Alfalfa / lucerneX-ray / CTRoot2D/3D reconstructionVisualization / data managementGrowth / development / phenology
The symbiotic relationship between the legume Medicago sativa and the soil bacteria Sinorhizobium meliloti results in the formation of nitrogen-fixing root nodules. Traditional destructive methods, including paraffin sectioning, vibratome sectioning, and cryosectioning, have been applied to visualize how bacteria occupy the nodule, making it extremely difficult to obtain reliable three-dimensional information. These approaches are often combined with fluorescent labeling or staining, which can introduce additional stress affecting plant growth and nodule formation. MicroCT has emerged as a relatively quick, easy, and robust tool for plant biology that can non-destructively visualize plant histological features in three dimensions (3D), thereby avoiding destructive artifacts during sample preparation and ensuring high-fidelity 3D reconstruction. While microCT has been applied to legume root nodules, a detailed established protocol that documents the process from plant harvest and sample preparation to scanning and software visualization is lacking. In this study, we show a step-by-step microCT workflow using Medicago sativa as a model. The protocol includes nodule excision from roots, fixation, contrast enhancement, mounting, scanning, and three-dimensional reconstruction. Critical parameters affecting elements such as image quality, tissue preservation, and contrast are highlighted. Using this approach, it is possible to visualize the overall tissue organization, bacteroid-infected cells, and vascular bundles in three dimensions without physically sectioning the nodules. The pipeline described here provides a reproducible method for non-destructive, high-resolution imaging of native root nodules and is likely adaptable to other legume species, offering researchers a practical tool for studying nodule structure and bacterial organization within nodules in 3D.
Why it matches plant phenotyping methods根粒の組織構造と感染細胞を3Dで取得するMicroCT撮像・再構成プロトコルが研究の中心であり、植物器官の形態状態を測定する実質的なフェノタイピング手法である。
abstractIn this study, we show a step-by-step microCT workflow using Medicago sativa as a model.
Precise characterization of alfalfa growth dynamics is essential for breeding accessions with superior regrowth capacity, persistence, and yield stability. However, traditional plot level and coarse scale observations suffer from low signal to noise ratios particularly before canopy closure when phenotypic data are strongly affected by weeds and soil background. Moreover, existing studies rarely capture the dynamic mechanisms of crop development across the entire growth cycle. To address this, the DINO-Pheno-Cluster framework is introduced as a foundation model driven and mechanism decomposed phenotyping approach. This decoupled framework first utilizes DINO-XMem, a few-shot individual plant segmentation network based on DINOv3 and a dual memory mechanism. It subsequently applies parameterized dynamic modeling guided by growth process knowledge. Validation utilized high frequency Unmanned Aerial Vehicle (UAV) imagery from 12 time points across three growing seasons covering 127 alfalfa accessions. DINO-XMem achieved an 89.54% mean Intersection over Union (mIoU) under a 10-shot setting and maintained 86.77% under extreme 1-shot conditions. It successfully resolved dense canopy oversegmentation outperforming fully supervised baselines by 4.08% to 11.97% in mIoU. Crucially, the extracted high purity time series trajectories were parameterized into specific biological indicators including maximum growth rate, comprehensive regeneration index, and seasonal stability index. Gaussian Mixture Model (GMM) clustering based on these mechanistic traits identified four distinct functional ideotypes comprising High yield/High regrowth, Upright/Sparse, High stability/Persistent, and Short/Dense, all validated by ground measured biomass. This workflow establishes a precision screening tool for multi harvest crops advancing crop phenomics toward process level analysis.
Why it matches plant phenotyping methods植物の時系列UAV画像から個体を分割し、成長・再生・安定性などの形質を抽出するフェノタイピング手法の開発と検証が中心である。
abstractthe DINO-Pheno-Cluster framework is introduced as a foundation model driven and mechanism decomposed phenotyping approach
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Fall dormancy (FD) and forage yield (FY) are two key traits in alfalfa ( Medicago sativa L.) breeding programs. However, genetic progress has remained limited over the past decades, largely due to the complexity of alfalfa breeding and the reliance on labor-intensive phenotyping methods. High-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation. The objectives of this study were to i) estimate FD using UAV-derived canopy height and RGB vegetation indices and ii) evaluate the predictive performance of machine learning (ML) models for FY estimation. A total of 210 alfalfa populations with diverse genetic backgrounds were evaluated over two growing seasons (2023 to 2025) across seven harvests under Mediterranean conditions in central Chile. FY was measured manually, while FD was estimated using both manual and UAV-based approaches. A total of 19 RGB-derived indices (VIs) including plant height (PH) were extracted and used as predictor variables. Five complex predictive ML models were evaluated: PLS, PCR, SVM, ANN, and MLR. The results showed that UAV-derived FD was significantly correlated with FD obtained through conventional methods ( R 2 = 0.88). The automated UAV-based FD phenotyping framework demonstrated slightly higher precision ( R 2 = 0.92) and broad-sense heritability ( H 2 = 0.69) compared to manual measurements ( R 2 = 0.87–0.89; H 2 = 0.64), providing a more reliable selection tool for breeders. Among the tested ML models, SVM and ANN achieved the highest accuracy ( R 2 ≈ 0.73) for FY prediction. These findings demonstrate that integrating low-cost RGB imagery with complex modeling offers a promising avenue that could assist in refining future selection strategies for this genetically complex species.
Why it matches plant phenotyping methodsUAV画像からアルファルファの休眠性と収量関連形質を推定する高スループット表現型解析手法を開発・検証し、手動測定との比較と機械学習モデル評価を行っているため、方法が研究の中心である。
abstractHigh-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation.
Accurately monitoring alfalfa nutritional quality is essential for optimal pasture management. Yet, current UAV remote sensing methods rely on single-temporal imagery and empirical indices, limiting their ability to handle multi-stage growth dynamics, canopy spectral saturation, and canopy-to-whole-plant scale differences. Furthermore, small sample sizes often cause purely data-driven models to overfit correlations, yielding biologically unrealistic results. Overcoming these challenges, we designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator. After screening 14,960 spectral operators across original and log-transformed spaces, we applied a dual dimensionality reduction strategy to isolate optimal features. Four-band dual-difference structures proved highly sensitive to fiber components (ADF/NDF, |r| = 0.896), while logarithmic decoupling operators accurately isolated protein and nitrogen signals (CP/N, |r| = 0.868). We then engineered a Physics-Informed Sparse Shallow Network (PI-SSN). By leveraging temporal attention decoupling, it adaptively assigns growth-stage weights to different components and uses carbon-nitrogen metabolic constraints to maintain biological accuracy during multi-task retrieval. Multi-stage temporal data significantly boosted accuracy over single-period spectra. PI-SSN delivered exceptional test set coefficients of determination ( R2 ) of 0.812-0.848 and RPDs >2.0 for N, CP, ADF, and NDF, easily outperforming standard baselines. To bridge the canopy-only observation gap, we introduced a 3D allometric transfer operator that incorporates canopy coverage and plant height. This effectively corrected vertical stem-leaf observation biases, enhancing Relative Feed Value (RFV) predictions. Ultimately, this approach offers a powerful new framework for high-throughput forage phenotyping.
Why it matches plant phenotyping methodsUAVリモートセンシングと物理制約ネットワーク、3Dアロメトリック演算子を統合し、アルファルファの栄養品質を推定する手法を開発・検証しており、植物表現型取得が中心である。
abstractwe designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator.
Reproduction assets foundThe paper's authors publicly release the pre-trained PI-SSN model weights, inference code, and usage instructions on GitHub. The raw spectral and ground-truth quality datasets are not public and are available only on request, so they do not qualify as public assets.Code · publiceptualization, Resources, Supervision, Writing-review & editing. Dongyan Zhang: Conceptualization, Funding acquisition, Project Administration, Supervision, Writing-original draft, Writing-review & editing.
Data and code availability
The pre-trained model weights, inference code, and usage instructions are publicly available at https://github.com/AeroPheno/PI-SSN.git . The raw spectral data and ground-truth quality data used in this study are not publicly available due to ongoing collaborative projects, but are available from the corresponding author on reasonable request.
Funding
This work was supported by the 2023 Hohhot to introduce high-level innovative and entrepreneurial talents (teamOpen asset ↗AeroPheno/PI-SSNlines:243-301Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Alfalfa (Medicago sativa L.), also known as lucerne, is the fourth most widely grown crop in the U.S. and plays key roles in animal nutrition and crop rotations. Several foliar diseases reduce yield and quality of herbage, impacting both plant and animal production and farm economy. Spring black stem and leaf spot, caused by Ascochyta medicaginicola (formerly Phoma medicaginis), is a damaging fungal leaf spot disease of alfalfa and needed levels resistance is lacking in commercial cultivars. To identify potential sources of resistance in alfalfa germplasm, an optimized greenhouse seedling inoculation protocol was developed. Several A. medicaginicola isolates were tested for pathogenicity and spore concentrations were optimized for ideal disease pressure. A rating scale, modified from an established protocol, with improved resolution was used to distinguish differences observed in disease phenotypes. After protocol improvements, 78 standard check alfalfa cultivars and 188 Medicago spp. accessions were screened for disease reaction in replicated trials and 2832 alfalfa accessions were screened for disease reaction in non-replicated evaluations. An ideal concentration of 5 × 10⁴ spores mL⁻¹ was determined by measuring the reaction of standard susceptible (Lahontan) and moderately resistant (Ramsey) check cultivars. Several cultivars and related Medicago species appeared to be more resistant than the moderately resistant checks. Many of the alfalfa accessions originating from colder and wetter environments (e.g., northern latitudes) also showed improved resistance compared to reference cultivars. Resistant germplasm selections were made from these screening efforts for further development of improved alfalfa populations.
Why it matches plant phenotyping methodsアルファルファ葉斑病抵抗性の評価に用いる接種プロトコルと、病徴表現型を識別する改良評価尺度を開発し、検証・大規模適用しているため、植物表現型取得法が中心である。
abstractan optimized greenhouse seedling inoculation protocol was developed
Accurate and temporally consistent multispectral observations are essential for monitoring alfalfa yield and quality, given its frequent harvest cycles and rapid regrowth. However, optical satellite imagery is often constrained by cloud cover, revisit intervals, and sensor availability. To overcome these limitations, we propose a novel Alfalfa Multimodal Generative Adversarial Network (AMGAN) designed for near-daily multispectral image reconstruction. Unlike conventional image-to-image or spatiotemporal fusion methods that overlook crop-specific characteristics, are restricted to observed timestamps, or depend heavily on dense temporal series, AMGAN leverages multisource (Landsat-8/9, Sentinel-1, PlanetScope) and multimodal (climate, geographic, temporal) information within an adversarial learning paradigm. This enables high-quality image generation from minimal inputs. Extensive experiments across five major alfalfa-producing states in the United States (2022-2024) show that AMGAN consistently surpasses four state-of-the-art (SOTA) deep learning baselines. It achieves higher reconstruction accuracy across all spectral bands, with pronounced gains in red-edge and near-infrared (NIR) regions critical for vegetation assessment. Multisource integration and multimodal cues enhance robustness, ensuring reliable performance under diverse observation scenarios. The reconstructed imagery was subsequently evaluated in alfalfa yield and quality prediction tasks. Results demonstrated high predictive accuracy for dry matter yield (DM) in the cross validation (CV) experiment with a coefficient of determination (R²) of 0.80, and moderate correlations for selected quality traits such as crude protein (CP), non-fiber carbohydrates (NFC), and minerals, while nutritive value traits tied to complex biochemical processes remained more challenging. Overall, this study underscores the potential of multimodal adversarial learning to bridge observational gaps in alfalfa monitoring. The proposed framework provides a scalable, crop-specific approach for generating temporally dense imagery, supporting precision management for biomass-related and proximate quality traits, while performance for digestibility traits remains limited.
Why it matches plant phenotyping methodsアルファルファの収量・品質形質推定を支えるマルチスペクトル画像再構成手法を開発し、複数手法との比較検証と形質予測評価を行っており、フェノタイピング手法が中心である。
abstractwe propose a novel Alfalfa Multimodal Generative Adversarial Network (AMGAN) designed for near-daily multispectral image reconstruction.
Plant nitrogen content (PNC) is a core physiological parameter characterizing crop nitrogen nutrition status. Its precise and dynamic monitoring is crucial for crop growth diagnosis, optimizing nitrogen fertilizer management, enhancing fertilizer use efficiency, and reducing agricultural nonpoint source pollution. This study utilized multispectral imagery from unmanned aerial vehicles (UAVs) to extract vegetation indices (VIs) and texture feature values (TFVs) during critical growth stages of alfalfa. By combining TFVs to construct texture indices (TIs), variables exhibiting extremely significant correlations with alfalfa PNC ( p R 2 increased by 5.4-19.7%, 1.7-16.4%, and 5.2-17.2% for the branching, budding, and initial flowering stages, respectively. (3) The XG-Boost model demonstrated optimal performance across all growth stages and input variables. Particularly during the budding stage, the VIs + TIs model achieved the highest fitting accuracy: training set R 2 = 0.81, RMSE = 0.15%; validation set R 2 = 0.80, RMSE = 0.12%. In summary, integrating multispectral vegetation indices and texture indices effectively enhances the accuracy of PNC estimation in alfalfa, providing scientific support for precision field management and fertilization decisions in alfalfa cultivation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物窒素含有量を推定する特徴抽出・モデル構築と検証が研究の中心であり、植物の生理形質を測定するフェノタイピング手法に該当する。
abstractThis study utilized multispectral imagery from unmanned aerial vehicles (UAVs) to extract vegetation indices (VIs) and texture feature values (TFVs) during critical growth stages of alfalfa.
Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.
Why it matches plant phenotyping methodsUAV画像から樹冠高・スペクトル情報を抽出し、機械学習で乾物収量とマメ科収量を推定する手法の開発・検証が研究の中心であるため。
abstractThis study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites.
Alfalfa (Medicago sativa L.), known for its high yield and nutritional value, is a widely cultivated perennial legume subject to various diseases including Alfalfa Mosaic Virus (AMV), Downy Mildew, and Leaf Spot. Timely and accurate identification of these diseases is highly important to maintain crop health, improve productivity, and minimize the use of chemicals. In this study it was aimed to develop a mobile application-based machine learning technique for the detection of major alfalfa diseases. Open-access image dataset of 557 images for four categories—AMV, Downy Mildew, Leaf Spot, and healthy leaves, a deep learning model was used in Google’s Teachable Machine platform. The model then integrated into a mobile application developed with MIT App Inventor 2. The model employs a Convolutional Neural Network (CNN) architecture optimized for mobile deployment via TensorFlow Lite. The application provides a user-friendly interface in Turkish and allows real-time disease classification through mobile phone’s camera. Furthermore, it incorporates cloud-based storage using Google Drive and Google Sheets to log images with metadata including user input, time, and GPS location. The trained model achieved 85% classification accuracy on the test set. The resulting application offers a cost-effective, accessible tool for disease diagnosis in alfalfa cultivation, supporting sustainable agricultural practices. Future studies could expand the application to include a broader range of crops and diseases. The study highlights the potential of integrating artificial intelligence and mobile technology to empower farmers with on-the-spot decision support tools.
Why it matches plant phenotyping methodsアルファルファ葉の画像から病害状態を推定するCNNモデルとモバイルアプリを開発・評価しており、植物病害表現型の取得・分類手法が中心である。
abstractIn this study it was aimed to develop a mobile application-based machine learning technique for the detection of major alfalfa diseases.
Abstract Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.
Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル情報を抽出し、乾物収量とマメ科収量を予測する手法の開発・検証が研究の中心であり、植物の収量形質を直接推定している。
abstractThis study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites.
Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.
Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル情報を抽出し、乾物収量という植物形質を予測する手法の開発・検証が中心です。
abstractThis study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites.
Alfalfa (Medicago sativa) is a globally important forage crop; however, improvements in its biomass yield have stagnated due to its complex genetic architecture and the costly, labor-intensive phenotyping. This study evaluated the potential of the normalized difference vegetation index (NDVI) to predict biomass yield and enhance selection efficiency in alfalfa breeding programs. Specifically, nineteen alfalfa experimental populations (AEXP 1–19) and one control cultivar (OS 66) were evaluated over two growing seasons in Croatia. NDVI was measured at four development stages using a GreenSeeker sensor and compared with forage yield, dry matter yield, and plant height. NDVI values varied significantly among genotypes, years, and growth stages, ranging from 0.23 to 0.87, and increased consistently from early to late vegetative phases. Strong positive correlations were observed between NDVI and forage yield (r = 0.543–0.843) and plant height (r = 0.537–0.738) at early vegetative, late vegetative, and early bud stages. Conversely, NDVI at the mid-vegetative stage correlated negatively with yield and height (r = –0.622 to –0.794). High-performing populations (AEXP 2, AEXP 15, AEXP 18) also exhibited the highest NDVI values. NDVI is a reliable, non-destructive indicator for early selection of high-yielding alfalfa genotypes, although multi-location validation is advised to confirm its broader applicability.
Why it matches plant phenotyping methodsNDVIセンサーによる非破壊的な生育・収量形質推定を育種選抜へ適用し、収量や草丈との相関で妥当性を評価しており、フェノタイピング手法が中心である。
abstractThis study evaluated the potential of the normalized difference vegetation index (NDVI) to predict biomass yield and enhance selection efficiency in alfalfa breeding programs.
Agro-Photovoltaic (APV) systems optimize land use by integrating agriculture with renewable energy production, offering a sustainable solution to contemporary address the growing global food demand and mitigate climate change impacts . However, the heterogeneous shading conditions inherent to APV systems may affect crop growth and productivity, highlighting the need for innovative and accurate monitoring methods to ensure consistent production levels. In this context, the present study proposed and tested a hybrid approach that combines proximal sensing with a process-based crop model to estimate biomass accumulation within an APV system. Firstly, the performance of two-dimensional (2D) and three-dimensional (3D) imagery in capturing the phenotypic adaptations of alfalfa ( Medicago sativ a L.) over two consecutive growing seasons (i.e., 2023 and 2024) was assessed by empirically modeling fluctuations in the fraction of absorbed photosynthetically active radiation (fAPAR). Although the comparative analysis always revealed a strong correlation between observed and image-derived fAPAR values (R² = 0.74), the 2D-based models demonstrated higher accuracy than the 3D approach (RMSE = 0.07 vs 0.09, rRMSE = 10.83 % vs 17.16 %, AIC = −44.91 vs −56.27, respectively). Consequently, the daily fAPAR estimates derived from 2D data were used to force the SSM-iCrop2 model, providing accurate predictions of alfalfa biomass accumulation both in 2023 (R² = 0.96, RMSE = 36.26 g m⁻², rRMSE = 21.35 %, AIC = 151.37) and 2024 (R² = 0.88, RMSE = 46.98 g m⁻², rRMSE = 30.55 %, AIC = 129.07) when compared with field survey data. These findings demonstrate how the proposed image-driven modeling approach can be used as a potential tool for monitoring plant adaptation to varying shading conditions in APV systems. Moreover, its proven ability to predict crop yield with high spatio-temporal accuracy could inform mowing management strategies, optimizing production efficiency while aligning with sustainable agricultural and energy goals.
Why it matches plant phenotyping methods画像からfAPARを推定し、2D・3D手法を比較検証してアルファルファのバイオマスを予測する方法が研究の中心である。
abstractthe present study proposed and tested a hybrid approach that combines proximal sensing with a process-based crop model to estimate biomass accumulation within an APV system.
Alfalfa is the most important forage crop in grassland agriculture. Efficient regional-scale estimates of alfalfa yields are crucial for the precision management of cultivated alfalfa production; however, obtaining rapid and accurate yield assessments remains challenging due to the scarcity of in situ sample data and limited integration of multi-source satellite remote sensing data. To address these issues, this study utilized a yield dataset from 78 sample plots collected during the alfalfa growing season (May–October) and multi-source heterogeneous satellite remote sensing data (Landsat 8, Sentinel-2, and Sentinel-1). By integrating simulated Landsat 8 red-edge bands and applying sample augmentation via the synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN), a high-accuracy framework for estimating cultivated alfalfa yields is proposed based on multi-source remote sensing data and sample augmentation. The study’s key outcomes are as follows. 1) Compared to alfalfa yield estimation models constructed using only Sentinel-2 or Landsat 8 data, incorporating simulated Landsat 8 red-edge bands or Sentinel-1 variables slightly improves the estimation accuracy, with an R² increase of 0.01–0.04, an RMSE decrease of 3.67–8.00 g/m², an RPD increase of 0.02–0.05, and an MAE decrease of 3.34–8.25 g/m². 2) Using the SMOGN algorithm to augment the sample data significantly enhances estimation accuracy, with R² increases of 0.09–0.28 and RMSE decreases of 0.30–38.57 g/m². 3) Integrating spectral bands and vegetation indices derived from the Sentinel-2, Landsat 8, and Sentinel-1 data improves the accuracy of alfalfa yield estimates, with the optimal model constructed using RF algorithm explaining 66 % of yield variation. Overall, multi-source satellite data and sample augmentation techniques represent an extremely promising approach for remote sensing-based alfalfa yield estimation. This study’s findings provide technical support for an innovative method framework for the precision management of cultivated alfalfa production.
Why it matches plant phenotyping methods衛星リモートセンシングと機械学習により、アルファルファの圃場収量を推定する方法枠組みを開発・比較検証しており、収量という植物形質の取得が中心である。
abstracta high-accuracy framework for estimating cultivated alfalfa yields is proposed based on multi-source remote sensing data and sample augmentation
Alfalfa ( Medicago sativa ), a globally important crop known for its high yields, wide adaptability, and high protein content, provides an excellent feed source for livestock. Smart breeding, an emerging technology that integrates genomics and phenomics, holds considerable promise for accelerating the development of elite varieties of alfalfa. Nevertheless, there are few phenotypic analysis tools available for alfalfa. Here, we present DU-Net-L, an effective and lightweight model for segmenting alfalfa images that enables preliminary analysis of branch phenotypes based on digital images. In our study, the DeepLabV3+ model struggled to handle petioles, while U-Net performed poorly with images captured under high light. To address these issues, we have created a new model utilizing ResNet34 as its feature extraction module and retaining the architectures of both DeepLabV3+ and U-Net. An analysis based on test data indicated that the new model overcame the shortcomings of using either of the two base models individually. Subsequently, we lightened the fused model by reducing output channels in each block, while maintaining its predictive capability. We have named the lightened model DU-Net-L. Ultimately, we adopted an exponential decay strategy for the learning rate and increased the number of training epochs to select an optimal parameter combination. This approach achieved 99.83% accuracy and a mean intersection over union of 0.9411, with a size of 25.42 MB. In summary, we have provided a lightweight model that effectively segments stems and leaves in alfalfa images, fulfilling the requirements for the preliminary analysis of branch phenotypes. Supplementary information The online version contains supplementary material available at 10.1007/s42994-025-00235-2.
Why it matches plant phenotyping methodsアルファルファ画像から茎・葉を分割し枝形態表現型の分析に用いる軽量モデルを開発・評価しており、表現型取得・抽出手法が研究の中心である。
abstractwe present DU-Net-L, an effective and lightweight model for segmenting alfalfa images that enables preliminary analysis of branch phenotypes based on digital images.
Accurate and timely assessment of alfalfa nutritional parameters is crucial for optimizing harvest management, maximizing yield, and ensuring high-quality forage in China’s Hexi Corridor, a key alfalfa-growing region. UAV-based hyperspectral remote sensing offers a nondestructive and efficient method for monitoring these parameters, providing high-resolution data and covering large areas efficiently. Previous studies have faced challenges related to the scarcity and imbalance of hyperspectral samples and the effective selection of spectral bands for evaluating crop nutrients. Additionally, the simultaneous evaluation of multiple nutrient parameters using a common set of spectral bands has rarely been reported. Least Absolute Shrinkage and Selection Operator (LASSO) is an important method for hyperspectral band selection, but its linear fitting process is challenged by the complex relationship between spectral reflectance and plant properties. In this study, we propose a new band selection strategy that identifies the most informative spectral bands and improves model performance by combining the strengths of both LASSO selection of bands and machine learning’s ability to fit complex relationships. To address the issue of imbalanced field samples, we generated high-quality synthetic data using the synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) algorithm. Three machine learning models (ANN, RF, and SVM) were then employed to predict alfalfa nutritional parameters. Our findings show that the proposed synergistic band selection strategy significantly improves model performance, yielding a 14–25 % reduction in RMSE while requiring only 37–59 % of the original spectral bands. By integrating this band selection strategy with the SMOGN method, our optimal model for estimating alfalfa nutrient parameters achieved R² values of 0.92–0.95 and PRMSE values of 5.1–7.1 %. We observed the importance of the spectral regions around 730 nm and 960 nm for predicting alfalfa quality parameters. This finding suggests that existing satellite platforms such as Sentinel-2 and Landsat could improve the accuracy and efficiency of alfalfa quality monitoring by incorporating these specific spectral bands. Overall, our approach provides a robust and transferable framework for improving the accuracy and reliability of remote sensing-based crop quality monitoring, which is important for optimizing the spectral band configurations of future satellite sensors for precision agriculture.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像からアルファルファの栄養形質を推定するため、バンド選択、データ拡張、機械学習モデルを開発・評価しており、形質取得手法が研究の中心である。
abstractwe propose a new band selection strategy that identifies the most informative spectral bands and improves model performance by combining the strengths of both LASSO selection of bands and machine learning’s ability to fit complex relationships.
Arbuscular mycorrhizal (AM) fungi, ubiquitously distributed across diverse terrestrial ecosystems, establish symbiotic associations with the majority of vascular plants, fulfilling essential physiological and ecological functions. Mycorrhizal development represents the initiation of host-fungus interactions and serves as a metric for assessing mutualistic efficacy. However, mycorrhizal detection underscores the urgent need to develop cost-effective, efficient, and environmentally benign dyestuff. Therefore, wild-collected and laboratory-grown roots of Medicago sativa were selected. Six reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one. Concurrently, root characteristics were quantified, and interrelationships among root traits, image quality, and colonization indices were analyzed to unravel the mechanism of their interactions. The findings demonstrated that wild roots exhibited pronounced lignification, achieving a mycorrhizal colonization rate of 100 %, which was better than the two laboratory groups. And the fungal community displayed a markedly greater colonization intensity compared to the Claroideoglomus etunicatum. Evaluation of the six reagents revealed distinct staining efficacy, with significant variations in image clarity, gray-level co-occurrence matrix (GLCM) indices, and colonization parameters across treatments. Specifically, aniline blue proved ineffective, while Sudan IV showed selective binding. Notably, black ink in glacial acetic acid achieved optimal mycorrhizal detection efficacy. Moreover, correlation matrix identified microscopic image quality as critical determinant of quantification accuracy, influenced by both reagent types and root properties, and AvgDiam exerted the most substantial impact (|R| > 0.75).
Why it matches plant phenotyping methods植物根のAM菌根菌感染状態を染色とコンピュータビジョンで定量する手法の開発・比較評価が中心であり、単なる生物学的測定ではない。
abstractSix reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one.
Maximising the nitrogen fixation occurring in rhizobia-legume associations represents an opportunity to sustainably reduce nitrogen fertiliser inputs in agriculture. High-throughput measurement of symbiotic traits has the potential to accelerate the identification of elite rhizobium/legume associations and enable novel research approaches. Plasmid-ID technology, recently deployed in Rhizobium leguminosarum, facilitates the concurrent assessment of rhizobium nitrogen-fixing effectiveness and competitiveness for root nodulation. This study adapts Plasmid-ID technology to function in Sinorhizobium species that are central models for studying rhizobium-legume associations and form economically important symbioses with alfalfa. New Sino-Plasmid-IDs were developed and tested for stability and their ability to measure competitiveness for root nodulation and nitrogen-fixing effectiveness. Rhizobial competitiveness is measured by identifying strain-specific nucleotide barcodes using next-generation sequencing, whereas effectiveness is measured by GFP fluorescence driven by the synthetic nifH promoter. Sino-Plasmid-IDs allow researchers to efficiently study competitiveness and effectiveness in a multitude of Sinorhizobium strains simultaneously.
Why it matches plant phenotyping methodsSinorhizobium向けにPlasmid-ID技術を適応・開発し、根粒形成競争性と窒素固定有効性を同時に測定する方法を安定性・性能とともに検証しており、植物共生形質の取得法が中心である。
abstractHigh-throughput measurement of symbiotic traits has the potential to accelerate the identification of elite rhizobium/legume associations
Arbuscular mycorrhizal (AM) fungi, ubiquitously distributed across diverse terrestrial ecosystems, establish symbiotic associations with the majority of vascular plants, fulfilling essential physiological and ecological functions. Mycorrhizal development represents the initiation of host-fungus interactions and serves as a metric for assessing mutualistic efficacy. However, mycorrhizal detection underscores the urgent need to develop cost-effective, efficient, and environmentally benign dyestuff. Therefore, wild-collected and laboratory-grown roots of Medicago sativa were selected. Six reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one. Concurrently, root characteristics were quantified, and interrelationships among root traits, image quality, and colonization indices were analyzed to unravel the mechanism of their interactions. The findings demonstrated that wild roots exhibited pronounced lignification, achieving a mycorrhizal colonization rate of 100 %, which was better than the two laboratory groups. And the fungal community displayed a markedly greater colonization intensity compared to the Claroideoglomus etunicatum. Evaluation of the six reagents revealed distinct staining efficacy, with significant variations in image clarity, gray-level co-occurrence matrix (GLCM) indices, and colonization parameters across treatments. Specifically, aniline blue proved ineffective, while Sudan IV showed selective binding. Notably, black ink in glacial acetic acid achieved optimal mycorrhizal detection efficacy. Moreover, correlation matrix identified microscopic image quality as critical determinant of quantification accuracy, influenced by both reagent types and root properties, and AvgDiam exerted the most substantial impact (|R| > 0.75).
Why it matches plant phenotyping methods植物根の菌根コロニー形成を定量する染色・画像解析法の開発と試薬間比較検証が研究の中心であり、単なる生物学的測定ではない。
abstractSix reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one.
Among various types of forages, Alfalfa (Medicago sativa) is a crucial forage crop that plays a vital role in livestock nutrition and sustainable agriculture. As a result of its ability to adapt to different weather conditions and its high nitrogen fixation capability, this crop produces high-quality forage that contains between 15 and 22 % protein. It is fortunately possible to improve the overall prediction of forage biomass and quality prior to harvest through remote sensing technologies. The recent advent of deep Convolution Neural Networks (deep CNNs) enables researchers to utilize these incredible algorithms. This study aims to build a model to count the number of alfalfa stems from proximal images. To this end, we first utilized a deep CNN encoder-decoder to segment alfalfa and other background objects in a field, such as soil and grass. Subsequently, we employed the alfalfa cover fractions derived from the proximal images to develop and train machine learning regression models for estimating the stem count in the images. This study uses many proximal images taken from significant number of fields in four provinces of Canada over three consecutive years. A combination of real and synthetic images has been utilized to feed the deep neural network encoder-decoder. This study gathered roughly 3447 alfalfa images, 5332 grass images, and 9241 background images for training the encoder-decoder model. With data augmentation, we prepared about 60,000 annotated images of alfalfa fields containing alfalfa, grass, and background utilizing a pre-trained model in less than an hour. Several convolutional neural network encoder-decoder models have also been utilized in this study. Simple U-Net, Attention U-Net (Att U-Net), and ResU-Net with attention gates have been trained to detect alfalfa and differentiate it from other objects. The best Intersections over Union (IoU) for simple U-Net classes were 0.98, 0.93, and 0.80 for background, alfalfa and grass, respectively. Simple U-Net with synthetic data provides a promising result over unseen real images and requires an RGB iPad image for field-specific alfalfa detection. It was also observed that simple U-Net has slightly better accuracy than attention U-Net and attention ResU-Net. Finally, we built regression models between the alfalfa cover fraction in the original images taken by iPad, and the mean alfalfa stems per square foot. Random forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGB) methods have been utilized to estimate the number of stems in the images. RF was the best model for estimating the number of alfalfa stems relative to other machine learning algorithms, with a coefficient of determination (R²) of 0.82, root-mean-square error of 13.00, and mean absolute error of 10.07.
Why it matches plant phenotyping methods近接画像からアルファルファをセグメンテーションし、茎数という植物形質を推定する画像解析・機械学習手法の開発と評価が中心である。
abstractThis study aims to build a model to count the number of alfalfa stems from proximal images.
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:1-55Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Maximizing the nitrogen fixation occurring in rhizobia-legume associations represents an opportunity to sustainably reduce nitrogen fertilizer inputs in agriculture. High-throughput measurement of symbiotic traits has the potential to accelerate the identification of elite rhizobium/legume associations and enable novel research approaches. Plasmid-ID technology, recently deployed in Rhizobium leguminosarum , facilitates the concurrent assessment of rhizobium nitrogen-fixing effectiveness and competitiveness for root nodulation. This study adapts Plasmid-ID technology to function in Sinorhizobium species that are central models for studying rhizobium-legume associations and form economically important symbioses with alfalfa. New Sino-Plasmid-IDs were developed and tested for stability and their ability to measure competitiveness for root nodulation and nitrogen-fixing effectiveness. Rhizobial competitiveness is measured by identifying strain-specific nucleotide barcodes using Next-Generation Sequencing while effectiveness is measured by GFP fluorescence driven by the synthetic nifH promoter. Sino-Plasmid-IDs allow researchers to efficiently study competitiveness and effectiveness in a multitude of Sinorhizobium strains simultaneously.
Why it matches plant phenotyping methods根粒形成競争と窒素固定効果という植物-微生物共生形質を高スループットに測定する技術を適応・開発し、安定性と測定性能を検証しているため、手法が中心である。
abstractHigh-throughput measurement of symbiotic traits has the potential to accelerate the identification of elite rhizobium/legume associations
Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e., SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM (R = 0.87, R² = 0.76 before and R = −0.82, R² = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands.
Why it matches plant phenotyping methods3D放射伝達モデルと蛍光モデルを用いて、作物の光合成系レベルの蛍光量子効率を推定する手法を開発し、地上・航空観測で検証・適用している。植物生理状態の取得手法が研究の中心である。
abstractOur proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach
Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e. , SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM ( R = 0.87, R 2 = 0.76 before and R = −0.82, R 2 = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands. • A novel solar-induced fluorescence (SIF) downscaling method based on DART modeling. • Method removes confounding structural impacts from top-of-canopy SIF observations. • Applied to in-situ SIF measurements, it produced FQE diurnal courses of alfalfa crop. • Adapted to airborne SIF images, it mapped FQE spatial variation.
Why it matches plant phenotyping methodsDARTとFluspect-CXを統合し、作物の光合成系レベルの蛍光量子効率を推定するセンシング・解析手法を開発し、地上および航空観測で検証・適用している。植物生理状態の取得が研究の中心である。
abstractHere, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level.
Vegetation phenology, encompassing critical events like leaf emergence and maturity, serves as an important indicator of adaptive plant responses to environmental factors. In the context of Saudi Arabia, existing crop phenology retrieval methods encounter several challenges related to local farm management operations. These can include unstable crop calendars with planting and harvesting at any time throughout the year, uncertainty in sub-field management with independent control of areas within a center-pivot field, and diverse crop rotations between fodder and non-fodder crops. To address these challenges, we present an innovative framework utilizing machine learning and Sentinel-2 NDVI time series data for mapping phenology stages of key crops at a national scale. The framework is composed of three modules that are implemented step-wise, including: (1) a within-field dynamic clustering module (termed WithinFDy) that monitors fields for potential subdivision based on pixel-level NDVI temporal dynamics; (2) a phenology estimation module (termed PhenoEst) that segments NDVI time series into growing seasons and extracts essential phenology stages (e.g., planting and harvesting dates) for each season; and (3) a crop type discrimination module (termed CropDis) that utilizes extracted phenology information as input features to discriminate between different crop types. Evaluated on 1,000 randomly selected fields in northern Saudi Arabia, our framework achieved overall accuracies of 93.38%, 96.40%, and 94.39% for WithinFDy, PhenoEst, and CropDis modules, respectively. When applied nationwide in 2020, the framework revealed valuable insights. In terms of field management, 21.8% of the fields were divided into two distinct subfields, featuring different planting and harvesting dates - and sometimes crop type, while 73.2% showed consistent practices across the entire field. For seasonal dynamics, 53.4%, 36.3%, and 8.7% of fields supported crops for one, two, and three seasons annually, respectively. Main planting and harvesting activities occurred during winter seasons (November to February), with another peak observed in June. Approximately 30% of fields were under production for 5 to 6 months, and 15.7% were under production year-round. The dominant crop types in 2020 were fodder crops (e.g. alfalfa and Rhodes grass), followed by winter crops like winter wheat. Our methodology represents a substantial advancement over previous approaches, expanding applicability beyond crops with regular growth patterns. The results not only enrich agricultural datasets in Saudi Arabia but also hold promise for enhancing food and water security studies globally.
Why it matches plant phenotyping methods作物の植付け・収穫などの生育ステージをNDVI時系列から抽出する手法を開発し、精度評価と全国適用を行っており、植物フェノタイピングが研究の中心である。
abstractwe present an innovative framework utilizing machine learning and Sentinel-2 NDVI time series data for mapping phenology stages of key crops at a national scale
Abstract Aims This study aimed to explore the effects of increasing image texture features and removing soil background on the alfalfa salt stress diagnosis accuracy. Methods This study extracted spectral reflectance to construct 15 vegetation indexes, and used gray level co-occurrence matrix to calculate eight image texture features. The Canny edge detection algorithm was used to remove the soil background, and set T1 (vegetation index non-removed soil background), T2 (vegetation index + image texture features non-removed soil background), T3 (vegetation index removed soil background), T4 (vegetation index + image texture features removed soil background), as independent variables to construct salt stress diagnosis model based on the support vector regression algorithm, and determined the best salt stress diagnosis model. Results Compared with the T1, the modeling and validation accuracies of salt stress diagnosis model constructed based on the T2 increased by 13.39% and 13.36%, respectively, and those of salt stress diagnosis model constructed based on the T3 increased by 6.30% and 5.33%. The salt stress diagnosis accuracy constructed based on T4 was the highest, with the modeling set R 2 , RMSE, and RPD of 0.675, 0.2143, and 1.7735, respectively, and the validation set R 2 , RMSE, and RPD of 0.652, 0.2349, and 15749, respectively. The modeling and validation accuracies of the salt stress diagnosis model constructed based on crop salt stress index (CSSI) reached more than 0.564 and 0.549, respectively, which can be used as a new indicator for diagnosing salt stress. Conclusions Both increasing image texture features and removing soil background can significantly improve the accuracy of alfalfa salt stress diagnosis.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数・テクスチャ特徴を抽出し、土壌背景除去とモデル検証を通じてアルファルファの塩ストレス状態を診断する方法が研究の中心である。
abstractThis study aimed to explore the effects of increasing image texture features and removing soil background on the alfalfa salt stress diagnosis accuracy.
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:145-180Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Alfalfa is a high-yielding forage crop that is widely grown in the United States for grazing, hay and silage making. A proper maintenance of these grasslands is necessary to ensure optimum productivity and profits. The pre-harvest estimation of biomass yield helps in quantifying the profits and optimizing the forage allocation in advance. Most traditional methods of forage estimation are relatively laborious and time-consuming. Recent developments in contact and remote sensing technologies opened numerous paths for performing aboveground biomass estimation tasks with flexibility and easiness. This study focused on the development of crop height measurement systems for estimating the aboveground biomass yield of Alfalfa (Medicago sativa L.). Five different systems for measuring crop height were evaluated on their ability to estimate aboveground wet and dry biomass. The crop height measurement systems used in this study were Structure-from-Motion, Ultrasound Sensor and Ski, Inertial Measurement Unit and Ski, Inertial Measurement Units and Roller, and a Depth Camera. The results indicated that the system using the Inertial Measurement Unit sensor and ski (IMU-Ski) performed the best among ground-based methods (R2= 0.79; SeY= 3166 kg-wet/ha). The Structure-from-Motion (SfM) method using UAV also provided satisfactory results for biomass predictions (R2= 0.74; SeY= 2543 kg-wet/ha). The models based on IMU-Ski and UAV-based SfM methods were facilitated with vegetation coverage as an additional independent variable to evaluate their effect on biomass predictions. The results indicated that the vegetation coverage did not improve the predictions in any of these systems. Thus, the models based on only the crop height (IMU-Ski and UAV-SfM) were the recommended approaches for Alfalfa biomass estimations. The addition of data points for wide ranges of crop height and vegetation coverage is recommended for future studies to improve the results and ensure the adaptability of these systems in varying environmental conditions.
Why it matches plant phenotyping methodsアルファルファの草丈から地上部バイオマスを推定する複数のフィールドロボティクス・センシング手法を開発・比較評価しており、植物形質の取得と推定が研究の中心である。
abstractThis study focused on the development of crop height measurement systems for estimating the aboveground biomass yield of Alfalfa (Medicago sativa L.).
Alfalfa / lucerneRootClassificationRoot system architecture
Background: Root system architecture (RSA) is of growing interest in implementing plant improvements with belowground root traits. Modern computing technology applied to images offers new pathways forward to plant trait improvements and selection through RSA analysis (using images to discern/classify root types and traits). However, a major stumbling block to image-based RSA phenotyping is image label noise, which reduces the accuracies of models that take images as direct inputs. To address the label noise problem, this study utilized an artificial intelligence model capable of classifying the RSA of alfalfa ( Medicago sativa L.) directly from images and coupled it with downstream label improvement methods. Images were compared with different model outputs with manual root classifications, and confident machine learning (CL) and reactive machine learning (RL) methods were tested to minimize the effects of subjective labeling to improve labeling and prediction accuracies. Results: The CL algorithm modestly improved the Random Forest model's overall prediction accuracy of the Minnesota dataset (1%) while larger gains in accuracy were observed with the ResNet-18 model results. The ResNet-18 cross-population prediction accuracy was improved (~8% to 13%) with CL compared to the original/preprocessed datasets. Training and testing data combinations with the highest accuracies (86%) resulted from the CL- and/or RL-corrected datasets for predicting taproot RSAs. Similarly, the highest accuracies achieved for the intermediate RSA class resulted from corrected data combinations. The highest overall accuracy (~75%) using the ResNet-18 model involved CL on a pooled dataset containing images from both sample locations. Conclusions: ResNet-18 DNN prediction accuracies of alfalfa RSA image labels are increased when CL and RL are employed. By increasing the dataset to reduce overfitting while concurrently finding and correcting image label errors, it is demonstrated here that accuracy increases by as much as ~11% to 13% can be achieved with semi-automated, computer-assisted preprocessing and data cleaning (CL/RL).
Why it matches plant phenotyping methodsアルファルファ根系構造を画像から分類するResNet-18と、ラベルノイズを補正する機械学習手法を開発・評価しており、植物形質取得ワークフローが中心である。
abstracta major stumbling block to image-based RSA phenotyping is image label noise
Reproduction assets foundThe paper's Data Availability statement deposits two paper-specific public assets on Zenodo: the Minnesota alfalfa root crown images (with tags removed and RootPainter-segmented images) and the Oklahoma root crown images together with the R statistical analysis code generated in this study. Both are directly usable, soDataset · publicThe original images (dataset 1 from USDA-ARS at St Paul, MN) with tags removed and segmented images from RootPainter for data analysis are available on Zenodo ( https://doi.org/10.5281/zenodo.5879778 ).Open asset ↗Zenodo · 10.5281/zenodo.5879778lines:341-365Dataset · publicDataset 2 from Oklahoma: Root crown images and R statistical analysis code generated from this study are available on Zenodo ( https://doi.org/10.5281/zenodo.2172832 ).Open asset ↗Zenodo · 10.5281/zenodo.2172832lines:341-365Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Crop height and biomass are the two important phenotyping traits to screen forage population types at local and regional scales. This study aims to compare the performances of multispectral and RGB sensors onboard drones for quantitative retrievals of forage crop height and biomass at very high resolution. We acquired the unmanned aerial vehicle (UAV) multispectral images (MSIs) at 1.67 cm spatial resolution and visible data (RGB) at 0.31 cm resolution and measured the forage height and above-ground biomass over the alfalfa ( Medicago sativa L.) breeding trials in the Canadian Prairies. (1) For height estimation, the digital surface model (DSM) and digital terrain model (DTM) were extracted from MSI and RGB data, respectively. As the resolution of the DTM is five times less than that of the DSM, we applied an aggregation algorithm to the DSM to constrain the same spatial resolution between DSM and DTM. The difference between DSM and DTM was computed as the canopy height model (CHM), which was at 8.35 cm and 1.55 cm for MSI and RGB data, respectively. (2) For biomass estimation, the normalized difference vegetation index (NDVI) from MSI data and excess green (ExG) index from RGB data were analyzed and regressed in terms of ground measurements, leading to empirical models. The results indicate better performance of MSI for above-ground biomass (AGB) retrievals at 1.67 cm resolution and better performance of RGB data for canopy height retrievals at 1.55 cm. Although the retrieved height was well correlated with the ground measurements, a significant underestimation was observed. Thus, we developed a bias correction function to match the retrieval with the ground measurements. This study provides insight into the optimal selection of sensor for specific targeted vegetation growth traits in a forage crop.
Why it matches plant phenotyping methodsUAVマルチスペクトル/RGB画像から飼料作物の草高・地上部バイオマスを推定し、センサー性能を比較・検証する手法研究であり、表現型取得が中心である。
abstractThis study aims to compare the performances of multispectral and RGB sensors onboard drones for quantitative retrievals of forage crop height and biomass at very high resolution.
Objectives: The objective of this study was to evaluate the accuracy of Brix in predicting sugar concentrations in fresh alfalfa (ALF; Medicago sativa L.) and orchardgrass (OG; Dactylis glomerata L.) forages to be used as an inexpensive and rapid field-level assessment of relative energy in forages. Materials and Methods: In a 2-yr study, fresh forages samples from ALF and OG monoculture pastures in central Pennsylvania, USA, were collected once monthly from May to September. Samples were immediately evaluated for Brix values with a hand-held digital refractometer, and the remaining biomass was frozen immediately with liquid N to halt cellular respiration. Samples were lyophilized and analyzed for individual sugars and wet chemistry nutritive analyses. Results and Discussion: Brix was positively correlated with total and individual sugars in ALF during late spring and late summer (0.53-0.93), but correlations were nonexistent or negative in mid-summer. However, Brix values did not correlate with any notable sugar parameters in OG beyond the first sampling date. These findings were attributed to greater fibrous fraction contamination, lower sugar concentrations found in grasses and legumes compared with horticultural crops, and changes in seasonal growth of ALF and OG. Implications and Applications: Brix values did not consistently predict sugar concentrations in fresh ALF and OG forages. Because Brix measures dissolved solids in solution (not just sugars), Brix readings collected from crushed ALF or OG samples may be confounded by fibrous fractions found in the solution. Brix accuracy may also be dependent on seasonal temperature patterns, plant growth stage, and daily weather patterns. Other solutions should be investigated that rapidly assess sugar profiles and nutritive values of fresh forages.
Why it matches plant phenotyping methodsアルファルファとオーチャードグラスの糖濃度という植物形質を対象に、携帯型屈折計によるBrix測定の予測精度と適用限界を検証しており、測定法の技術評価が研究の中心である。
abstractThe objective of this study was to evaluate the accuracy of Brix in predicting sugar concentrations in fresh alfalfa (ALF; Medicago sativa L.) and orchardgrass (OG; Dactylis glomerata L.) forages to be used as an inexpensive and rapid field-level assessment of relative energy in forages.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Measuring canopy height is important for phenotyping as it has been identified as the most relevant parameter for the fast determination of plant mass and carbon stock, as well as crop responses and their spatial variability. In this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode. The tool was lab-tested and field-tested on crop systems of different geometry and spacings: in a static setting on faba bean (Vicia faba L.) and in an on-the-go setting on chia (Salvia hispanica L.), alfalfa (Medicago sativa L.), and wheat (Triticum durum Desf.). Cross-correlation (CC) or a dynamic time-warping algorithm (DTW) was used to analyze and correct shifts between manual and sensor data in chia. Sensor data were able to reproduce with minor shifts in canopy profile and plant status indicators in the field when plant heights varied gradually in narrow-spaced chia (R2 = 0.98), faba bean (R2 = 0.96), and wheat (R2 = up to 0.99). Abrupt height changes resulted in systematic errors in height estimation, and short-scale variations were not well reproduced (e.g., R2 in widely spaced chia was 0.57 to 0.66 after shifting based on CC or DTW, respectively)). In alfalfa, ultrasound data were a better predictor than NDVI (Normalized Difference Vegetation Index) for Leaf Area Index and biomass (R2 from 0.81 to 0.84). Maps of ultrasound-determined height showed that clusters were useful for spatial management. The good performance of the tool both in a static setting and in the on-the-go setting provides flexibility for the determination of plant height and spatial variation of plant responses in different conditions from natural to managed systems.
Why it matches plant phenotyping methods超音波センサーを用いた近接型植物高測定ツールを開発し、複数作物・運用条件で検証しており、植物形質取得法が研究の中心である。
abstractIn this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode.
Multi-spectral imaging (MSI) collection by unoccupied aerial vehicles (UAV) is an important tool to measure growth of forage crops. Information from estimated growth curves can be used to infer harvest biomass and to gain insights in the relationship of growth dynamics and harvest biomass stability across cuttings and years. In this study, we used MSI to evaluate Alfalfa ( Medicago sativa L. subsp. sativa ) to understand the longitudinal relationship between vegetative indices (VIs) and forage/biomass, as well as evaluation of irrigation treatments and genotype by environment interactions (GEI) of different alfalfa cultivars. Alfalfa is a widely cultivated perennial forage crop grown for high yield, nutritious forage quality for feed rations, tolerance to abiotic stress, and nitrogen fixation properties in crop rotations. The direct relationship between biomass and VIs such as Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red edge normalized difference vegetation index (NDRE), and Near infrared (NIR) provide a non-destructive and high throughput approach to measure biomass accumulation over subsequent alfalfa harvests. In this study, we aimed to estimate the genetic parameters of alfalfa VIs and utilize longitudinal modeling of VIs over growing seasons to identify potential relationships between stability in growth parameters and cultivar stability for alfalfa biomass yield across cuttings and years. We found VIs of GNDVI, NDRE, NDVI, NIR and simple ratios to be moderately heritable with median values for the field trial in Ithaca, NY to be 0.64, 0.56, 0.45, 0.45 and 0.40 respectively, Normal Irrigation (NI) trial in Leyendecker, NM to be 0.3967, 0.3813, 0.3751, 0.3239 and 0.3019 respectively, and Summer Irrigation Termination (SIT) trial in Leyendecker, NM to be of 0.11225, 0.1389, 0.1375, 0.2539 and 0.1343, respectively. Genetic correlations between NDVI and harvest biomass ranged from 0.52 - .99 in 2020 and 0.08 - .99 in 2021 in the NY trial. Genetic correlations for NI trial in NM for NDVI ranged from 0.72 - .98 in 2021 and SIT ranged from 0.34-1.0 in 2021. Genotype by genotype by interaction (GGE) biplots were used to differentiate between stable and unstable cultivars for locations NY and NM, and Random regression modeling approaches were used to estimate growth parameters for each cutting. Results showed high correspondence between stability in growth parameters and stability, or persistency, in harvest biomass across cuttings and years. In NM, the SIT trial showed more variation in growth curves due to stress conditions. The temporal growth curves derived from NDVI, NIR and Simple ratio were found to be the best phenotypic indices on studying the stability of growth parameters across different harvests. The strong correlation between VIs and biomass present opportunities for more efficient screening of cultivars, and the correlation between estimated growth parameters and harvest biomass suggest longitudinal modeling of VIs 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から植生指数とバイオマスを推定し、成長曲線・遺伝パラメータ・品種安定性を評価することが研究の中心であり、植物表現型取得と解析手法の実質的な応用に該当する。
abstractMulti-spectral imaging (MSI) collection by unoccupied aerial vehicles (UAV) is an important tool to measure growth of forage crops.
Alfalfa is a forage of vast importance around the world. In the past, near-infrared spectroscopy (NIRS) technique have been explored in the lab to determine quality traits such as fibre content in dried and ground material. During the last decade, portable hyperspectral devices have emerged as a tools for in-field prediction, of not only crop yield but also a large range of quality and physiological markers. The objective of this study was to estimate quality parameters in an alfalfa crop using hyperspectral data acquired from a full-range (350–2500 nm) spectrometer under field conditions. Reflected spectra were measured in single leaves as well as at the canopy level, then reflectance was related to target parameters such as biomass, leaf pigments, sugars, protein, and mineral contents. Due to their large effect on crop quality parameters, meteorological conditions and phenological stages were included as predictors in the models. We found that meteorological and phenological variables improved the accuracies and percentage of variance explained (R²) for most of the parameters evaluated. Based on R² values, the best prediction models were obtained for biomass (0.71), sucrose (0.65), flavonoids (Flav) (0.56) and nitrogen (0.70) with normalized root mean squared errors of 0.196, 0.32, 0.087 and 0.08, respectively. These parameters were associated mainly with visible (VIS) (approx. 350–700 nm) and near infrared (NIR) (700–1250 nm) regions of the spectrum. Regarding mineral composition, the best prediction models were developed for P (0.51), B (0.50) and Zn (0.44), associated with the short-wave infra-red (SWIR) region (1250–2500 nm). The results of this study demonstrated the potential of hyperspectral techniques to be used as a base for performing initial evaluations in the field of quality traits in alfalfa crops.
Why it matches plant phenotyping methodsアルファルファの葉・群落を対象に、野外ハイパースペクトル計測から biomass、色素、糖、タンパク質、鉱物などの植物形質を推定する手法が研究の中心であり、モデル精度も評価しているため。
abstractThe objective of this study was to estimate quality parameters in an alfalfa crop using hyperspectral data acquired from a full-range (350–2500 nm) spectrometer under field conditions.
Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa.
Why it matches plant phenotyping methodsLiDAR・ハイパースペクトル画像から植物の構造・スペクトル形質を自動抽出し、アルファルファ耐塩性を評価する手法を開発・比較検証しており、表現型取得と解析が中心である。
abstractTherefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI).
Alfalfa (Medicago sativa) is an important forage source for grassland agricultural development, so it would be worthwhile to explore accurate and fast methods of alfalfa remote sensing identification and yield estimation. However, the traditional methods of identifying large areas of crops and yield estimation have some problems, such as the limited spatial resolution of remote sensing data and heavy reliance training data. In this study, based on Sentinel-2 high-resolution images and the Google Earth Engine (GEE) platform to establish a cloud-free normalized difference vegetation index (NDVI) time-series dataset, we proposed an effective method for alfalfa feature extraction and yield estimation method. The results show that (1) The producer’s accuracy, user’s accuracy, overall accuracy, and Kappa coefficient of alfalfa identification using a trough recognition algorithm were 98.51%, 91.67%, 94.26%, and 0.88, respectively. The total area of cultivated alfalfa identified in the study area in 2020 was estimated at 46,793.21 hm2, which was mainly distributed in areas in north of the Qilian Mountains; (2) NDVI had a highly significant correlation with alfalfa hay yield, and the power function regression model was the greatest, with an R2 greater than 0.65; (3) The annual unit hay yield of four alfalfa cuttings was estimated at 17,497.55-32,962.10 kg/hm2, with a total hay yield of 48.38×107 kg and an average hay yield of 4,464.95 kg/hm2. The method proposed has important application potential for automatic and rapid remote sensing identification and yield evaluation of large-scale cultivated alfalfa.
Why it matches plant phenotyping methodsSentinel-2 NDVI時系列とGEEを用いて、アルファルファの特徴抽出および収量推定手法を開発・評価しており、植物の収量形質取得が中心である。
abstractwe proposed an effective method for alfalfa feature extraction and yield estimation method.
Climate change is one of the biggest challenges facing food production worldwide, particularly in Mediterranean climate regions where rainfall has been decreasing and temperature increasing. Alfalfa is a perennial forage crop that could play an important role in increasing the resilience and sustainability of rainfed production systems in these environments. In this study, we tested how a prolonged drought period of 5–6 months in a Mediterranean environment of central Chile affects the plant persistence and productivity of an alfalfa diversity panel of 69 landraces, cultivars, and diverse pre-breeding populations (hereafter accessions) from Kazakhstan, Azerbaijan, Spain, Australia, USA, and Chile, with putative drought tolerance. The objectives were i) to evaluate the phenotypic variability in plant survival and productivity among the alfalfa diversity panel, under rainfed and with supplemental irrigation, in a Mediterranean environment; and ii) to study the use of an NDVI sensor, and RGB and thermal cameras for field phenotyping and evaluation of canopy traits, such as leaf area index (LAI) and forage yield (FY). Results showed large phenotypic variability for plant survival after the three drought periods, ranging from 30 % to 76 % under rainfed conditions and from 48 % to 90 % with supplementary irrigation. The FY during winter was significantly different between cultivars, with more than 4 Mg ha⁻¹ per year in some accessions. The average FY for three growing seasons (2018–2020) ranged between 2.26 and 10.80 Mg ha⁻¹ under rainfed and 3.92–11.14 Mg ha⁻¹ per year with supplementary irrigation. Among the most productive accessions under both water regimes were two populations of the Medicago arborea x sativa hybrid (AF3448 and AF3347), cultivars Genesis and Venus, and the landraces Aragon from Spain and Alta Sierra 2 from Chile. The normalized difference vegetation index (NDVI) showed exponential and positive relationships with the leaf area index (LAI) and FY. RGB images were obtained at 60 cm above the top of the canopy with a digital camera and at 30 m using a drone. The RGB indices “intensity”, a* and greener area obtained from images taken at 60 cm above the top of the canopy, were highly correlated (r = 0.76 – 0.98; P 0.73 – 0.94; P < 0.0001) with the NDVI and FY of accessions growing under both water regimens in 2020. The canopy temperature (CT) obtained from thermal images from a drone was higher under the rainfed regime. The stress degree day (SDD), defined as the difference between canopy temperature (Tc) and air temperature (Ta) was negatively related to FY. Under the rainfed regimen, the population AF3348 and cultivars Genesis and Aragon exhibited the lowest SDD and the highest FY.
Why it matches plant phenotyping methodsNDVIセンサー、RGB・熱画像カメラを用いた圃場フェノタイピングと、LAI・飼料収量・キャノピー温度の推定が研究目的として明示され、手法と形質の関係も評価されているため。
abstractto study the use of an NDVI sensor, and RGB and thermal cameras for field phenotyping and evaluation of canopy traits, such as leaf area index (LAI) and forage yield (FY).
Highlights A UAV with NDVI and RGB cameras captured aerial images of alfalfa plots before and after harvest. Structure from Motion (SfM) and crop surface models were used to determine change in alfalfa crop height. An equation to predict dry matter fraction (DMF) as a function of NDVI was developed. Dry alfalfa yield models using DMF and change in crop height were developed. Abstract. Alfalfa is an important forage crop grown for hay, silage, and pasture production. Accurate yield estimations before harvest or grazing are crucial for producers to optimize forage utilization. The goal of this research was to develop an aboveground dry biomass prediction function for alfalfa using remote sensing from an unmanned aerial vehicle (UAV). A DJI Mavic Pro equipped with RGB and NDVI cameras captured aerial images. High-resolution orthomosaics and digital surface models were obtained using Structure from Motion (SfM) with Agisoft Metashape. An equation to estimate wet biomass was developed using three variables: change in canopy height (∆H) from digital elevation and crop surface models, and NDVI based canopy density index (CDI) data. The dry biomass yield was estimated as the product of a wet biomass prediction function and a correlation equation to estimate the dry matter fraction (DMF). The best correlation equation for wet biomass (BMwet) only required SfM methods to measure ΔH. The linear regression equation for BMwet had an R2 of 0.963 and the mean coefficient of variation (CV) was ±23%. The best prediction equation for DMF was a quadratic equation with an R2 of 0.642 with a mean CV of ±11%. It was also determined that the DMF varied significantly by season of harvest. As a result, the dry biomass could be estimated using the DMF equation or seasonal mean DMF values. Comparison of the observed dry biomass measurements with the empirically determined biomass prediction function that used the NDVI data to estimate the DMF showed excellent agreement. The model underpredicted the dry biomass by 1.1% with a standard error of the estimate of 236 kg DM/ha (CV = ±26%). The model predictions using the seasonal mean DMF values were not significantly different.
Why it matches plant phenotyping methodsUAV画像、SfM、作物表面モデル、NDVIを用いてアルファルファの草高・バイオマス・乾物率を推定する方法を開発し、精度を検証しているため、表現型取得・推定が研究の中心です。
abstractThe goal of this research was to develop an aboveground dry biomass prediction function for alfalfa using remote sensing from an unmanned aerial vehicle (UAV).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Crop breeding relies on the numbers game. The higher the number of locations and entries evaluated, the higher the probability of developing superior cultivars. One major challenge facing breeders of perennial cool season forage crops is the number of biomass harvests per season. In regions with mild winters, alfalfa is harvested every four weeks, six to seven times a year. This creates an operational bottleneck limiting the number of entries and testing locations. Substantial investments were made in the development of automated solutions for precision Ag for row crops. The adoption of these platforms to forage crops rests on their accuracy in estimating biomass yield and cost-effectiveness. This work focused on evaluating the sensitivity of popular unmanned aerial vehicles (UAVs) and imaging strategies for optimal real-time biomass estimates in perennial forage crops. Experimental plots consisting of single plants, row plots, and sward plots were used for a hybrid data collection approach including direct measurements and remote sensing. UAV platforms equipped with a 42-megapixel RGB camera (Sony Alpha 7Rii), a five-band multispectral system (MicaSense RedEdge MX), a hyperspectral sensor (Resonon-Pika L), and a LiDAR (LiDARUSA Revolution 120) were tested. Images were used to generate 3D canopy models of vegetation in the field and to compute morphometric and spectral indices descriptive of vegetation coverage, health and vigor. Harvested biomass yield was used to validate the values derived from UAVs. Preliminary results suggest that the simple red-green vegetation index may be sufficient to give a reliable estimate of biomass yield.
Why it matches plant phenotyping methodsUAV画像・LiDAR・マルチスペクトル等を用いた飼料作物のバイオマス収量推定を評価し、収穫バイオマスで検証しているため、植物表現型取得法が中心である。
abstractThis work focused on evaluating the sensitivity of popular unmanned aerial vehicles (UAVs) and imaging strategies for optimal real-time biomass estimates in perennial forage crops.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Vegetative indices (VIs) collected from an unoccupied aerial vehicle (UAV) equipped with a multi-spectral camera can be used to study growth and development of alfalfa throughout each growth cycle. Random regression models are well suited to fit longitudinal phenotypes such as VIs collected over time to estimate growth curves using covariance functions. Using these functions genetic variation in growth through time can be estimated and the relationships between VIs and end-use traits, like forage yield and quality, can be assessed. The main objectives of this project are (1) to incorporate aerial high-throughput phenotyping to predict performance and genetic merit of the breeding materials, (2) to fit longitudinal random regression models to estimate genotype-specific growth curves and estimate the heritability of key growth parameters. Univariate and multivariate models were used to estimate heritability of image features for alfalfa trial of Helfer, 2020 and 2021. The heritability of different image features in alfalfa ranged from 0-0.78. The preliminary results showed the strongest correlation for Green NDVI and biomass yield (0.4053, 0.7875, and 0.6779), followed by Red edge NDVI and biomass yield (0.417, 0.7898, and 0.6417) for the first, second and third cuttings respectively of the experimental trial located at Helfer, Ithaca for 2020, while the genetic correlations for 2021 were strongest for Red edge NDVI and biomass yield (0.76, 0.74, and 0.66) followed by Green NDVI and biomass yield (0.75, 0.76 and 0.60) for the first, second and third cuttings. The potential of random regression models was investigated using Legendre polynomial functions. Random regression model converged for most of the time points and showed potential for modeling genetic parameters associated with growth and development.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による高スループット表現型取得と、画像特徴量の遺伝パラメータ推定を中心に扱っており、単なる生物学的測定ではない。
abstractThe main objectives of this project are (1) to incorporate aerial high-throughput phenotyping to predict performance and genetic merit of the breeding materials
Alfalfa / lucerneField / plotRootClassificationRoot system architecture
Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.
Why it matches plant phenotyping methodsアルファルファ根系構造を対象に、画像増強と機械学習・深層学習による表現型分類手法を開発・比較しており、フェノタイピング手法が研究の中心である。
abstractThis research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms
Reproduction assets foundThe paper's root images (originals with tags removed and RootPainter segmentations) used for the alfalfa RSA phenotyping/ML analysis are publicly deposited on Zenodo (doi: 10.5281/zenodo.5879778), as stated in the Data Availability section. No allowed URL in the supplied list matches this deposit, so no URL is providedDataset · publicThe original images with tags removed and segmented images from RootPainter for data analysis are available on Zenodo doi: 10.5281/zenodo.5879778 [ 85 ].Zenodo · 10.5281/zenodo.5879778lines:627-653Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.
Why it matches plant phenotyping methodsマルチスペクトル画像と多変量解析により、アルファルファ種子の活力・生存性・発芽率を非破壊推定する手法が研究の中心であるため。
abstractAs a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation.
Reproduction assets foundThe authors provide a public Google Drive supplement containing the paper's own multispectral imaging data: mean reflectance at 19 wavelengths for all seeds (Table S1), morphological feature data for all seeds (Table S2), and multispectral images of the alfalfa seed samples (Figures S1–S6). These directly reproduce theDataset · publicThe following are available online at https://drive.google.com/file/d/13CXchEm81qnbIZCXLqdvupDPib7BS8FM/view?usp=sharing , Table S1: Mean reflectance of 19 wavelengths in all seeds, Table S2: Data of morphological feature in all seeds. Figure S1: Multispectral image of seeds harvested in 2004. Figure S2: Multispectral image of seeds harvested in 2008. Figure S3: Multispectral image of seeds harvested in 2019. Figure S4: Multispectral image of seeOpen asset ↗lines:79-239Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Why it matches plant phenotyping methodsUAS multispectral画像の分類法を比較し、アルファルファの被害面積・キャノピー損傷という植物状態を評価する方法が中心的に検討されている。
abstractseveral alternatives for estimating the damage to alfalfa fields by voles through unmanned vehicle systems (UASs) and multispectral cameras are presented
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa ( Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と空間解析を用いたアルファルファ収量形質のHTP手法を、選抜効率・予測精度・遺伝的パラメータ推定の観点から実質的に評価しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 8 Sept 2026
Alfalfa / lucerneGrowth chamberRootClassificationMorphology / geometry measurementRoot system architecture
Abstract Background The root system architecture (RSA) of alfalfa ( Medicago sativa L.) affects biomass production by influencing water and nutrient uptake, including nitrogen fixation. Further, roots are important for storing carbohydrates that are needed for regrowth in spring and after each harvest. Previous selection for a greater number of branched and fibrous roots significantly increased alfalfa biomass yield. However, phenotyping root systems of mature alfalfa plant is labor-intensive, time-consuming, and subject to environmental variability and human error. High-throughput and detailed phenotyping methods are needed to accelerate the development of alfalfa germplasm with distinct RSAs adapted to specific environmental conditions and for enhancing productivity in elite germplasm. In this study methods were developed for phenotyping 14-day-old alfalfa seedlings to identify measurable root traits that are highly heritable and can differentiate plants with either a branched or a tap rooted phenotype. Plants were grown in a soil-free mixture under controlled conditions, then the root systems were imaged with a flatbed scanner and measured using WinRhizo software. Results The branched root plants had a significantly greater number of tertiary roots and significantly longer tertiary roots relative to the tap rooted plants. Additionally, the branch rooted population had significantly more secondary roots > 2.5 cm relative to the tap rooted population. These two parameters distinguishing phenotypes were confirmed using two machine learning algorithms, Random Forest and Gradient Boosting Machines. Plants selected as seedlings for the branch rooted or tap rooted phenotypes were used in crossing blocks that resulted in a genetic gain of 10%, consistent with the previous selection strategy that utilized manual root scoring to phenotype 22-week-old-plants. Heritability analysis of various root architecture parameters from selected seedlings showed tertiary root length and number are highly heritable with values of 0.74 and 0.79, respectively. Conclusions The results show that seedling root phenotyping is a reliable tool that can be used for alfalfa germplasm selection and breeding. Phenotypic selection of RSA in seedlings reduced time for selection by 20 weeks, significantly accelerating the breeding cycle.
Why it matches plant phenotyping methodsアルファルファ幼植物の根系構造をスキャナとWinRhizoで高スループットに取得・測定し、機械学習で識別・検証した根系フェノタイピング手法の開発が中心である。
abstractIn this study methods were developed for phenotyping 14-day-old alfalfa seedlings to identify measurable root traits that are highly heritable and can differentiate plants with either a branched or a tap rooted phenotype.
High-throughput phenotyping and genotyping have provided a vast source of information for evaluating the genetic merit of different breeding materials, but their implementation has been limited in alfalfa due to the complexity of the genome and the perennial nature of the crop. Vegetative indices (VIs) collected from an unmanned aerial vehicle (UAV) equipped with multi-spectral camera can be used to study forage growth and development throughout each growth cycle. Random regression models could be implemented to fit such longitudinal phenotypes like VIs collected over time to estimate growth curves, to access genetic variation in growth and the relations of VIs to end-use traits like forage yield and quality. The main objectives of this project are (1) to incorporate aerial high-throughput phenotyping to predict performance and genetic merit of the breeding materials, (2) to fit longitudinal random regression model to estimate genotype-specific growth curves, and (3) to develop a genotyping approach to estimate genetic relationships between alfalfa populations. The imaging of the alfalfa experimental trials was done every ~ 4.3 days throughout the growing season. The Vegetative indices (VIs) close to the harvest date were extracted and used to fit multi-traits models to evaluate the genetic correlations between VIs and forage biomass yield. The VIs considered were Normalized Vegetative index (NDVI), Green NDVI, Red Edge NDVI, simple ratio of Near Infrared to Red (NIR), and Digital Surface Map (DSM). The preliminary results showed highest correlation of Green NDVI and biomass yield (0.4053, 0.7875, and 0.6779), followed by Rededge NDVI and biomass yield (0.417, 0.7898, and 0.6417) for the first, second and third cuttings respectively for the experimental trial located at Helfer, Ithaca. Heritability estimates ranging from 0.03 to 0.75 was observed indicating the presence of genetic variation in these VIs. Pairwise Fst values estimated from population-level genotyping approach was found to be efficient estimates of genetic relatedness between populations. Random regression models with a linear spline function and legendre polynomials including other environmental trials are under evaluation to see the potentiality of these models to fit VIs from multiple time points.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を抽出し、成長曲線・遺伝的能力・収量との関連を評価する高スループット表現型解析が研究の中心である。
abstractThe main objectives of this project are (1) to incorporate aerial high-throughput phenotyping to predict performance and genetic merit of the breeding materials
When dealing with plant roots, a multiscale description of the functional root structure is needed. Since the beginning of 21st century, new devices such as laser confocal microscopes have been accessible for coarse root structure measurements, including three-dimensional (3D) reconstruction. Most researchers are familiar with using simple 2D geometry visualization that does not allow quantitative determination of key morphological features from an organ-like perspective. We provide here a detailed description of the quantitative methods available for 3D analysis of root features at single-cell resolution, including root asymmetry, lateral root analysis, cell size and nuclear organization, cell-cycle kinetics, and chromatin structure analysis. Quantitative maps of the root apical meristem (RAM) are shown for different species, including Arabidopsis thaliana (L.), Heynh, Nicotiana tabacum L., Medicago sativa L., and Setaria italica (L.) P. Beauv. The 3D analysis of the RAM in these species showed divergence in chromatin organization and cell volume distribution that might be used to study root zonation for each root tissue. Detailed protocols and possible pitfalls in the usage of the marker lines are discussed. Therefore, researchers who need to improve their quantitative root biology portfolio can use them as a reference.
Why it matches plant phenotyping methods根の3D形態・細胞特性を定量化する方法とプロトコルを中心に扱う方法論的レビューであり、植物フェノタイピング手法が主題である。
abstractWe provide here a detailed description of the quantitative methods available for 3D analysis of root features at single-cell resolution
Prediction of lucerne phenological and morphological development is important for optimising the defoliation schedule and time of other management events. A challenge for any lucerne phenology module is to capture the seasonality of development processes in response to environment, management and genotype. To date, lucerne phenological modules have not been evaluated under different defoliation regimes or with genotypes of different fall dormancy (FD) classes. This research integrated data of lucerne phenological development into the Agricultural Production Systems sIMulator (APSIM) next generation (APSIM NextGen) model framework to develop and verify a phenology module. Relationships derived from the FD5 genotype, grown under a 42 day (LL) defoliation treatment were used for model development. These were further tested for two genotypes with contrasting FD (FD2 and FD10) under frequent (28 day: S) or long (84 day: H) defoliation regimes, all under irrigated conditions. Development was parameterized based on thermal time targets to reach specific phenological stages and modified by photoperiod responses. Development stage and node appearance were shown to be independent of defoliation treatment and FD class. Simulation results showed good agreement for prediction of development stages (NSE of 0.77 for days to buds visible and 0.67 for days to flowering stage) and number of main stem nodes (NSE values were ranged from 0.53 to 0.84). However, both defoliation management treatment and FD classes affected stem height. For FD5, there was good agreement for the 84 day treatment (NSE of 0.83) and the 42 day treatment (NSE of 0.66), but it was poor for the 28 day treatment (NSE of -0.08). This was probably due to reduced stem extension rates, limited by low C and N reserves in perennial organs under the frequent (28 day) defoliation regime. For FD2 and FD10, two separate sets of parameters were used to improve model prediction of height to account for their contrasting seasonal C partitioning patterns. These results show that the APSIM NextGen lucerne phenology module was able to simulate crops grown under unconstrained growing conditions. However, the reason for under estimation of stem height for the 28 day treatment needs further investigation.
Why it matches plant phenotyping methodsルーサンの発育段階、節数、茎高という植物形質を予測するAPSIM NextGenのフェノロジーモジュールを開発・検証しており、モデルによる形質推定が研究の中心である。
abstractThis research integrated data of lucerne phenological development into the Agricultural Production Systems sIMulator (APSIM) next generation (APSIM NextGen) model framework to develop and verify a phenology module.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Winter freezing temperature impacts alfalfa ( Medicago sativa L.) persistence and seasonal yield and can lead to the death of the plant. Understanding the genetic mechanisms of alfalfa freezing tolerance (FT) using high-throughput phenotyping and genotyping is crucial to select suitable germplasm and develop winter-hardy cultivars. Several clones of an alfalfa F 1 mapping population (3010 x CW 1010) were tested for FT using a cold chamber. The population was genotyped with SNP markers identified using genotyping-by-sequencing (GBS) and the quantitative trait loci (QTL) associated with FT were mapped on the parent-specific linkage maps. The ultimate goal is to develop non-dormant and winter-hardy alfalfa cultivars that can produce extended growth in the areas where winters are often mild. Results Alfalfa FT screening method optimized in this experiment comprises three major steps: clone preparation, acclimation, and freezing test. Twenty clones of each genotype were tested, where 10 samples were treated with freezing temperature, and 10 were used as controls. A moderate positive correlation (r ~ 0.36, P CBF . The BLAST alignment of a CBF sequence of M . truncatula , a close relative of alfalfa, against the alfalfa reference showed that the gene’s ortholog resides around 75 Mb on chromosome 6. Conclusions The indoor freezing tolerance selection method reported is useful for alfalfa breeders to accelerate breeding cycles through indirect selection. The QTL and associated markers add to the genomic resources for the research community and can be used in marker-assisted selection (MAS) for alfalfa cold tolerance improvement.
Why it matches plant phenotyping methodsアルファルファの凍結耐性を測定する屋内スクリーニング法を最適化し、手順と有用性を示しているため、表現型取得法が研究の中心である。
abstractAlfalfa FT screening method optimized in this experiment comprises three major steps: clone preparation, acclimation, and freezing test.
The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R2CV) higher than 0.80 for all three crops.
Why it matches plant phenotyping methodsSentinel-2画像から植物キャノピーのLAIを推定する手法を開発・比較し、地上測定との交差検証で精度評価しているため、植物形質推定法が中心である。
abstractThe objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach.
Alfalfa canopy structure reveals useful information for managing this forage crop, but manual measurements are impractical at field-scale. Photogrammetry processing with images from Unmanned Aerial Vehicles (UAVs) can create a field-wide three-dimensional model of the crop canopy. The goal of this study was to determine the appropriate flight parameters for the UAV that would enable reliable generation of canopy models at all stages of alfalfa growth. Flights were conducted over two separate fields on four different dates using three different flight parameters. This provided a total of 24 flights. The flight parameters considered were the following: 30 m altitude with 90° camera gimbal angle, 50 m altitude with 90° camera gimbal angle, and 50 m altitude with 75° camera gimbal angle. A total of 32 three-dimensional canopy models were created using photogrammetry. Images from each of the 24 flights were used to create 24 separate models and images from multiple flights were combined to create an additional eight models. The models were analyzed based on Model Ground Sampling Distance (GSD), Model Root Mean Square Error (RMSE), and camera calibration difference. Of the 32 attempted models, 30 or 94% were judged acceptable. The models were then used to estimate alfalfa yield and the best yield estimates occurred with flights at a 50 m altitude with a 75° camera gimbal angle; therefore, these flight parameters are suggested for the most consistent results.
Why it matches plant phenotyping methodsUAVフォトグラメトリによるアルファルファ群落の3D形状モデル化と収量推定を中心に、飛行条件、精度、再現性を評価しており、植物表現型取得手法が主題である。
abstractPhotogrammetry processing with images from Unmanned Aerial Vehicles (UAVs) can create a field-wide three-dimensional model of the crop canopy.
ABSTRACT When dealing with plant roots, a multi-scale description of the functional root structure is needed. Since the beginning of XXI century, new devices like laser confocal microscopes have been accessible for coarse root structure measurements, including 3D reconstruction. Most researchers are familiar with using simple 2D geometry visualization that does not allow quantitatively determination of key morphological features from an organ-like perspective. We provide here a detailed description of the quantitative methods available for three-dimensional (3D) analysis of root features at single cell resolution, including root asymmetry, lateral root analysis, xylem and phloem structure, cell cycle kinetics, and chromatin determination. Quantitative maps of the distal and proximal root meristems are shown for different species, including Arabidopsis thaliana , Nicotiana tabacum and Medicago sativa . A 3D analysis of the primary root tip showed divergence in chromatin organization and cell volume distribution between cell types and precisely mapped root zonation for each cell file. Detailed protocols are also provided. Possible pitfalls in the usage of the marker lines are discussed. Therefore, researchers who need to improve their quantitative root biology portfolio can use them as a reference.
Why it matches plant phenotyping methods根の3D形態・細胞構造を定量化する方法と詳細プロトコルが中心であり、植物フェノタイピング手法として適格です。
abstractWe provide here a detailed description of the quantitative methods available for three-dimensional (3D) analysis of root features at single cell resolution
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Alfalfa (Medicago sativa L.) is an important forage crop grown worldwide for animal feed, green manure, and as a land cover. However, very few approaches exist for timely field scale mapping of crop status, yield and quality attributes for management of inputs, harvest and storage resources, budgeting, crop insurance, etc. This study aims to apply high-resolution aerial multispectral and thermal infrared remote sensing (7 cm/pixel) to characterize above crop attributers. Imaged were two crop cutting cycles in 2018 season. Eight crop vigor index (VI) and a Crop Water Stress Index (CWSI) features were derived from collected imagery data. Modified Non-Linear Index (MNLI), Modified Simple Ratio (MSR) and CWSI reliably evaluated the spatial variations in crop vigor and stress traits (Coefficient of variation [CV] in the ranges of 24–69%). Yield was then predicted with indices as predictor variables through nine simple linear regression (LRs, variable: one image feature per model), seven multiple linear regression (MLRs, variables: one VI and CWSI per model), a stepwise linear regression (SLR), a partial least square regression (PLSR) and a least absolute shrinkage and selection operator (LASSO) models. The SLR, PLSR and LASSO initially used all image features for model training. Amongst simple models, MLR-4 (Variables: MNLI and CWSI) performed the best (Root mean square error [RMSE] = 0.45 kg, R² = 0.64) and LR-5 (Variable: MNLI) was the second-best model (RMSE = 0.51 kg, R² = 0.54). The complex SLR, PLSR and LASSO models predicted yield with similar accuracy as MLR-4 (RMSE in the ranges of 0.45–0.46 kg, R² in the ranges of 0.63–0.64). MNLI (canopy vigor) and CWSI (stress) were significant and sufficient for effective alfalfa crop status and yield prediction for their non-saturation and non-linearity features. Overall, high-resolution aerial remote sensing in the visible-NIR and thermal infrared domain showed potential for site-specific crop monitoring.
Why it matches plant phenotyping methods高解像度航空マルチスペクトル・熱赤外画像から作物活力、ストレス、収量を抽出・予測するセンシングおよび解析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 aims to apply high-resolution aerial multispectral and thermal infrared remote sensing (7 cm/pixel) to characterize above crop attributers.
Forage analysis by near infrared reflectance (NIR) spectroscopy has had many advancements since it began in the 1970s. There have been steady improvements in instrumentation, in computers, and chemometric algorithms for developing calibrations. Thus, making NIR the most used technique to routinely analyze samples for forage producers, plant breeders, animal nutritionists, cattle farmers, and feed companies. This study compared the performance of prediction across three different portable instruments compared to a bench top laboratory NIR instrument, using a wide range of alfalfa and grass preprocessed dried, ground forage samples. Laboratory instrument scans were replicated with a reduced spectral range to match the range of each portable instrument. Alfalfa tended to have better calibration and test-set statistics than the grasses in this study. Portable instruments evaluated did not scan the upper portion of the spectral range (1652–2498 mn), which had some negative impact on forage calibration. The SCiO instrument scanned a very narrow range (740–1070 nm); and, although it had comparable results to the laboratory instrument constrained to same wavelength range, most major peaks related to forage quality traits are outside this range. The expensive bench top laboratory instrument had the best performance as expected, while the very inexpensive SCiO portable instrument had much greater error of prediction to the point that, for most traits, the prediction would not be considered reliable. However, the AuroraNir and the NIR-S-G1 digital light processing portable NIR may provide an alternative to expensive bench top laboratory equipment while still providing sufficiently accurate predictions. Therefore, some portable instruments have the potential to be used for on-farm analysis of wet, coarsely chopped forage, and this option must be evaluated in future studies.
Why it matches plant phenotyping methods乾燥・粉砕したアルファルファおよび草試料の飼料品質特性を推定する携帯型NIR装置を比較・検証しており、植物試料から形質を取得するセンシング手法の性能評価が中心である。
abstractThis study compared the performance of prediction across three different portable instruments compared to a bench top laboratory NIR instrument
Key message We describe a simple method to view meiotic cells in whole anthers from a range of plants. The method retains spatial organisation and enables simultaneous analysis of many meiotic cells. Understanding the process of male meiosis in flowering plants, and the role of genes involved in this process, offers potential for plant breeding, such as through increasing the level of genetic variation or the manipulation of ploidy levels in the gametes. A key to the characterisation of meiotic gene function and meiosis in non-model crop plants, is the analysis of cells undergoing meiosis, a task made difficult by the inaccessible nature of these cells. Here, we describe a simple and rapid method to analyse plant male meiosis in intact anthers in a range of plant species. This method allows analysis of numerous cells undergoing meiosis and, as meiotic cells stay within the anther, it retains information of the three-dimensional organisation and the location of organelles in meiotic cells. We show that the technique provides information on male meiosis by looking at the synchrony of meiotic progression between and within locules, and comparing wildtype and mutant plants through the chromosome separation stages in Arabidopsis thaliana. Additionally, we demonstrate that the protocol can be adopted to other plants with different floral morphology using Medicago truncatula as an example with small floral buds and the non-model plant kiwifruit (Actinidia chinensis) with larger buds and anthers.
Why it matches plant phenotyping methods植物の雄性減数分裂細胞を無傷の葯内で可視化・解析する画像取得法の開発が中心であり、細胞状態、進行同期、三次元配置を測定するため、植物フェノタイピング手法として適格です。
abstractWe describe a simple method to view meiotic cells in whole anthers from a range of plants.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50-70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green-Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.
Why it matches plant phenotyping methodsUAV画像、完全自動プロット分割、特徴量抽出、別圃場での予測検証を用いてアルファルファ biomass を推定する手法が研究の中心であり、植物表現型取得・推定法として適格。
abstractusing UAV images with fully automatic field plot segmentation for high-throughput phenotyping
Highlights The canopy height distributions from photogrammetry were mostly Gaussian distributions. The mean canopy height and standard deviation from photogrammetry can be used to predict yield and nutritive values. Including average field maturity and pest pressures improves the models to R2 values of around 0.8. The best predictive model types were generally Gaussian random processes. Abstract. Alfalfa producers would be able to manage their crop production practices better if they knew the distribution of yield and nutritive values of the alfalfa growing throughout their fields. Unmanned aerial vehicles (UAVs) equipped with cameras and photogrammetry techniques provide methods to quickly capture the plant canopy structure at field scale. The goal of this study was to determine how to use the point clouds produced by the photogrammetry process to estimate the yield and nutritive value of alfalfa throughout its growth cycle. During the 2017 growing season, weekly measurements were taken of 1 m2 quadrats (~20 per week, 325 total) in a field of alfalfa managed for forage production. Measurements in each quadrat included manual measurements of maximum and average height, weed presence, disease damage, insect damage, maturity level, stand plant density, and many images of the quadrat from a UAV. After processing to remove outliers, the canopy heights from the photogrammetry point clouds were largely Gaussian distributions. Models were developed using supervised machine learning to estimate yield and nutritive values, including acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP), with different numbers of predictor (input) variables. Simple models with two predictor variables were only based on the mean and standard deviation of the heights of the photogrammetry point cloud. The models with three predictor variables added average field maturity level. Finally, the models with six predictor variables added weed presence, insect damage, and disease damage. A linear regression with all interaction terms was found to be the best type of model for predicting yield with six variables. For all other outputs and numbers of predictor variables, a Gaussian random process (GRP) model was best. The models improved with additional predictor variables, so the six-variable models were best able to predict yield and nutritive value. The R2 values for the six-variable models for predicting yield, ADF, NDF, and CP were 0.81, 0.81, 0.78, and 0.79, respectively. Keywords: Alfalfa, Machine learning, Nutritive value, Photogrammetry, Unmanned aerial vehicle, Yield.
Why it matches plant phenotyping methodsUAVフォトグラメトリの点群から作物キャノピー構造を抽出し、アルファルファの収量・栄養価を推定する手法とモデルを開発・評価しており、表現型取得が研究の中心である。
abstractUnmanned aerial vehicles (UAVs) equipped with cameras and photogrammetry techniques provide methods to quickly capture the plant canopy structure at field scale.
Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.
Why it matches plant phenotyping methodsアルファルファ種子の形態・スペクトル形質を取得し、マルチスペクトル画像と多変量解析で品種識別する手法が研究の中心であり、植物表現型計測法として適格。
abstractthe potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed.
Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An R 2 value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.
Why it matches plant phenotyping methods新規DVDIによる作物の洪水被害状態（損傷・損失）の推定手法を開発し、NDVIおよび収量損失で検証しており、植物状態の取得・推定が中心である。
abstractThe main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 9 Sept 2026
Abstract Background Winter freezing temperature impacts alfalfa (Medicago sativa L.) persistence and seasonal yield and can lead to the death of the plant. Understanding the genetic mechanisms of alfalfa freezing tolerance (FT) using high-throughput phenotyping and genotyping is crucial to select suitable germplasm and develop winter-hardy cultivars. Several clones of an alfalfa F1 mapping population (3010 x CW 1010) were phenotyped for FT using a cold chamber. The population was genotyped with SNP markers identified using genotyping by sequencing (GBS) and the QTL associated with FT were mapped on the parent-specific linkage maps. The ultimate goal is to develop non-dormant and winter-hardy alfalfa cultivars that can produce extended growth in the areas where winters are often mild. Results Alfalfa FT screening method optimized in this experiment comprises three major steps; clone preparation, acclimation, and freezing test. Twenty clones of each genotype were tested, where 10 samples were treated with freezing temperature, and 10 were used as controls. A moderate positive correlation (r ~ 0.36, P < 0.01) was observed between indoor FT and field-based winter hardiness (WH), suggesting that the indoor FT test is useful as an indirect selection method for winter hardiness of alfalfa germplasm. We detected a total of 20 QTL for four traits; visual rating-based FT, percentage survival (PS), treated to control regrowth ratio (RR), and treated to control biomass ratio (BR). Some QTL overlapped with WH QTL reported previously, suggesting a genetic relationship between FT and WH. Some favorable QTL from the winter-hardy parent (3010) potentially represented the genic region of a cold tolerance gene, the c-repeat binding factor (CBF). These QTL were located on the terminal end of chromosome 6 which is considered a location of the CBF homologs in alfalfa.Conclusions The indoor freezing tolerance selection method reported here is valuable for alfalfa breeders to accelerate breeding cycles through indirect selection. The QTL and associated markers add to the genomic resources needed by the alfalfa research community and can be used in marker-assisted selection (MAS) for alfalfa cold tolerance improvement.
Why it matches plant phenotyping methodsアルファルファの凍結耐性を測定する室内スクリーニング法を最適化し、圃場の冬季耐寒性との相関で有用性を検証しているため、植物表現型取得法が研究の中心である。
abstractAlfalfa FT screening method optimized in this experiment comprises three major steps; clone preparation, acclimation, and freezing test.
Background Physical dormancy (hard seed) occurs in most species of Leguminosae family and has great consequences not only for ecological adaptation but also for agricultural practice of these species. A rapid, nondestructive and on-site screening method to detect hard seed within species is fundamental important for maintaining seed vigor and germplasm storage as well as understanding seed adaptation to various environment. In this study, the potential of multispectral imaging with object-wise multivariate image analysis was evaluated as a way to identify hard and soft seeds in Acacia seyal , Galega orientulis , Glycyrrhiza glabra , Medicago sativa , Melilotus officinalis , and Thermopsis lanceolata . Principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) methods were applied to classify hard and soft seeds according to their morphological features and spectral traits. Results The performance of discrimination model via multispectral imaging analysis was varied with species. For M. officinalis , M. sativa , and G. orientulis , an excellent classification could be achieved in an independent validation data set. LDA model had the best calibration and validation abilities with the accuracy up to 90% for M. sativa . SVM got excellent seed discrimination results with classification accuracy of 91.67% and 87.5% for M. officinalis and G. orientulis , respectively. However, both LDA and SVM model failed to discriminate hard and soft seeds in A. seyal , G. glabra , and T. lanceolate . Conclusions Multispectral imaging together with multivariate analysis could be a promising technique to identify single hard seed in some legume species with high efficiency. More legume species with physical dormancy need to be studied in future research to extend the use of multispectral imaging techniques.
Why it matches plant phenotyping methodsマルチスペクトル画像と多変量解析により、個々の種子の硬実・軟実状態を非破壊推定する方法の評価・検証が主題であり、植物表現型取得法が中心である。
abstractthe potential of multispectral imaging with object-wise multivariate image analysis was evaluated as a way to identify hard and soft seeds
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting the genetic architecture of these traits toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users’ experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crops, including alfalfa, canola, lentil, wheat, chickpea, and soybean. GridFree was implemented in Python with a friendly graphical user interface to allow users to easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website ( https://zzlab.net/GridFree ).
Why it matches plant phenotyping methods穀粒画像からの分割・計数・形質測定を目的とするソフトウェアを開発し、既存ソフトウェアとの性能比較も行っており、植物フェノタイピング手法が研究の中心である。
abstractIn this study, we developed a software package, named GridFree, to overcome these challenges.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: Monitoring the timing of seedling emergence and early development via high-throughput phenotyping with computer vision is a challenging topic of high interest in plant science. While most studies focus on the measurements of leaf area index or detection of specific events such as emergence, little attention has been put on the identification of kinetics of events of early seedling development on a seed to seed basis. RESULT: Imaging systems screened the whole seedling growth process from the top view. Precise annotation of emergence out of the soil, cotyledon opening, and appearance of first leaf was conducted. This annotated data set served to train deep neural networks. Various strategies to incorporate in neural networks, the prior knowledge of the order of the developmental stages were investigated. Best results were obtained with a deep neural network followed with a long short term memory cell, which achieves more than 90% accuracy of correct detection. CONCLUSION: This work provides a full pipeline of image processing and machine learning to classify three stages of plant growth plus soil on the different accessions of two species of red clover and alfalfa but which could easily be extended to other crops and other stages of development.
Why it matches plant phenotyping methods画像取得と深層学習により、発芽・子葉開き・第一葉出現という幼植物の発達段階を自動分類するフルパイプラインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractMonitoring the timing of seedling emergence and early development via high-throughput phenotyping with computer vision
Organic farmers, who rely on legumes as an external nitrogen (N) source, need a fast and easy on-the-go measurement technique to determine harvestable biomass and the amount of fixed N (NFix) for numerous farm management decisions. Especially clover- and lucerne-grass mixtures play an important role in the organic crop rotation under temperate European climate conditions. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) are new promising tools for a non-destructive assessment of crop and grassland traits on large and remote areas. One disadvantage of multispectral information and derived vegetations indices is, that both ignore spatial relationships of pixels to each other in the image. This gap can be filled by texture features from a grey level co-occurrence matrix. The aim of this multi-temporal field study was to provide aboveground biomass and NFix estimation models for two legume-grass mixtures through a whole vegetation period based on UAV multispectral information. The prediction models covered different proportions of legumes (0-100% legumes) to represent the variable conditions in practical farming. Furthermore, the study compared prediction models with and without the inclusion of texture features. As multispectral data usually suffers from multicollinearity, two machine learning algorithms, Partial Least Square and Random Forest (RF) regression, were used. The results showed, that biomass prediction accuracy for the whole dataset as well as for crop-specific models were substantially improved by the inclusion of texture features. The best model was generated for the whole dataset by RF with an rRMSE of 10%. For NFix prediction accuracy of the best model was based on RF including texture (rRMSEP = 18%), which was not consistent with crop specific models.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とテクスチャ特徴量を用いて、植物群落の地上部バイオマスと窒素固定量を推定するモデルを開発・比較しており、形質取得手法が研究の中心である。
abstractThe aim of this multi-temporal field study was to provide aboveground biomass and NFix estimation models for two legume-grass mixtures through a whole vegetation period based on UAV multispectral information.
The North American Alfalfa Improvement Conference (NAAIC) is the curator of a series of standard tests that allow validation of any alfalfa (Medicago sativa L.) cultivar's pest resistance and agronomic attributes. One NAAIC test is for grazing tolerance, an important trait in the tropics. Our objectives were to validate the NAAIC grazing tolerance test and to describe physiological characteristics related to alfalfa grazing persistence in a tropical environment. Treatments were combinations of five cultivars (varying in fall dormancy and grazing tolerance) and two grazing methods, applied from March 2001 to January 2002 (330 d) in Piracicaba, Brazil. Plots were grazed every 7 d (mimicking continuous stocking) or every 28/42 d in spring–summer/autumn‐winter (graze‐rest scheme of rotational stocking), always to 7‐cm stubble. ‘Alfagraze’ (fall dormant, tolerant) and ‘ABT 805’ (fall nondormant, tolerant) had greater survival under both grazing environments compared with the nontolerant checks ‘Pioneer 5432’ (fall dormant, intolerant) and ‘CUF101’ (fall nondormant, intolerant). The widely used Brazilian cultivar ‘Crioula’ showed poor survival, suggesting that adaption did not affect the test, and Crioula is now described as grazing intolerant. Rotational stocking favored persistence of even the tolerant checks. This was related to maintenance of carbohydrate reserve pools, which declined by 80% in roots and by 85% in plant crowns when plots were grazed every 7 d, but only by 46 and 59%, respectively, under the 28/42 d grazing regime. The NAAIC standard test should be useful to screen cultivars for grazing tolerance in tropical or temperate environments. The use of rotational stocking is best for even grazing‐tolerant cultivars.
Why it matches plant phenotyping methodsアルファルファの放牧耐性という植物形質を評価するNAAIC標準試験の妥当性検証が研究目的の中心であり、単なる生理・農業測定ではない。
abstractOur objectives were to validate the NAAIC grazing tolerance test and to describe physiological characteristics related to alfalfa grazing persistence in a tropical environment.
Alfalfa / lucerneLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
In plant-fungus phenotyping, determining fungal hyphal and plant root lengths by digital image analysis can reduce labour and increase data reproducibility. However, the degree of software sophistication is often prohibitive and manual measuring is still used, despite being very time-consuming. We developed the HyLength tool for measuring the lengths of hyphae and roots in in vivo and in vitro systems. The HyLength was successfully validated against manual measures of roots and fungal hyphae obtained from all systems. Compared with manual methods, the HyLength underestimated Medicago sativa roots in the in vivo system and Rhizophagus irregularis hyphae in the in vitro system by about 12 cm per m and allowed to save about 1 h for a single experimental unit. As regards hyphae of R. irregularis in the in vivo system, the HyLength overestimated the length by about 21 cm per m compared with manual measures, but time saving was up to 20.5 h per single experimental unit. Finally, with hyphae of Aspergillus oryzae, the underestimation was about 8 cm per m with a time saving of about 10 min for a single germinating spore. By benchmarking the HyLength against the AnaMorf plugin of the ImageJ/Fiji, we found that the HyLength performed better for dense fungal hyphae, also strongly reducing the measuring time. The HyLength can allow measuring the length over a whole experimental unit, eliminating the error due to sub-area selection by the user and allowing processing a high number of samples. Therefore, we propose the HyLength as a useful freeware tool for measuring fungal hyphae of dense mycelia.
Why it matches plant phenotyping methods根長を含む植物・菌類の長さを画像から抽出するHyLengthツールの開発と手動測定・既存プラグインとの検証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。
abstractWe developed the HyLength tool for measuring the lengths of hyphae and roots in in vivo and in vitro systems.
Background Fine root production is one of the least well understood components of the carbon cycle in terrestrial ecosystems. Minirhizotrons allow accurate and non-destructive sampling of fine root production. Small and large scale studies across a range of ecosystems are needed to have baseline data on fine root production and further assess the impact of global change upon it; however, the expense and the low adaptability of minirhizotrons prevent such data collection, in worldwide distributed sampling schemes, in low-income countries and in some ecosystems (e.g. tropical forested wetlands). Results We present EnRoot, a narrow minirhizotron of 25 mm diameter, that is partially 3D printable. EnRoot is inexpensive (€150), easy to construct (no prior knowledge required) and adapted to a range of ecosystems including tropical forested wetlands (e.g. mangroves, peatlands). We tested EnRoot's accuracy and precision for measuring fine root length and diameter, and it yielded Lin's concordance correlation coefficient values of 0.95 for root diameter and 0.92 for length. As a proof of concept, we tested EnRoot in a mesocosm study, and in the field in a tropical mangrove. EnRoot proved its capacity to capture the development of roots of a legume ( Medicago sativa ) and a mangrove species (seedlings of Rhizophora mangle ) in laboratory mesocosms. EnRoot's field installation was possible in the root-dense tropical mangrove because its narrow diameter allowed it to be installed between larger roots and because it is fully waterproof. EnRoot compares favourably with commercial minirhizotrons, and can image roots as small as 56 µm. Conclusion EnRoot removes barriers to the extensive use of minirhizotrons by being low-cost, easy to construct and adapted to a wide range of ecosystem. It opens the doors to worldwide distributed minirhizotron studies across an extended range of ecosystems with the potential to fill knowledge gaps surrounding fine root production.
Why it matches plant phenotyping methods根の画像取得用ミニライゾトロンを開発し、細根長・直径の精度と再現性を検証しており、植物表現型取得法が研究の中心である。
abstractWe present EnRoot, a narrow minirhizotron of 25 mm diameter, that is partially 3D printable.
In the present study a new luminescent dye 3-N-(2-pyrrolidinylacetamido)benzanthrone (AZR) was synthesized. Spectroscopic measurements of the novel benzanthrone 3-aminoderivative were performed in seven organic solvents showing strong fluorescence. The capability of the prepared dye for visualization has been tested on flax, red clover and alfalfa to determinate the embryo in plant callus tissue cultures. Callus cells were stained with AZR and further analysed utilizing confocal laser scanning fluorescence microscopy. Performed experiments show high visualization effectiveness of newly synthesized fluorescent dye AZR that is efficient in fast and relatively inexpensive diagnostics of callus embryos that are problematic due to in vitro culture specificity.
Why it matches plant phenotyping methods新規蛍光色素と共焦点顕微鏡を用いて植物カルス中の胚を可視化・診断する手法が研究の中心であり、植物の発生状態を取得する画像ベースのフェノタイピング手法に該当する。
abstractThe capability of the prepared dye for visualization has been tested on flax, red clover and alfalfa to determinate the embryo in plant callus tissue cultures.
An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R2 = 0.59–0.81) as the ruler height measurements (R2 = 0.58–0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types.
Why it matches plant phenotyping methodsUAV RGB画像とSfMによるキャノピー高抽出を用いて乾物収量を予測する手法を開発・比較評価しており、植物形質の取得・推定方法が研究の中心である。
abstractPhotogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH).
Core Ideas Remote sensing technologies enable rapid and nondestructive phenotyping of plants in the field. Biomass estimate accuracy from images and sensors was similar to yields harvested manually. Biomass yield variation identified the most productive accessions under low‐input conditions. High‐throughput phenotyping technologies enable monitoring plant growth and development nondestructively throughout the growing season. Crop losses associated with abiotic and biotic factors threaten the sustainability of crops used for feed, fiber, and fuel. The process to develop improved cultivars with enhanced crop yields includes phenotyping hundreds of plants under a target set of conditions. However, the manual collection of data is often laborious and time consuming. Strategies that integrate remote sensing technologies including unmanned aerial vehicles (UAVs) and sensors mounted on “phenomobiles” can streamline phenotyping efforts in plant breeding programs. The objectives of this study were to compare the phenotypic data collected from the field using UAVs, sensors, and manual approaches and to assess the potential of high‐throughput approaches to rank the productivity of alfalfa (Medicago sativa L.) accessions growing in the field. Phenotypic data were collected from 100 alfalfa accessions established and grown under low‐input conditions. Specific traits evaluated using both UAVs and sensors mounted on a phenomobile prior to physically harvesting the biomass during four harvests include biomass yield, plant height, normalized difference vegetation index, leaf area index, and ground coverage. The results from both the UAV and the sensors were highly correlated to the physical measurements obtained for the multiple traits evaluated. Therefore, field‐based high‐throughput phenotyping strategies represent a viable option for efficiently screening germplasm in the field to increase phenotyping efficiencies in plant breeding programs.
Why it matches plant phenotyping methodsUAVとセンサー搭載フェノモバイルによるアルファルファ形質取得を、手作業測定と比較・検証する高スループット表現型解析研究であり、方法が中心的です。
abstractThe objectives of this study were to compare the phenotypic data collected from the field using UAVs, sensors, and manual approaches and to assess the potential of high‐throughput approaches to rank the productivity of alfalfa (Medicago sativa L.) accessions growing in the field.
Root system architecture (RSA) is critical for plant growth, which is influenced by several edaphic, environmental, genetic and biotic factors including beneficial and pathogenic microbes. Studying root architecture and the dynamic changes that occur during a plants lifespan, especially for perennial crops growing over multiple growing seasons, is still a challenge because of the nature of their growing environment in soil. We describe the utility of an imaging platform called RhizoVision Crown to study RSA of alfalfa, a perennial forage crop affected by Phymatotrichopsis Root Rot (PRR) disease. Phymatotrichopsis omnivora is the causal agent of PRR disease that reduces alfalfa stand longevity. During the lifetime of the stand, PRR disease rings enlarge and the field can be categorized into three zones based upon plant status: asymptomatic, disease front and survivor. To study root architectural changes associated with PRR, a four-year old 25.6-hectare alfalfa stand infested with PRR was selected at the Red River Farm, Burneyville, OK during October 2017. Line transect sampling was conducted from four actively growing PRR disease rings. At each disease ring, six line transects were positioned spanning 15 m on either side of the disease front with one alfalfa root sampled at every 3 m interval. Each alfalfa root was imaged with the RhizoVision Crown platform using a backlight and a high-resolution monochrome CMOS camera enabling preservation of the natural root architectural integrity. The platforms image analysis software, RhizoVision Analyzer, automatically segmented images, skeletonized, and extracted a suite of features. Data indicated that the survivor plants compensated for damage or loss to the taproot through the development of more lateral and crown roots, and that a suite of multivariate features could be used to automatically classify roots as from survivor or asymptomatic zones. Root growth is a dynamic process adapting to ever changing interactions among various phytobiome components, by utilizing a low-cost, efficient and high-throughput Rhizo-Vision Crown platform we showed quantification of these changes occurring in a mature perennial forage crop.
Why it matches plant phenotyping methodsRhizoVision CrownとRhizoVision Analyzerによる根系形態の画像取得・自動解析が研究の中心であり、根系構造特徴の抽出と分類を実施しているため、植物フェノタイピング手法として含める。
abstractWe describe the utility of an imaging platform called RhizoVision Crown to study RSA of alfalfa
Reproduction assets foundThe paper's Data Availability section explicitly deposits the root crown images and R statistical analysis code on Zenodo (doi 10.5281/zenodo.2172832), a paper-specific public asset containing the phenotyping images and analysis code.Dataset · publicical analysis code generated from this study are available on
382
Zenodo.
383
York, Larry M., Young, Carolyn A., Mattupalli, Chakradhar, & Seethepalli, Anand. (2018). Images
384
and statistical analysis of alfalfa root crowns from inside and outside disease rings caused by
385
cotton root rot (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2172832
386
387
ACKNOWLEDGEMENTS. We thank the Noble Research Institute, LLC for funding this project.
388
389
LITERATURE CITED.
390
Arias, M. M. D., Leandro, L. F., and Munkvold, G. P. 2013. Aggressiveness of Fusarium species and
391
impact of root infection on growth and yield of soybeans. Phytopathology 103:822-832.
392
Arif, M., FlOpen asset ↗Zenodo · 10.5281/zenodo.2172832pdf-raw-page:18 lines:1-49Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Oct 2018Ying yong sheng tai xue bao = The journal of applied ecologyCited by 3 · OpenAlex ↗
The rapid and accurate measurement of LAI is of great importance for the research of ecological processes. Photos from typical land use types in the northern Loess Plateau, including Caragana, Salix, alfalfa, wild grass, soybean and maize, were measured by digital hemispherical photography (DHP). Meanwhile, photos were daily taken by video camera with fisheye lens and the pictures were analyzed by image processing software to obtain the dynamics of LAI in soybean, maize and Caragana fields. The results showed that a linear correlation existed between the LAI measured by DHP and LAI-2200. The coefficient of determination (R 2 ) was 0.85 (P<0.05) and root mean square error (RMSE) was 0.256. The key parameters of professional software were affected by the local solar radiation. When the downward lens was used, the green index was the key parameter which increased with the increase of solar radiation. However, the brightness index decreased with the increase of solar radiation when the lens was upward. Through the adjustment of the key parameters, the results of LAI of maize, soybean, and Caragana were consistent with the LAI-2200 results, well reflecting LAI dynamics during the plant growth. The downward lens in Caragana field was better. The fisheye camera could be used for monitoring the dynamic LAI of different vegetations.
Why it matches plant phenotyping methods魚眼カメラと画像処理によるLAI（葉面積指数）測定・動態監視法を開発・調整し、DHPおよびLAI-2200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。
abstractphotos were daily taken by video camera with fisheye lens and the pictures were analyzed by image processing software to obtain the dynamics of LAI
Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is routinely used to determine the spatial distributions of various biomolecules in tissues. Recently, there has been an increased interest in creating higher resolution images using sources with more focused beams. One such source, an atmospheric pressure (AP) MALDI source from MassTech, has a laser capable of reaching spatial resolutions of 10 μm. Here, the AP-MALDI source coupled with a Q Exactive HF Orbitrap platform is compared to the commercial MALDI LTQ Orbitrap XL system using Medicago truncatula root nodules. AP-MALDI parameters, such as the S-lens value, capillary temperature, and spray voltage, were optimized on the Q Exactive-HF platform for optimal detection of plant metabolites. The performance of the two systems was evaluated for sensitivity, spatial resolution, and overall ability to detect plant metabolites. The commercial MALDI LTQ Orbitrap XL was superior regarding the number of compounds detected, as at least two times more m/z were detected compared to the AP-MALDI system. However, although the AP-MALDI source requires a spatial resolution higher than 10 μm to get the best signal, the spatial resolution at 30 μm is still superior compared to the 75 μm spatial resolution achieved on the MALDI platform. The AP-MALDI system was also used to investigate the metabolites present in M. truncatula roots and root nodules under high salt and low salt conditions. A discriminative analysis with SCiLS software revealed m/z ions specific to the control and salt conditions. This analysis revealed 44 m/z ions present at relatively higher abundances in the control samples, and 77 m/z enriched in the salt samples. Liquid chromatography-tandem MS was performed to determine the putative molecular identities of some of the mass ions enriched in each sample, including, asparagine, adenosine, and nicotianamine in the control samples, and arginine and soyasaponin I in the salt treated samples.
Why it matches plant phenotyping methods植物組織中の代謝物を空間的に可視化するMALDI-MSIプラットフォームを比較・最適化し、感度と空間分解能を評価しているため、植物フェノタイピング手法が中心です。
abstractHere, the AP-MALDI source coupled with a Q Exactive HF Orbitrap platform is compared to the commercial MALDI LTQ Orbitrap XL system using Medicago truncatula root nodules.
In-field estimation of alfalfa (Medicago sativa L.) yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as model covariates. An alternative GDU calculation (GDUALT) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDUALT increased prediction accuracy for all response variables by 9–17%. Models using a common set of seven wavebands, combined with GDUALT, explained 81–90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-h in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with air temperature information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.
Why it matches plant phenotyping methodsリモートセンシングとLiDARによるキャノピー計測から、アルファルファの収量・栄養価を推定する手法を開発・評価しており、表現型取得と推定が研究の中心である。
abstractRemote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale.
Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R 2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R 2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.
Why it matches plant phenotyping methods作物キャノピー水分含量という植物生理状態を、ハイパースペクトルデータから推定する新規指標を開発・検証しており、フェノタイピング手法が中心である。
abstractWe propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale.
CORE IDEAS: Predictive equations can help producers determine when to harvest their forage fields.Equations developed in New York State could be used to predict aNDFom, ADFom, RFV, and RFQ in Quebec.Equations developed in New York State cannot be used to predict NDFdom in Quebec. Predictive equations of pre‐harvest nutritive attributes of alfalfa (Medicago sativa L.)–grass mixtures using simple plant or climate data were developed in New York State for the spring growth, but they must be validated before being used outside their development area. Our objective was to validate these predictive equations for their use in Quebec, Canada. Samples (n = 679) of alfalfa–grass mixtures were collected during spring growth at three sites for 2 consecutive years and analyzed for several nutritive attributes. Alfalfa maximum height, the most mature stage of development of alfalfa, growing degree days, grass proportion, and grass maximum height were also measured and used as input in several existing predictive equations. Predicted values were then compared with laboratory‐determined values using several validation statistics. The most promising predictive equations of neutral detergent fiber (NDF) and acid detergent fiber concentrations, relative feed value, and relative forage quality had coefficients of determination (r²) of the linear regression between observed and predicted values between 0.74 and 0.81, and an index of agreement (d) between 0.87 and 0.93. Several equations were, however, significantly biased as indicated by slopes and intercepts of the regressions. The NDF digestibility was not predicted satisfactorily with the New York State equations. Among all equations evaluated, an equation for NDF concentration has the most potential for use to predict the spring growth pre‐harvest nutritive value of alfalfa–grass mixtures in Quebec.
Why it matches plant phenotyping methodsアルファルファ・イネ科混播の収穫前栄養価を予測する既存方程式を、植物形質・気候データと実測値で体系的に検証しており、予測手法の検証が研究の中心である。
abstractOur objective was to validate these predictive equations for their use in Quebec, Canada.
For all crop species, an accurate, quick, and simple method for determination of mean development stage of plants has a key role for scientific and practical use. The investigation was performed to validate a modified methodology for determination of mean stage of development in alfalfa (Medicago sativa L.). The modified methodology, mean stage by fresh weight (MSFW), assumes that mean stage by weight (MSW) could be determined by fresh, and not only by dry weight, as was required by the original methodology. A total of 198 alfalfa samples were collected in 2010 and 2011. Sampling completely covered three growth cycles: spring growth and first and second regrowths. The correlation (r) between MSW and MSFW was >0.99. In 92% of samples, absolute deviation between MSW and MSFW was <0.15 on a scale between 0 and 9. The equation for prediction of MSFW based on measured MSW, MSFWₚᵣₑdᵢcₜₑd = 0.9808 × MSW, was developed with the data from the first year (n = 141) and tested with second-year samples (n = 57). Prediction error, expressed by root mean squared deviation (RMSD), was 0.045, while components of mean squared deviation (MSD), such as squared bias (SB), nonunity slope (NU), and lack of correlation (LC), were 8.12 × 10⁻⁴, 5.45 × 10⁻⁴, and 6.67 × 10⁻⁴, respectively. Values close to zero in all three MSD components show that validation line had a ≈ 0, b ≈ 1, and r² ≈ 1. As a simpler methodology, MSFW gives an opportunity to be more applicable in practice with all of the benefits of the MSW methodology.
Why it matches plant phenotyping methodsアルファルファの発育段階という植物形質を測定する改良手法を開発し、既存法との相関・予測誤差で検証しており、測定手法が研究の中心である。
abstractThe investigation was performed to validate a modified methodology for determination of mean stage of development in alfalfa (Medicago sativa L.).
Alfalfa (Medicago sativa L.) is the most commonly grown forage crop due to its better quality characteristics and high adaptability in China. However, there was 20%-80% hard seeds in alfalfa which could not be identified easily from non hard seeds which would cause the loss of seed utilization value and plant production. This experiment was designed for 121 samples of alfalfa. Seeds were collected according to different regions, harvested year and varieties. 31 samples were artificial matched as hard rates ranging from 20% to 80% to establish a model for hard seed rate by near infrared spectroscopy (NIRS) with Partial Least Square (PLS). The objective of this study was to establish a model and to estimate the efficiency of NIRS for determining hard rate of alfalfa seeds. The results showed that the correlation coefficient (R2(cal)) of calibration model was 0.981 6, root mean square error of cross validation (RMSECV) was 5.32, and the ratio of prediction to deviation (RPD) was 3.58. The forecast model in this experiment presented the satisfied precision. The proposed method using NIRS technology is feasible for identification and classification of hard seed in alfalfa. A new method, as nondestructive testing of hard seed rate, was provided to theoretical basis for fast nondestructive detection of hard seed rates in alfalfa.
Why it matches plant phenotyping methodsアルファルファ種子の硬実率という植物形質を、NIRSとPLSで非破壊推定するモデルを構築・評価しており、形質取得手法が研究の中心である。
abstractto establish a model and to estimate the efficiency of NIRS for determining hard rate of alfalfa seeds
The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b899. The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part.
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ルーサンの生体 phytomass を推定し、地上測定との相関で手法性能を比較・検証しているため、植物形質取得が中心的である。
abstractwe quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360.
Background A screening method for elicitor and priming agents does not only allow detecting new bioactive substances, it can also be used to understand structure–function relationships of known agents by testing different derivatives of them. This can not only provide new lead compounds for the development of novel, more environment-benign, bio-based agro-chemicals, it may eventually also lead to a better understanding of defense mechanisms in plants. Reactive oxygen species (ROS) are sensitive indicators of these mechanisms but current assay formats are not suitable for multiplex screening, in particularly not in the case of monocot systems. Results Here we describe continuous monitoring of ROS in 96-well microtiter plates using the chemiluminescent probe L012, a luminol derivative producing chemiluminescence when oxidised by ROS like hydrogen peroxide, superoxide, or hydroxyl radical that can thus be used as an indicator for these ROS. We were able to measure ROS in both monocot ( Oryza sativa ) and dicot ( Medicago truncatula ) cell suspension cultures and record dose dependencies for the carbohydrate elicitors and priming agents ulvan and chitosan at low substrate concentrations (0.3–2.5 µg/ml). The method was optimized in terms of cell density, L012 concentration, and pre-incubation time. In contrast to the single peak observed using a cuvette luminometer, the improved method revealed a double burst in both cell systems during the 90-min measuring period, probably due to the detection of multiple ROS rather than only H 2 O 2 . Conclusion We provide a medium throughput screening method for monocot and dicot suspension-cultured cells that enables direct comparison of monocot and dicot plant systems regarding their reaction to different signaling molecules. Electronic supplementary material The online version of this article (doi:10.1186/s13007-016-0110-1) contains supplementary material, which is available to authorized users.
Why it matches plant phenotyping methods植物細胞培養における酸化バーストを測定するアッセイの改良が主題であり、植物の生理状態を取得する測定法の開発に該当する。
titleAn improved microtiter plate assay to monitor the oxidative burst in monocot and dicot plant cell suspension cultures