Cover crops offer essential agroecosystem benefits, including reduced soil erosion, weed suppression, and improved soil health. Aboveground biomass (AGB) is a key indicator of these benefits; however, field-based quantification is often limited, which hinders effective cover crop management decisions. This study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas. Ground-truth and imagery data were collected over three years (2023–2025) for winter rye ( Secale cereale L.) in Lamesa and two years (2023–2024) for winter wheat ( Triticum aestivum L.) in Chillicothe under varying irrigation regimes. Five ML algorithms, random forest, support vector regression, extreme gradient boosting, partial least squares regression (PLSR), and artificial neural network (ANN), were evaluated across four individual and eleven feature fusion datasets. The ANN model consistently achieved the highest predictive accuracy, particularly when vegetation indices were combined with structural features (R² = 0.87, RMSE = 9.08 g m - ²), while PLSR showed the weakest performance. Grouped validation (leave-one-year-out, leave-one-species-out, and leave-one-treatment-out) revealed reduced model performance compared to random (70/30) splitting of pooled data, yet the ANN maintained moderate predictive ability, indicating reasonable generalizability across years, species, and management conditions. Shapley additive explanations (SHAP) revealed key predictors in the ANN model, including plant height, chlorophyll vegetation index, chlorophyll sensitive index, blue band reflectance, modified chlorophyll absorption in reflectance index, dissimilarity, correlation, and enhanced green vegetation index. These findings demonstrate the effectiveness of UAV-ML integration for accurate AGB estimation and highlight the potential for scalable, data-driven cover crop monitoring in water-limited environments and beyond.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を統合し、植物の地上部バイオマスを推定する手法を開発・比較検証しており、表現型取得が研究の中心である。
abstractThis study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas.
Hyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale. Hyperspectral images of healthy plants, obtained under laboratory conditions using a Cubert Ultris 20 camera (450–874 nm range, 106 channels), were utilized. The effectiveness of various preprocessing schemes was compared: full (including normalization, smoothing, calculation of derivatives, and identification of extreme features), reduced, and minimal. Machine learning models were exploited for classification: logistic regression, support vector machine, and gradient boosting, trained on averaged spectra. It is shown that the use of a full pipeline optimized for phytopathological diagnostics leads to reduced classification accuracy in phenotyping tasks. The best results (F1 = 0.97 ± 0.025) were achieved using the original averaged spectral curves without additional transformations. It is concluded that for healthy wheat, barley, and rye phenotyping, absolute reflectance levels are informative, whereas for disease diagnostics, changes in the shape of the spectral curve are more important. The obtained results clarify the applicability limits of pipelines developed for phytosanitary purposes and can inform the development of remote monitoring and phenotyping systems for cereal crops.
Why it matches plant phenotyping methods穀類の健全植物フェノタイピングに対するハイパースペクトル画像処理パイプラインの適用性を比較評価しており、前処理と分類性能の検証が研究の中心である。
abstractHyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale.
BarleyRyeWheatField / plotLeafRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Summary Cereal architecture is underpinned by the coordinated development of modular phytomer units. While above-ground phenology is well characterized by metrics such as the phyllochron, an equivalent framework for root system development is lacking. Because each phytomer node initiates both leaves and adventitious roots, root and shoot development are inherently linked. Here, we quantified this coordination in wheat, barley, and rye across contrasting temperature regimes and validated the results under field conditions. We introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes. Root development followed a highly conserved thermal sequence synchronized with shoot phenology. Across species and environments, the rhizochron averaged 146.1°C d, closely matching the phyllochron (126.6°C d). We also identified a consistent thermal offset, with nodal roots emerging approximately 185.3°C d after the corresponding leaf on the same phytomer node. The root appearance interval averaged 45.3°C d, reflecting continuous root deployment across active nodes. By integrating root phenology into a node-based framework, the rhizochron provides a predictive tool for crop modeling, trait-based breeding, and more target phenotyping aimed at improving resource acquisition and climate resilience.
Why it matches plant phenotyping methods根系と地上部の発達を定量化する新しい熱時間指標（rhizochron等）を導入し、複数種・環境および圃場条件で検証しており、表現型測定法が研究の中心である。
abstractWe introduce the rhizochron, defined as the thermal time (growing degree-days, °C d) period between the emergence of nodal roots on successive stem nodes, and the root appearance interval, describing the emergence rate of individual root axes.
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:197-235Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Computers and Electronics in Agriculture.
Better matching of the timing and amount of fertilizer inputs to plant requirements will improve nutrient use efficiency and crop yields and could reduce negative environmental impacts. Deep learning can be a powerful digital tool for on-site, real-time, non-invasive diagnosis of crop nutrient deficiencies. A drone-based RGB image dataset was generated together with ground truthing data in winter wheat (2020) and in winter rye (2021) during tillering and booting in the long-term fertilizer experiment (LTFE) Dikopshof. In this LTFE, the crops were fertilized with the same amounts for decades. The selected treatments included full fertilization including manure (NPKCa+m+s), mineral fertilization (NPKCa), mineral fertilization but no nitrogen (N) application (_PKCa), no phosphorus (P) application (N_KCa), no potassium (K) application (NP_Ca), or no liming (Ca) (NPK_), as well as an unfertilized treatment. The image dataset consisting of more than 3600 UAV-based RGB images was used to train and evaluate in total of eight CNN-based and transformer-based models as baselines within each crop-year and across the two crop-year combinations, aiming to detect the specific fertilizer treatments, including the specific nutrient deficiencies. The field observations showed a strong biomass decline in the case of N omission and no fertilization, though the effects were lower in the case of P, K, and lime omission. The mean detection accuracy within one year was 75% (winter wheat) and 81% (winter rye) across models and treatments. Hereby, the detection accuracy for winter wheat was highest for the NPKCa+m+s (100%) and the unfertilized (96%) treatments as well as the _PKCa treatment (92%), whereas for treatments N_KCa and NPKCa the accuracy was lowest (about 50%). The results were similar for winter rye. In the cross-year and cross-cereal species transfer (training on winter wheat, application on winter rye, and vice versa), the mean accuracy was about 18%. The results highlight the potential of deep learning as a digital tool for decision-making in smart farming but also the difficulties of transferring models across years and crops.
Why it matches plant phenotyping methodsUAV RGB画像から作物の栄養欠乏・施肥状態を推定するデータセットと深層学習モデルを構築・評価しており、植物状態の取得・推定手法が研究の中心である。
abstractDeep learning can be a powerful digital tool for on-site, real-time, non-invasive diagnosis of crop nutrient deficiencies.
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:135-161Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Phosphorus (P) deficiency is a critical limiting factor in crop production, significantly impacting growth and yield, particularly in winter rye. Traditional methods for detecting P deficiency often face challenges in terms of accuracy and timeliness, especially during the early stages of crop development. Hyperspectral remote sensing presents a promising alternative for monitoring nutrient stress, while feedforward neural networks (FNNs) offer robust predictive capabilities for data analysis. This study seeks to develop an innovative method for detecting phosphorus deficiencies in winter rye during early growth stages by integrating hyperspectral data with feedforward neural networks. The primary objective is to improve the efficiency and scalability of phosphorus (P) deficiency detection, with a particular focus on reducing root mean square error (RMSE) and enhancing the responsiveness of detection compared to traditional phosphorus detection methods, eliminating the need for time-consuming sample collection and chemical analysis. Field experiments were conducted at two distinct locations: the Professor Marian Górski Experimental Station in Skierniewice, Poland, and experimental fields in Połczyn, West Pomeranian Voivodeship, during the months of April and May 2023. Hyperspectral data were acquired through drone-based, FieldSpec ground-based, and satellite measurements. Plant and soil samples were analyzed to assess nutrient content. The hyperspectral reflectance data were processed using feedforward neural networks (FNNs), which were trained to predict phosphorus levels based on spectral data collected from both ground-based and drone-based sensors. Additionally, spectral channels were aligned with the Sentinel-2 and PlanetScope satellite bands for broader applicability. The integration of hyperspectral data with FNNs significantly improved the accuracy and timeliness of phosphorus deficiency detection. The spectral ranges of 500–550 nm and 950–1000 nm were identified as crucial for accurate prediction. The model achieved a root mean square error (RMSE) of 0.84 g/kg for phosphorus content and 64 % accuracy in identifying deficiency points, demonstrating the efficacy of this approach for early-stage nutrient monitoring in winter rye. This study contributes to the advancement of precision agriculture by providing an efficient, accurate method for early-stage phosphorus deficiency detection in winter rye. The findings hold practical implications for optimizing fertilization strategies, improving crop management practices, and promoting sustainable farming.
Why it matches plant phenotyping methods冬ライムギのリン欠乏という植物状態を、ハイパースペクトル計測とFNNで推定する手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。
abstractThis study seeks to develop an innovative method for detecting phosphorus deficiencies in winter rye during early growth stages by integrating hyperspectral data with feedforward neural networks.
Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. While the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training.
Why it matches plant phenotyping methods葉の検出・セグメンテーション・計数を対象とする統一画像解析モデルを開発・評価しており、植物形態形質の抽出手法が研究の中心である。
abstractAccurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture.
RyeWheatFlowerVisualization / data managementFruit / seed / panicle traits
Successful pollination and fertilization are crucial for grain setting in cereals. Wheat is an allohexaploid autogamous species. Due to its evolutionary history, the genetic diversity of current bread wheat ( Triticum aestivum ) cultivars is limited. Introducing favorable alleles from related wild and cultivated wheat species is a promising breeding strategy for resolving this issue. However, wide hybridization between bread wheat and its relatives is hampered by the presence of suppressor genes and difficulties in crossing. Optimized methods for observing pollen tubes are essential for understanding the mechanism of crossability between wheat and its relatives. Here, we improved the crossing procedure between bread wheat and rye ( Secale cereale ) and established an optimized protocol for visualizing pollen tube behavior. Crossing via detached spike culture significantly enhanced crossing efficiency and phenotypic stability. A combination of canonical aniline blue staining and optimized clearing and sectioning allowed us to visualize pollen tube behavior. The proportion of rye pollen tubes reaching the micropyle was lower than that for pollen tubes germinated on the stigmatic hair, explaining why the hybrid seed-setting rate was approximately 75% instead of 100%. This method sheds light on wide hybridization through deeper visualization of the insides of pistils.
Why it matches plant phenotyping methodsコムギ雌ずい内の花粉管挙動を可視化する染色・透明化・切片化手法の最適化が研究の中心であり、植物の生殖状態を測定する実質的な表現型取得法に該当する。
abstractOptimized methods for observing pollen tubes are essential for understanding the mechanism of crossability between wheat and its relatives.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and above 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. This highlights that while the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training. This work demonstrates the potential of AI-driven models to advance automated phenotyping for large-scale precision agriculture.
Why it matches plant phenotyping methods複数作物の葉を自動検出・セグメンテーションし、統合モデルと作物別モデルの性能を比較する画像解析手法が中心で、葉の抽出・計数という植物表現型取得に直接関係する。
abstractAccurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture.
This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R² ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m² during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R² (0.67) and lowest RMSE (83.13 g/m²) and MAE (48.13 g/m²) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m² but decreased for biomass greater than 200 g/m². When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R² and RMSE of the models reaching up to 0.82 and 61.67 g/m² respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.
Why it matches plant phenotyping methodsUASマルチスペクトル画像と機械学習により、作物バイオマスという植物形質を推定・検証する手法が研究の中心である。
titleusing machine learning on images from an unmanned aerial system
Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density-height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha⁻¹, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r² = 0.813) through levels of 9,000 kg ha⁻¹. Based on independent data validation, predicted biomass and measured biomass were linearly related (r² = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.
Why it matches plant phenotyping methodsSfM画像から作物高さ・点群密度を抽出し、バイオマスを推定・検証・空間マッピングする方法が研究の中心である。
abstractThis study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass.
Abstract Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha−1, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r2 = 0.813) through levels of 9,000 kg ha−1. Based on independent data validation, predicted biomass and measured biomass were linearly related (r2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.
Why it matches plant phenotyping methods地上型SfMによる3D画像から、イネ科被覆作物のバイオマスを推定・検証・マッピングする手法が研究の中心であり、植物形質の非破壊取得を扱うため含める。
abstractThis study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass.
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:1-59Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract Cover crops provide valuable roles in sustainable agriculture, provided they produce enough biomass. To accurately measure their services to field management, spatial estimates would be useful to producers. This study used multispectral drone imagery to produce maps of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and a digital surface model (DSM) of cover crop plots on sandy, Mid‐Atlantic soils. Cover crops included cereal rye ( Secale cereale ), mixtures of rye and crimson clover ( Trifolium incarnatum ), and mixtures of rye and hairy vetch ( Vicia villosa ). Their biomass was sampled in the spring of 2019, 2020, and 2021, dried, weighed, and analyzed for total nitrogen (N) content. Measurements of NDVI became saturated (i.e., reached a linear plateau) at 3.86 Mg biomass ha −1 , NDRE at 5.72 Mg biomass ha −1 , and the DSM at 5.11 Mg biomass ha −1 . The measured N content became saturated at 80.9, 139.1, and 75 kg N ha −1 for NDVI, NDRE, and the DSM, respectively. Based on log transformations, NDVI was a stronger predictor of biomass and N, but not C:N. The NDRE was important for biomass, N, and C:N, while the DSM interactions with cover crop species helped predict both the N content and C:N of cover crop tissues. Accumulated growing degree days was important as an individual variable for biomass and N and as an interaction with cover crop species.
abstractThis study used multispectral drone imagery to produce maps of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and a digital surface model (DSM) of cover crop plots
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018-2019 and 2019-2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck.
Why it matches plant phenotyping methodsRGB画像とYOLOv4を用いて圃場の穂数を推定するフェノタイピング手法を開発・評価しており、形質取得と精度検証が研究の中心である。
abstractHere, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.
Why it matches plant phenotyping methods圃場高スループット表現型計測とセンサー特徴量・機械学習による被覆作物バイオマス推定が研究の中心であり、手法の比較・性能評価も実施している。
abstractFour ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated.
Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (σVV0, σVH0), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, σVH0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola σVH0 of Sentinel-1 offered higher performance (r = 0.88). Generally, σVV0, σVH0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture.
Why it matches plant phenotyping methodsSARセンサー由来指標と時系列フィルタリングを比較・評価し、作物の生育段階や草丈などの植物形質追跡への有用性を検証しており、フェノタイピング手法の評価が中心である。
abstractthis work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato.
Context: Variability in biomass production poses a challenge for growers when using cover crops for weed control. However, most methods for assessing cover crop biomass are laborious and impractical on a field scale. Objective: The goal of the present study was to determine the feasibility of using Structure-from-Motion (SfM) photogrammetry to estimate biomass in cereal rye (Secale cereale L.) and winter wheat (Triticum aestivum L.) cover crops by correlating biomass with 3-D point cloud pixel density and crop height. Methods: Point clouds were generated using a SfM algorithm from RGB (red, green, and blue) videos collected by a hand-held GoPro camera over sixteen crop fields in North Carolina, Iowa, and Maryland, USA, throughout two growing seasons (2021-2023). Crop height, leaf area index (LAI), and photosynthetically active radiation (PAR) were also measured. Results: Biomass was positively correlated with crop height for both cereal rye (R² = 0.621) and wheat (R² = 0.55). LAI was positively correlated with biomass accumulation and crop height for both species, increasing linearly in rye and exponentially in wheat. Conversely, PAR penetration below the canopy decreased with biomass accumulation and crop height in both species, with a more rapid extinction in wheat than rye. Point cloud pixel density showed a positive linear relationship with biomass in rye but saturated after 2.5 tonnes ha⁻¹ (2500 kg ha⁻¹). In wheat, point cloud pixel density was weakly and negatively correlated with biomass due to a denser canopy causing faster saturation of tissue detection by SfM point clouds. However, considering crop height and point cloud density integrating them both in the model allowed obtaining a positive relationship with biomass through levels of 8 tonnes ha⁻¹ (8000 kg ha⁻¹) in both species. When models were validated with independent data, predicted and measured biomass were positively correlated for both rye (R² = 0.86) and wheat (R² = 0.78). Conclusions: Based on the results, using SfM to generate 3-D point clouds can provide a more accurate estimation of biomass than canopy height alone by capturing species-level differences in canopy architecture. Implications: The results of this study suggest that SfM can potentially be used as a non-destructive tool for growers to monitor biomass production in cereal cover crops other systems such as energy/forage crops, which can help inform management decisions and conserve resources.
Why it matches plant phenotyping methodsSfMフォトグラメトリと3D点群を用いて作物バイオマスを推定し、独立データで検証した、植物表現型取得手法の開発・適用研究である。
abstractThe goal of the present study was to determine the feasibility of using Structure-from-Motion (SfM) photogrammetry to estimate biomass
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Background Phosphorus nutrition is important for obtaining high yields of crop plants. However, wheat plants are known to be almost incapable of taking up phosphorus from insoluble phosphate sources, and reduced height genes are supposed to decrease this ability further. Methods We performed a pot experiment using Triticum durum Desf. tall spring variety LD222, its near-isogenic semidwarf line carrying Rht17 ( Reduced height 17 ) gene, and winter rye ( Secale cereale L.) variety Chulpan. The individual plants were grown in quartz sand. The phosphorus was provided either as phosphate rock powder mixed with sand, or as monopotassium phosphate solution (normal nutrition control) or was not supplemented at all (no-phosphorus control). Other nutrients were provided in soluble form. During experiment the plants were assessed using the TraitFinder (Phenospex Ltd., Heerlen, Netherlands) digital phenotyping system for a standard set of parameters. Double scan with 90 degrees turns of pots around vertical axis vs . single scan were compared for accuracy of phenotyping. Results The phenotyping showed that at least 20 days of growth after seedling emergence were necessary to get stable differences between genotypes. After this initial period, phenotyping confirmed poor ability of wheat to grow on substrate with phosphate rock as the only source of phosphorus compared to rye; however, Rht17 did not cause an additional reduction in growth parameters other than plant height under this variant of substrate. The agreement between digital phenotyping and conventionally measured traits was at previously reported level for grasses (R 2 = 0.85 and 0.88 for digital biomass and 3D leaf area vs . conventionally measured biomass and leaf area, single scan). Among vegetation indices, only the normalized differential vegetation index (NDVI) and the green leaf index (GLI) showed significant correlations with manually measured traits, including the percentage of dead leaves area. The double scan improved phenotyping accuracy, but not substantially.
Why it matches plant phenotyping methodsTraitFinderデジタルフェノタイピングを用いた形質取得と、スキャン方式の精度比較・従来測定との妥当性検証が明示されており、フェノタイピング手法が技術的に実質的な役割を果たしている。
abstractDuring experiment the plants were assessed using the TraitFinder (Phenospex Ltd., Heerlen, Netherlands) digital phenotyping system for a standard set of parameters.
Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass ( r = 0-0.74) compared to imagery from November ( r = 0.01-0.41) and April ( r = 0.03-0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band ( r = 0.74) during March. The R 2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.
Why it matches plant phenotyping methods衛星画像と機械学習を用いて被覆作物のバイオマスという植物形質を推定し、取得時期・波長・予測性能を評価することが研究の中心である。
titleMixed-Species Cover Crop Biomass Estimation Using Planet Imagery
In general, remote sensing studies assessing cover crop growth are species nonspecific, use imagery from satellites or modified unmanned aerial vehicles (UAVs), and rely on multispectral vegetation indexes (VIs). However, using RGB imagery and visible-spectrum VIs from commercial off-the-shelf (COTS) UAVs to assess species specific cover crop growth is limited in the current scientific literature. Thus, this study evaluated RGB imagery and visible-spectrum VIs from COTS UAVs for suitability to estimate concentration (%) and content (kg ha−1) based cereal rye (CR) biomass, carbon (C), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S). UAV surveys were conducted at two fields in Indiana and evaluated five visible-spectrum VIs—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Greenness (ExG). This study utilized simple linear regression (VI only) and stepwise multiple regression (VI with weather and geographic data) to produce individual models for estimating CR biomass, C, N, P, K, and S concentration and content. The goodness-of-fit statistics were generated using repeated K-fold cross-validation to compare individual model performance. In general, the models developed using simple linear regression were inferior to those developed using the multiple stepwise regression method. Furthermore, for models developed using the multiple stepwise regression method all five VIs performed similarly when estimating concentration-based CR variables; however, when estimating content-based CR variables the models developed with GLI, MGRVI, and RGBVI performed similarly explaining 74–81% of the variation in CR data, and outperformed VARI and ExG. However, on an individual field basis, MGRVI consistently outperformed GLI and RGBVI for all CR characteristics. This study demonstrates the potential to utilize COTS UAVs for estimating in-field CR characteristics; however, the models generated in this study need further development to expand geographic scope and incorporate additional abiotic factors.
Why it matches plant phenotyping methodsCOTS UAVのRGB画像と可視域植生指数から、ライムギのバイオマスおよび栄養特性を推定する手法を構築・比較検証しており、表現型取得が研究の中心である。
abstractthis study evaluated RGB imagery and visible-spectrum VIs from COTS UAVs for suitability to estimate concentration (%) and content (kg ha−1) based cereal rye (CR) biomass, carbon (C), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S).
Nondestructive proximal sensors can be an efficient source of information of N status in crops for localized and rapid adjustment of fertilization applications. The aim of this study was to compare two transmittance/reflectance‐based sensors (SPAD, ASD) and a florescence‐based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level. Measurements of leaves and canopies from six fertilization field trials in 2019 and 2020 were analyzed to establish relationships between sensor information and laboratory‐determined N content in crops. Analyses included linear regression for single sensor variables and machine learning for multivariate approaches, to assess the relative accuracy of the proximal sensors to measure N. The ASD is time‐intensive and requires post hoc analyses of the spectra. However, the spectral outputs of this device were clearly correlated with the N status of leaves and canopies. At the leaf level, SPAD showed higher accuracy than any of the single Multiplex variables to predict plant N. Multiplex performance could be improved by combining three of its variables. At the canopy level, interpolated SPAD values and the best‐performing Multiplex variables showed similar accuracy. It could be concluded that the relationship sensor‐N status is species specific. Despite the high standard deviation recorded in some raw Multiplex variable, the derived indices showed a comparable low standard deviation. At both, leaf and canopy levels an integrated sensor solution would combine the multidimensionality of Multiplex and ASD, and the accuracy and practicality of SPAD.
Why it matches plant phenotyping methods作物の窒素状態という植物生理形質を対象に、複数の近接センサーの測定精度を比較・評価し、センサー情報と実験室測定値の関係を解析しているため、センシング手法の技術評価が中心である。
abstractThe aim of this study was to compare two transmittance/reflectance‐based sensors (SPAD, ASD) and a florescence‐based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level.
The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.
Why it matches plant phenotyping methods光学衛星画像とSARを統合し、カバークロップのバイオマスという植物形質を推定する手法を開発・評価しており、技術性能も定量的に検証しているため。
abstractWe assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland.
Traditional crop cutting experiment-based yield estimation method captures the regional yield variability but lacks field-level information. Satellite images hold enormous crop information at finer spatial resolution. Crop yield mapping with optical images is particularly challenging if cloud-free images are unavailable during the crucial crop developmental stages. All-weather availability and sensitivity to crop structure, dielectric properties make synthetic aperture radar (SAR) images an excellent resource for yield estimation. Both types of data provide complementary information about crop conditions. A random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation. We utilized the crop harvest and quality survey (BEE) yield data set collected by the Hessisches Statistisches Landesamt (HSL), Wiesbaden, Germany, over 490 fields. We prepared 20 m resolution yield maps for winter wheat, winter barley, winter rye and winter rapeseed. Input features for the yield estimation model are selected based on the prior knowledge of remote sensing of vegetation. Baseline random forest regression models are developed for all the four crop types with optical and SAR input features. An optimized random forest regression model with genetic algorithm-based feature selection results in performance improvement. Dissimilarity in genetic algorithm selected image features highlights the significance of crop-specific feature selection for yield estimation. The optimized models reliably estimate yield by achieving correlation coefficient (r) of 0.65–0.86, mean absolute error 0.93–1.16 t ha–1 and root mean square error 1.12–1.56 t ha–1 with BEE yield on testing data set. The proposed models could estimate the intra-field yield variation when winter wheat, winter barley, winter rye were in the shooting phase to the beginning of ear-shifting, and winter rapeseed began to flower or was already flowering. These results demonstrate the merits of our model for early-stage crop yield estimation at the field level with mono-temporal image and adaptability for the cropping season with high cloud cover.
Why it matches plant phenotyping methods衛星光学・SAR画像から圃場内の作物収量を推定するモデルを開発・検証しており、植物の収量という形質の取得・推定手法が研究の中心である。
abstractA random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.
Why it matches plant phenotyping methods穀類スパイクの画像検出・セグメンテーション手法を比較評価し、SpikeAppとして植物画像の自動フェノタイピングを提供しており、方法開発・検証が中心である。
abstractwe performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images
The pure spectra acquisition of plant disease symptoms is essential to improving the reliability of remote sensing methods in crop protection. The reflectance values read from the pure spectra can be used as valuable training data for development of algorithms designed for plant disease detection at leaf and canopy scale. The aim of this paper is to identify and distinguish spectrally the leaf rust symptoms caused by two closely related special forms (f. sp.) of Puccinia recondita f. sp. tritici on wheat and Puccinia recondita f. sp. recondita on rye at leaf scale. Spectral measurements were made with FieldSpec 3 spectrometer in the wavelength range of 350-2500 nm. The spectrometer was connected to a microscope by optical fiber. Raw spectra of uredinia, chlorotic discoloration, green leaves, senescent inoculated leaves and senescent uninoculated leaves of wheat and rye, all of which obtained for this study, were investigated with a view towards making an automized classification of plant species and their phases. The created Random Forest models were tested separately using pure spectra, and from these vegetation indices were derived as predictors. Three vegetation indices, namely CRI, PRI and GNDVI, appeared to be the most robust in terms of distinguishing uredinia from other symptoms on rye and wheat leaves. PRI, EVI, NDVI 705 , and GNDVI were the most suitable for distinguishing uredinia, chlorotic discoloration, and green leaf stages on rye. That tusk on wheat leaves can be recognized if seven indices (PRI, MSAWI, SAVI, NDVI, NDVI 705 , GNDVI and RVI) are used together. For the classification of all disease symptoms for both plant species, the most useful were wavelengths in the VIS range: 431-436, 696-703 and 646-686 nm. However, the ranges of SWIR wavelengths (1938, 1955) and NIR wavelengths (1099-1104) also have a high contribution to the discrimination accuracy of the model. In the classification of all disease symptoms, the most important vegetation indices were CRI, OSAVI, and GNDVI. Analysis of the results revealed the advantage of the model based on the selected spectral wavelengths (Hit Rate of 96.6%) in comparison with predictions based on vegetation indices alone (Hit Rate of 91.7%). Both approaches show the highly applicable character of utilizing high quality spectral products such as satellite images in reducing operational costs of crop protection.
Why it matches plant phenotyping methods葉の病徴を分光計測し、スペクトルおよび植生指数から病害症状を分類する方法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractThe pure spectra acquisition of plant disease symptoms is essential to improving the reliability of remote sensing methods in crop protection.
Over-wintering cereal cover crops contribute to greater cropping system sustainability by reducing risk of erosion, adding carbon (C), and sequestering inorganic nitrogen (N) between fall harvest and spring planting of annual crops. Carbon and N pools can be measured by manual sampling and laboratory analysis of roots and shoots. However, this is expensive and unscalable to field levels. This study aimed to (1) determine relationships between shoot biomass and total biomass, C and N uptake of winter wheat (Triticum aestivumL.), cereal rye (Secale cerealeL.), and triticale (XTriticosecaleWittm.); and (2) develop models to estimate shoot biomass using green cover (%) from digital images. Shoot and root biomass, C, and N uptake of cover cropped fields were determined by destructive sampling within rectangular quadrats (97.8 × 20.3 cm) in fall 2011 prior to snowfall (58 fields) and spring 2012 prior to termination (30 fields). In addition, shoot biomass and digital images (top view of crop canopy taken at shoulder height) were collected in fall 2013 and spring 2012–2014 (266 quadrats) using four different cameras (Canon, Nikon, Apple iPhone, Motorola Droid) for a total of 589 images. Four approaches were evaluated to estimate green cover: (i) estimation from “Canopeo” software; (ii) RGB color space with a set cutoff; (iii) the Lab color space with fixed adjustment of the “a” channel per camera; and (iv) the Otsu thresholding method applied to the “a” channel of the Lab color space. Shoot biomass ranged from 37 to 2657 kg DM ha⁻¹ and was linearly correlated with total biomass (R² = 0.99), total C (R² = 0.99), and total N (R² = 0.92), independent of species or season. The Otsu method best estimated shoot biomass from green cover (R² = 0.66; green cover ranging from 10 to 70%). This method also reduced differences among cameras and the influence of lighting conditions on green cover estimations. Exponential models for shoot biomass estimation from green cover (%) determined with the Otsu method had the highestR² for cereal rye (R² = 0.79), followed by triticale (R² = 0.54), and wheat (R² = 0.52). We conclude these models, combined with linear equations to predict total biomass, C, and N uptake from shoot biomass can be used to estimate the same from imagery. Additional research is needed to validate models across more locations, different growth stages, and to expand across a larger range of green coverage.
Why it matches plant phenotyping methodsデジタル画像から緑被率を抽出し、作物の地上部バイオマス、炭素・窒素量を推定する手法を開発・比較・検証しており、フェノタイピング手法が中心である。
abstractdevelop models to estimate shoot biomass using green cover (%) from digital images
Abstract Image-based plant phenotyping is the major approach to quantitative assessment of important plant properties. For automated analysis of a large amount of image data from high-throughput greenhouse measurements, efficient techniques for image segmentation are required. However, conventional approaches to whole plant and plant organ segmentation are hampered by high variability of plant and background illumination, and naturally occurring changes in geometry and colors of growing plants. Consequently, application of advanced machine learning techniques for automated image segmentation is required. Here, we investigate six advanced neural network (NN) methods for detection and segmentation of grain spikes in RGB images including three detection deep NNs (SSD, Faster-RCNN, YOLOv3/v4), two deep (U-Net, DeepLabv3+) and one shallow segmentation NNs. Our experimental results show superior performance of deep learning NNs that achieve in average more than 90% accuracy by detection and segmentation of wheat as well as barley and rye spikes. However, different methods demonstrate different performance on matured, emergent and occluded spikes. In addition to comprehensive comparison of six NN methods, a GUI-based tool (SpikeApp) provided with this work demonstrates the application of detection and segmentation NNs to fully automated spike phenotyping. Further improvements of evaluated NN approaches are discussed.
Why it matches plant phenotyping methods穀粒穂の画像検出・セグメンテーション手法を比較評価し、SpikeAppによる自動フェノタイピングを実証しており、植物表現型取得手法が中心である。
abstractImage-based plant phenotyping is the major approach to quantitative assessment of important plant properties.
Reproduction assets foundThe paper's data availability statement explicitly provides demo software (SpikeApp with pre-trained U-Net, YOLOv3, and shallow ANN models) and example spike images for public download from the authors' IPK page. The DeepLabv3+ GitHub link is a cited third-party library, and psi.cz is the imaging facility, not a paper-Code · publics work was also supported by the project
of specific research provided by the Masaryk University.
Conflict of interest statement
The authors declare no competing interests.
Data availability statement
In addition to data presented in the main text, demo software as well as examples of spike images are provided for download
from https://ag-ba.ipk-gatersleben.de/spikeapp.html.11/14Open asset ↗spikeapp.htmlpdf-raw-page:12 lines:1-30Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
KEY MESSAGE: Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.
Why it matches plant phenotyping methodsUAVハイパースペクトルデータからバイオマス関連形質を予測する手法を開発・比較検証しており、植物表現型取得と予測が研究の中心である。
abstractThe early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Cover crops can positively impact productivity and the environment. While improved estimates of cover crop N benefits could help promote their adoption, little information is currently available at the broad scale. We conducted a multi‐site study to determine whether use of satellite images and site factors could fill this gap. Six spectral indices were extracted from Sentinel‐2 satellite imagery and used with modeling covariates to estimate cover crop properties and biomass N credits (biomass × N contents). The partial least squares regression (PLSR) models were calibrated and validated with samples from 42 cover crop fields located in the midwestern and northeastern United States collected in 2017–2018. Remote sensing (RS)‐derived spectral indices strongly correlated ( r > .7) with red clover ( Trifolium pratense L.) but not with rye ( Secale cereale L.) biomass. Growing degree days (GDDs), cover height, ground cover percentage, and temperature often had high importance (variable importance in projection >1) in PLSR models. Model predictive power was limited for estimates of biomass N credits when data from all validation sites and cover types were used (adjusted [adj] R 2 = .52). However, models for both biomass (adj R 2 = .81) and biomass N credits (adj R 2 = .89) were successful for red clover fields. This suggests N benefits could be more effectively modeled for specific cover crop types. We also found RS‐based estimation of C/N ratios performed moderately well when applied to the complete dataset (adj R 2 = .54), suggesting a way to differentiate grass and legume cover crops that can potentially inform biogeochemical models.
Why it matches plant phenotyping methodsSentinel-2画像とPLSRを用いてカバークロップのバイオマス、Nクレジット、C/N比を推定し、複数サイトで較正・検証しているため、植物形質推定手法が研究の中心です。
abstractSix spectral indices were extracted from Sentinel‐2 satellite imagery and used with modeling covariates to estimate cover crop properties and biomass N credits (biomass × N contents).
Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments. Results We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings. Conclusion Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Why it matches plant phenotyping methods種子の発芽状態を画像と機械学習で自動抽出し、手動計数と性能比較しているため、植物表現型取得法が中心です。
abstractWe here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.
Reproduction assets foundThe paper's authors publicly released the labeled germination image dataset (~24,000 annotated images of 2449 seeds) on Mendeley Data and their machine learning analysis code on GitHub, both explicitly stated in the Availability of data and materials section.Dataset · publicThe generated and labeled training data is freely available on Mendeley Data: http://dx.doi.org/10.17632/4wkt6thgp6.2 .Open asset ↗Mendeley Data · 10.17632/4wkt6thgp6.2lines:164-248Code · publicThe code for our proposed machine learning–based model can be found on GitHub: https://github.com/grimmlab/GerminationPrediction .Open asset ↗GitHub · grimmlab/GerminationPredictionlines:164-248Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Cereal rye (Secale cereale sp.) is a cover crop species known to improve soil and water quality. Late‐season biomass production is information growers need to maximize cover crop benefits and schedule field operations. Statistical learning, built upon statistical and computational algorithms that “learn” from data, may help to improve predictions of cover crop biomass as a function of initial soil inorganic nitrogen levels. Three models—Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Random Forest (RF)—were trained and optimized on a 3‐yr allometric and remote sensing dataset of cereal rye responses to N fertilization in the mid‐Atlantic northern and southeastern United States. Shoot biomass (mean, 9,800 kg ha⁻¹) was accurately predicted with a RF model (RMSE, 2,039 kg ha⁻¹). Targeting shoot N content (mean, 107 kg ha⁻¹), on the other hand, LASSO made accurate and more stable predictions (RMSE, 34 kg ha⁻¹). Early‐season information (cover crop C/N ratio, tiller counts, and ground‐sensed normalized difference vegetation index) contributed to enhancing biomass and N content predictions. A final test on untrained data revealed that 92 and 73% of the predictions from either algorithm corresponded to ground‐truthed biomass and shoot N content observed under different N regimes. Modern data‐intensive approaches, such as statistical learning, show promise to characterize end‐season performance of a cover crop and may contribute to better farm decision‐making.
Why it matches plant phenotyping methods統計学習とリモートセンシングを用いてカバークロップのバイオマスおよび窒素含量を推定し、未学習データで検証しているため、植物形質推定法が研究の中心である。
abstractThree models—Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Random Forest (RF)—were trained and optimized on a 3‐yr allometric and remote sensing dataset
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
The present study investigated genotypic differences in canopy temperature of 218 testcross progenies of a recombinant inbred lines mapping population of winter rye. For this purpose, we repeatedly measured canopy temperature (Tc) and air temperature (Tₐ) differences (Tc − Tₐ) in field trials on two locations with sandy soils in northern Germany, two water treatments (rainfed and irrigated) in two experimental years. The experimental conditions therefore covered a wide range of drought stress conditions between stem elongation and ripening under a high variation of environmental boundary conditions. Genotype effects on (Tc − Tₐ) were significant in both water treatments and were correlated to grain yield reduction under drought stress. With the chosen statistical model, we were able to account for moderate time-series effects and short-term fluctuations caused by changing boundary condition during single screens of (Tc − Tₐ). We could show that under certain weather conditions (Tc − Tₐ) can detect differences in drought stress tolerance more accurate than grain yield determination of rye.
Why it matches plant phenotyping methodsキャノピー温度を用いた干ばつ耐性の高スループット圃場フェノタイピング手法を評価し、統計モデルと穀粒収量との比較によって技術性能を検証しているため。
titleIs canopy temperature suitable for high throughput field phenotyping of drought resistance of winter rye in temperate climate?
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 9 Sept 2026
KEY MESSAGE: Hyperspectral and genomic data are effective predictors of biomass yield in winter rye. Variable selection procedures can improve the informativeness of reflectance data. Integrating cutting-edge technologies is imperative to sustainably breed crops for a growing global population. To predict dry matter yield (DMY) in winter rye (Secale cereale L.), we tested single-kernel models based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices, a multi-kernel model combining both matrices and a bivariate model fitted with plant height as a secondary trait. In total, 274 elite rye lines were genotyped using a 10 k-SNP array and phenotyped as testcrosses for DMY and plant height at four locations in Germany in two years (eight environments). Spectral data consisted of 400 discrete narrow bands ranging between 410 and 993 nm collected by an unmanned aerial vehicle (UAV) on two dates on each environment. To reduce data dimensionality, variable selection of bands was performed, resulting in the least absolute shrinkage and selection operator (Lasso) as the best method in terms of predictive abilities. The mean heritability of reflectance data was moderate ([Formula: see text] = 0.72) and highly variable across the spectrum. Correlations between DMY and single bands were generally significant (p < 0.05) but low (≤ 0.29). Across environments and training set (TRN) sizes, the bivariate model showed the highest prediction abilities (0.56-0.75), followed by the multi-kernel (0.45-0.71) and single-kernel (0.33-0.61) models. With reduced TRN, HBLUP performed better than GBLUP. The HBLUP model fitted with a set of selected bands was preferred. Within and across environments, prediction abilities increased with larger TRN. Our results suggest that in the era of digital breeding, the integration of high-throughput phenotyping and genomic selection is a promising strategy to achieve superior selection gains in hybrid rye.
Why it matches plant phenotyping methodsUAVハイパースペクトル計測を用いた形質データの取得と、バンド選択・予測モデルの技術評価が、バイオマス収量予測および育種への応用の中心である。
abstractSpectral data consisted of 400 discrete narrow bands ranging between 410 and 993 nm collected by an unmanned aerial vehicle (UAV) on two dates on each environment.