイオマスを推定する処理ワークフローを開発・精度評価しており、植物形質取得が研究の中心である。
abstractWe investigate the accuracy of mapping C. scoparius in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh-density point cloud data (>1,000 pts/m 2 ).
Reproduction assets foundThe paper's UAS LiDAR point clouds, field presence-absence records, and harvested shrub biomass measurements are explicitly deposited in the Dryad Data Repository with a public DOI link, making the paper-specific phenotyping data directly actionable.Dataset · publicDATA AVAILABILITY STATEMENT
The data used in this study are available through the Dryad Data Repository: https://doi.org/10.5061/dryad.547d7wm55 .Open asset ↗Dryad Data Repository · 10.5061/dryad.547d7wm55lines:280-396Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Breeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat ( Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.
Why it matches plant phenotyping methodsUAVによる航空高スループット表現型計測を用いて植生指数を取得し、収量予測・選抜への有効性を評価しており、表現型取得法の適用と技術的評価が研究の中心である。
abstractWith the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield.
Reproduction assets foundThe paper's phenotypic and genotypic data (vegetation index BLUPs, grain yield, phenology, and SNP data for the yield trials and small plots) are publicly deposited on CIMMYT Dataverse. No author analysis code or trained models are explicitly deposited; QGIS is a generic library and excluded.Dataset · public18 study are available on CIMMYT Dataverse (http://hdl.handle.net/11529/10548379).Open asset ↗CIMMYT Dataverse · 11529/10548379pdf-page:22 lines:1-56Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had little effect. Visually scored delayed senescence was positively correlated with GY ranging from r = 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.
Why it matches plant phenotyping methodsコムギの老化動態をハイパースペクトル反射から推定する頑健な表現型計測法の開発が研究の中心であり、スペクトル指標と全スペクトルモデルを比較・評価している。
abstractThe aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance.
Reproduction assets foundThe paper's data availability statement points to an ETH research-collection deposit for the experimental phenotyping data (hyperspectral reflectance, visual senescence scorings, GY/GPC) and states that all analysis scripts are publicly available, with a GitHub development repository and an archived ETH version.Dataset · publicExperimental data supporting the conclusions of this article can be downloaded here: https://doi.org/10.3929/ethz-b-000365618 .Open asset ↗10.3929/ethz-b-000365618lines:853-864Code · publicAll analysis scripts required to reproduce the results published in this article are publicly available.Open asset ↗lines:853-864Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
To explore the relationship between the attributes of the rice panicle and its weight parameters, 6 different rice cultivars from Sihong City, Jiangsu Province, China were selected for sampling in 2017. Then, their weight parameters were measured. The images of rice panicles were scanned to obtain grain area. The significant correlation between the grain area and the panicle weight was found on the base of the analysis for the data obtained [1]. Now the weight and area data were present here for exploring the rapid yield estimation models and crop phenotype research.
Why it matches plant phenotyping methodsイネ穂の画像スキャンから粒面積を取得し、収量推定や作物表現型研究に再利用するデータを提示しており、画像ベース形質データセットとして方法論的価値がある。
abstractThe images of rice panicles were scanned to obtain grain area.
Reproduction assets foundThis Data in Brief article presents the paper's own rice panicle phenotyping measurements (grain area, panicle/grain weight parameters for 6 cultivars, 1200 panicles) and states that the raw data files (.xlsx) were uploaded as supplementary material, publicly accessible online at the article DOI. No separate author-depDataset · publica-
tional Natural Science Fund, China (31701321).
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relation-
ships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104667.
Reference
[1] S. Zhao, H. Zheng, M. Chi, X. Chai, Y. Liu, Rapid yield prediction in paddy fields based on 2D image modelling of rice panicles,
Comput. Electron. Agric. 162 (2019) 759e766, https://doi.org/10.1016/j.compag.2019.05.020.Open asset ↗pdf-layout-page:6 lines:1-23Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
In this paper, we present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting. The method combines 3D plants modeling and deep segmentation of the higher leaves, during a period of 25–30 days, related to their growth. The novelty of our approach is in providing 3D reconstruction, leaf segmentation, geometric surface modeling, and deep network estimation for weight prediction to effectively measure plant growth, under three relevant phenotype features: height, weight and leaf area. Together with the vision based measurements, to verify the soundness of our proposed method, we also harvested the plants at specific time periods to take manual measurements, collecting a great amount of data. In particular, we manually collected 2592 data points related to the plant phenotype and 1728 images of the plants. This allowed us to show with a good number of experiments that the vision based methods ensure a quite accurate prediction of the considered features, providing a way to predict plant behavior, under specific conditions, without any need to resort to human measurements.
Why it matches plant phenotyping methods植物フェノタイピングのための3D再構成・葉セグメンテーション・深層学習による形質推定法の開発と手測定による検証が研究の中心である。
abstractwe present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting
Reproduction assets foundThe paper's leaf segmentation analysis code (a Tensorflow Mask R-CNN implementation used for the plant phenotyping measurements) is explicitly stated to be freely available on the authors' GitHub organization. The collected image/phenotype dataset (1728 images, 2592 manual data points) is described but no public data-Code · publicWe used our
implementation in Tensorflow, which is freely available on GitHub (See https://github.com/alcor-lab).Open asset ↗alcor-labpdf-page:10 lines:1-109Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published21 Sept 2019Plant science : an international journal of experimental plant biologyCited by 30 · OpenAlex ↗
This study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs. A set of 38 (winter and facultative) wheat cultivars were planted in Valladolid (Spain) under irrigation and two contrasting planting dates: normal (late autumn), and late (late winter). The late plating trial exhibited a 1.5 °C increase in average crop temperature. Measurements with different remote sensing techniques were performed at heading and grain filling, as well as carbon isotope composition (δ 13 C) and nitrogen content analysis. Multispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY) across the whole set of genotypes in the normal compared with the late planting, with indices (such as the RGB indices Hue, a* and the spectral indices NDVI, EVI and CCI) measured at grain filling performing the best. Aerially assessed remote sensing indices only performed better than ground-acquired ones at heading. Nitrogen content and δ 13 C correlated with GY at both planting dates. Correlations within winter and facultative genotypes were much weaker, particularly in the facultative subset. For both planting dates, the best GY prediction models were achieved when combining remote sensing indices with δ 13 C and nitrogen of mature grains. Implications for phenotyping in the context of increasing temperatures are further discussed.
Why it matches plant phenotyping methods小麦収量を対象に、マルチスペクトル・RGB・熱赤外リモートセンシングと同位体指標を比較し、収量予測性能を評価することが中心であり、表現型取得・推定手法の検証に該当する。
titleRemote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance
Reproduction assets foundThe paper used the authors' MosaicTool software (a FIJI plugin) to crop and process UAV RGB/thermal/multispectral plot images and compute vegetation indices for this wheat phenotyping study. MosaicTool is publicly available via the authors' GitLab repository and project page, both listed in the article text and in the,Code · publiclater overlaps up to 30 images (with at least 80%
ro
overlap) and removes UAV flight effects to produce accurate ortho-mosaics. Afterwards, regions
of interest (plots) were cropped and processed using the MosaicTool software (Prof. Shawn C.
Kefauver, https://integrativecropecophysiology.com/software-development/mosaictool/,
-p
https://gitlab.com/sckefauver/MosaicTool/, University of Barcelona, Barcelona, Spain)
integrated as a plugin for the open source image analysis platform FIJI (Fiji is Just ImageJ;
http://fiji.sc/Fiji) [40].
re
Extracted RGB vegetation indices collected from both ground and aerial platforms were obtained
using an updated version of the original Breedpix 2.0 software [41Open asset ↗sckefauver/MosaicToolpdf-layout-page:7 lines:1-78Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red–green–blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn’t any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield.
Why it matches plant phenotyping methodsUAV・地上のRGB／マルチスペクトル／熱画像を用いた圃場表現型取得と、プラットフォーム間の性能比較が研究の中心であるため。
abstractRecent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly.
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 · publicregions of interest corresponding to each plot were segmented
and exported using the MosaicTool (Shawn C. Kefauver, https://integrativecropecophysiology.com/
software-development/mosaictool/, https://gitlab.com/sckefauver/MosaicTool, University of Barcelona,
Barcelona, Spain)Open asset ↗sckefauver/MosaicToolpdf-page:4 lines:1-56Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
In the present study we analyzed the responses of wheat to mild salinity and drought with special emphasis on the so far unclarified interaction of these important stress factors by using high-throughput phenotyping approaches. Measurements were performed on 14 genotypes of different geographic origin (Austria, Azerbaijan and Serbia). The data obtained by non-invasive digital RGB imaging of leaf/shoot area reflect well the differences in total biomass measured at the end of the cultivation period demonstrating that leaf/shoot imaging can be reliably used to predict biomass differences among different cultivars and stress conditions. On the other hand, the leaf/shoot area has only a limited potential to predict grain yield. Comparison of gas exchange parameters with biomass accumulation showed that suppression of CO2 fixation due to stomatal closure is the principal cause behind decreased biomass accumulation under drought, salt and drought plus salt stresses. Correlation between grain yield and dry biomass is tighter when salt- and drought stress occur simultaneously than in the well-watered control, or in the presence of only salinity or drought, showing that natural variation of biomass partitioning to grains is suppressed by severe stress conditions. Comparison of yield data show that higher biomass and grain yield can be expected under salt (and salt plus drought) stress from those cultivars which have high yield parameters when exposed to drought stress alone. However, relative yield tolerance under drought stress is not a good indicator of yield tolerance under salt (and salt plus drought) drought stress. Harvest index of the studied cultivars ranged between 0.38-0.57 under well watered conditions and decreased only to a small extent (0.37-0.55) even when total biomass was decreased by 90% under the combined salt plus drought stress. It is concluded that the co-occurrence of mild salinity and drought can induce large biomass and grain yield losses in wheat due to synergistic interaction of these important stress factors. We could also identify wheat cultivars, which show high yield parameters under the combined effects of salinity and drought demonstrating the potential of complex plant phenotyping in breeding for drought and salinity stress tolerance in crop plants.
Why it matches plant phenotyping methods非侵襲RGB画像による葉・シュート面積測定を用いてバイオマス予測の信頼性を評価しており、表現型取得法の応用・技術検証が研究の中心に含まれる。
Reproduction assets foundThe article reports wheat phenotyping measurements (RGB-imaged leaf/shoot area, biomass, grain yield, water use, gas exchange, ETR, proline) for 14 cultivars under four stress treatments. No author analysis code, images, or standalone dataset deposit is mentioned. The only paper-specific public asset is the article's在线Supplement · publicThe Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2019.00501/full#supplementary-material
Click here for additional data file.
References
Addinsoft ( 2019 ). XLSTAT.
Available at: https://www.xlstat.com
Ahn C. H. Hossain M. A. Lee E. Kanth B. K. Park P. B.
( 2018 ).
Increased salt and drought tolerance by D-pinitol production in transgenic Arabidopsis thaliana .
Biochem. Biophys. Res. Commun.
504
315 – 320 .
10.1016Open asset ↗10.3389/fpls.2019.00501lines:178-342Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment ( G × E ) interactions across environments within a bread wheat ( Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトル画像でコムギ育種試験のキャノピー反射を取得し、収量予測のための関係行列として技術的に評価・適用しており、表現型取得法が中心的です。
abstractHyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield.
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 · publicAll phenotypic and genotypic data required to confirm the results presented in this study are available on CIMMYT Dataverse link: hdl:11529/10548109. The “GID” column denotes the unique identifiers for the genotypes. Supplemental material available at Figshare: https://doi.org/10.25387/g3.7653473 .Open asset ↗Figshare · 10.25387/g3.7653473lines:629-629Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With the development of new phenomics techniques we have the opportunity to collect image data on the plants, and extend the traditional genomic prediction models where we incorporate diverse set of information collected on the plants. In our research, we developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield for the soybean nested association mapping (SoyNAM) panel. To obtain the testing and training sets, we clustered the individuals based on their marker and canopy information using 2 different clustering techniques, and we compared 5 different cross-validation schemes. The results showed that the predictive ability of the models was the highest when both the canopy and marker information was included, and it was the lowest when only the canopy information was included.
Why it matches plant phenotyping methodsキャノピー画像情報を組み込んだ穀物収量予測モデルを開発しており、画像由来の植物情報を用いる計算手法が研究の中心である。
abstractwe developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield
Reproduction assets foundThe paper's canopy coverage and phenotypic/genotypic measurements derive from the publicly available SoyNAM population dataset hosted at SoyBase, which the authors explicitly identify as the data source for their study. No author analysis code, scripts, or trained models are mentioned with any availability statement,soDataset · publicFor the evaluation of our model, we used data collected from the SoyNAM population ( https://www.soybase.org/SoyNAM/ ), which is a nested association mapping population originally consisting of 5600 F5-derived recombinant inbred lines (RILs). The RILs were derived by crossing a common, high-yielding parent (IA3023) to 40 other parents. Out of the 40 parents, 17 were high-yielding, elite lines from 8 different states from the United States, 15Open asset ↗SoyNAMlines:32-44Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Background Wet tropical forests of Chocó, along the Pacific Coast of Colombia, are known for their high plant diversity and endemic species. With increasing pressure of degradation and deforestation, these forests have been prioritized for conservation and carbon offset through Reducing Emissions from Deforestation and forest Degradation (REDD+) mechanisms. We provide the first regional assessment of forest structure and aboveground biomass using measurements from a combination of ground tree inventories and airborne Light Detection and Ranging (Lidar). More than 80,000 ha of lidar samples were collected based on a stratified random sampling to provide a regionally unbiased quantification of forest structure of Chocó across gradients of vegetation structure, disturbance and elevation. We developed a model to convert measurements of vertical structure of forests into aboveground biomass (AGB) for terra firme, wetlands, and mangrove forests. We used the Random Forest machine learning model and a formal uncertainty analysis to map forest height and AGB at 1-ha spatial resolution for the entire pacific coastal region using spaceborne data, extending from the coast to higher elevation of Andean forests. Results Upland Chocó forests have a mean canopy height of 21.8 m and AGB of 233.0 Mg/ha, while wetland forests are characterized by a lower height and AGB (13.5 m and 117.5 Mg/a). Mangroves have a lower mean height than upland forests (16.5 m), but have a similar AGB as upland forests (229.9 Mg/ha) due to their high wood density. Within the terra firme forest class, intact forests have the highest AGB (244.3 ± 34.8 Mg/ha) followed by degraded and secondary forests with 212.57 ± 62.40 Mg/ha of biomass. Forest degradation varies in biomass loss from small-scale selective logging and firewood harvesting to large-scale tree removals for gold mining, settlements, and illegal logging. Our findings suggest that the forest degradation has already caused the loss of more than 115 million tons of dry biomass, or 58 million tons of carbon. Conclusions Our assessment of carbon stocks and forest degradation can be used as a reference for reporting on the state of the Chocó forests to REDD+ projects and to encourage restoration efforts through conservation and climate mitigation policies.
Why it matches plant phenotyping methods航空LiDARと地上インベントリを用いて森林の構造・樹冠高・地上部バイオマスを推定するモデルを開発し、不確実性解析と広域マッピングまで行っており、植物群落の形質取得・推定手法が中心です。
abstractWe provide the first regional assessment of forest structure and aboveground biomass using measurements from a combination of ground tree inventories and airborne Light Detection and Ranging (Lidar).
Reproduction assets foundThe paper's Chocó lidar and field-plot phenotyping/biomass data are stated to be available through the authors' BioREDD project, whose project URL (http://bioredd.org/projects/) is given in the article and is an allowed URL. Other data sources mentioned (ALOS PALSAR, SRTM, Global Wood Density Database, R) are generic外部Dataset · publicData in Chocó are available as part of the Biodiversity—Reducing Emissions from Deforestation and forest Degradation (BioREDD) project.Open asset ↗BioREDDlines:344-425Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
High throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding. We aimed to assess whether the combined use of hyperspectral data with modern marker technology could be used to improve across location pre-harvest yield predictions using different statistical models. A maize bi-parental doubled haploid (DH) population derived from F1, which consisted of 97 lines was evaluated in testcross combination under heat stress as well as combined heat and drought stress during the 2014 and 2016 summer season in Ciudad Obregon, Sonora, Mexico (27°20" N, 109°54" W, 38 m asl). Full hyperspectral data, indicative of crop physiological processes at the canopy level, was repeatedly measured throughout the grain filling period and related to grain yield. Partial least squares regression (PLSR), random forest (RF), ridge regression (RR) and Bayesian ridge regression (BayesB) were used to assess prediction accuracies on grain yield within (two-fold cross-validation) and across environments (leave-one-environment-out-cross-validation) using molecular markers (M), hyperspectral data (H) and the combination of both (HM). Highest prediction accuracy for grain yield averaged across within and across location predictions (rGP) were obtained for BayesB followed by RR, RF and PLSR. The combined use of hyperspectral and molecular marker data as input factor on average had higher predictions for grain yield than hyperspectral data or molecular marker data alone. The highest prediction accuracy for grain yield across environments was measured for BayesB when molecular marker data and hyperspectral data were used as input factors, while the highest within environment prediction was obtained when BayesB was used in combination with hyperspectral data. It is discussed how the combined use of hyperspectral data with molecular marker technology could be used to introduce physiological genomic estimated breeding values (PGEBV) as a pre-harvest decision support tool to select genetically superior lines.
Why it matches plant phenotyping methods作物キャノピーの反復ハイパースペクトル計測を用いた表現型情報の取得と、収量予測における統計モデルの比較評価が研究の中心であり、実質的な植物表現型計測の応用研究である。
abstractHigh throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding.
Reproduction assets foundThe paper's underlying hyperspectral, marker, and grain yield phenotyping data are publicly deposited in the CIMMYT Research Data & Software Repository Network, with an explicit availability statement and handle URL. No author analysis code repository is stated; R packages cited are generic libraries.Dataset · publicData Availability: The data underlying this study have been uploaded to the CIMMYT data repository and are accessible using the following link: http://hdl.handle.net/11529/10548168 .Open asset ↗CIMMYT data repository · hdl.handle.net/11529/10548168lines:130-141Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Published12 Mar 2019Metabolomics : Official journal of the Metabolomic SocietyCited by 21 · OpenAlex ↗
Introduction Oats (Avena sativa L.) are a whole grain cereal recognised for their health benefits and which are cultivated largely in temperate regions providing both a source of food for humans and animals, as well as being used in cosmetics and as a potential treatment for a number of diseases. Oats are known as being a cereal source high in dietary fibre (e.g. β-glucans), as well as being high in antioxidants, minerals and vitamins. Recently, oats have been gaining increased global attention due to their large number of beneficial health effects. Consumption of oats has been proven to lower blood LDL cholesterol levels and blood pressure, thus reducing the risk of heart disease, as well as reducing blood-sugar and insulin levels. Objectives Oats are seen as a low input cereal. Current agricultural guidelines on nitrogen application are believed to be suboptimal and only consider the effect of nitrogen on grain yield. It is important to understand the role of both variety and of crop management in determining nutritional quality of oats. In this study the response of yield, grain quality and grain metabolites to increasing nitrogen application to levels greater than current guidelines were investigated. Methods Four winter oat varieties (Mascani, Tardis, Balado and Gerald) were grown in a replicated nitrogen response trial consisting of a no added nitrogen control and four added nitrogen treatments between 50 and 200 kg N ha -1 in a randomised split-plot design. Grain yield, milling quality traits, β-glucan, total protein and oil content were assessed. The de-hulled oats (groats) were also subjected to a rapid Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) metabolomic screening approach. Results Application of nitrogen had a significant effect on grain yield but there was no significant difference between the response of the four varieties. Grain quality traits however displayed significant differences both between varieties and nitrogen application level. β-glucan content significantly increased with nitrogen application. The UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples. The method captured a wide range of compounds, inclusive of primary metabolites such as the amino acids, organic acids, vitamins and lipids, as well as a number of key secondary metabolites, including the avenanthramides, caffeic acid, and sinapic acid and its derivatives and was able to identify distinct metabolic phenotypes for the varieties studied. Amino acid metabolism was massively upregulated by nitrogen supplementation as were total protein levels, whilst the levels of organic acids were decreased, likely due to them acting as a carbon skeleton source. Several TCA cycle intermediates were also impacted, potentially indicating increased TCA cycle turn over, thus providing the plant with a source of energy and reductant power to aid elevated nitrogen assimilation. Elevated nitrogen availability was also directed towards the increased production of nitrogen containing phospholipids. A number of both positive and negative impacts on the metabolism of phenolic compounds that have influence upon the health beneficial value of oats and their products were also observed. Conclusions Although the developed method has broad applicability as a rapid screening method or a rapid metabolite profiling method and in this study has provided valuable metabolic insights, it still must be considered that much greater confidence in metabolite identification, as well as quantitative precision, will be gained by the application of higher resolution chromatography methods, although at a large expense to sample throughput. Follow up studies will apply higher resolution GC (gas chromatography) and LC (reversed phase and HILIC) approaches, oats will be also analysed from across multiple growth locations and growth seasons, effectively providing a cross validation for the results obtained within this preliminary study. It will also be fascinating to perform more controlled experiments with sampling of green tissues, as well as oat grains, throughout the plants and grains development, to reveal greater insight of carbon and nitrogen metabolism balance, as well as resource partitioning into lipid and secondary metabolism.
Why it matches plant phenotyping methodsUHPLC-MSによる植物代謝表現型の迅速取得法を開発・反復性評価し、大規模穀類サンプルへの適用可能性と限界も示しているため、代謝測定が単なる生物学的実験の補助ではなく方法論の中心である。
abstractThe UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples.
Reproduction assets foundThe paper's UHPLC-MS metabolite profiling data (oat nitrogen supplementation study) is publicly deposited in MetaboLights (MTBLS804), and the authors' ASCA/PLS-S analysis scripts are publicly available on GitHub.Code · publicASCA and PLS-S with RFE were performed within MATLAB 2016a using in-house scripts which are made available freely online at https://github.com/Biospec/cluster-toolbox-v2.0 .Open asset ↗GitHub · Biospec/cluster-toolbox-v2.0lines:106-112Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate quantification of below-ground biomass (BGB) of woody vegetation is critical to understanding ecosystem function and potential for climate change mitigation from sequestration of biomass carbon. We compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB. We found that the relationship between BGB and stem diameter was generic, with a simple power-law model having a BGB prediction efficiency of 72–93% for four broad plant functional types: (i) shrubs and Acacia trees, (ii) multi-stemmed mallee eucalypts, (iii) other trees of relatively high wood density, and; (iv) a species of relatively low wood density, Pinus radiata D. Don. There was little improvement in accuracy of model prediction by including variables (e.g. climatic characteristics, stand age or management) in addition to stem diameter alone. We further assessed the generality of the plant functional type models across 11 contrasting stands where data from whole-plot excavation of BGB were available. The efficiency of model prediction of stand-based BGB was 93%, with a mean absolute prediction error of only 6.5%, and with no improvements in validation results when species-specific models were applied. Given the high prediction performance of the generalised models, we suggest that additional costs associated with the development of new species-specific models for estimating BGB are only warranted when gains in accuracy of stand-based predictions are justifiable, such as for a high-biomass stand comprising only one or two dominant species. However, generic models based on plant functional type should not be applied where stands are dominated by species that are unusual in their morphology and unlikely to conform to the generalised plant functional group models.
Why it matches plant phenotyping methods植物の地下部バイオマスという明示的な形質を推定する汎用アロメトリーモデルを開発し、複数の機能型・林分で予測性能を検証しており、形質測定法が研究の中心です。
abstractWe compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB.
Reproduction assets foundThe paper's below-ground biomass allometry is built from the authors' Australian Individual Tree Biomass Library (Paul et al. 2017b), a public dataset deposited with a DOI and ÆKOS portal URL, which qualifies as a paper-specific public phenotype dataset. The ecoregions map and Dryad wood density database are generic/cdDataset · publicH, England JR, Davies MJ, Luck H (2017a) Measurements of stem diameter:
786 implications for individual- and stand-level errors. Environmental Monitoring and Assessment, 189, 416, 1-
787 14.
788 Paul KI, Larmour, J., Zerihun, A., et al. (2017b) Australian Individual Tree Biomass Library, Version 3.
789 10.4227/05/566629ADA95DA. http://www.aekos.org.au/dataset/223706. Obtained from Australian
790 Ecological Knowledge and Observation System Data Portal (ÆKOS, http://www.portal. aekos.org.au/), ,
Generic allometrics 38Open asset ↗aekos.org.au · 10.4227/05/566629ADA95DApdf-layout-page:38 lines:1-43Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.
Why it matches plant phenotyping methodsUAVフォトグラメトリと画像解析により樹体構造・品種・個体収量を推定・地図化する手法を開発し、検証・実 orchard 適用しており、植物フェノタイピングが中心である。
abstractIn this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems.
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 are available online at http://www.mdpi.com/2072-4292/10/12/1900/
s1, Figure S1: Image abacus used by expert in the field to estimate load index for (a) ‘Kent’, (b) ‘Keitt’, (c) and
‘BDH’ cultivar. Load index categories (low, medium and high) are displayed in column and different tree heights
(small, medium and tall) are represented in line. Table S1: Mean fruit weight and standard deviation (SD) for the
three variety in Niayes region. Table S2: Description of the 150 calibration trees: cultivar; number of fruit detected
by the KNN-based machine vision and yield measured; load index; and tree structure parameters (tree height,
crown area and volume).Open asset ↗pdf-page:18 lines:1-56Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p
Why it matches plant phenotyping methodsVIIRS・MODISによる作物フェノロジー（緑化開始、成長段階、最大緑度）の推定手法を提示し、地上観測報告との精度比較で検証しているため、植物フェノタイピング手法が中心である。
abstractwe presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets
Reproduction assets foundThe paper uses two public datasets directly in its phenotyping analysis: USDA NASS Crop Progress field observations (via QuickStats) for evaluation, and the Cropland Data Layer (via CropScape/Cropland release) to identify 'pure' corn and soybean VIIRS pixels. No author code, models, or derived data deposits are stated.Dataset · publicwe used CDL in 2014 and
2015 that were publicly available at https://www.nass.usda.gov/Research_and_Science/Cropland/
Release/index.php or at USDA CropScape (https://nassgeodata.gmu.edu/CropScape/)Open asset ↗pdf-page:6 lines:1-62Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment ( G × E ) interactions across environments within a bread wheat ( Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトル画像によるキャノピー反射率フェノタイピングを用い、穀粒収量予測への有効性と予測精度を評価しており、表現型取得法の応用が中心的です。
abstractHyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants.
Reproduction assets foundThe authors state that all phenotypic and genotypic data needed to reproduce the study's hyperspectral-reflectance genomic prediction results are publicly deposited on the CIMMYT Dataverse under handle hdl:11529/10548109. This is a paper-specific, publicly actionable phenotype dataset asset. No author analysis code or Dataset · publicbioRxiv preprint doi: https://doi.org/10.1101/389825; this version posted November 27, 2018. The copyright holder for this preprint (which was
not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
under a CC-BY-NC-ND 4.0 International license.
1 All phenotypic and genotypic data required tOpen asset ↗pdf-layout-page:19 lines:1-60Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.
Why it matches plant phenotyping methodsサトウキビのバイオメトリクスからバイオマスを推定する手法を提案し、地上測定データセットと測定精度も提示しており、植物形質の取得・推定が中心である。
abstractHere, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil).
Reproduction assets foundThe paper's ground reference sugarcane dataset (3,500 observations of biometrics, LAI, and biomass from four fields in São Paulo state) is published openly on the 4TU Centre for Research Data repository. The authors' MATLAB analysis scripts are only available upon request, so they do not qualify as public assets.Dataset · publicThe ground measurements, published for public use, can be found on the 4TU Centre for Research Data
repository, see (Data Citation 1).Open asset ↗4TU Centre for Research Datapdf-page:11 lines:1-40Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera. We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H 2 < 0.93), as well as estimated fruit number ( H 2 = 0.68). In addition, we validated the method for estimating fruit number with rev5 , a mutant with increased flower abortion. The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.
Why it matches plant phenotyping methods画像から植物乾物重・果実数・成長動態を推定する手法を開発し、交差検証と変異体で検証しており、表現型取得・推定法が研究の中心である。
abstractHere, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.
Reproduction assets foundThe paper's availability statement explicitly links authors' code on GitHub and the phenotypic dataset on Dryad; supplementary files also contain the ImageJ macros and R code used for the phenotyping analysis.Code · publicCodes are available on Github ( https://github.com/fvasseur ), and phenotypic data are available on the Dryad repository ( https://doi.org/10.5061/dryad.343bd84 ) [ 43 ].Open asset ↗github.com/fvasseurlines:312-394Dataset · publicphenotypic data are available on the Dryad repository ( https://doi.org/10.5061/dryad.343bd84 ) [ 43 ].Open asset ↗Dryad · 10.5061/dryad.343bd84lines:312-394Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness ( R ² = 0.9457-0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship ( R ² = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis.
Why it matches plant phenotyping methodsKinectによる多視点3D再構成とアルゴリズムを用いて、植物の高さ・葉面積・体積・バイオマス関連形質を自動測定し、精度と頑健性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。
abstractThe paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables.
Reproduction assets foundThe paper's Supplementary Materials, available at the MDPI s1 URL, explicitly contain the paper-specific phenotyping assets: point clouds and meshes shown in figures, the datasets used for the scatter plots of Kinect-measured growth parameters vs. reference measurements, and interactive MATLAB 3D scatter plots. No codeDataset · publicThe following are available online at http://www.mdpi.com/1424-8220/18/3/806/s1 . Supplementary data associated with this article have been provided. These data include the point clouds and meshes appeared in figures, the data sets used by scatter plots, and interactive MATLAB 3D scatter plots.Open asset ↗lines:114-135Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Why it matches plant phenotyping methods圃場型HTPシステムを用いたNDVI・群落温度の取得と、ゲノム選抜モデルへの統合を技術的に評価しており、表現型取得基盤の適用が中心的です。
abstractA portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates.
Reproduction assets foundThe authors explicitly state that all data sets and analysis scripts for this wheat HTP/genomic-selection study are publicly deposited in the Dryad Digital Repository under DOI 10.5061/dryad.7f138. This covers the paper-specific phenotype data (NDVI, canopy temperature, grain yield BLUPs) and scripts. Note: no Dryad/DODataset · publicAll data sets and scripts are available from the Dryad Digital Repository: 10.5061/dryad.7f138 .Dryad Digital Repository · 10.5061/dryad.7f138lines:223-263Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Contemporary terrestrial laser scanning (TLS) is being used widely in forest ecology applications to examine ecosystem properties at increasing spatial and temporal scales. Harvard Forest (HF) in Petersham, MA, USA, is a long-term ecological research (LTER) site, a National Ecological Observatory Network (NEON) location and contains a 35 ha plot which is part of Smithsonian Institution's Forest Global Earth Observatory (ForestGEO). The combination of long-term field plots, eddy flux towers and the detailed past historical records has made HF very appealing for a variety of remote sensing studies. Terrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics. TLS approaches include three-dimensional reconstructions of a plot over time, establishing the impact of ice storm damage on forest canopy structure, and characterizing eastern hemlock ( Tsuga canadensis ) canopy health affected by an invasive insect, the hemlock woolly adelgid ( Adelges tsugae ). Efforts such as those deployed at HF are demonstrating the power of TLS as a tool for monitoring ecological dynamics, identifying emerging forest health issues, measuring forest biomass and capturing ecological data relevant to other disciplines. This paper highlights various aspects of the ForestGEO plot that are important to current TLS work, the potential for exchange between forest ecology and TLS, and emphasizes the strength of combining TLS data with long-term ecological field data to create emerging opportunities for scientific study.
Why it matches plant phenotyping methodsTLSを用いた森林キャノピー構造、健康状態、バイオマスの測定・監視を中心に扱うレビューであり、植物状態の取得手法が主要テーマです。
abstractTerrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics.
Reproduction assets foundThe paper's TLS work is grounded in the Harvard Forest ForestGEO plot census data (HF253), which the authors explicitly make available in the Data accessibility statement via the Harvard Forest Data Archive. This is a paper-specific, publicly accessible field/phenotype dataset (stem surveys, DBH, mortality assessments)Dataset · publicAdditional data are available from: http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf253 .Open asset ↗hf253lines:111-224Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 10 Sept 2026
Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species.
Why it matches plant phenotyping methods画像由来特徴量から植物バイオマスを推定する予測モデルを開発し、複数実験で性能評価しており、表現型取得・推定手法が研究の中心である。
abstractwe constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation.
Reproduction assets foundThe paper's authors publicly deposited their analysis code (HTPmod R package on GitHub), raw image datasets in the IPK PGP repository under three DOIs, and supporting data (metadata, raw images, archival code copy) in the GigaScience GigaDB repository. All are paper-specific, public, and actionable.Dataset · publicThe raw image datasets, as well as analyzed data supporting the results of this article, are available in the PGP repository [ 44 ] under https://dx.doi.org/10.5447/IPK/2017/24 , https://dx.doi.org/10.5447/IPK/2017/25 , and https://dx.doi.org/10.5447/IPK/2017/26Open asset ↗PGP repository · 10.5447/IPK/2017/24lines:102-163Dataset · publicThe raw image datasets, as well as analyzed data supporting the results of this article, are available in the PGP repository [ 44 ] under https://dx.doi.org/10.5447/IPK/2017/24 , https://dx.doi.org/10.5447/IPK/2017/25 , and https://dx.doi.org/10.5447/IPK/2017/26Open asset ↗PGP repository · 10.5447/IPK/2017/25lines:102-163Dataset · publicThe raw image datasets, as well as analyzed data supporting the results of this article, are available in the PGP repository [ 44 ] under https://dx.doi.org/10.5447/IPK/2017/24 , https://dx.doi.org/10.5447/IPK/2017/25 , and https://dx.doi.org/10.5447/IPK/2017/26Open asset ↗PGP repository · 10.5447/IPK/2017/26lines:102-163Dataset · publicSupporting data, including metadata tables, raw image files, and an archival copy of HTPmod are also available via the GigaScience repository, Giga DB [ 46 ].Open asset ↗lines:102-163Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Traditional phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, dynamic phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We used these traits to predict the grain yield in the early growth stage, and 30% of the grain yield variance was explained by 2 tiller traits in the early growth stage. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and tiller angles of rice accessions containing haplotype H3 were much smaller. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.\n\nHighlightCombining high-throughput micro-CT-RGB phenotyping facility and genome-wide association study to dissect the genetic architecture of rice tiller development by using the indica subpopulation.
Why it matches plant phenotyping methods高スループットのマイクロCT-RGB画像システムを開発し、イネの形態形質を多数・時系列で抽出することが研究の中心であるため。GWASはその応用にあたる。
abstractwe developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points.
Reproduction assets foundThe paper's own micro-CT-RGB phenotyping outputs (CT images, side-view RGB images, and extracted phenotypic traits for 234 rice accessions at 9 time points) are explicitly stated to be publicly viewable and downloadable from the authors' Huazhong Agricultural University plant phenotyping database. RiceVarMap is an exteDataset · public276
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Phenotyping database extracted by HCR at 9 time points
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During the tillering stage, 234 rice plants were automatically measured by HCR at 9
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different development time points (once every 3 d, starting from 41 ~ 67 d after
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sowing). All the phenotypic data and images can be viewed and downloaded via the
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link http://plantphenomics.hzau.edu.cn/checkiflogin_en.action and then following
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these steps: (1) select ‘rice’; (2) select ‘2015-tiller’ in the year section; (3) select one
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of the accession IDs in the ID section and then press ‘search images’; (4) 9 CT images
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and 9 side-view color images can be viewed and downloaded; (5) a similar process
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can be used to viOpen asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:10 lines:1-76Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Low soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a * and u * , which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option.
Why it matches plant phenotyping methods地上・空撮RGB／マルチスペクトル計測から表現型形質を抽出し、収量との相関を比較評価することが研究の中心であり、技術的なフェノタイピング手法の検証に該当する。
abstractremote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way
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 · publicRGB pictures were subsequently analyzed using a version of the Breedpix 0.2 software adapted to JAVA8 and integrated as a plugin within FIJI; https://github.com/George-haddad/CIMMYT ).Open asset ↗George-haddad/CIMMYTlines:48-107Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 14 Sept 2026
Abstract Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.
Why it matches plant phenotyping methods画像由来の形質から非破壊的に植物バイオマスを推定する計算手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe propose an approach to biomass estimation based on image derived phenotypic traits.
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 · publicWe analyzed a barley image dataset downloaded from http://iapg2p.sourceforge.net/modeling/#dataset . The Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany generated this high-throughput phenotype data setOpen asset ↗iapg2p.sourceforge.netlines:38-78Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best‐performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single‐time‐point data.
Why it matches plant phenotyping methodsハイパースペクトル画像からトウモロコシ収量を推定する予測手法を複数比較・評価しており、植物形質の取得・推定法が研究の中心である。
abstractwe examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs.
Reproduction assets foundThe paper's hyperspectral reflectance and maize grain yield phenotyping data (11 CIMMYT trials, 2014) are stated to be publicly available at the CIMMYT data repository. No specific dataset identifier is given, and no author analysis code URL is provided (scripts only appear in Supplemental Methods without a public URL,Dataset · publicat bands related to water use efficiency is
informative of the crop performance under drought stress Supplemental Material Available
(reported for C3 cereals in Araus et al., 2002). However, Supplemental material for this article is available online.
the CWMI in this study showed poor performance rela- Data is also available at http://data.cimmyt.org/dvn/.
tive to other VIs tested. Winterhalter et al. (2011) also
found low correlations between CWMI and maize grain Acknowledgments
yield and attributed the relatively poor correlation to the The authors acknowledge financial support from CIMMYT
ability of maize to recover from drought stress. and from ARVALIS–Institut du vegetal. TOpen asset ↗pdf-layout-page:7 lines:1-55Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. Results In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. Conclusions We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.
Why it matches plant phenotyping methodsハイパースペクトル画像から小麦の穀粒収量を推定するベイズ機能回帰モデルを開発・比較しており、植物形質の取得・推定手法が研究の中心である。
abstractwe propose Bayesian functional regression models that take into account all available bands
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 phenotypic, genotypic, HTP data and other materials used in this study can be downloaded from the link: https://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm .Open asset ↗lines:3867-3999Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
To ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. Here we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor-intensive measures of flowering time, height, biomass, grain yield, and harvest index. Genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.
Why it matches plant phenotyping methods圃場ハイスループット表現型解析を大規模イネ集団に適用し、従来測定との精度・有効性を比較してQTL検出に利用しており、表現型取得法が研究の中心である。
abstractHere we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits.
Reproduction assets foundThe paper's QTL/phenotyping analysis code is publicly hosted on Bitbucket, and the authors state that the code and original phenotyping data files are deposited in Dryad under a DOI. Both are paper-specific, public, and actionable.Code · publicR code is currently hosted at bitbucket.org ( https://bitbucket.org/paultanger/rilpopr ) and code as well as original data files will be deposited into http://dx.doi.org/10.5061/dryad.53bj8 and www.datadryad.org for publication and availability.Open asset ↗bitbucket.org/paultanger/rilpoprlines:62-105Dataset · publicR code is currently hosted at bitbucket.org ( https://bitbucket.org/paultanger/rilpopr ) and code as well as original data files will be deposited into http://dx.doi.org/10.5061/dryad.53bj8 and www.datadryad.org for publication and availability.Open asset ↗Dryad · 10.5061/dryad.53bj8lines:62-105Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. Results This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. Conclusions We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
Why it matches plant phenotyping methodsキャノピーのハイパースペクトル反射を用いて穀粒収量を推定するモデルを提案し、複数の手法および植生指数と予測精度を比較しており、表現型取得・推定手法が中心である。
titlePredicting grain yield using canopy hyperspectral reflectance in wheat breeding data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe data and materials used in this study can be downloaded from the link: http://hdl.handle.net/11529/10693 . The links contains file corresponding to the phenotypic and bands data for each environments, Drought.Phe_and_Bands.RData, EarlyHeat.Phe_and_Bands.RData, Irrigated.Phe_and_Bands.RData, Irrigated.Phe_and_Bands.RData.Open asset ↗hdl.handle.net · 11529/10693lines:4711-4843