Key message Our scalable two-step method estimates genotypic performance and genetic parameters from ranking data, producing reliable results comparable to quantitative analyses, enabling the integration of ranking data into breeding pipelines. Plant breeding research has chiefly relied on on-station experiments to evaluate varietal performance. Nevertheless, these trials often fail to represent on-farm growing conditions and farmers' preferences, potentially leading to poorly defined breeding targets. Recent work has demonstrated the potential of using on-farm verification trials combined with ranking data to support farmers in evaluating varieties while providing information that is representative of farmers' needs. Despite this potential, scalable methods for quantifying genetic differences and assessing the strength of the genetic signal in such trials remain limited. Here, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters. The approach follows a common strategy in quantitative genetics, in which parameters are estimated from tables of genotypic means and their variances. In our framework, these estimates are obtained from Thurstonian and/or Plackett-Luce models, which treat rankings as observations of an underlying continuous trait associated with genotypic performance. Using simulated data, we showed that genotypic mean estimates derived from ranking analyses are linearly related to those obtained from quantitative trait analyses and that their variances adequately capture estimation uncertainty. We further demonstrated that incorporating these estimates and their variances into a second-step mixed-effects model yields accurate estimates of variance components. Analyses of groundnut, maize, and sweetpotato datasets confirmed the applicability of the approach and showed that ranking data can provide reliable estimates of genetic parameters. We argue that this framework can be scaled to obtain genotypic performance estimates from multi-trial on-farm data.
Why it matches plant phenotyping methods作物品種の遺伝型性能をランキングデータから推定する統計的方法そのものが研究の中心であり、育種に再利用可能な植物性能の推定手法を開発・検証している。
abstractHere, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters.
Reproduction assets foundThe paper's Data availability statement provides public access to the observed groundnut and sweetpotato ranking/trial datasets (Zenodo 17112492), the authors' R functions and simulation workflow (GitHub hdorado/tricot-ranking-analysis, archived Zenodo 17942919), and supplementary material with methods and figures (ZenDataset · publicThe observed data for groundnut and sweetpotato used in this study are publicly available and can be accessed at: Global multi-crop agricultural trial data supported by citizen science, Zenodo [ https://doi.org/10.5281/zenodo.17112492 ]Open asset ↗Zenodo · 10.5281/zenodo.17112492lines:205-225Code · publicThe R functions and simulation workflow used in this study are publicly available at: - Source code available from: [ https://github.com/hdorado/tricot-ranking-analysis ]Open asset ↗GitHub · hdorado/tricot-ranking-analysislines:205-225Code · public- Archived software available from: [ https://doi.org/10.5281/zenodo.17942919 ] - License: [MIT License]Open asset ↗Zenodo · 10.5281/zenodo.17942919lines:205-225Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract In peanut ( Arachis hypogaea L.), plant stand establishment, seedling vigor, and canopy growth are key determinants of crop performance; yet traditional ground‐based assessment methods can be destructive, labor‐intensive, and limited in throughput. This study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut. Six runner‐type peanut cultivars were evaluated in 2024 and seven in 2025, with each cultivar represented by two seed size classes (small and large), to generate variation in these traits. Within‐row vegetation discontinuity‐based plant stand ratings for estimating plant stand count ( R 2 = 0.81–0.90), together with canopy coverage for assessing seedling biomass ( R 2 = 0.77–0.82) and light interception ( R 2 = 0.96–0.98), were the best‐performing vegetation metrics. These vegetation metrics provided similar or greater cultivar separation compared with ground‐based measurements. In contrast, several vegetation indices exhibited strong correlations with ground‐based measurements but provided inconsistent cultivar rankings and statistical groupings. MS imagery outperformed RGB imagery for plant stand and seedling biomass assessment. Overall, these results demonstrate that UAV‐derived canopy metrics provide reliable, high‐throughput tools for early‐ to mid‐season crop assessment and offer scalable alternatives to traditional ground‐based approaches for agronomic, crop physiological, and plant breeding research.
Why it matches plant phenotyping methodsUAV画像から植物体の出芽・苗勢・バイオマス・光 interception を推定する植 phenotyping 手法を開発・評価しており、取得指標の性能検証が研究の中心です。
abstractThis study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut.
Abstract Sclerotinia minor (causing Sclerotinia blight) is a devastating pathogen in peanut production, with severe outbreaks causing up to 50% yield loss due to rapid oxalic acid (OA) accumulation. Early diagnosis is challenging because canopy-level symptoms typically emerge only after infection is well established, while early cues are subtle, stem-localized, and nonspecific; in contrast, molecular assays are time- and resource-intensive. Despite established mechanistic links between oxalate accumulation and disease progression, so far, there are no sensors developed or tested for detecting Sclerotinia blight in peanut plants. This paper reports a low-cost, lithography-free, and label-free electrochemical sensor for metabolite-targeted, presymptomatic monitoring of S. minor in peanut plants based on clear mechanistic links between oxalate accumulation and disease progression. The sensor platform comprises 3D-printed resin substrates with platinum (Pt) electrodes and a nanostructured reduced graphene oxide (rGO)−chitosan interface functionalized with an oxaloacetic acid (OAA) interfacial layer. Using ferri/ferrocyanide as a redox probe, the sensor exhibited a linear calibration to oxalate (prepared from OA) from 0.05 µM to 1 mM (R2 = 0.99), with a sensitivity of 6.37 µA/decade, limit of detection of 17.6 nM, and excellent coefficient of variation of 0.93−3.32% across standards (n = 4). In real plant trials, stem sap from S. minor-inoculated peanut plants produced significantly elevated voltammetric responses relative to healthy and Nothopassalora personata controls as early as five days post-inoculation (dpi), enabling longitudinal monitoring through 20 dpi (p
Why it matches plant phenotyping methods植物体内のシュウ酸を指標に、ピーナッツの病害を早期・無症状段階で検出する電気化学センサーを開発し、校正性能と実植物での識別・経時モニタリングを検証している。病害状態の取得法が研究の中心である。
abstractThis paper reports a low-cost, lithography-free, and label-free electrochemical sensor for metabolite-targeted, presymptomatic monitoring of S. minor in peanut plants
Plant diseases are crucial for improving crop yield and ensuring sustainable agricultural practices, particularly for staple crops such as groundnut and paddy leaf. However, existing methods often suffer from limited feature discrimination, inadequate attention to disease-affected regions, and reduced performance under real-world conditions. To address these limitations, this research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification. Initially, the input leaf images are enhanced using Bilateral Filtering (BF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise and improve contrast. Subsequently, HSV color space segmentation is employed to precisely isolate disease-affected regions. The proposed Dual Attention Network (DuAtNet) integrates channel and spatial attention mechanisms within a ConvNeXt backbone to capture discriminative disease-specific features. An efficient Fuzzy Extreme Learning Machine (FELM) classifier is then utilized for final categorization into Healthy, Leaf Spot, Bacterial Wilt, and Leaf Blight classes. The effectiveness of the GOPI-NET is evaluated using precision, recall, specificity, accuracy, and F1-score. The experimental results demonstrate that GOPI-NET achieves an overall accuracy of 98.32%. The GOPI-NET improves classification accuracy by 1.29%, 1.40%, and 2.23% compared to GLDICCNN, DNN-CSA, and LeafNet respectively.
Why it matches plant phenotyping methods植物葉画像から病斑領域を抽出し、病害状態を分類する深層学習手法が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractthis research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification.
Seeds storage-year have a significant impact on high-oleic peanut seed vigor and quality. Therefore, it is essential to identify different storage-year seeds for planting, direct consumption, industrial processing, and marketing. In this study, hyperspectral images with 616 spectral bands (from visible light to near-infrared) were employed to classify different storage-year peanut seeds. To extract characteristic information for classification, we proposed a hybrid band selection (HBS) method based on the successive projection algorithm (SPA) by fusing the color-sensitive bands and moisture-sensitive bands. Then three classifiers, support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbors (KNN), were selected for storage-year classification. The experimental results demonstrated that the features extracted with the HBS method can obtain higher classification accuracy than other methods'. Specifically, the HBS-ELM model achieved the highest classification performance, with accuracy of 90.22%.
Why it matches plant phenotyping methodsハイパースペクトル画像から落花生種子の貯蔵年を推定するバンド選択法を開発・比較しており、種子の状態・品質の表現型抽出が研究の中心である。
abstracthyperspectral images with 616 spectral bands (from visible light to near-infrared) were employed to classify different storage-year peanut seeds.
Three-dimensional (3D) reconstruction based on structure from motion and multi-view stereo (SfM-MVS) is increasingly used in plant phenotyping, but its performance is influenced by crop architecture, viewpoint configuration, and image preprocessing. For compact crops such as peanut, dense branching and severe within-canopy occlusion make reliable reconstruction challenging. This study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging. A total of 10,800 RGB images from 30 plants were used to compare representative implementations of incremental and global SfM-MVS pipelines in terms of geometric quality, phenotypic accuracy, and processing efficiency. At the 1° baseline, the tested global pipeline implementation reduced the root mean square reprojection error (RMSRE), point-density coefficient of variation (CV), vertical root mean square error (VRMSE), and the 95th percentile of the absolute point-cloud distance values (P95) by 15.05%, 14.08%, 39.87%, and 33.33%, respectively, and increased average phenotypic accuracy from 96.08% to 97.37%, compared with the tested incremental pipeline implementation. In contrast, the tested incremental implementation showed a lower voxel void ratio and shorter processing time. In both pipelines, increasing the angular interval reduced processing time but also reduced geometric stability, internal voxel filling, and phenotypic accuracy. In the present dataset, angular intervals of 3°–5° provided a favourable balance between reconstruction accuracy and efficiency. Cropping reduced peripheral redundancy, whereas cropping combined with background removal produced the best overall results, with the lowest reprojection error and the highest phenotypic accuracy. These results provide practical guidance for selecting reconstruction pipeline, viewpoint configuration, and preprocessing strategy in close-range indoor 3D phenotyping of peanut plants and crops with similar canopy architectures.
Why it matches plant phenotyping methods落花生の3D表現型取得について、SfM-MVSパイプライン、視点間隔、画像前処理を比較・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging.
Abstract Agriculture is a highly dynamic area that sustains global food security, and crop health plays a critical role in obtaining agricultural output. Peanuts, commonly known as groundnuts, hold a significant value due to their nutritional importance and economic benefits in many regions. However, it faces numerous challenges due to its vulnerability to a range of diseases that have a severe impact on both quality and yield. Disease detection methods based on traditional techniques were time-consuming, vulnerable to human mistakes and Labor-intensive. To handle these issues, we suggest a cutting-edge technique for the detection of peanut diseases using deep learning models. In this research work, we initially proposed some pre-trained deep learning models such as EfficientNet-B0, EfficientNet-B4, and ConvNeXt-Base, but none of them produced state-of-the-art results. To address this challenge, we leveraged the ResNet-50 Architecture using transfer learning enriched with the combination of advanced methodologies such as Data augmentation, OneCycleLR Learning rate scheduler, and weighted loss. This approach dramatically boosted the performance across various metrics, demonstrating the strength of transfer learning to handle imbalanced data and refine generalization. Deep learning models were trained with 1720 publicly available images of the dataset. The dataset includes both healthy and diseased images of groundnut leaves, including Alternaria leaf spot, Rosette, Rust and Leaf spot (early and late). The dataset was partitioned into 80% training images, 10% test images, and 10% value accuracy images. Our proposed methodology outperformed on the same dataset and gave better results than the older ones on the same dataset. The earlier same dataset had an accuracy rate of 96.51%. Our experiments showed that the suggested technique achieved a 97.28% accuracy rate and outperformed the existing state-of-the-art models. Results from these experiments demonstrate the merit of the proposed model for application in real-world agricultural problems, establishing a new baseline for detecting groundnut leaf diseases and establishing the feasibility of AI-based solutions for enhancing transferable sustainable agricultural practices.
Why it matches plant phenotyping methods落花生葉の病徴を画像から分類する深層学習手法を開発・評価しており、植物の病害状態推定が研究の中心であるため含める。
abstractwe suggest a cutting-edge technique for the detection of peanut diseases using deep learning models.
Nodule color and morphology are key readouts of legume symbiotic performance. However, long-term preservation of post-excavation nodules with intact morphology, color, and microbial cleanliness remains a major challenge. This study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization. A comprehensive evaluation was subsequently established, incorporating composite morphological score (0-5), color difference (ΔE) and its piecewise slope over time ([Formula: see text]), and visible contamination grade (0-3). Peanut and soybean nodules from multiple regions and cultivars were tracked for 24 months under five preservation methods: TAPP, FormalinCu, TAPP-Resin, Resin, and AirDry. TAPP showed the best overall preservation, with composite morphological scores of 4.65 ± 0.14 for peanut and 4.63 ± 0.22 for soybean at 24 months, and no visible mold. Color change slowed over time: [Formula: see text] decreased from 1.83 to 1.10 ΔE·month - 1 during 0-1 month to 0.16 ΔE·month - 1 during 12-24 months, yielding final ΔE values of 10.53 ± 1.88 and 10.32 ± 1.93, respectively. Notably, TAPP pretreatment markedly improved resin-embedded samples, demonstrating scalability and flexible deployment. In addition, this study further proposes a stage-wise workflow that integrates on-site pre-fixation, long-distance transport, and long-term storage to enable cross-regional circulation and collaborative phenomics of oxidation-prone, dehydration-sensitive nodules. Together, this work establishes a standardized, traceable workflow to preserve and benchmark legume root nodule phenotypes, supporting cross-laboratory comparability and longitudinal cross-source analyses.
Why it matches plant phenotyping methodsマメ科根粒の形態・色・汚染状態という植物表現型を長期保存し、定量評価・比較する手法と標準化ワークフローが研究の中心であるため。
abstractThis study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization.
Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R2 of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming.
Why it matches plant phenotyping methodsUAV RGB画像と機械学習による作物生理形質・キャノピー状態・葉緑素の推定手法を中心に評価しており、植物表現型取得・推定が実質的な貢献である。
abstractThis study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation.
Determining the optimal harvest time is among the most critical economic decisions for peanut ( Arachis hypogaea L.) growers, directly influencing yield, quality, and market value. Unlike many other crops, peanuts are indeterminate, continuing to flower and produce pods throughout their life cycle. As a result, pod development and maturation are asynchronous, making harvest timing particularly challenging. Conventional maturity estimation techniques, including the hull scrape method, pod blasting, and visual maturity profiling, are invasive, labor-intensive, time-consuming, and spatially limited. Moreover, differences in cultivar maturity rates and agroclimatic conditions exacerbate inconsistencies in maturity prediction. These challenges highlight the urgent need for scalable, objective, and data-driven methods to support growers in achieving optimal harvest outcomes. This review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation. It aims to identify the limitations of conventional techniques and explore the integration of advanced sensing technologies, artificial intelligence (AI), and geospatial analytics to enhance precision and scalability in peanut maturity assessment and harvest decision-making. This review examines traditional destructive techniques such as the hull scrape method and pod blasting, followed by emerging non-invasive methods employing proximal and remote sensing platforms. Applications of vegetation indices, multispectral and hyperspectral imaging, and AI-based data analytics are discussed in the context of maturity prediction. Additionally, the potential of multimodal remote sensing data fusion and digital frameworks integrating spatial big data analytics, centralized data management, and cloud-based graphical interfaces is explored as a pathway toward end-to-end decision-support systems. Recent advances in non-invasive sensing and AI-assisted modeling have demonstrated significant improvements in scalability, precision, and automation compared with traditional manual approaches. However, their effectiveness remains constrained by the limited inclusion of agroclimatic, phenological, and cultivar-specific variables. Furthermore, the translation of model outputs into actionable, field-level harvest decisions is still underdeveloped, underscoring the need for integrated, user-centric digital infrastructure. Achieving a robust and transferable digital peanut maturity estimation system will require comprehensive ground-truth data across cultivars, regions, and growing seasons. Multidisciplinary collaborations among agronomists, data scientists, growers, and technology providers will be essential for developing practical, field-ready solutions. Integrating AI, multimodal sensing, and geospatial analytics holds immense potential to transform peanut maturity estimation. Such innovations promise to enhance harvest precision, economic returns, and sustainability while reducing manual effort and uncertainty, ultimately improving the efficiency and quality of life for peanut producers worldwide.
Why it matches plant phenotyping methodsピーナッツ莢の成熟度という植物状態を対象に、従来法と非侵襲センシング、画像解析、AIによる推定手法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation.
Introduction Manual detection of peanut leaf diseases is plagued by a significant time lag, which frequently enables diseases to develop from isolated, sporadic outbreaks into large-scale epidemics. This delay ultimately leads to substantial regional yield losses in peanut production. Consequently, the precise detection capability of intelligent monitoring equipment is essential for mitigating the risk of large-scale peanut disease outbreaks. Detection algorithms serve as the core technology underpinning intelligent detection devices, highlighting the need for optimized, high-performance algorithms to address this challenge. Methods This study takes the YOLOv12 algorithm as the baseline model and proposes an improved model named YOLO-SDA. To enhance the model's performance while reducing its computational burden, three key modules-StarNet, DySample, and A2C2f_SCSA-are integrated into the original YOLOv12 framework. The integration of these modules is designed to optimize feature extraction, sampling efficiency, and feature fusion, thereby improving the model's detection accuracy and reducing its resource consumption. Results Experimental results demonstrate that the proposed YOLO-SDA model outperforms the baseline YOLOv12 model in both performance and efficiency. Specifically, compared with YOLOv12, the YOLO-SDA model achieves a 44% reduction in parameters, a 38.5% decrease in GFLOPs (giga floating-point operations per second), and a 43.6% reduction in model size. Simultaneously, the model's detection precision and mAP@0.5-0.95 (mean average precision at intersection over union thresholds from 0.5 to 0.95) are improved by 2.0% and 2.5%, respectively. Discussion The superior performance of the YOLO-SDA model confirms the effectiveness of integrating StarNet, DySample, and A2C2f_SCSA modules into the YOLOv12 framework. The significant reduction in parameters, GFLOPs, and model size addresses the practical challenge of deploying intelligent detection algorithms on resource-constrained equipment, making it more suitable for on-site peanut leaf disease monitoring. The concurrent improvement in detection precision and mAP@0.5-0.95 ensures that the model can accurately identify peanut leaf diseases even in complex field environments, providing a reliable technical support for preventing large-scale disease outbreaks and safeguarding peanut yield.
Why it matches plant phenotyping methods落花生葉の病害状態を画像から推定するYOLOベースの検出モデルを開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study takes the YOLOv12 algorithm as the baseline model and proposes an improved model named YOLO-SDA.
Why it matches plant phenotyping methods発芽油種子の油含量・分布という植物器官の状態を、LF-NMR/MRIで非破壊測定する手法を開発・検証しており、表現型取得法が研究の中心である。
abstractThis study evaluated the feasibility of using low-field nuclear magnetic resonance (LF-NMR) coupled with magnetic resonance imaging (MRI) as a non-destructive approach for monitoring oil changes in germinating oilseeds.
Peanut (Arachis hypogaea L.), a major legume crop valued for its high oil content, displays complex genotypic-phenotypic interactions shaped by environmental influences, yet these relationships remain poorly understood. We present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization. Using over 6500 pods collected across China, we identify a geographically distinct morphological signature and demonstrate accurate cultivar discrimination. This scalable approach establishes the foundation for a Large Geometric Model capable of predicting phenotypic traits and accelerating precision agriculture. Our pipeline offers a transformative tool for peanut breeding and sustainable crop improvement.
Why it matches plant phenotyping methodsデジタル顕微鏡・スマートフォン画像と多様体学習を統合し、ピーナッツ莢の形態を大規模に取得・解析する高スループット表現型解析フレームワークが中心である。
abstractWe present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization.
Reproduction assets foundThe authors state that the peanut pod image dataset, extracted phenotypic trait data, and the Orange Data Mining workflow (.ows) used for analysis are publicly available in their GitHub repository.Dataset · publicThe image dataset of peanut pods analyzed in this study and the extracted phenotypic trait data are publicly available in the GitHub repository: https://github.com/pengwengkung/Complex-geometry-peanut .Open asset ↗pengwengkung/Complex-geometry-peanutlines:169-192Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2026Computers and Electronics in Agriculture.
Empirical and physical models are widely used for monitoring equivalent water thickness (EWT) to adjust plant moisture management. However, model transferability to different times and locations, and insufficient training data remain the two key challenges of field spectroscopy analysis. Therefore, this study aims to construct a hybrid model, which combines the physical models optimized by Wasserstein Generative Adversarial Nets (WGAN) and empirical models for performing hyperparameter searches (the process of finding optimal model settings) to monitor the peanut EWT. Specifically, we develop a large spectral dataset consisting of field-measured data which including 246 peanut varieties in five peanut farms across China and synthetic datasets generated from the physical models optimized by WGAN. Furthermore, the PWLEH was constructed by hyperparameter tuning and pre-training which using synthetic datasets, and then fine-tuned by modular training with field data of peanut canopy water content. Comparing the model constructed with field data (R² = 0.5618, mean squared error (MSE) = 0.0725) and PROSAIL (a widely used canopy radiative transfer model) (R² = 0.7105, MSE = 0.0473), PWLEH achieved high accuracy in predicting peanut water content (R² = 0.7650, MSE = 0.0519). Unlike pure data-driven approaches, the new hybrid model incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. This study demonstrates the potential of applying an optimized PROSAIL, hyperparameter search and modular training to improve the accuracy and transferability of the EWT prediction model, providing a new approach for sustainable agricultural management.
Why it matches plant phenotyping methods落花生のキャノピー分光データから等価含水厚（EWT）を推定するハイブリッドモデルを開発・評価しており、植物水分形質の取得・推定手法が中心である。
abstractTherefore, this study aims to construct a hybrid model, which combines the physical models optimized by Wasserstein Generative Adversarial Nets (WGAN) and empirical models for performing hyperparameter searches
Nodule formation and their involvement in biological nitrogen fixation are critical features of leguminous plants, with phenotypic characteristics closely linked to plant growth and nitrogen fixation efficiency. However, the phenotypic analysis of root nodules remains technically challenging due to their small size, weak texture, dense clustering, and occlusion. To address these challenges, this study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions. In addition, A hybrid small-object detection method, SCO-YOLOv8s, was proposed, integrating Swin Transformer and CBAM attention mechanisms into the YOLOv8s framework to enhance global and local feature representation. Furthermore, an Otsu segmentation-based post-processing module was incorporated to validate and refine detection results based on geometric features, boundary sharpness, and image entropy, effectively reducing false positives and enhancing robustness in complex scenes. Using this integrated approach, over 3375 nodules were identified from a single plant sample in under 1 min, with extracted phenotypic features such as diameter, color, and texture. A total of 10,879 high-quality annotated images were collected from 39 peanut varieties across 14 provinces and 31 soybean varieties across 12 provinces in China, addressing the current lack of large-scale datasets for legume root nodules. The SCO-YOLOv8s model achieved a precision of 97.29 %, a mAP of 98.23 %, and an overall identification accuracy of 95.83 %. This integrated approach provides a practical and scalable solution for high-throughput nodule phenotyping, and may contribute to a deeper understanding of nitrogen fixation mechanisms.
Why it matches plant phenotyping methods根粒の画像取得・検出・セグメンテーション・形質抽出を統合した高スループット表現型解析手法を開発し、精度評価と大規模データセット構築も行っているため、方法が研究の中心である。
abstractthis study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions.
Accurate monitoring of crop canopy chlorophyll content (CCC) is of significance for precision agriculture and sustainable development. However, current remote sensing retrieval of crop chlorophyll is often limited to a single growth stage, overlooking dynamic changes in canopy structure from seedling to maturity, prevents the dynamic monitoring of peanut CCC throughout the entire stage. Therefore, this study acquired UAV multispectral imagery at four key growth stages of peanut and extracted a feature set including 54 VIs, 32 TFs, and 64 DWT features. The LassoLarsCVFS algorithm was employed to identify the optimal feature subset for each growth stage. Subsequently, retrieval models were developed for using Lasso, ENR, SVR, and MLP. The results showed that the combination of VIs and the MLP model achieved the best performance at the seedling stage (R² = 0.588). At the flowering and pinning stage, the VIs+TFs feature set combined with the SVR model yielded the optimal result (R² = 0.676). At the podding stage, the VIs+DWT feature set with the SVR model performed best (R² = 0.733). At the maturity stage, the VIs+TFs feature set paired with the MLP model produced the highest accuracy (R² = 0.828). Compared with the global model, the proposed stage-specific strategy demonstrated superior performance, achieving improvements in R² of 36.7 %, 0.7 %, 1.8 %, and 6.6 % at the seedling, flowering and pinning, podding, and maturity stages, respectively. These findings indicate that dynamic feature selection and model optimization tailored to different growth stages are crucial for retrieving crop chlorophyll content throughout the entire growth stage. The stage-wise optimization strategy, by integrating spectral and multi-scale structural information, offers an effective approach to overcome the challenges of remote sensing retrieval in dense canopies and provides reliable support for precision nutrient management in peanut and other crops.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から落花生キャノピーのクロロフィル含量を推定する特徴抽出・機械学習モデルを開発し、成長段階別に性能比較しており、表現型取得手法が中心である。
abstractcurrent remote sensing retrieval of crop chlorophyll is often limited to a single growth stage
Serotonin, widely recognized as a mammalian pineal hormone, is also present in plants, yet its in vivo dynamics and physiological roles remain poorly understood due to the absence of real-time sensing tools. Herein, we report nitrogen-doped carbon quantum dot (N-CQD) nanosensors (∼5 nm; quantum yield 36%) for the selective detection and visualization of serotonin in plant systems. The sensing mechanism involves static-dominated mixed fluorescence quenching accompanied by a blue shift, corroborated by UV-Vis spectral changes, Stern-Volmer analysis, and fluorescence lifetime decay. The nanosensor exhibits a low detection limit of 0.391 µM and a linear response range of 4.74-75 µM. Using Arachis hypogaea seedlings as a model, stronger and more consistent serotonin-dependent fluorescence responses were observed compared with those in other plant species, enabling reliable in vivo monitoring. Real-time sensing revealed a condition-dependent regulatory role for serotonin, including growth inhibition under non-stress conditions and growth enhancement under stress, indicating a dual function in stress adaptation. Fluorescence microscopy further confirmed the intracellular association of serotonin with N-CQDs, providing direct visual evidence of its localization. This work establishes a nanosensor-based platform for real-time detection of serotonin in plants and advances understanding of serotonin-mediated signalling in plant growth and stress responses.
Why it matches plant phenotyping methods植物内セロトニンをリアルタイム検出・可視化するナノセンサーを開発し、植物の生理状態やストレス応答の測定に実質的に用いているため、方法中心の研究として含める。
abstractwe report nitrogen-doped carbon quantum dot (N-CQD) nanosensors (∼5 nm; quantum yield 36%) for the selective detection and visualization of serotonin in plant systems.
Leaf area index (LAI) is a key indicator of crop growth and development and is widely used in both agricultural research and precision farming applications. PlanetScope imagery is generally used for monitoring crop growth due to its high revisit frequency, broad spatial coverage, and cost-effective access to consistent high-resolution multispectral data. Therefore, we developed regression models to estimate peanut LAI, combining PlanetScope spectral bands and vegetation indices (VIs). Specifically, we compared the performance of random forest (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR) regression algorithms for peanut LAI estimation. Our results showed that most of the VIs exhibited strong relationships with LAI. Thirteen VIs were individually evaluated for estimating LAI using the aforementioned algorithms, and our results showed that the best single predictors of LAI are: TSAVI (RF: R 2 = 0.87, RMSE = 0.83 m 2 /m 2 , RRMSE = 24.20%; XGBoost: R 2 = 0.77, RMSE = 0.95 m 2 /m 2 , RRMSE = 27.96%); and RTVIcore (PLSR: R 2 = 0.68, RMSE = 1.12 m 2 /m 2 , RRMSE = 32.88%). The top six ranked VIs were used to calibrate the RF, XGBoost, and PLSR algorithms. Model validation indicated that RF achieved the highest accuracy (R 2 = 0.844, RMSE = 0.858 m 2 /m 2 , RRMSE = 25.17%), followed by XGBoost (R 2 = 0.808, RMSE = 0.92 m 2 /m 2 , RRMSE = 26.99%), whereas PLSR showed comparatively lower performance (R 2 = 0.76, RMSE = 0.983 m 2 /m 2 , RRMSE = 28.85%). Further results showed that PlanetScope VIs provided superior model accuracy in estimating peanut LAI compared to the use of spectral bands alone. Additionally, integrating spectral bands with VIs reduced LAI estimation accuracy, underscoring the importance of selecting predictor variables in ensuring optimal model performance. Overall, the presented results are significant for future crop monitoring using RF to reduce overreliance on multiple models for peanut LAI estimation.
Why it matches plant phenotyping methodsPlanetScopeスペクトルデータと複数の回帰手法により、ピーナッツのLAIという植物形質を推定し、モデル性能を比較・検証している。形質取得・推定手法が研究の中心である。
abstractwe developed regression models to estimate peanut LAI, combining PlanetScope spectral bands and vegetation indices (VIs).
This study aimed to establish an efficient screening method for identifying biocontrol strains effective against soil-borne pathogens of plants. To achieve this objective, we developed a quantitatively-controlled potted plant testing (QC-PPT) system by optimizing cultivation devices, growth substrates, pathogen inoculation methods, and quantitative evaluation of root infection. This design effectively confines the roots and facilitates uniform pathogen infection. The optimal inoculation timing was determined to be between the 10th and 16th day of growth. Furthermore, a substrate composed of vermiculite with 4% organic compost was identified as ideal, supporting vigorous peanut growth while allowing sufficient pathogen infection required for reliable biocontrol evaluation. Using this system, seven strains with strong antagonistic effects against Sclerotium rolfsii were isolated from White-spotted Flower Chafer (WSFC, Protaetia brevitarsis) larval frass. In vitro assays showed that strain X13 inhibited Sclerotium rolfsii growth by 44.56%, while strain X15 performed more excellently in the QC-PPT system: it increased peanut root dry weight by 32.8% and reduced root lesion area by 51.2% compared to the control group. Genomic sequencing data revealed that the superior strain X15 possesses the most diverse set of secondary metabolite biosynthetic gene clusters. Collectively, strain X15 is a promising candidate for biopesticide development, and the QC-PPT system we established can be extended to other crop-soil-borne pathogen systems. This study not only provides an effective biocontrol resource for peanut southern blight but also facilitates the development of sustainable disease management strategies in agriculture.
Why it matches plant phenotyping methods植物根の感染状態を定量評価するQC-PPTシステムを開発・最適化し、根病斑面積などの植物病徴を測定する方法が研究の中心であるため。
abstractwe developed a quantitatively-controlled potted plant testing (QC-PPT) system by optimizing cultivation devices, growth substrates, pathogen inoculation methods, and quantitative evaluation of root infection.
In response to the demand for real-time monitoring of the nitrogen nutritional status of peanut populations, this paper proposes a real-time monitoring system for the nitrogen nutritional status of peanut populations based on the YOLOv11 framework and spectral attention module. Traditional nitrogen detection methods have problems such as low efficiency and difficulty in achieving population-scale monitoring, while crop phenotyping technology based on computer vision faces challenges such as small leaf targets, severe occlusion, easy confusion of nitrogen deficiency symptoms, and difficulty in deploying deep learning models on mobile terminals. This study improves the YOLOv11 model, introduces the ASF (Attentional Scale Fusion) module and the DySample dynamic upsampling mechanism, enhances the model’s perception and feature expression capabilities for multi-scale targets, and effectively improves the monitoring accuracy and robustness of the nitrogen nutritional status of peanut populations. Experimental results show that the ADS-YOLO model performs well in evaluation indicators such as accuracy, recall, and mean average precision (mAP), providing technical support for precision fertilization of peanuts.
Why it matches plant phenotyping methodsピーナッツ集団の窒素栄養状態という植物状態を画像から推定するYOLO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。
abstractthis paper proposes a real-time monitoring system for the nitrogen nutritional status of peanut populations based on the YOLOv11 framework and spectral attention module.
Accurate assessment of plant photosynthetic responses under drought and high-temperature stress is critical for understanding crop resilience. Chlorophyll content is a key indicator of photosynthetic efficiency, but conventional methods are destructive and time-consuming. Here, we developed a non-destructive detection system that captures Red (R), Green (G), and Blue (B) values from peanut (Arachis hypogaea L.) leaves and predicts chlorophyll content using machine learning. We optimized sensor distance (3–6 mm) and found 3 mm provided the most reliable RGB readings. Among Bayesian ridge and linear regression models, linear regression performed best (coefficient of determination R2 = 0.93), yielding a robust predictive formula: chlorophyll = [−0.0308 × [2 × G − R − B] + 4.386]. Integration of this formula into the detection system enabled real-time estimation of chlorophyll as a proxy for photosynthetic status and stress response. By enabling low-cost, non-destructive and rapid chlorophyll monitoring, this framework can help support resource-efficient crop monitoring and high-throughput screening for stress-resilient cultivars, with potential relevance to sustainable production in water-limited environments.
Why it matches plant phenotyping methodsピーナッツ葉のRGBセンサーと機械学習によるクロロフィル含量の非破壊推定システムを開発・最適化・検証しており、植物形質取得手法が研究の中心である。
abstractHere, we developed a non-destructive detection system that captures Red (R), Green (G), and Blue (B) values from peanut (Arachis hypogaea L.) leaves and predicts chlorophyll content using machine learning.
The early detection of plant diseases is an indispensable task to improve crop yields and production quality. Crop disease observations by experienced pathologists are difficult and might take a long time. Therefore, deep learning (DL) techniques have been utilized to present an automated detection technique that could accurately and timely detect plant diseases. Several DL models in the literature were proposed, but no paper conducted a comparative study between those models to determine which of them was the best alternative for this task. Therefore, twenty-one DL models are compared in this review paper to show which of them could achieve better classification accuracy when applied to detect plant diseases. Three publicly available datasets, namely PlantVillage, Tomato Leaves, and Groundnut Plant Leaf, are used to assess the performance of those models under five different performance metrics, such as accuracy, precision, recall, F1-score, and area under curve (AUC). The extensive experiments conducted in the same environments under the same number of epochs and batch size for all models show that EfficientNetB0 is the best for both PlantVillage and Tomato Leaves datasets, with a classification accuracy of around 99% and 98%, respectively, and ResNet152 is the best for the Groundnut Plant Leaf dataset, with a classification accuracy of 99.7%.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を21モデルで比較・評価しており、植物病害状態の表現型推定と技術ベンチマークが中心である。
abstractTherefore, twenty-one DL models are compared in this review paper to show which of them could achieve better classification accuracy when applied to detect plant diseases.
Peanut leaf diseases have a major impact on peanut yield and quality. Timely, rapid, and accurate early diagnosis and control of peanut leaf diseases are key to ensuring high quality and yield of peanuts. This work focuses on the early diagnosis of peanut diseases and pests and conducts systematic research on the hardware system for imaging and spectral sensing of peanut plant leaves, as well as the software for deep learning classification algorithms. First, we designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases, including scab, scorch spot, and anthracnose. Second, we constructed a convolutional neural network to extract the basic features of spectral images. Third, an adaptive channel attention mechanism is introduced to update the weights of different channels. Fourth, a sparse second-order attention mechanism driving network is constructed to enhance the discriminative ability of deep feature information. Finally, the classification is completed utilizing the Softmax classifier. The experimental results demonstrate that the spectral image information improves the robustness of deep learning models to data transformation and achieves a high-precision classification score of 98.45% for asymptomatic peanut leaf diseases. Compared to traditional optical devices and software algorithms, the proposed multispectral imaging system and deep learning algorithm significantly improve detection ability and classification accuracy, which can assist botanists in making more accurate diagnoses of peanut leaf diseases.
Why it matches plant phenotyping methodsピーナッツ葉の病害状態を対象に、マルチスペクトル・蛍光画像取得システムと深層学習分類手法を開発・評価しており、植物病害表現型の取得・判定が中心的な技術貢献である。
abstractwe designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases
Three-dimensional phenotyping technology is paramount in the field of peanut breeding and cultivation. The intricate topological structure of plants substantially complicates the development of effective peanut phenotyping technologies. In this study, we present the development of a point-cloud-based pipeline for three-dimensional phenotypic analysis of peanut plants. An efficient multi-view image acquisition system and three-dimensional reconstruction techniques were employed to generate point clouds of peanut plants. A dataset comprising 188 labelled samples of peanut point clouds was constructed for the development of semantic and leaf-instance segmentation models based on the transformer architecture. The segmentation accuracy of these models surpassed that of the conventional general segmentation techniques for plant point clouds. Based on the results of the segmentation, 11 three-dimensional phenotypic traits were automatically calculated at both the plant and leaf scales. Among these, five phenotypic traits, including plant height and leaf length, exhibited a mean absolute percentage error (MAPE) of less than 0.12 compared to the measured values. In addition, the Jensen-Shannon divergence (JS divergence) between the probability distributions of the three leaf phenotypic traits and their corresponding measured values was below 0.1. The three-dimensional phenotypic analysis pipeline developed in this study exhibited satisfactory generalisation capabilities, thereby offering an efficacious and expeditious high-throughput phenotyping analysis instrument for the intelligent breeding and cultivation of peanuts.
Why it matches plant phenotyping methodsピーナッツの3D画像取得、点群再構成、分割、形質自動算出を統合したフェノタイピングパイプラインの開発と精度検証が中心である。
abstractwe present the development of a point-cloud-based pipeline for three-dimensional phenotypic analysis of peanut plants
Peanuts rank as the seventh-largest crop in the United States with a farm value exceeding $1 billion. Conventional peanut yield estimation methods involve digging, harvesting, transporting, and weighing, which are labor-intensive and inefficient for large-scale research operations. This inefficiency is particularly pronounced in peanut breeding, which requires precise pod yield estimations of each plot in order to compare genetic potential for yield to select new, high-performing breeding lines. To improve efficiency and throughput for accelerating genetic improvement, we proposed an automated robotic imaging system to predict peanut yields in the field after digging and inversion of plots. A workflow was developed to estimate yield accurately across different genotypes by counting the pods from stitched plot-scale images. After the robotic scanning in the field, the sequential images of each peanut plot were stitched together using the Local Feature Transformer (LoFTR)-based feature matching and estimated translation between adjusted images, which avoided replicated pod counting in overlapped image regions. Additionally, the Real-Time Detection Transformer (RT-DETR) was customized for pod detection by integrating partial convolution into a lightweight ResNet-18 backbone and refining the up-sampling and down-sampling modules in cross-scale feature fusion. The customized detector achieved a mean Average Precision (mAP50) of 89.3% and a mAP95 of 55.0%, improving by 3.3% and 5.9% over the original RT-DETR model with lighter weights and less computation. To determine the number of pods within the stitched plot-scale image, a sliding window-based method was used to divide it into smaller patches to improve the accuracy of pod detection. In a case study of a total of 68 plots across 19 genotypes in a peanut breeding yield trial, the result presented a correlation (R 2 =0.47) between the yield and predicted pod count, better than the structure-from-motion (SfM) method. The yield ranking among different genotypes using image prediction achieved an average consistency of 84.8% with manual measurement. When the yield difference between two genotypes exceeded 12%, the consistency surpassed 90%. Overall, our robotic plot-scale peanut yield estimation workflow showed promise to replace the human measurement process, reducing the time and labor required for yield determination and improving the efficiency of peanut breeding.
Why it matches plant phenotyping methodsロボット撮像と画像解析により圃場区画の落花生莢数・収量を推定するワークフローを開発し、検出精度や手動測定との整合性を検証しており、フェノタイピング手法が中心である。
abstractwe proposed an automated robotic imaging system to predict peanut yields in the field after digging and inversion of plots.
Detecting leaf diseases is crucial for ensuring crop health and boosting agricultural productivity. An advanced deep learning-based framework is introduced for cassava and groundnut leaf disease detection, incorporating a suite of innovative techniques to enhance classification accuracy. Real-time leaf images are collected from various agricultural environments to capture a wide range of conditions. To improve image quality and segmentation precision, the Contextual Image Enhancement Wiener Filter (CIEWF) is employed for effective noise reduction. Data augmentation is performed using a Generative Adversarial Network (GAN), increasing dataset diversity and improving model generalization. A novel Region of Interest-based Multi-Dimensional Attention Network (ROI-MDAN) is developed to identify and segment critical disease-affected areas within the leaves. For robust feature extraction, the MSFNet-CAM model is proposed, leveraging parallel multi-scale features and incorporating Coordinate Attention to enhance feature fusion and improve classification performance. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) is used to interpret the model's decision-making process by highlighting the influential regions contributing to disease classification. Experimental results validate the effectiveness of the proposed approach, setting a new benchmark for AI-assisted plant disease diagnosis.
Why it matches plant phenotyping methods葉画像から病徴部位を分割・分類し、植物病害状態を推定する画像解析手法の開発が中心であるため、植物フェノタイピング手法として含める。
abstractA novel Region of Interest-based Multi-Dimensional Attention Network (ROI-MDAN) is developed to identify and segment critical disease-affected areas within the leaves.
As an important economic crop, peanut is frequently affected by leaf diseases during its growth period, which severely threaten its yield and quality. Therefore, early and accurate disease detection is critical. However, existing lightweight deep learning methods often struggle to balance model size, real-time detection accuracy, and edge device deployment, limiting their widespread application in large-scale agricultural scenarios. This study proposes a lightweight real-time detection model, YOLO-PLNet, designed for edge deployment. The model is based on YOLO11n, with lightweight improvements to the backbone network and Neck structure. It introduces a Lightweight Attention-Enhanced (LAE) convolution module to reduce computational overhead and incorporates a Channel-Spatial Attention Mechanism (CBAM) to enhance feature representation for small lesions and edge-blurred targets. Additionally, the detection head adopts an Asymptotic Feature Pyramid Network (AFPN), leveraging staged cross-level fusion to improve detection performance across multiple scales. These improvements significantly enhance the detection accuracy of peanut leaf diseases under complex backgrounds while improving adaptability for edge device deployment. Experimental results show that YOLO-PLNet achieves a parameter count, computational complexity, and model size of 2.13M, 5.4G, and 4.51MB, respectively, representing reductions of 18.07%, 16.92%, and 15.70% compared to the baseline YOLO11n. The mAP@0.5 and mAP@0.5:0.95 reach 98.1% and 94.7%, respectively, improving by 1.4% and 1.7% over YOLO11n. When deployed on the Jetson Orin NX platform with real-time video input from a CSI camera, the model achieves a latency of 19.1 ms and 28.2 FPS at FP16 precision. At INT8 precision, latency is reduced to 11.8 ms, with real-time detection speed increasing to 41.3 FPS, while GPU usage and power consumption are significantly reduced with only a slight decrease in detection accuracy. In summary, YOLO-PLNet achieves high detection accuracy and robust edge deployment performance, providing an efficient and feasible solution for intelligent monitoring of multiple categories of peanut leaf diseases.
Why it matches plant phenotyping methods落花生葉の病徴を画像から検出する軽量モデルを開発し、精度・計算量・エッジ実装性能を評価しており、植物状態の取得手法が中心である。
abstractThis study proposes a lightweight real-time detection model, YOLO-PLNet, designed for edge deployment.
Background: The purpose of this project is to evaluate the prospective use of drone-based remote sensing for assessing legume crop development and predicting their yields. Traditional agricultural procedures often fall short in delivering timely and accurate monitoring, necessitating the adoption of innovative techniques. Methods: The study considers vegetative indicators such as NDVI, GNDVI and canopy cover to track the growth of three legume crops-peanut, soybean and common bean. Machine learning models, including random forest, support vector machines and multiple linear regression, were developed to predict agricultural production using remote sensing data. Statistical analysis was performed to verify the trustworthiness of vegetation indicators against ground-truth measurements. Result: The models achieved high accuracy, with R² values reaching up to 0.92. Statistical analysis confirmed strong relationships between vegetation indicators and ground-truth data. Among the studied crops, soybeans exhibited the highest growth vigor and yield. The study demonstrates that integrating machine learning with drone photography can enhance precision agriculture, making it more scalable and sustainable. Future research is recommended to explore different crop varieties and environmental conditions to further optimize the application of these technologies.
Why it matches plant phenotyping methodsドローンリモートセンシングと機械学習を用いて作物生育指標および収量を推定し、地上実測値で検証することが研究の中心である。
abstractThe purpose of this project is to evaluate the prospective use of drone-based remote sensing for assessing legume crop development and predicting their yields.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Plant height (PH) is closely linked to yield potential, lodging resistance, and mechanized harvesting efficiency in peanut cultivation. However, breeding efforts for optimized PH are hindered by limited understanding of its genetic architecture. In this study, we utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials. Using UAV-LiDAR data, we precisely measured time-series PH and applied Gaussian fitting and principal component analysis (PCA) to extract five dynamic growth parameters: parameter a (maximum plant height), b (time to reach maximum height), c (variation extent of PH), (interpreted as average height), and (growth rate). Genome-wide association studies (GWAS) identified 1,133 candidate genes associated with parameters a , b , c , and , and differential expression of genes (DEGs) analysis combined with weighted correlation network analysis (WGCNA) further identified Arahy.1026BX as a candidate gene. This gene is involved in the shikimate pathway and is crucial for the synthesis of auxin and lignin. Reverse transcription quantitative real-time PCR (RT-qPCR) and virus-induced gene silencing (VIGS) experiments validated the significant effect of Arahy.1026BX on peanut PH. Overall, our study integrates advanced UAV-LiDAR time-series phenotyping with genome-wide association study to identify potential candidate genes associated with PH, which providing valuable breeding insights for developing peanut varieties with ideal PH and improving peanut yield.
Why it matches plant phenotyping methodsUAV-LiDARによる時系列の草丈取得と動的成長パラメータ抽出が研究の中心的手法であり、植物表現型解析プラットフォームを実質的に適用している。
abstractwe utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials.
Abstract. The seedling emergence rate is a crucial indicator for evaluating the growth status of crops in agricultural production and can provide valuable recommendations for subsequent crop planting and field management strategies. Currently, the determination of the emergence rate relies on manual seedling counting, which is not only labour-intensive and time-consuming, but also prone to human errors. Therefore, we utilize drone-captured images of peanut seedlings and employs deep learning networks to estimate seedling numbers. Specifically, we incorporate the BIFPN (Bidirectional Feature Pyramid Network) feature fusion module into the original Centernet model, which would combine multi-scale feature information. This modification not only enhances the accuracy of identification but also improves the localization of seedlings. To address the issue of false positives caused by complex field backgrounds in seedling recognition, we integrate the Contrastive Loss module to increase the discrepancy between positive and negative samples. The results demonstrate that the proposed method significantly enhances both precision and recall rates for peanut seedling recognition under three different scenes, compared to the original model. Furthermore, the proposed method is also applied in real peanut breading field, fulfilling the practical requirements for emergence rate calculation.
Why it matches plant phenotyping methodsUAV画像と深層学習により、ピーナッツ幼苗の識別・計数から出芽率を推定する手法を開発・評価しており、植物形質取得が中心である。
abstractwe utilize drone-captured images of peanut seedlings and employs deep learning networks to estimate seedling numbers.
Abstract. Plant height is, as a crucial indicator, capable of reflecting the health status and growth vigor at various growth stages. It provides essential information for increasing crop yield, optimizing cultivation strategies, and improving varieties. Traditional plant height measurements using tapes or rods are labour-intensive, time-consuming, subject to human errors, and inadequate for large-scale observations. In recent years, unmanned aerial vehicles (UAVs) equipped with RGB cameras have demonstrated significant advantages in terms of efficiency and cost-effectiveness, enabling detailed 3D reconstruction of complex farmland environments through photogrammetry techniques. Therefore, we develop a high-throughput plant height measurement approach for the field peanuts from low-cost UAV photogrammetry. First, a UAV platform equipped with RGB camera is used to collect high-resolution imagery, covering the entire peanut growth stages. Following this, the aerial images are processed and precisely aligned with positional and orientation system (POS) data, subsequently generating Digital Surface Models (DSMs). Among these DSMs, the one representing the bare soil period was considered as the Digital Elevation Model (DEM). Afterwards, each plot is clipped based on its minimum bounding rectangles, creating Canopy Height Models (CHMs) by subtracting the DEM from the corresponding DSMs. Finally, Peanut plant heights are estimated via histogram distribution analysis of CHMs and validated with manually measured heights in Wangbian Community, Ningyang County, Tai'an City, Shandong Province. Experimental results indicate excellent effectiveness and reliability, achieving coefficients of determination (R2) of 0.9424 and RMSE of 2.26 cm. These observations demonstrate UAV photogrammetry's practical potential for large-scale crop phenotyping applications.
Why it matches plant phenotyping methodsUAVフォトグラメトリとCHM解析による落花生の草丈推定法を開発し、手測定で検証しており、植物形質取得が研究の中心である。
abstractTherefore, we develop a high-throughput plant height measurement approach for the field peanuts from low-cost UAV photogrammetry.
Abstract. High-throughput phenotyping monitoring has become increasingly important in modern agriculture, as it can collect plant images to extract and analyse phenotype data related to growth and yield, thereby reducing crop monitoring costs. Aboveground biomass (AGB) is a key indicator for evaluating plant health, growth, and productivity, and reflects the impact of environmental factors (such as water, soil nutrients, and temperature) on plants. However, traditional methods for measuring AGB are often labor-intensive, costly, and limited in spatial coverage. Unmanned aerial vehicles (UAVs)-based remote sensing offer new solutions, enabling large-scale, high-resolution data collection in agricultural fields. Therefore, this study evaluates the use of Vegetation indices (VIs) and Texture features (TFs), as well as their combinations, derived from UAV multispectral imagery to estimate peanut AGB across different growth stages. Specifically, nine VIs and eight TFs with different parameter settings were first derived from RGB and four single-band UAV images. Based on random forest (RF) regression, the study explored the impact of different parameter combinations on the performance of AGB models and analysed the potential of combining VIs and TFs to improve AGB estimation. The results show that TFs effectively complement VIs, significantly enhancing peanut AGB estimation performance. The optimal window size was 7×7, with a direction of 90° and a grey level of 16. The combined VIs and TFs yield a regression with R² and RMSE of 0.929 and 0.032, respectively. These findings suggest that the strategy of extracting image textures and combining features significantly improves the accuracy of AGB estimation, providing a more precise method for monitoring AGB.
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、RFでピーナッツ地上部バイオマスを推定する手法の評価が研究の中心であり、植物形質推定への技術的貢献が明確。
abstractthis study evaluates the use of Vegetation indices (VIs) and Texture features (TFs), as well as their combinations, derived from UAV multispectral imagery to estimate peanut AGB across different growth stages.
ObjectivePeanuts plant height is a key structural trait for assessing crop growth and nitrogen response. Accurate and efficient height acquisition is essential for monitoring canopy vigor, supporting genotype selection, and enabling precision management. However, conventional ground control point (GCP)-based methods require substantial field deployment and are highly sensitive to local misregistration between multi-temporal digital surface models (DSMs) and digital elevation models (DEMs). In low-stature, prostrate peanut canopies on uneven terrain, such residual elevation errors propagate directly into the canopy height model, severely reducing estimation accuracy. To overcome these limitations, a robust unmanned aerial vehicle (UAV)-based method is developed for peanut plant height estimation using bare-soil invariant constraints. The workflow incorporates crop-mask-assisted fine registration to optimize DSM-DEM alignment and eliminates the need for dense GCP distribution.MethodsField experiments were conducted at a peanut experimental station in Wangbian community, Ningyang county, Tai'an city, Shandong Province, China, using multiple UAV platforms (DJI Mavic 3 Multispectral and DJI MATRICE 350 RTK equipped with a DJI Zenmuse P1 camera), two growth stages (42 d and 49 d after sowing, DAS), and two nitrogen fertilization levels (high nitrogen and low nitrogen). To validate the peanut plant height estimates, representative plants in each plot were selected before and after each UAV image acquisition, and manual measurements from the ground surface to the canopy apex were recorded as the reference plant height. High-resolution digital orthomosaic (DOM) images were then generated from the UAV data, and peanut canopy regions were extracted using the excess green (ExG) index. To mitigate threshold instability caused by variable illumination and soil background conditions, a fixed empirical threshold was combined with an adaptive strategy that integrated Otsu's between-class variance method and the median absolute deviation (MAD), thereby ensuring robust canopy segmentation across growth stages and nitrogen treatments. After canopy extraction, the peanut canopy mask derived from the DOM was used to remove corresponding pixels from the DSM on a per-pixel basis. The DSMs with and without canopy points were then separately used for 3D reconstruction, yielding a canopy point cloud and a bare-soil point cloud. This bare-soil point cloud and a bare-soil DSM acquired before crop emergence (used as the DEM reference) were jointly input into the iterative closest point (ICP) algorithm to solve for a three-dimensional rigid transformation matrix. The resulting matrix was used to jointly optimize translations and rotations along the X, Y, and Z directions. It was applied uniformly to the DSM containing peanut canopy points, thereby achieving fine-scale alignment between the DSM and DEM at the block level. Following registration, the DEM was used as the ground reference, and the canopy height model was constructed by differencing the DSM and DEM pixel by pixel. The 95th percentile (P95) of canopy height within each plot, derived from the canopy height histogram, was used as the representative plant height to reduce the influence of local noise on the statistics.Results and DiscussionsThe results showed that varying the ExG threshold among 0.05, 0.10 and 0.15 had only a limited effect on overall plant height estimation accuracy, with the best performance observed at 0.10. At this threshold, the Mavic 3 platform achieved an R2 of 0.864 7 and a root-mean-square error (RMSE) of 2.57 cm. In contrast, the P1 platform achieved an R2 of 0.918 6 and an RMSE of 2.05 cm, indicating that the proposed threshold selection strategy provided a good balance between accuracy and robustness. Error analysis across different canopy-height percentiles showed that, as the percentile increased from P90 to P99, R2 and RMSE exhibited a typical concave pattern, first improving and then degrading. Among these percentiles, P95 yielded the highest R2 and the lowest RMSE, representing the best trade-off between noise suppression and canopy-top information retention; therefore, P95 was adopted as the representative plant height for this method. Under the P95-based definition of plant height, the traditional GCP method produced R2 values of only 0.592 3－0.669 9 and RMSE values of 4.60~4.94 cm, and the "GCP+ICP" workflow, in which canopy points were not removed prior to ICP registration, was most strongly affected by noise in the point clouds, with R2 dropping below 0.3 in some cases. In contrast, the proposed method maintained R2 values of 0.864 7~0.918 6 and RMSE values of 2.05~2.57 cm across both platforms, markedly improving the agreement between estimated and measured plant height relative to the traditional GCP-based approach. Further platform-specific analysis showed that, owing to its higher spatial resolution, the P1 platform reconstructed a more complete canopy-top structure and yielded better plant height estimates than the Mavic 3 platform at each growth stage. Nevertheless, when combined with the proposed plant height extraction workflow, the Mavic 3 platform still achieved reliable performance (R2 > 0.817 7) in regions with different nitrogen contents, confirming the method's multi-platform applicability. From the perspective of canopy cover and nitrogen level, as the crop progressed from 42 to 49 DAS, the peanut canopy gradually approached full closure, the proportion of high-value pixels in the canopy height model increased, the canopy-top point cloud in the DSM became more continuous, and plant height estimation accuracy improved accordingly. Under high nitrogen treatment, the canopy was denser and structurally more complete than under low nitrogen treatment, resulting in slightly higher R2 and slightly lower RMSE on both platforms; however, these differences remained within a controllable range, demonstrating that the bare-soil-based registration workflow was robust to fertility differences and that the proposed method was stable and transferable across growth stages and fertility conditions.ConclusionsOverall, the proposed method for estimating peanut plant height substantially alleviates the constraints imposed by the misregistration of residual DSM and DEM on plant height inversion for low-stature, prostrate crops. It achieves centimetre-level accuracy for plant height retrieval across platforms and nitrogen treatments. By significantly reducing the dependence on densely distributed GCPs and offering a simple, reproducible, and low-cost processing pipeline, the method provides a scalable technical route for monitoring peanut nitrogen responses, deriving high-throughput agronomic structural traits, and measuring plant height in other low-stature, prostrate crops.
Why it matches plant phenotyping methodsUAV画像・DSM/DEM位置合わせ・キャノピー抽出・点群処理を統合し、ピーナッツ草高という植物形質を推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractTo overcome these limitations, a robust unmanned aerial vehicle (UAV)-based method is developed for peanut plant height estimation using bare-soil invariant constraints.
Unmanned aerial systems (UAS) are reliable tools for field phenotyping, enabling rapid, large-scale, and cost-effective data collection to support breeding programs. However, many UAS-based approaches rely on manual data processing, limiting scalability and efficiency. This study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN). SAM auto-mask generator mode was used to identify field extent and orientation, while SAM interactive mode enabled individual plot segmentation using auto-generated point prompts. Terrain points automatically sampled near each plot were used to model the ground surface and compute the canopy height model, allowing CH estimations at the plot level. CH estimations showed strong agreement with manual measurements (R² = 0.78, RMSE = 3 cm, MAPE = 10 %). For MP and GH estimation, three pre-trained CNN models (AlexNet, ResNet18, and EfficientNet-B0) were evaluated, with AlexNet achieving the highest accuracy (89 % for GH, 83 % for MP). To assess the feasibility of using these HTP-derived estimations in plant breeding, quantitative trait loci (QTL) analysis was performed, identifying major-effect loci associated with these traits. The results were consistent with conventional QTL mapping methods, demonstrating that UAS-based phenotyping provides reliable trait data for genetic studies in peanut breeding. Overall, our deep learning-based data processing workflow minimizes manual efforts, providing an efficient and scalable approach that can accelerate genetic studies and trait selection in large-scale breeding programs.
Why it matches plant phenotyping methodsUAS画像、SAM、CNNを統合したピーナッツの草冠高・生育型・主茎優勢度の自動推定パイプラインを開発・検証しており、表現型取得と抽出手法が研究の中心である。
abstractThis study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN).
Reproduction assets foundThe authors deposited the paper's phenotyping inputs (plot-level aerial RGB images and nDSM maps for GH/MP classification) publicly on Zenodo. The analysis source code is only available upon request, so it does not qualify as a public asset.Dataset · public0126 .
Contributor Information
Peggy Ozias-Akins, Email: pozias@uga.edu.
Changying Li, Email: cli2@ufl.edu.
Appendix A.
Supplementary data
The following is the supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
The datasets supporting this study are publicly available on Zenodo [ https://doi.org/10.5281/zenodo.17274012 ]. They include plot-level aerial RGB images and nDSM maps from peanut breeding fields for classification of Growth Habit and Mainstem Prominence. The source code used for data processing and analysis will be made available upon request.
References
1. U. S. Department of Agriculture . USDA National Agricultural Statistics ServiOpen asset ↗Zenodo · 10.5281/zenodo.17274012lines:327-356Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Why it matches plant phenotyping methods植物病斑を画像から認識する軽量深層学習手法を開発し、複数データセットで精度・圧縮性能・組込み推論速度を評価しているため、病害状態の画像ベース表現型計測が中心である。
abstractThis study aims to develop a lightweight yet accurate framework, referred to as FCDRNet, which integrates feature enhancement and compression techniques to facilitate practical deployment in the field.
Manual assessment of toxic fungal infection levels in crop seeds is important for developing antifungal-resistant cultivars, yet it has long been recognized as health-risking and inherently subjective. This study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds. The edge computing-based computer vision approach, termed Edge CV, was developed using the Jetson Nano, embedded cameras, and deployed with the proposed Edge CV model to enable intelligent evaluation with constrained computing resources and GPU power. The Edge CV model: First, leveraging semantic segmentation in computer vision tasks to differentiate between A. flavus -infected and uninfected; Second, utilizing post-processing techniques to accurately separate connected peanut seeds while merging segments belonging to the same ones; Third, analyzing and quantifying infection indices, as well as results presentation. Finally, deep transfer learning was employed to validate the model's transferability for other crop seeds. As a result, Edge CV inference showed agreement with manual measurements (R 2 = 0.901, RMSE = 0.07) and superior consistency, with only a 0.01 % fluctuation compared to 4.2 % for human assessments. Moreover, Edge CV demonstrated its transferability to other crop seeds, such as maize (R 2 = 0.968, RMSE = 0.13) and rice (R 2 = 0.949, RMSE = 0.26). These results underscore the potential of Edge CV as a transferable solution for assessing toxic fungal infections. The approach developed also offers valuable insights for enhancing proximal machine vision, improving the distinction of adjacent seeds, and enabling more accurate calculation of the infection index.
Why it matches plant phenotyping methods種子の真菌感染状態を画像から定量化するエッジコンピューティング画像手法を開発し、手動測定との一致および他作物種への移 transfer 性を検証しており、植物状態の取得・抽出が研究の中心である。
abstractThis study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' data and code for the Edge CV peanut A. flavus infection assessment pipeline.Code · publicThe data and code will be made available on this URL: https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessment.Open asset ↗https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessmenthtml-lines:349-374Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Accurate estimation of leaf biochemical parameters is crucial for understanding crop physiology and monitoring nutritional status. Remote sensing algorithms perform well on limited germplasm, but the transferability to high-throughput phenotyping with diverse genotypes remains unclear. This study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions. Results showed that Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and Modified Simple Ratio (mSR) were strongly correlated with Cab, Cw, and Cm, respectively, highlighting their importance in the inversion models. Comparative analysis revealed that the RF model achieved the highest accuracy for Cab (R 2 = 0.77, RMSE = 8.14 µg cm −2 ), Cw (R 2 = 0.67, RMSE = 1.1 × 10 −3 g cm −2 ), and Cm (R 2 = 0.50, RMSE = 6.2 × 10 −4 g cm −2 ), followed by the PROSPECT model, with R 2 and RMSE of 0.76 and 8.21 µg cm −2 for Cab, 0.61 and 1.2 × 10 −3 g cm −2 for Cw, and 0.38 and 7.7 × 10 −4 g cm −2 for Cm, respectively. However, the PROSPECT model was most effective in Cab inversion across diverse germplasm resources (R 2 = 0.58, RMSE = 7.68 µg cm −2 ), demonstrating its superior transferability and stability. These results underscore its value in high-throughput phenotyping and improving the accuracy and generalizability of crop biochemical parameter estimation. • The PROSPECT model exhibited superior transferability compared to other models across diverse peanut germplasm resources. • Different hyperspectral inversion models exhibited variations in estimating peanut leaf parameters. • Leaf chlorophyll content estimating showed high accuracy than other leaf parameters.
Why it matches plant phenotyping methodsハイスループット分光計測によりピーナッツ葉の生化学的形質を推定し、複数の反転モデルの精度・移植性を比較評価しており、フェノタイピング手法が中心である。
abstractThis study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions.
Accurate estimation of leaf biochemical parameters is crucial for understanding crop physiology and monitoring nutritional status. Remote sensing algorithms perform well on limited germplasm, but the transferability to high-throughput phenotyping with diverse genotypes remains unclear. This study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions. Results showed that Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and Modified Simple Ratio (mSR) were strongly correlated with Cab, Cw, and Cm, respectively, highlighting their importance in the inversion models. Comparative analysis revealed that the RF model achieved the highest accuracy for Cab (R² = 0.77, RMSE = 8.14 µg cm⁻²), Cw (R² = 0.67, RMSE = 1.1 × 10⁻³ g cm⁻²), and Cm (R² = 0.50, RMSE = 6.2 × 10⁻⁴ g cm⁻²), followed by the PROSPECT model, with R² and RMSE of 0.76 and 8.21 µg cm⁻² for Cab, 0.61 and 1.2 × 10⁻³ g cm⁻² for Cw, and 0.38 and 7.7 × 10⁻⁴ g cm⁻² for Cm, respectively. However, the PROSPECT model was most effective in Cab inversion across diverse germplasm resources (R² = 0.58, RMSE = 7.68 µg cm⁻²), demonstrating its superior transferability and stability. These results underscore its value in high-throughput phenotyping and improving the accuracy and generalizability of crop biochemical parameter estimation.
Why it matches plant phenotyping methods植物葉の生化学的形質をハイスループット分光センシングと反転モデルで推定し、複数モデルの精度・移植性を比較評価しており、表現型取得手法が中心である。
abstractThis study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions.
Background: Groundnuts, commonly known as peanuts, are a significant legume cultivated globally, with China and India being the leading producers. Groundnut production faces challenges from pests, diseases and climate change, impacting yield and quality. In addition to notable diseases like rust, early and late leaf spots, plant health is also impacted by nutritional deficiencies. Sustainable production requires the management of diseases and the supply of appropriate nourishment. Methods: This study applies machine learning (ML), specifically Convolutional Neural Networks (CNNs), to detect groundnut disease symptoms. A CNN-based tool is developed to assess disease severity efficiently. The model is trained and validated using a dataset of 3,058 groundnut leaf images, classified into five disease categories. Result: After 100 epochs, the CNN model reached a training accuracy of 91.94% and a validation accuracy of 90.97%. Performance metrics such as precision, recall and F1-score confirm the model’s effectiveness in disease classification. The study acknowledges certain limitations, including a small dataset and a focus only on leaf infections. Future work may expand the dataset, include other plant parts and compare various ML approaches.
Why it matches plant phenotyping methodsCNNで葉の病徴を直接解析し、病害カテゴリと重症度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す。
abstractThis study applies machine learning (ML), specifically Convolutional Neural Networks (CNNs), to detect groundnut disease symptoms.
Aflatoxin, a secondary metabolite synthesized by moldy peanuts, poses a significant and potentially fatal threat to human health. Hyperspectral imaging technology combined with supervised learning algorithms has become an essential method for rapid, non-destructive identification of moldy peanuts. However, it is impossible to measure the aflatoxin content of peanut kernel at pixel-level, weak labels is commonly phenomenon when initially labeling moldy regions within peanut hyperspectral images, which leads to an inevitable issue-noisy label. In this study, a label quality quantification framework which integrate confidence level and statistical testing (CL-ST) is proposed to find label errors of moldy peanut hyperspectral images. First, confidence level of initial artificial labeling is estimated using out-of-sample probability. Furthermore, label quality is assessed through statistical testing and ranked in descending order. Finally, the optimal noisy rate (NR) is determined based on moldy peanut kernel-scale identification performance, and the models with clean data as input is rebuilt to identify moldy peanuts. Experimental results show that CL-ST can reliably quantify label quality of hyperspectral images and effectively identify potential noisy labeled pixels. The rebuilt successive projection algorithm-extreme learning machine achieve optimal performance, improving overall accuracy and precision from 74.63 % and 66.21 % to 98.96 % and 97.09 %, respectively. Feature visualization analysis reveals that noisy labels are the primary cause of incorrect decision boundaries, although their impact on feature selection is limited. CL-ST does not require hyperparameters and can be combined with any model to quantify label quality, demonstrating significant potential in food quality assessment using hyperspectral images. The source code of CL-ST will be available at https://github.com/yuandeshuai/CL-ST.
Why it matches plant phenotyping methodsカビ感染ピーナッツの状態をハイパースペクトル画像から推定するラベル品質評価・再構築手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstracta label quality quantification framework which integrate confidence level and statistical testing (CL-ST) is proposed to find label errors of moldy peanut hyperspectral images.
Abstract Leaf spot is a devastating disease in cultivated peanut ( Arachis hypogaea L.) that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components that make breeding for leaf spot‐resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and objective phenotyping strategy must be implemented. In this work, data derived from leaf scans, unoccupied aerial vehicle‐captured red, green, blue and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, “visible atmospherically resistant index” was selected as the most appropriate proxy for visual ratings and should be deployed by researchers and plant breeders in the peanut community for the objective evaluation of leaf spot resistance.
Why it matches plant phenotyping methods落花生葉斑病の客観的表現型評価を目的に、葉スキャンおよびUAVのRGB・マルチスペクトル画像を用いるデジタル手法を評価・標準化しており、病害表現型の取得法が中心である。
abstractan accurate and objective phenotyping strategy must be implemented
Reproduction assets foundThe paper deposits its phenotyping datasets (visual ratings, leaf scans, UAV RGB/multispectral imagery) in Dryad and hosts analysis scripts and supporting information in a public GitHub repository, both explicitly linked by the authors.Dataset · publicUS
Department of Agriculture is an equal opportunity provider
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C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated during and/or analyzed during the cur-
rent study are available in the Dryad repository: https://doi.org/10.5061/dryad.rn8pk0pnm.O RC I D
RyanAndres https://orcid.org/0000-0001-8635-4077
JeffreyDunne https://orcid.org/0000-0003-0544-9889
R E F E R E N C E S
Anco, D. J., Thomas, J. S., Jordan, D. L., Shew, B. B., Monfort, W. S.,
Mehl, H. L., Small, I. M., Wright, D. L., Tillman, B. L., Dufault, N.
S., Hagan, A. K., & Campbell, H. L. (2020). Peanut yield losOpen asset ↗Dryad · 10.5061/dryad.rn8pk0pnm.Opdf-raw-page:15 lines:1-82Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 7 Sept 2026
ABSTRACT Late leaf spot (LLS), caused by Nothopassalora personata , is the most damaging foliar disease in peanut production worldwide, leading to significant yield losses if not properly managed. Accurate disease severity assessment is crucial for evaluating fungicide efficacy and implementing effective management strategies. This study aimed to develop and validate an automated image analysis model, LLS-SevEst , for quantifying LLS severity in peanut leaves. A dataset of 190 scanned leaf images was analyzed using three approaches: a fixed threshold-based segmentation, morphological preprocessing, and K-means clustering. Exploratory analyses revealed distinct brightness patterns between healthy and diseased tissues, guiding the development of classification functions. The threshold-based model yielded high false positive rates due to its inability to account for natural leaf variation, while the morphological preprocessing method improved segmentation marginally but still required manual adjustments. The K-means clustering approach achieved superior segmentation by objectively differentiating healthy tissue, lesions, and background, and showed high potential for automated, reproducible disease severity estimation. Future work should focus on integrating deep learning and expanding the dataset to improve model robustness and adaptability to other foliar pathosystems.
Why it matches plant phenotyping methods落花生葉の病斑から葉面病害重症度を自動推定する画像解析手法を開発・比較検証しており、植物表現型（病害状態）の取得が研究の中心です。
abstractThis study aimed to develop and validate an automated image analysis model, LLS-SevEst , for quantifying LLS severity in peanut leaves.
Yield prediction of root-fruit crops before harvest is significant for implementing precise field management. However, unlike crops such as wheat and corn, non-destructively predicting the yield of root-fruit crops non-destructively is challenging owing to their edible parts being located underground. Remote sensing offers a potential solution to this problem. Studies on predicting peanut yield through remote sensing are rare. Most of these studies relying on specific vegetation indices, such as the normalized difference vegetation index (NDVI), but have limitations in terms of model accuracy when other phenological parameters influencing peanut yield formation are not considered. 355 peanut yield samples were collected from two distinct cultivation patterns in 2022, 2023 and 2024 and In the study of peanut yield prediction, two modeling methods, linear regression and random forest, were employed to develop prediction models. Considering the contributions of early-stage material accumulation and late- stage material transfer to peanut yield, the results showed that incorporating multiple phenological parameters into peanut yield prediction models enhances accuracy beyond that achieved by models relying solely on early growth stage vegetation indices such as maximum NDVI.. Furthermore, the random forest algorithm has demonstrated its effectiveness in predicting peanut yields, particularly for summer peanuts, as evidenced by its successful application in related studies. The R² reached a high of 0.8201, while the lowest MAE and RMSE values were recorded at 0.2878 and 0.4048 t/ha, respectively. This study’s findings have significantly contributed to remote sensing-based yield prediction for root-fruit crops, further refining precision management practices in the cultivating of crops such as peanuts.
Why it matches plant phenotyping methodsリモートセンシングと回帰・ランダムフォレストによるピーナッツ収量推定モデルの開発が中心で、植物形質（収量）を定量化している。
abstracttwo modeling methods, linear regression and random forest, were employed to develop prediction models.
Plant height and SPAD values are critical indicators for evaluating peanut morphological development, photosynthetic efficiency, and yield optimization. Recent unmanned aerial vehicle (UAV) technology advancements have enabled high-throughput phenotyping at field scales. As a globally strategic oilseed crop, peanut plays a vital role in ensuring food and edible oil security. This study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains. Multispectral UAV and ground data were collected across four growth stages (2023–2024). Using spectral indices and Texture features, four models (PLSR, SVM, ANN, RFR) were trained on 2024 data and independently validated with 2023 datasets. The ensemble machine learning models (RFR) significantly enhanced estimation accuracy (R2 improvement: 3.1–34.5%) and robustness compared to the linear model (PLSR). Feature stability analysis revealed that combined spectral-textural features outperformed single-feature approaches. The SVM model achieved superior plant height prediction (R2 = 0.912, RMSE = 2.14 cm), while RFR optimally estimated SPAD values (R2 = 0.530, RMSE = 3.87) across heterogeneous field conditions. This UAV-based multi-modal integration framework demonstrates significant potential for temporal monitoring of peanut growth dynamics.
Why it matches plant phenotyping methodsUAVマルチソースデータと機械学習により、ピーナッツの草丈およびSPAD値を推定するフレームワークを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractThis study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains.
Background Late and early leaf spot in peanuts is a foliar disease contributing to a significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity of leaf spot infection. The objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field. This was accomplished using image capture protocols and segmentation neural networks that extracted lesion areas from plot-level images to determine an appropriate rating for infection severity. Results The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1-9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. We were able to demonstrate automated scoring, as compared to expert visual scoring, with a root mean square error of 0.996 visual scores, on individual images (one image per plot), and 0.800 visual scores when three images were captured of each plot. Conclusion Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut.
Why it matches plant phenotyping methods落花生の葉斑病重症度という植物病害表現型を、画像取得・セグメンテーション・画像処理で自動推定するパイプラインの開発と評価が中心であるため。
abstractThe objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field.
Peanut ( Arachis hypogea L.) crops in the southeastern U.S. suffer significant yield losses from diseases like leaf spot, southern blight, and stem rot. Traditionally, growers use conventional boom sprayers, which often leads to overuse and wastage of agrochemicals. However, advances in computer technologies have enabled the development of precision or variable-rate sprayers, both ground-based and drone-based, that apply agrochemicals more accurately. Historically, crop disease scouting has been labor-intensive and costly. Recent innovations in computer vision, artificial intelligence (AI), and remote sensing have transformed disease identification and scouting, making the process more efficient and economical. Over the past decade, numerous studies have focused on developing technologies for peanut disease scouting and sprayer technology. The current research trend shows significant advancements in precision spraying technologies, facilitating smart spraying capabilities. These advancements include the use of various platforms, such as ground-based and unmanned aerial vehicle (UAV)-based systems, equipped with sensors like RGB (red-blue-green), multispectral, thermal, hyperspectral, light detection and ranging (LiDAR), and other innovative detection technologies, as highlighted in this review. However, despite the availability of some commercial precision sprayers, their effectiveness is limited in managing certain peanut diseases, such as white mold, because the disease affects the roots, and the chemicals often remain in the canopy, failing to reach the soil where treatment is needed. The review concludes that further advances are necessary to develop more precise sprayers that can meet the needs of large-scale farmers and significantly enhance production outcomes. Overall, this review paper aims to provide a review of smart spraying techniques, estimating the required agrochemicals and applying them precisely in peanut fields.
Why it matches plant phenotyping methodsピーナッツ病害の識別・スカウティングに用いる画像、AI、リモートセンシング、各種センサー技術をレビューしており、植物の病害状態を推定する方法が主要な内容である。精密散布も扱うが、病害フェノタイピング手法のレビュー要素が明確である。
abstractRecent innovations in computer vision, artificial intelligence (AI), and remote sensing have transformed disease identification and scouting
Indian agriculture is vital sector in the country's economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers' livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can lead to inaccuracies due to the invisible nature of early disease symptoms and the labor-intensive process, making them inefficient for large-scale agricultural management. To recover from this and, address these challenges, this study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection. Deep learning architectures, like convolutional neural network, can autonomously learn and extract complicated characteristics and patterns from huge datasets. Our research, conducted on mango and groundnut leaves collected during field visits in western Maharashtra and supplemented by online datasets, demonstrates a CNN model that achieves an impressive 96% accuracy as compared to machine learning techniques that follow tedious feature extraction. Furthermore, image processing contributes to enhancing the dataset through normalization, resizing, and augmentation for better classification results. Overall, CNN can continuously improve and adapt its performance through iterative training, resulting in higher accuracy rates and reduced false positives in contrast to conventional machine learning methods.
Why it matches plant phenotyping methodsCNNによる植物葉の病害状態分類と画像前処理・性能比較が研究の中心であり、植物病害表現型の取得・推定手法に該当する。
abstractthis study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Exploring the relationships between plant phenotypes and genetic information requires advanced phenotypic analysis techniques for precise characterization. However, the diversity and variability of plant morphology challenge existing methods, which often fail to generalize across species and require extensive annotated data, especially for 3D datasets. This paper proposes a zero-shot 3D leaf instance segmentation method using RGB sensors. It extends the 2D segmentation model SAM (Segment Anything Model) to 3D through a multi-view strategy. RGB image sequences captured from multiple viewpoints are used to reconstruct 3D plant point clouds via multi-view stereo. HQ-SAM (High-Quality Segment Anything Model) segments leaves in 2D, and the segmentation is mapped to the 3D point cloud. An incremental fusion method based on confidence scores aggregates results from different views into a final output. Evaluated on a custom peanut seedling dataset, the method achieved point-level precision, recall, and F1 scores over 0.9 and object-level mIoU and precision above 0.75 under two IoU thresholds. The results show that the method achieves state-of-the-art segmentation quality while offering zero-shot capability and generalizability, demonstrating significant potential in plant phenotyping.
Why it matches plant phenotyping methods植物の葉を対象とするマルチビュー3Dインスタンスセグメンテーション手法を開発し、植物フェノタイピング用途としてデータセット上で技術評価しているため、方法が研究の中心である。
abstractThis paper proposes a zero-shot 3D leaf instance segmentation method using RGB sensors.
Peanut / groundnutRootMorphology / geometry measurementRoot system architecture
Accurately characterizing root systems in mature, field-grown crops presents a significant challenge due to the complexity of root architecture and the limitations of existing phenotyping techniques. While recent technological advancements, such as in-situ photographing, have improved root assessment, widely accessible and cost-effective methods remain scarce. Root phenotyping in peanut is typically limited to terminal excavation or expensive, spatially constrained imaging systems. In this exploratory methods paper, we describe a simplified, low-cost approach—referred to as the 'root box method'—to visualize and characterize root architecture in peanut under controlled conditions. We document the construction and use of a box system made from widely available materials, enabling manual root washing, high-contrast imaging, and basic image-based phenotyping. We compare root metrics obtained with this system to those generated by a commercial root scanner to explore its utility as an accessible alternative for small-scale or early-stage research. While the method has clear limitations, it offers a practical starting point for observing root architectural variation in peanut genotypes. Our goal is to provide a transparent evaluation of this approach to support broader participation in root research and tool development in resource-limited contexts. Additionally, it offers significant potential for breeding, agronomy, and extension research, particularly in studies related to drought resilience, nutrient acquisition, and soil health. The root box method serves as an accessible tool for researchers, agronomists, and growers, providing critical insights into root architecture that can inform crop management strategies and improve agricultural sustainability.
Why it matches plant phenotyping methods低コストな根系フェノタイピング手法を開発し、商用スキャナーとの比較で評価しており、根系形態の取得・解析が研究の中心である。
abstractIn this exploratory methods paper, we describe a simplified, low-cost approach—referred to as the 'root box method'—to visualize and characterize root architecture in peanut under controlled conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
Abstract Drought stress is a major production constraint of groundnut in Africa and Asia where it is largely grown as rainfed crop. The experiments aim to design an early testing approach for drought tolerance in the groundnut breeding pipeline to ensure sustainable production. A population of 600 multi parent advanced generation inter-cross (MAGIC) lines (MLs) (F 8/9 generation) and 100 advance breeding lines (ABLs) were studied in LeasyScan, a high throughput phenotyping platform (HTPP) to assess early canopy growth, and under a managed stress environment (MSE). MSE ensures uniform water application in well-watered and water-stressed plots, while intermittent drought is imposed in water-stressed plots from 1000 0 cumulative thermal time (CTT) during pod-filling stage. Digital biomass, leaf area 3D and plant height measured under HTPP recorded high heritability along with high genetic gain and were identified for use as selection criteria for early canopy vigour. The second selection criteria is Mean Score Index (MSI) (1 to 10 scale), which accounts for both resilience and productivity capacity indices (RCI and PCI), with the MSI ranging from 1.4 to 8.4. Based on results, a two-step selection approach is proposed for selection of traits required for adaption under drought stress. The approach involves HTPP (LeasyScan) to select early canopy vigour followed by selection based on MSI under MSE. MSE is field based and expensive, hence screening of a large number of selection candidates under HTTP helps to select a relatively small subset of early vigour lines for screening under MSE for agronomic performance.
Why it matches plant phenotyping methodsLeasyScan高スループット表現型解析プラットフォームを用いたデジタルバイオマス、葉面積、草丈の取得と、圃場ストレス評価を組み合わせた選抜ワークフローが研究の中心であり、単なる作物試験のルーチン測定ではない。
abstractstudied in LeasyScan, a high throughput phenotyping platform (HTPP) to assess early canopy growth
Key message Polarized auxin transport regulates fruit shape determination by promoting anisotropic cell growth. Angiosperms produce organs with distinct shape resultant from adaptive evolution. Understanding the cellular basis underlying the development of plant organ has been a central topic in plant biology as it is key to unlock the mechanisms leading to the diversification of plants. Variations in the location of synthesis, polarized auxin transport (PAT) have been proposed to account for the development of diverse organ shapes, but the exact cellular mechanism has yet to be elucidated. The Capsella rubella develops a perfect heart-shaped fruit from an ovate shape gynoecium that is tightly linked to the localized auxin synthesis in the valve tips and provides a unique opportunity to address this question. In this study, we studied auxin movement in the fruits and the cellular effect of N-1-Naphthylphthalamic Acid (NPA) on the fruit shape determination by constructing the pCrPIN3:PIN3:GFP reporter and live-imaging. We found PAT in the valve epidermis is in congruent with fruit shape development and NPA treatment disrupts the heat-shaped fruit development mainly by repressing cell anisotropic growth with minor effect on division. As the Capsella fruit is unusually big in size, we also included a detailed step-by-step protocol on how to conduct live-imaging experiment. We further test the utility of this protocol by conducting a live-imaging analysis of the gynophore in Arachis hypogaea. Collectively, the results of this study elucidated the mechanism on how auxin signal was translated into instructions guiding cell growth during organ shape determination. In addition, the description of the detailed live-imaging protocol will encourage further studies of the cellular mechanisms underlying shape diversification in angiosperms.
Why it matches plant phenotyping methods果実および細胞成長を観察・抽出するライブイメージング手法の詳細プロトコルを提示し、別種でも有用性を検証しており、手法的貢献が明示されています。
abstractwe also included a detailed step-by-step protocol on how to conduct live-imaging experiment.
Leaf spot is a devastating disease in cultivated peanut that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components which make breeding for leaf spot resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and effective phenotyping strategy must be implemented. In this work, data derived from leaf scans and UAV-captured RGB and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, ‘Visible Atmospherically Resistant Index’ is selected as the most appropriate proxy for immediate use by researchers and plant breeders in the peanut community.
Why it matches plant phenotyping methodsUAV画像・葉スキャンによる落花生葉斑病の表現型取得法を開発・比較・検証し、従来の主観的評価の代替指標を選定しているため、方法が中心的です。
abstractdata derived from leaf scans and UAV-captured RGB and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present.
Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images are collected from traditional databases, and then they are given to the pre-processing stage. Then, relevant features are drawn out from the preprocessed images in two stages. In the first stage, the preprocessed image is segmented using adaptive TransResunet++, where the variables are tuned with the help of designed Hybrid Position of Beluga Whale and Cuttle Fish (HP-BWCF) and finally get the feature set 1 using Kaze Feature Points and Binary Descriptors. In the second stage, the same Kaze feature points and the binary descriptors are extracted from the preprocessed image separately, and then obtain feature set 2. Then, the extracted feature sets 1 and 2 are concatenated and given to the Hybrid Convolution-based Adaptive Resnet with Attention Mechanism (HCAR-AM) to detect the ground nut leaf diseases very effectively. The parameters from this HCAR-AM are tuned via the same HP-BWCF. The experimental outcome is analysed over various recently developed ground nut leaf disease detection approaches in accordance with various performance measures.
Why it matches plant phenotyping methods落花生葉の画像から病害をセグメンテーション・特徴抽出・深層学習で検出する手法開発が論文の中心であり、植物の病害状態を直接推定している。
titleHybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.
Abstract Background: Late and early leaf spot in peanuts is a foliar disease contributing to a significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity of leaf spot infection. The objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field. This was accomplished using image capture protocols and segmentation neural networks that extracted lesion areas from plot-level images to determine an appropriate rating for infection severity. Results: The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. The pipeline was evaluated using field data from plots with varying leaf spot severity, creating a dataset of thousands of images that spanned conventional visual severity scores ranging from 1-9. These predictions were based on the amount of infected leaf area and the presence of defoliated leaves in the surrounding area. We were able to demonstrate automated scoring, as compared to exprt visual scoring, with a root mean square error of 0.996 visual scores, on individual images (one image per plot), and 0.800 visual scores when three images were captured of each plot. Conclusion: Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut.
Why it matches plant phenotyping methods落花生の葉斑病重症度という植物状態を、画像取得・セグメンテーション・画像処理で自動推定するパイプラインの開発と評価が中心である。
abstractThe objective of this study was to develop an objective end-to-end pipeline that can serve to replace an expert human scorer in the field.
Reproduction assets foundThe paper explicitly states that the complete dataset of RGB images and semantic segmentation labels, generated/analysed during the study, is publicly available in the authors' GitHub repository (Automated Leaf Spot Scoring), which directly reproduces this paper's peanut leaf spot phenotyping images and annotations.Dataset · publicThe datasets generated and/or analysed during the current study are available in the Automated
Leaf Spot Scoring GitHub repository:
https://github.ncsu.edu/jclarse2/AutomatedLeafSpotScoringOpen asset ↗AutomatedLeafSpotScoringpdf-page:19 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction The emergence rate of crop seedlings is an important indicator for variety selection, evaluation, field management, and yield prediction. To address the low recognition accuracy caused by the uneven size and varying growth conditions of crop seedlings under salt-alkali stress, this research proposes a peanut seedling recognition model, MS-YOLOv8. Methods This research employs close-range remote sensing from unmanned aerial vehicles (UAVs) to rapidly recognize and count peanut seedlings. First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. Additionally, the module automatically adjusts the channel weights of each group based on their contribution, improving the feature fusion effect. Second, the neck network structure is reconstructed to enhance recognition capabilities for small objects, and the MPDIoU loss function is introduced to effectively optimize the detection boxes for seedlings with scattered branch growth. Results Experimental results demonstrate that the proposed MS-YOLOv8 model achieves an AP50 of 97.5% for peanut seedling detection, which is 12.9%, 9.8%, 4.7%, 5.0%, 11.2%, 5.0%, and 3.6% higher than Faster R-CNN, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and RT-DETR, respectively. Discussion This research provides valuable insights for crop recognition under extreme environmental stress and lays a theoretical foundation for the development of intelligent production equipment.
Why it matches plant phenotyping methodsUAVリモートセンシング画像からピーナッツ幼苗を認識・計数するモデルを開発し、検出性能を比較検証している。幼苗数・出現率という植物状態の推定が研究の中心である。
abstractthis research proposes a peanut seedling recognition model, MS-YOLOv8
Reproduction assets foundThe paper's data availability statement explicitly deposits the peanut seedling UAV image dataset (and associated model resources) in a public GitHub repository, matching an allowed URL.Dataset · publicy close-range remote sensing. It provides a certain theoretical guidance for the development of an intelligent monitoring platform for peanut.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/zfvincent1997/MS-YOLOV8 .
Author contributions
FZ: Investigation, Resources, Software, Writing – original draft. LZ: Conceptualization, Supervision, Writing – review & editing. DW: Investigation, Writing – review & editing. JW: Investigation, Writing – review & editing. IS: Software, Visualization, Writing – review & editing. JL: Conceptualization, Open asset ↗https://github.com/zfvincent1997/MS-YOLOV8lines:667-765Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 7 Sept 2026
Background Stem rot, caused by the soil-borne pathogen Sclerotium rolfsii, pose a serious challenge in the groundnut (Arachis hypogaea L) cultivation. Although this disease is widespread globally but had most adverse impact in groundnut growing regions of United States, India, and Australia. The pathogen primarily targets the crown region of the plant, resulting in systemic collapse and potentially leading to yield losses up to 80%. Effective genetic control measures are essential to mitigate the impact of this disease on groundnut production. Realizing the time and resource-consuming complex field-based phenotyping, the availability of easy and repeatable phenotyping methods may fasten the process of donor and gene discovery efforts. Results Multi-season phenotyping was performed for stem rot on 184 minicore germplasm accessions, including checks, under two conditions: sick field screening and response to oxalic acid assay. This study demonstrated medium to high heritability (52-63% broad-sense heritability) and significant environmental influence (36%). The response to the oxalic acid assay showed a high proportion of similarity (approximately 80%) with the percent mortality observed in the sick field indicating an easy way of performing precise phenotyping. Notably, seven genotypes-ICG163, ICG721, ICG10479, ICG875, ICG11457, ICG111, and ICG2857-exhibited stable resistance, with less than 30% mortality against stem rot disease. Among these, ICG163, ICG875, and ICG111 displayed low mortality and consistent stability across multiple seasons in both the sick field and controlled conditions of the oxalic acid assay. Conclusions The oxalic acid assay developed in this study effectively complements field phenotyping, as a reliable method for assessing stem rot resistance. Seven resistant genotypes identified through this assay can be utilized for the introgression of stem rot resistance into elite genotypes. Given the significant influence of the environment on stem rot resistance, it is essential to implement multi-season phenotyping to obtain precise results. Furthermore, the response to oxalic acid serves as a valuable supplement to traditional field phenotyping, since maintaining uniform disease pressure during field screenings is often challenging.
Why it matches plant phenotyping methods落花生の茎腐病抵抗性を評価するシュウ酸アッセイを開発し、圃場表現型との一致性・遺伝率・再現性を検証した研究であり、植物病害表現型の取得法が中心である。
abstractRealizing the time and resource-consuming complex field-based phenotyping, the availability of easy and repeatable phenotyping methods may fasten the process of donor and gene discovery efforts.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species — peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatial–spectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.
Why it matches plant phenotyping methods地下部植物根のRGB・ハイパースペクトル画像データセットを構築し、根のセマンティックセグメンテーション手法を比較・評価しており、植物フェノタイピング手法が中心である。
abstractHere we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools.
Timely and accurate identification of peanut pests and diseases, coupled with effective countermeasures, is pivotal for ensuring high-quality and efficient peanut production. Despite the prevalence of pests and diseases in peanut cultivation, challenges such as minute disease spots, the elusive nature of pests, and intricate environmental conditions often lead to diminished identification accuracy and efficiency. Moreover, continuous monitoring of peanut health in real-world agricultural settings demands solutions that are computationally efficient. Traditional deep learning models often require substantial computational resources, limiting their practical applicability. In response to these challenges, we introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet. LSCDNet preserves only the transition layers to reduce feature map dimensionality, simplifying the model's complexity. The inclusion of a sandglass block bolsters features extraction capabilities, mitigating potential information loss due to dimensionality reduction. Additionally, the incorporation of coordinate attention addresses issues related to positional information loss during feature extraction. Experimental results showcase that LSCDNet achieved impressive metrics with accuracy, precision, recall, and Fl score of 96.67, 98.05, 95.56, and 96.79%, respectively, while maintaining a compact parameter count of merely 0.59 million. When compared with established models such as MobileNetV1, MobileNetV2, NASNetMobile, DenseNet-121, InceptionV3, and X-ception, LSCDNet outperformed with accuracy gains of 2.65, 4.87, 8.71, 5.04, 6.32, and 8.2%, respectively, accompanied by substantially fewer parameters. Lastly, we deployed the LSCDNet model on Raspberry Pi for practical testing and application and achieved an average recognition accuracy of 85.36%, thereby meeting real-world operational requirements.
Why it matches plant phenotyping methodsピーナッツの病害状態を識別する軽量画像認識モデルを開発し、比較評価とRaspberry Pi上での実運用検証を行っており、植物病害フェノタイピング手法が中心である。
abstractwe introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet.
Groundnut is an oil seed cash crop, that can be consumed directly by humans and animals. It is also used as an intelligence in the industrial production of butter and other products. However, Groundnut seeds can become infected with fungi, viruses, pests, physical damage, and high heat, which can negatively impact crop yield, quality, and economic value, and pose health hazards. To address these challenges, computer vision technology detects and classifies defects. This study introduced an ensemble deep learning defected classification model that combines VGG16 and InceptionV3 using seed images. To enhance model performance, collected images are preprocessed using novel techniques tailored to different image types. Preprocessing techniques are chosen based on image-quality evaluation metrics. Watershed segmentation is applied followed by detecting the region of interest in the image using YOLOv3. The image dataset is augmented and balanced using a Generative Adversarial Network (GAN). The model development involves a combination of classical and deep-based features, comparing features extracted with (HOG and GLCM) to those extracted with InceptionV3 and VGG16. The ensemble model achieves an accuracy of 96.25% with a split ratio of 10% for testing, 10% for validation, and 80% for training set. This research benefits farmers looking to improve yield, consumers of groundnut products, and researchers studying seed defects. The work provides valid insights for future researchers regarding dataset creation, augmentation, methods, and feature selection for modeling. However, the model experienced misclassifications due to the similar appearance of sample images in different classes. Future researchers should address these challenges and consider factors such as oil content and defect grading.
Why it matches plant phenotyping methods落花生種子の欠陥状態を画像から分類するコンピュータビジョン手法の開発が研究の中心であり、植物器官の表現型状態を推定している。
abstractThis study introduced an ensemble deep learning defected classification model that combines VGG16 and InceptionV3 using seed images.
Pests and viral plant diseases have proliferated in agriculture. Rats spread plant diseases. Hard to detect viral infections or insect infestations that impair plant growth and yield. It decreases crop size, quality, and marketability, incurring major economic losses. Strong, disease-resistant crops are needed for agriculture. Bacterial, fungal, and insect/pest diseases destroy 70% and 15% of crops. Above 85% of disease severity destroys crops. Early detection systems track pests and diseases. High-definition equipment is costly for rural farmers. Correct diagnosis permits quick infection severity and type assessment and control. In precision agriculture, neural networks and image processing eliminate human error. Sustainable agriculture and organic agricultural cultivation must start early. The proposed system classifies diseases and pests for plant safety. A healthy, fast-growing plantation is our goal. Early detection of viral diseases and insect infestations boosts crop productivity. Plant disease diagnostics focuses on leaves and buds. Stem and insect infections are covered. The suggested method classifies plant health and disease automatically. Peanut leaves develop Bacterial Blight from contagious illnesses like Cercospora Leaf Peanut. Boll Weevils, European Corn Borers, and Fall Army Worms attack cotton. The conditions are categorised. Image accuracy depends on lighting, resolution, location, and backdrop complexity. Various low-resolution pictures are enriched to better classification. Complex leaf intensity variations cause the system to misdiagnose illness or insect infestation. To avoid misclassification, the system segments the sample image using various methods to find lesions. Multilevel Segmentation (MS) improves Deep neural network feature extraction using approximate outlines. This method protects important pixels, eliminates unneeded pixels, and amplifies the lesion to control segmentation and reduce insufficient and excessive segmentation.
Why it matches plant phenotyping methods植物画像から病害・病徴を自動検出し、病変分割とニューラルネットワーク分類を行う手法開発が中心であり、植物の健康状態・病害状態を直接推定している。
abstractThe suggested method classifies plant health and disease automatically.
Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F , with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.
Why it matches plant phenotyping methods落花生種子の活力をハイパースペクトルデータから推定する高スループット表現型計測法を開発・検証し、機械学習モデルによる推定表現型をGWASに利用しているため、表現型取得法が研究の中心である。
abstractThis study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes.
Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.
Why it matches plant phenotyping methods落花生種子の品質・カビ・欠損・乾燥・発芽状態を、テラヘルツ画像とCNNで非破壊的に識別する手法が研究の中心であり、種子状態という植物表現型を抽出している。
abstractwe present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach.
Groundnut (Arachis hypogaea) is a widely cultivated legume crop that plays a vital role in global agriculture and food security. It is a major source of vegetable oil and protein for human consumption, as well as a cash crop for farmers in many regions. Despite the importance of this crop to household food security and income, diseases, particularly Leaf spot (early and late), Alternaria leaf spot, Rust, and Rosette, have had a significant impact on its production. Deep learning (DL) techniques, especially convolutional neural networks (CNNs), have demonstrated significant ability for early diagnosis of the plant leaf diseases. However, the availability of groundnut-specific datasets for training and evaluation of DL models is limited, hindering the development and benchmarking of groundnut-related deep learning applications. Therefore, this study provides a dataset of groundnut leaf images, both diseased and healthy, captured in real cultivation fields at Ramchandrapur, Purba Medinipur, West Bengal, using a smartphone camera. The dataset contains a total of 1720 original images, that can be utilized to train DL models to detect groundnut leaf diseases at an early stage. Additionally, we provide baseline results of applying state-of-the-art CNN architectures on the dataset for groundnut disease classification, demonstrating the potential of the dataset for advancing groundnut-related research using deep learning. The aim of creating this dataset is to facilitate in the creation of sophisticated methods that will aid farmers accurately identify diseases and enhance groundnut yields.
Why it matches plant phenotyping methods落花生葉の健全・病葉画像データセットを提供し、植物病害状態の画像ベース判定を可能にすることが中心で、ベースライン評価も含むため。
abstractTherefore, this study provides a dataset of groundnut leaf images, both diseased and healthy, captured in real cultivation fields
Reproduction assets foundThe paper's own groundnut leaf image dataset (1720 images, diseased and healthy) is publicly deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL.Dataset · publiccategorised based on disease criteria with the assistance of a pathologist.
Data source location
Ramchandrapur, Purba Medinipur, West Bengal, India, Pin: 721429 Latitude 21.930146 and Longitude 87.556852
Data accessibility
Repository name: Mendeley Data.
Data identification number: DOI: 10.17632/x6x5jkk873.2
Direct URL to data: https://data.mendeley.com/datasets/x6x5jkk873/2
Instructions for accessing these data: All the image can be downloaded by the following link: https://data.mendeley.com/datasets/x6x5jkk873/2
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We address four prominent diseases that specifically target groundnut leaves, causing significant damage to numerous groundnut fields. Researchers and practiOpen asset ↗Mendeley Data · 10.17632/x6x5jkk873.2lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Peanut / groundnutMultimodalLiDAR / point cloudFruitSegmentation
Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%.
Why it matches plant phenotyping methods落花生莢の接触画像を対象に、3D点群と2D画像を融合した個体分割・輪郭抽出法を開発しており、植物器官の形状取得が研究の中心である。
abstracta multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds.
Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss of groundnut plant growth, which will directly diminish the yield and quality. Therefore, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System (GLDI-WDCGAN-AOA) is proposed in this paper. The pre-processed output is fed to Hesitant Fuzzy Linguistic Bi-objective Clustering (HFL-BOC) for segmentation. By using Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN), the input leaf images are classified into Healthy leaf, early leaf spot, late leaf spot, nutrition deficiency, and rust. Finally, the weight parameters of WDCGAN are optimized by Aquila Optimization Algorithm (AOA) to achieve high accuracy. The proposed GLDI-WDCGAN-AOA approach provides 23.51%, 22.01%, and 18.65% higher accuracy and 24.78%, 23.24%, and 28.98% lower error rate analysed with existing methods, such as Real-time automated identification and categorization of groundnut leaf disease utilizing hybrid machine learning methods (GLDI-DNN), Online identification of peanut leaf diseases utilizing the data balancing method along deep transfer learning (GLDI-LWCNN), and deep learning-driven method depending on progressive scaling method for the precise categorization of groundnut leaf infections (GLDI-CNN), respectively.
Why it matches plant phenotyping methods葉画像から病害状態を分類する画像ベースの植物表現型推定手法を開発・比較しており、病害識別が研究の中心であるため。
abstractan Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System (GLDI-WDCGAN-AOA) is proposed in this paper.
Plant pathogens are commonly identified in the field based on the typical disease that they can cause. For effective management measures and the selection of highly resistant breeding stock, effective early disease detection and disease identification and localisation are essential. However, traditional detection methods, which rely on field administrators’ experience, are inefficient for large-scale production. This study introduces an automated leaf disease detection system, including edge computing equipment deploying an improved lightweight detection algorithm, Huawei 5G communication equipment and specialised management software. However, existing lightweight deep learning-based methods often fall short in overall performance when considering aspects such as model size, parameters or FLOPs collectively. To enhance the performance of the system, this study introduces an improved YOLOv8n network model that incorporates advancements in the FasterNeXt, DSConv modules and the Generalized Intersection over Union loss function. This improvement aims to lightweight the model to adapt to edge computing devices and achieve real-time peanut leaf disease detection while maintaining high detection accuracy. Experimental results indicate a noteworthy reduction in the number of model parameters and FLOPS, by 31.01% and 45.40%, respectively, when compared with the original YOLOv8n, while achieving a mean average precision of 91.10% and a precision of 89.80%. Moreover, the detection speed on the central processing unit (CPU) and graphics processing unit (GPU) platforms were 19.10 and 72.30 img/s, which were 35.07% and 7.05% better than the original algorithm. Notably, our approach leverages an improved YOLOv8 algorithm for leaf disease detection, supplemented with location data acquired via QR codes for the generated region-wide disease condition map. Field trials have shown that the efficiency of the system in monitoring diseases has increased by 74.19% compared with that in humans, contributing to the early detection of diseases and breeding of peanut leaf disease-resistant varieties, improvement of monitoring efficiency and reduction of labour costs.
Why it matches plant phenotyping methods落花生葉の病徴を画像から検出・局在化する軽量深層学習モデルとエッジ実装を開発・評価しており、植物病害状態の表現型取得が研究の中心である。
abstractThis study introduces an automated leaf disease detection system, including edge computing equipment deploying an improved lightweight detection algorithm, Huawei 5G communication equipment and specialised management software.
Abstract Predicting soil water status remotely is appealing due to its low cost and large‐scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre‐dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought‐induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn ( Zea mays ), which disconnected from the soil during intense drought, and peanut ( Arachis hypogaea ), which did not. Sweet corn's hydraulic disconnection led to an extended ‘hydrated’ phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (−5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species‐specific considerations in soil water status predictions from canopy reflectance.
Why it matches plant phenotyping methods植物の葉のハイパースペクトル反射から土壌・根圏の水分状態を推定する手法を明示的に評価し、種間で予測性能を検証しているため、手法応用・検証として中心的です。
abstractdetermine the potential for predicting soil water status from plant spectral reflectance
High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.
Why it matches plant phenotyping methods画像処理・形態計測・色彩計測と機械学習を用いて遺伝資源の表現型記述子を開発・比較し、遺伝資源管理ワークフローへ統合することが中心であるため。
abstractHigh-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms.
Reproduction assets foundThe paper's Data Availability statement explicitly points to a public GitHub repository containing the phenomics and traditional descriptor data underlying the study, which directly reproduces the paper's plant-phenotyping measurements. Figures and tables in the article are not treated as separate assets.Dataset · publicData Availability: The data underlying the results presented in the study are available from https://github.com/agrocompuepidemlab/Digital-descriptors-genebank The data of the phenomics and traditional descriptors of the evaluated accessions are associated to this one.Open asset ↗agrocompuepidemlab/Digital-descriptors-genebanklines:143-155Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Aflatoxins are highly carcinogenic secondary metabolites of some fungal species, particularly Aspergillus flavus. Aflatoxins often contaminate economically important agricultural commodities, including peanuts, posing a high risk to human and animal health. Due to the narrow genetic base, peanut cultivars demonstrate limited resistance to fungal pathogens. Therefore, numerous wild peanut species with tolerance to Aspergillus have received substantial consideration by scientists as sources of disease resistance. Exploring plant germplasm for resistance to aflatoxins is difficult since aflatoxin accumulation does not follow a normal distribution, which dictates the need for the analyses of thousands of single peanut seeds. Sufficiently hydrated peanut (Arachis spp.) seeds, when infected by Aspergillus species, are capable of producing biologically active stilbenes (stilbenoids) that are considered defensive phytoalexins. Peanut stilbenes inhibit fungal development and aflatoxin production. Therefore, it is crucial to analyze the same seeds for peanut stilbenoids to explain the nature of seed resistance/susceptibility to the Aspergillus invasion. None of the published methods offer single-seed analyses for aflatoxins and/or stilbene phytoalexins. We attempted to fulfill the demand for such a method that is environment-friendly, uses inexpensive consumables, and is sensitive and selective. In addition, the method is non-destructive since it uses only half of the seed and leaves the other half containing the embryonic axis intact. Such a technique allows germination and growth of the peanut plant to full maturity from the same seed used for the aflatoxin and stilbenoid analysis. The integrated part of this method, the manual challenging of the seeds with Aspergillus, is a limiting step that requires more time and labor compared to other steps in the method. The method has been used for the exploration of wild Arachis germplasm to identify species resistant to Aspergillus and to determine and characterize novel sources of genetic resistance to this fungal pathogen.
Why it matches plant phenotyping methods単一種子のアフラトキシン・スチルベノイドを非破壊測定し、同一種子の抵抗性評価と生育を可能にする手法の開発が中心であり、植物の病害抵抗性状態の表現型取得に直接結びつく。
abstractWe attempted to fulfill the demand for such a method that is environment-friendly, uses inexpensive consumables, and is sensitive and selective.
Peanut / groundnutMultispectral / hyperspectralClassificationGrowth / development / phenology
Artificial intelligence has played a significant role in the expansion of the agriculture industry in recent times by evaluating data and making recommendations for better production. An automated method for determining significant information in seed quality analysis is the peanut maturity analysis in image processing through sensory images. The majority of the time, changes in picture intensity result in feature independence and precise maturity level determination. Therefore, agricultural precision in identifying essential features is low. To address this issue, we suggest employing a Cross-Layer Multi-Perception Neural Network (CLMPNN) for hyperspectral sensory image feature observation in order to determine the optimal assessment of peanut maturity in agriculture. The sensing unit first determines the angular cascade projection’s (ACP) structural dependencies for the peanut pod structure. With the aid of color-intensive saturation, the entity projection of pod growth is found using the Slicing Fragment Segmentation (SFS) technique. This generates the various entity variations by integrating relational maturity and non-maturity findings with spectral values. Next, cross-layer multi-perception neural networks are trained with hyperspectral values optimized by LSTM to distinguish between mature and immature pods. In comparison to the other system, this one does exceptionally well in precision agriculture, with a 98.6 well recall rate, a 97.3% classification accuracy, and a 98.9% production accuracy.
Why it matches plant phenotyping methods落花生莢の成熟状態をハイパースペクトル画像、画像分割、ニューラルネットワークで推定する手法が研究の中心であり、植物器官の状態を直接評価している。
abstractwe suggest employing a Cross-Layer Multi-Perception Neural Network (CLMPNN) for hyperspectral sensory image feature observation in order to determine the optimal assessment of peanut maturity in agriculture.
Peanut pod rot is one of the major plant diseases affecting peanut production and quality over China, which causes large productivity losses and is challenging to control. To improve the disease resistance of peanuts, breeding is one significant strategy. Crucial preventative and management measures include grading peanut pod rot and screening high-contributed genes that are highly resistant to pod rot should be carried out. A machine vision-based grading approach for individual cases of peanut pod rot was proposed in this study, which avoids time-consuming, labor-intensive, and inaccurate manual categorization and provides dependable technical assistance for breeding studies and peanut pod rot resistance. The Shuffle Attention module has been added to the YOLOv5s (You Only Look Once version 5 small) feature extraction backbone network to overcome occlusion, overlap, and adhesions in complex backgrounds. Additionally, to reduce missing and false identification of peanut pods, the loss function CIoU (Complete Intersection over Union) was replaced with EIoU (Enhanced Intersection over Union). The recognition results can be further improved by introducing grade classification module, which can read the information from the identified RGB images and output data like numbers of non-rotted and rotten peanut pods, the rotten pod rate, and the pod rot grade. The Precision value of the improved YOLOv5s reached 93.8%, which was 7.8%, 8.4%, and 7.3% higher than YOLOv5s, YOLOv8n, and YOLOv8s, respectively; the mAP (mean Average Precision) value was 92.4%, which increased by 6.7%, 7.7%, and 6.5%, respectively. Improved YOLOv5s has an average improvement of 6.26% over YOLOv5s in terms of recognition accuracy: that was 95.7% for non-rotted peanut pods and 90.8% for rotten peanut pods. This article presented a machine vision- based grade classification method for peanut pod rot, which offered technological guidance for selecting high-quality cultivars with high resistance to pod rot in peanut.
Why it matches plant phenotyping methods落花生莢の腐敗状態・腐敗率・等級をRGB画像から推定する機械視覚手法を開発・評価しており、植物病害表現型の取得が中心である。
abstractA machine vision-based grading approach for individual cases of peanut pod rot was proposed in this study
Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper’s improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する深層学習手法を提案・比較しており、植物フェノタイピング手法が研究の中心である。
abstractthis paper proposes an effective plant disease image recognition method aECA-ResNet34
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 15 Sept 2026
MaizePeanut / groundnutLaboratory / benchtopMultimodalMultispectral / hyperspectralRootSegmentationRoot system architecture
Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species - peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatialspectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRIs hyperspectral and spatial information improves semantic segmentation of target objects.
Why it matches plant phenotyping methods地下部根系のRGB・ハイパースペクトル画像データセットを構築し、根のセマンティックセグメンテーション手法を比較する研究であり、植物表現型取得・抽出が中心です。
abstractHere we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools.
Accurate prediction of peanut growth and maturity is crucial to improve crop management and strengthening breeding programs. Remote sensing technology, such as satellites and drones, can facilitate in-season crop growth monitoring through the collection of high temporal and spatial imagery that capture differences in crop spectral reflectance. Current robust algorithms for predicting multiple peanut growth variables have not been proposed. This study aimed to develop algorithms for prediction of multiple peanut growth variables using a multi-output regression (MTR) approach. Two commercial irrigated fields, one of 8.6 ha located in Society Hill Alabama (AL), U.S. (study area 1), and the second of 54.76 ha (ha) located in Eufaula, Alabama (AL), U.S. (Study area 2) were used for data collection. Peanut biomass samples were collected weekly from 20 locations which each field. Two Peanut Maturity Indices (PMI orange to black and brown to black) were measured from manual assessment of maturity using the peanut profile board. MTR models were built to establish a functional relationship between peanut aboveground biomass, maturity, and spectral reflectance changes of the canopy over time using Random Forest (RF) and K-nearest neighbor. Reflectance from individual spectral bands and vegetation indices (VI) of the biomass sampling location were extracted from Planet scope® satellite images. The algorithms were developed using toolkits available in the Scikit-learn python library and were evaluated using the mean absolute error (MAE) metric. The RF algorithm was able to output multiple numeric values of peanut maturity indices upon VI and spectral bands, supporting the hypothesis that MTR can predict peanut maturity at the field level. The use of spectral reflectance from satellite images resulted in a small prediction error of 9 % for PMI using brown to black pods and 10 % when predicting PMI using orange to black pods. The MTR model was also accurate in predicting aboveground biomass (MAE = 1301 kg ha⁻¹) compared to pod weight (MAE = 1103 kg ha⁻¹). The study demonstrated a promising method to assess within-field variability of peanut maturity using remote sensing images, which could reduce the subjectivity of the manual method.
Why it matches plant phenotyping methods衛星リモートセンシングと多目的回帰により、ピーナッツの成熟度およびバイオマスという植物形質を推定する手法を開発・評価しており、表現型取得・抽出が研究の中心である。
abstractThis study aimed to develop algorithms for prediction of multiple peanut growth variables using a multi-output regression (MTR) approach.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop’s genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.
Why it matches plant phenotyping methodsUAV画像からキャノピー形質を抽出し、成長曲線と機械学習で落花生収量を推定する高スループット表現型解析が研究の中心である。
abstractthis study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut.
Why it matches plant phenotyping methodsマルチスペクトル画像からピーナッツの病害による落葉率を推定するアルゴリズムを開発し、交差検証と独立試験で性能検証しているため、植物フェノタイピング手法が中心である。
abstractThis study aimed to develop an algorithm to quantify peanut disease defoliation using multispectral imagery captured by an unmanned aircraft system.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Root phenes are associated with the absorptive efficiency of water and fertilizers. However, there are few reports on the genetic variation and stability of peanut (Arachis hypogaea L.) root architecture under different environments. In this study, the diversity, variance and stability of root phenes of 89 peanut varieties were investigated with shovelomics (high throughput phenotyping of root system architecture) for two years in both field and laboratory experiments. The root phenes of these peanut genotypes presented rich diversity; for example, the value of total root length (TRL) ranged from 347.84 cm to 1013.80 cm in the field in 2018, and from 55.14 cm to 206.22 cm in the laboratory tests. The root phenes of different genotypes varied differently; for example, the coefficient of variation (CV) of TRL ranged from 24.0 to 83.5 across the two-year field test. Field and laboratory evaluations were highly correlated, especially on lateral root density (LRD) and root angle (RA), and the quadrant graph analysis of LRD and RA implied that 69.7% of the roots belong to the same type. These not only further reflect root phenes stability through different environment but also demonstrate that some root phenes identified at early stage can indicate their status at later growth stage. In addition, root phenes showed a strong correlation with shoot growth, especially root dry weight (RDW), TRL and（nodule number）NN. Thus, laboratory tests in combination with field shovelomics can efficiently screen and select genotypes with contrasting root phenes to optimize water and nutrient management.
Why it matches plant phenotyping methodsピーナッツ根系形態を対象に、shovelomicsによるハイスループット表現型計測を圃場・実験室で適用し、環境間の安定性と評価の相関を検証している。根形質の測定・比較手法が研究の中心である。
abstractinvestigated with shovelomics (high throughput phenotyping of root system architecture) for two years in both field and laboratory experiments.
Root phenes are associated with the absorptive efficiency of water and fertilizers. However, there are few reports on the genetic variation and stability of peanut (Arachis hypogaea L.) root architecture under different environments. In this study, the diversity, variance and stability of root phenes of 89 peanut varieties were investigated with shovelomics (high throughput phenotyping of root system architecture) for two years in both field and laboratory experiments. The root phenes of these peanut genotypes presented rich diversity; for example, the value of total root length (TRL) ranged from 347.84 cm to 1013.80 cm in the field in 2018, and from 55.14 cm to 206.22 cm in the laboratory tests. The root phenes of different genotypes varied differently; for example, the coefficient of variation (CV) of TRL ranged from 24.0 to 83.5 across the two‐year field test. Field and laboratory evaluations were highly correlated, especially on lateral root density (LRD) and root angle (RA), and the quadrant graph analysis of LRD and RA implied that 69.7% of the roots belong to the same type. These not only further reflect root phenes stability through different environment but also demonstrate that some root phenes identified at early stage can indicate their status at later growth stage. In addition, root phenes showed a strong correlation with shoot growth, especially root dry weight (RDW), TRL and（nodule number）NN. Thus, laboratory tests in combination with field shovelomics can efficiently screen and select genotypes with contrasting root phenes to optimize water and nutrient management.
Why it matches plant phenotyping methodsピーナッツ根系形態を対象に、shovelomicsによるハイスループット根系表現型測定を圃場・実験室で比較検証し、安定性や遺伝子型選抜への適用を評価しており、表現型取得法が研究の中心です。
abstractinvestigated with shovelomics (high throughput phenotyping of root system architecture) for two years in both field and laboratory experiments
Off‐target drift of herbicides can seriously reduce peanut (Arachis hypogea L.) growth and yield and is of great concern to growers who will need to manage sensitive crops near new herbicide‐tolerant crops. Field experiments were conducted in 2021 and 2022 with 25% labeled rates of dicamba, glufosinate, glyphosate, lactofen, and paraquat to simulate drift on peanut. The objective was to evaluate the effects of low‐rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery‐based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury from the herbicides applied at vegetative (V3) and reproductive (R3) growth stages. Peanut suffered greater yield reduction (33%) when exposed to the herbicides at R3 than at V3 growth stage (19%) across all herbicides applied. The order of herbicides that induced yield reductions in peanut was glyphosate > glufosinate = dicamba > paraquat = lactofen. Regardless of exposure timing, NDVI values generated from UAV imagery could not differentiate paraquat or lactofen injury from the weed‐free check. However, NDVI values could differentiate between injured and weed‐free check plants up to 2 and 4 weeks after treatment (WAT) for dicamba at R3 and V3 exposure timing, respectively, up to 4 WAT for glufosinate, and 8 WAT for glyphosate. NDVI from aerial imagery may be helpful to accelerate the detection of injury in large hectarages with greater accuracy compared with visual injury rating, which can be influenced by individual estimation bias.
Why it matches plant phenotyping methodsUAV画像由来NDVIによる除草剤傷害（植物状態）の推定精度を、目視評価と比較して検証しており、植物フェノタイピング手法の技術評価が明示的な目的の一部である。
abstractThe objective was to evaluate the effects of low‐rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery‐based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury
Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model ‘CNN8GN’. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy.
Why it matches plant phenotyping methods落花生葉の病害状態を画像から推定するCNN手法の開発・比較検証とデータセット構築が中心であり、植物表現型計測に該当する。
abstractThis paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model ‘CNN8GN’.
A millimeter-wave FMCW radar-based mass flow sensor was developed and evaluated to monitor peanut mass flow rate towards development of a peanut yield monitor. The radar sensor components were placed outside a customized plastic duct in the pneumatic conveyor of a peanut combine. Two systems to simulate the mass flow conditions during harvest were built: one for research-scale mass flow rate using a retrofitted 2-row combine blower and one for commercial-scale mass flow rate using a modified 6-row combine. The ground truth for mass flow rate was obtained and the radar sensor was used to acquire range-velocity image time-series. Two datasets were generated using a sliding window techniqueand several machine learning models (i.e., linear regression, k-neighbor regressor, support vector regressor, random forest regressor, and multi-layer perceptron) were trained to predict peanut mass flow rate from radar data. After evaluation of 5-fold cross validation, k-neighbor regression achived the best performance for the research-scale combine system with an RMSE of 0.14 kg/s, a sMAPE of 15 %, and an R² value of 0.85, while random forest regression achieved the best performance for the commericial-scale combine system with an RMSE of 0.52 kg/s, a sMAPE of 10 %, and an R² value of 0.71. Moreover, the sensor can potentially provide the combine operator with information about the velocity of the peanuts to adjust the air pressure of the pneumatic conveyor to reduce undesired peanut shelling. While the peanut mass flow prediction results are promising, further field investigation is necessary to evaluate the effects of noise caused by combine movement, foreign materials, peanut varieties, moisture content, and soil type.
Why it matches plant phenotyping methodsmmWaveレーダーと機械学習により、収穫時のピーナッツ収量（質量流量）を推定するセンサー手法の開発・評価が研究の中心である。
abstractA millimeter-wave FMCW radar-based mass flow sensor was developed and evaluated to monitor peanut mass flow rate towards development of a peanut yield monitor.
Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.
Why it matches plant phenotyping methodsハイパースペクトル画像とCNN・機械学習を用いてピーナッツ種子の生存性を非破壊的に判定する手法を開発・比較しており、種子状態の取得・推定が研究の中心である。
abstractHyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet.
Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop, assessing seed viability of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50°C over 24-hour intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky-Golay (SG) and Multiplicative Scatter Correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of meta-heuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), Genetics (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 seconds) and LCA (44.9389 seconds) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected by the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), Multilayer Perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and NaiveBayes (NB) classifiers. The DSOS-DT and FOA-MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS-LR and DSOS-KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and non-destructive approach for detecting seed viability.
Why it matches plant phenotyping methodsピーナッツ種子の生存性という植物状態をVis/NIR分光で非破壊推定し、波長選択アルゴリズムと分類器を比較・評価する方法研究であり、表現型取得と解析手法が中心である。
abstractthis study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds
Published1 Oct 2023Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Phytochemically, peanuts provide significant nutritional value to humans, animals, and the food industry as a whole. This study was conducted to explore the potential for near-infrared (NIR) spectroscopy and effective variable selection algorithms to quantify phenolic compounds in peanut seeds. The phenolics were extracted and then identified and quantified using high-performance liquid chromatography (HPLC). The spectroscopic data were acquired from the peanut samples using a tabletop NIR spectrometer with a wavelength range of 10,000–4000 cm–¹. The acquired spectra were preprocessed using a synergistic effect of first- and second-order derivative (FOD and SOD) preprocessing techniques, and multivariate algorithms were used, examined, and evaluated using correlation coefficients of the validation set (Rₚ), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). The competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model produced optimum performance for chlorogenic acid (Rₚ = 0.933, RPD = 2.77), kaempferol (Rₚ = 0.928, RPD = 2.68), p-coumaric acid (Rₚ = 0.900, RPD = 2.32), and quercetin (Rₚ = 0.932, RPD = 2.88), respectively. Therefore, this study proved that NIR spectroscopy in combination with CARS-PLS was capable of nondestructively predicting phenolic content in peanut seeds.
Why it matches plant phenotyping methodsピーナッツ種子のフェノール含量を非破壊推定するNIR分光法とCARS-PLSモデルの開発・検証が研究の中心であり、植物器官の化学的形質を定量する方法論的貢献が明確である。
abstractThis study was conducted to explore the potential for near-infrared (NIR) spectroscopy and effective variable selection algorithms to quantify phenolic compounds in peanut seeds.
India is the world's second-largest groundnut producer after Brazil. An major crop of oilseeds is groundnuts. Because of this, the crop's quality and yield have declined, which has had a detrimental effect on the agricultural economy. This is partly because the crop is more susceptible to various diseases. It is required to create more precise and reliable automated approaches to address this problem and improve the identification of groundnut leaf diseases. This article proposes a deep learning-driven approach based on a progressive scaling technique for the accurate classification and identification of groundnut leaf diseases. The five main groundnut leaf diseases that are the subject of this study are leaf spot, armyworm effect, wilts, yellow leaf, and healthy leaf. The proposed model is trained using both progressive resizing and conventional techniques, and its performance is assessed using cross-entropy loss. A fresh dataset is meticulously curated in Gujarat state, India's Saurashtra region, for training and validation. Due to the dataset's uneven sample distribution across disease categories, an extended focus loss function was used to correct this class imbalance. In order to evaluate the performance of the suggested model, a number of performance metrics are utilized, including accuracy, sensitivity, F1-score, precision, and sensitivity. Notably, the suggested model has a 96.12% success rate, which signifies a considerable increase in the disease identification accuracy. It's important to note that the model incorporating progressive resizing beats the basic neural network-based model based on cross-entropy loss, highlighting the potency of the recommended approach.
Why it matches plant phenotyping methods落花生葉の病害状態を画像から分類する深層学習手法を開発・評価しており、植物病害表現型の取得・推定が中心です。
abstractThis article proposes a deep learning-driven approach based on a progressive scaling technique for the accurate classification and identification of groundnut leaf diseases.
Abstract Agronomic and physiological traits in peanut ( Arachis hypogaea ) are important to breeders for selecting high‐yielding and resilient genotypes. However, direct measurement of these traits is labor‐intensive and time‐consuming. This study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress. Two different approaches were evaluated. The first approach employed eighty narrowband vegetation indices as input features for an ensemble model that included K‐nearest neighbors, support vector regression, random forest, and multi‐layer perceptron (MLP). The second approach utilized mean and standard deviation of canopy spectral reflectance per band. The resultant 400 features were used to train a deep learning (DL) model consisting of one‐dimensional convolutional layers followed by an MLP regressor. Predictions of the agronomic traits obtained using feature learning and DL ( R 2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models ( R 2 = 0.44–0.61, sMAPE = 27%–59%). In contrast, the ensemble model had a slightly better performance in predicting physiological traits ( R 2 = 0.35–0.57; sMAPE = 37%–70%) compared to the results obtained from the DL model ( R 2 = 0.36–0.52; sMAPE = 47%–64%). The results showed that the combination of UAV‐based hyperspectral imaging and ML techniques have the potential to assist breeders in rapid screening of genotypes for improved yield and drought tolerance in peanut.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習による植物形質の推定手法を開発・比較評価しており、表現型取得・抽出が研究の中心である。
abstractThis study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress.
Peanuts are an important cash crop in the national economy, and their chlorophyll content can reflect the fertilizer status of peanuts. Therefore, it is urgent to obtain a rapid and accurate method for monitoring chlorophyll content. In this study, two types of peanuts, Yanghua 1 and Yueyou 45, were planted according to different plant densities, and eight vegetation indices were calculated using Parrot Bluegrass multispectral drone photography, so as to establish different monitoring model, and to compare the accuracy of each model according to the coefficient of determination, root mean square error, and absolute mean error. The chlorophyll content of Yanghua 1 peanut leaves was generally higher than that of Yueyou 45, and the plant density had a greater influence on Yanghua 1 peanut. The one-dimensional linear regression models of NDVI(Normalized Difference Vegetation Index) and GNDVI(Green Normalized Difference Vegetation Index) had a much higher degree of fit and precision than the other indices; and the multiple linear regression model had a more accurate predictive ability than the one-dimensional linear regression model. After testing, BP neural network(Back Propagation Neural Network)is the most suitable model for monitoring the chlorophyll content of peanut with better fit and accuracy than the random forest model. The multispectral drone could rely on the highly accurate predictive model to quickly obtain information on the chlorophyll content in the field and infer the most suitable crop type and planting density for local planting.
Why it matches plant phenotyping methodsドローン multispectral 画像から植葉のクロロフィル含量を推定する特徴抽出・回帰モデルを開発し、精度比較と検証を行っており、フェノタイピング手法が中心である。
titleMonitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction
Compared to the well-studied model legumes, where symbiosis is established via root hair entry, the peanut is infected by Bradyrhizobium through the crack entry, which is less common and not fully understood. Crack entry is, however, considered a primitive symbiotic infection pathway, which could be potentially utilized for engineering non-legume species with nitrogen fixation ability. We utilized a fluorescence-labeled Bradyrhizobium strain to help in understanding the crack entry process at the cellular level. A modified plasmid pRJPaph-bjGFP, harboring the codon-optimized GFP gene and tetracycline resistance gene, was created and conjugated into Bradyrhizobium strain Lb8, an isolate from peanut nodules, through tri-parental mating. Microscopic observation and peanut inoculation assays confirmed the successful GFP tagging of Lb8, which is capable of generating root nodules. A marking system for peanut root potential infection sites and an optimized sample preparation protocol for cryostat sectioning was developed. The feasibility of using the GFP-tagged Lb8 for observing crack entry was examined. GFP signal was detected at the nodule primordial stage and the following nodule developmental stages with robust GFP signals observed in infected cells in the mature nodules. Spherical bacteroids in the root tissue were visualized at the nodules' inner cortex under higher magnification, reflecting the trace along the rhizobial infection path. The GFP labeled Lb8 can serve as an essential tool for plant-microbe studies between the cultivated peanut and Bradyrhizobium, which could facilitate further study of the crack entry process during the legume-rhizobia symbiosis.
Why it matches plant phenotyping methodsGFP標識菌の蛍光顕微鏡観察に加え、感染部位のマーキングと凍結切片作製プロトコルを開発し、植物根・根粒内の感染状態を可視化する方法が中心である。
abstractA marking system for peanut root potential infection sites and an optimized sample preparation protocol for cryostat sectioning was developed.
Peanut is a key food commodity, and diseases of its leaves can have a negative impact on the quantity and quality of the product. The seed has high levels of potassium, magnesium, calcium, riboflavin, niacin, folic acid, vitamin E, resveratrol, and amino acids. It also contains 36% to 54% oil, 16% to 36% protein, and 10% to 20% carbs. Early, late, and rusty leaf spots, among other frequent illnesses, are recognized by this equipment. Techniques for image augmentation have been used, such as twisting, rotating, and scaling. The variant employs a Multi Class Convolutional neural network with 5 output which include Normal Leaf, Images Without Leaf and the images with the leaf been infected by the diseases.
Why it matches plant phenotyping methods葉画像の分類・セグメンテーションにより、植物体の病害状態を画像から推定する手法が研究の中心であるため。
titleClassification and Segmentation of Leaf Images based on Deep Learning for Peanut Plant Disease Detection
During the seedling stage, real-time monitoring and detection of seed germination are important for testing the quality of seeds, crop field management, and yield estimation. However, owing to the low efficiency of traditional manual seedling rate monitoring, survey methods have been gradually replaced by unmanned aerial vehicles (UAVs) and real-time peanut video counting models. In this study, we propose an efficient and fast real-time peanut video counting model (combining the improved YOLOV5s, DeepSort, and OpenCV programs) to accurately distinguish peanut seedlings from weeds, and to count peanut seedlings based on videos. The improved YOLOV5s combines a vision transformer with CSNet to replace the original CSNet backbone. The field experiment results show that the real-time peanut video counting model count capabilities is close to those of humans with an accuracy of 98.08%; however, the seedling calculation model takes only one-fifth of the time required for human detection. Therefore, the video-based model outperforms the image-based target detection algorithm, and was more suitable for application in practical germination rate investigation in peanut production.
Why it matches plant phenotyping methodsUAV動画と深層学習により、落花生苗の出芽率を自動計数する手法を開発・評価しており、植物状態の取得が研究の中心である。
abstractwe propose an efficient and fast real-time peanut video counting model
The use of machine learning is rapidly expanding across many industries, including agriculture and the IT sector. However, data is essential for machine learning models, and a substantial amount of data is required prior to training a model. We have collected data of groundnut plant leaves in the form of digital photographs taken in the Koppal (Karnataka, India) area with the assistance of a pathologist in natural settings. Images of leaves are categorized into six distinct groups according to their condition. Collected images are pre-processed and the processed images of groundnut leaves are kept in 6 folders as: the "healthy leaves" folder with 1871 images, the "early leaf spot" folder with 1731 images, the "late leaf spot" folder with 1896 images, the "Nutrition deficiency" folder with 1665 images, the "rust" folder with 1724 images, and the "early rust" folder with 1474 images. The total number of images in the dataset is 10361. This dataset will be useful to train and validate deep learning and machine learning algorithms for groundnut leaf disease classification and recognition. Disease detection in plants is crucial for limiting crop losses and our dataset will help disease detection in groundnut plants. This dataset is freely accessible to public at https://data.mendeley.com/datasets/22p2vcbxfk/3 and at https://doi.org/10.17632/22p2vcbxfk.3.
Why it matches plant phenotyping methods落花生葉の画像データセット自体を構築・公開し、葉の健康状態や病徴分類に利用する研究であり、植物病害状態の画像ベース表現型取得が中心です。
titleDataset of groundnut plant leaf images for classification and detection
Reproduction assets foundThe paper is a data descriptor for a public groundnut leaf image dataset (10,361 annotated images across six disease/health classes) deposited on Mendeley Data, with explicit public URLs and DOI. This is a paper-specific plant image dataset directly used for the phenotyping/disease-classification analysis. No author's Dataset · publichis dataset will be useful to train and validate deep learning and machine learning algorithms for groundnut leaf disease classification and recognition. Disease detection in plants is crucial for limiting crop losses and our dataset will help disease detection in groundnut plants. This dataset is freely accessible to public at https://data.mendeley.com/datasets/22p2vcbxfk/3 and at https://doi.org/10.17632/22p2vcbxfk.3
Keywords: Classification of leaf diseases, Image dataset, Diagnosis of disease, Computer Vision
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2023 Mar 10; Revised 2023 Apr 14; Accepted 2023Open asset ↗10.17632/22p2vcbxfk.3lines:1-56Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 7 Sept 2026
We deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population. Sensors mounted on an unmanned aerial vehicle (UAV) were used to derive various vegetative indices as well as canopy temperatures. A combination of aerial imaging and manual scoring showed that CSSL 100, CSSL 84, CSSL 111, and CSSL 15 had remarkably low tomato spotted wilt virus (TSWV) incidence, a devastating disease in South Georgia, USA. The four lines also performed well under leaf spot pressure. The vegetative indices showed strong correlations of up to 0.94 with visual disease scores, indicating that aerial phenotyping is a reliable way of selecting under disease pressure. Since the yield components of peanut are below the soil surface, we deployed ground penetrating radar (GPR) technology to detect pods non-destructively. Moderate correlations of up to 0.5 between pod weight and data acquired from GPR signals were observed. Both the manually acquired pod data and GPR variables highlighted the three lines, CSSL 84, CSSL 100, and CSSL 111, as the best-performing lines, with pod weights comparable to the cultivated check Tifguard. Through the combined application of manual and HTP techniques, this study reinforces the premise that chromosome segments from peanut wild relatives may be a potential source of valuable agronomic traits.
Why it matches plant phenotyping methodsUAV画像・センサーによる地上部形質およびGPRによる地下ポッド形質の取得と、目視評価との相関検証が研究の中心であるため、実質的な植物フェノタイピング手法の適用・検証に該当する。
abstractWe deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population.
Peanut is an essential food and oilseed crop. One of the most critical factors contributing to the low yield and destruction of peanut plant growth is leaf disease attack, which will directly reduce the yield and quality of peanut plants. The existing works have shortcomings such as strong subjectivity and insufficient generalization ability. So, we proposed a new deep learning model for peanut leaf disease identification. The proposed model is a combination of an improved X-ception, a parts-activated feature fusion module, and two attention-augmented branches. We obtained an accuracy of 99.69%, which was 9.67%-23.34% higher than those of Inception-V4, ResNet 34, and MobileNet-V3. Besides, supplementary experiments were performed to confirm the generality of the proposed model. The proposed model was applied to cucumber, apple, rice, corn, and wheat leaf disease identification, and yielded an average accuracy of 99.61%. The experimental results demonstrate that the proposed model can identify different crop leaf diseases, proving its feasibility and generalization. The proposed model has a positive significance for exploring other crop diseases' detection. Supplementary information The online version contains supplementary material available at 10.1007/s11032-023-01370-8.
Why it matches plant phenotyping methods植物葉の病害状態を画像から推定する深層学習手法の開発・比較が中心であり、植物フェノタイピング手法に該当する。
abstractwe proposed a new deep learning model for peanut leaf disease identification.
The condition of plant root systems plays an important role in plant growth and development. The Minirhizotron method is an important tool to detect the dynamic growth and development of plant root systems. Currently, most researchers use manual methods or software to segment the root system for analysis and study. This method is time-consuming and requires a high level of operation. The complex background and variable environment in soils make traditional automated root system segmentation methods difficult to implement. Inspired by deep learning in medical imaging, which is used to segment pathological regions to help determine diseases, we propose a deep learning method for the root segmentation task. U-Net is chosen as the basis, and the encoder layer is replaced by the ResNet Block, which can reduce the training volume of the model and improve the feature utilization capability; the PSA module is added to the up-sampling part of U-Net to improve the segmentation accuracy of the object through multi-scale features and attention fusion; a new loss function is used to avoid the extreme imbalance and data imbalance problems of backgrounds such as root system and soil. After experimental comparison and analysis, the improved network demonstrates better performance. In the test set of the peanut root segmentation task, a pixel accuracy of 0.9917 and Intersection Over Union of 0.9548 were achieved, with an F1-score of 95.10. Finally, we used the Transfer Learning approach to conduct segmentation experiments on the corn in situ root system dataset. The experiments show that the improved network has a good learning effect and transferability.
Why it matches plant phenotyping methods植物根系画像から根系表現型を抽出するセグメンテーション手法を開発し、性能比較と別作物データセットでの転移性検証を行っており、方法が研究の中心である。
abstractwe propose a deep learning method for the root segmentation task.
Peanut is one of the important oil crops. Its kernel quality directly affects its processing characteristics and is an important index for peanut quality evaluation. Establishing a high-throughput phenotyping model for peanut kernel quality and evaluating peanut kernel quality quickly and efficiently might significantly improve the efficiency of peanut breeding. In this study, the spectra of 175 peanut kernel samples (140 RIL populations derived from Yuhua 14 × LOP 215 and 35 other breeding lines) were collected by Antaris II Fourier Transform Near Infrared Spectroscopy Analyzer (Thermo company), and the oil content, protein content, sugar content, and fatty acid content of seed kernel were determined by Soxhlet extraction method, Dumas nitrogen method, anthrone colorimetry, and gas chromatography, respectively. Partial least squares (PLS) was used to construct the near-infrared calibration models of oil content, protein content, sugar content and some fatty acid content of peanut kernel. 30 other peanut materials which were not involved in the modelling were selected to verify the model externally. The determination coefficients (R2) of the models were greater than 0.90, indicating that the models could be applied to the high-throughput prediction of peanut kernel quality traits. This study provides a detection platform for high-throughput phenotypic analysis of peanut kernel quality traits.
Why it matches plant phenotyping methods近赤外分光とPLSによるピーナッツ種子品質形質の高スループット推定モデルを構築し、外部検証しており、形質取得・予測法が研究の中心である。
abstractEstablishing a high-throughput phenotyping model for peanut kernel quality and evaluating peanut kernel quality quickly and efficiently might significantly improve the efficiency of peanut breeding.
Background Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. Fast and accurate are always the goals of the plant 3D model reconstruction research. Results We proposed a 3D reconstruction method based on dual RGB-D cameras for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits' height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was also carried out during the experiment processing. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result showed that the average accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. Conclusions The reconstruction method for the 3D model of the peanut plant described in this paper is capable of rapidly and accurately establishing a 3D model of the peanut plant while also meeting the modeling requirements for other species' breeding processes. This study offers a potential tool to further explore the 3D model for improving traits and agronomic qualities of plants.
Why it matches plant phenotyping methodsピーナッツ植物の3D再構成手法を開発し、植物形質の定量と手動測定・ICP法による精度および速度比較で検証しており、フェノタイピング手法が研究の中心である。
abstractWe proposed a 3D reconstruction method based on dual RGB-D cameras for the peanut plant 3D model quickly and accurately.
Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R 2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.
Why it matches plant phenotyping methods落花生種子の活力という植物形質をハイパースペクトル画像と機械学習で非破壊推定する手法が研究の中心であり、分類・回帰性能も評価している。
abstractIn this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' original contributions (hyperspectral seed vigor data and analysis). The repository URL in the text (https://github.com/cjkka/cjkka/tree/main) is under the allowed base URL https://github.com/cjkka/. No separate code orDataset · publicavailable. This data can be found here: https://github.com/cjkka/ absence of any commercial or financial relationships that could beOpen asset ↗cjkkapdf-page:12 lines:1-54Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
The sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi ( A. flavus , A. niger , Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of peanut seed health quality were found. The incubation period of 216 h was the one that most contributed to discriminating healthy seeds from those containing fungi through multispectral images. Texture (Percent Run), color (CIELab L *) and reflectance (490 nm) were highly effective in discriminating the sanitary quality of peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status (90 to 100%). Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.
Why it matches plant phenotyping methodsマルチスペクトル画像と機械学習を用いて、ピーナッツ種子の健全性・真菌感染状態を画像特徴から識別する手法が研究の中心であり、植物器官の病害状態を測定するフェノタイピングに該当する。
abstractMultispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds
The edge features of peanut grain image are not considered in peanut grain integrity detection, and there are many noises, resulting in low accuracy of peanut grain integrity detection and poor effect of image edge noise removal. Therefore, this paper designs a peanut seed integrity detection method based on deep learning convolution neural network. The edge contour of peanut grain image is closed, the edge contour curve characteristics of peanut grain image are extracted, the noise area of peanut grain edge image is determined according to the filter window of Gaussian kernel function, and the wavelet descriptor in contour description operator is used to reduce the edge noise of peanut grain image. Input the image into the convolution neural network, update the weight by gradient descent method, construct the peanut seed integrity detection model, output the peanut seed integrity detection results, and realize the peanut seed integrity detection. The experimental results show that the proposed detection method can improve the accuracy of peanut grain detection and has certain feasibility.
Why it matches plant phenotyping methodsピーナッツ種子画像から完全性という植物器官形質を抽出する画像解析・CNN手法の開発が中心であり、植物フェノタイピング手法に該当する。
abstractthis paper designs a peanut seed integrity detection method based on deep learning convolution neural network
This paper presents the early identification of water stress in groundnut canopy using unmanned aerial vehicle (UAV) based hyperspectral imaging (in 385-1020 nm) and machine learning (ML) techniques. An efficient hyperspectral imaging (HSI) data analysis pipeline was presented which includes image quality assessment, denoising, band selection, and classification. A novel ML-based ensemble feature selection (FS) algorithm has been proposed for optimal water stress sensitive waveband selection. The data analysis pipeline and the selected bands were validated on HSI data acquired at two different water stress levels. Wavelengths 515.05, 552.16, 711.92, 724.75, and 931.92 nm were identified as optimal water stress sensitive bands in groundnut canopy, using which we could identify early stress with 96.46% accuracy. The proposed data analysis pipeline and ensemble FS algorithm will benefit crop phenotyping applications such as early abiotic stress detection.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像から作物キャノピーの水ストレスを抽出する解析パイプラインとアンサンブル特徴選択法を開発・検証しており、植物フェノタイピング手法が中心である。
abstractAn efficient hyperspectral imaging (HSI) data analysis pipeline was presented which includes image quality assessment, denoising, band selection, and classification.
A healthy crop is required to provide high-quality food for daily consumption. Crop leaf diseases have more influence on agronomic production and our country. Earlier, many scholars relied on traditional techniques to detect and classify leaf diseases. Furthermore, classification at an early stage is impossible when there are not enough experts and inadequate research facilities. As technology progresses into our day to day life, an Artificial Intelligence subset called Deep Learning (DL) models plays a vital role in the automatic identification of groundnut leaf diseases. The essential for controlling diseases that are spread to the healthy development of groundnut farming. Deep Learning can resolve the issues of finding leaf diseases early and effectively. Most of the researchers concentrate on detecting leaf diseases by doing research in Machine Learning (ML) approaches, which leads to low accuracy and high loss. To achieve better accuracy and decreases the loss in the DL model by identifying the leaf diseases of groundnut crops at an early stage, we propose the Progressive Groundnut Convolutional Neural Network (PGCNN) model. This paper mainly focuses on identifying and classifying groundnut leaf diseases with a self-collected dataset which is collected from the various climatic conditions around the village located nearby Pudukkottai district, Tamil Nadu, India. The common diseases that occurred in those areas were gathered namely early spot, late spot, rust, and rosette. Model Performance metrics analysis was done to evaluate the model performance and also compared with the various DL architectures like AlexNet, VGG11, VGG13, VGG16, and VGG19. The proposed models have achieved a training accuracy of 99.39% and a validation accuracy of 97.58%, continuing with an overall accuracy of 97.58%.
Why it matches plant phenotyping methods植物葉の画像から病害状態を自動識別・分類する深層学習手法を提案し、データセット上で性能比較・検証しているため、植物フェノタイピング手法が中心です。
abstractwe propose the Progressive Groundnut Convolutional Neural Network (PGCNN) model.
Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features' extracting ability of plant leaf diseases. Based on the model's optimum feature extraction condition, unimportant channels are removed to reduce the model's parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection.
Why it matches plant phenotyping methods植物葉の病徴を画像から識別する深層学習モデルを開発し、公開・独自データセットで精度と計算性能を評価しており、植物フェノタイピング手法が中心である。
abstractThe accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset.
Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
Why it matches plant phenotyping methodsピーナッツ種子の品種分類とカビ状態検出を目的に、ハイパースペクトル画像と前処理・機械学習モデルを中心的に開発・比較しており、植物の状態を推定するフェノタイピング手法に該当する。
abstracthyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds.
Reproduction assets foundThe paper's data availability statement explicitly deposits the original study contributions (peanut seed hyperspectral data) in a public GitHub repository, which is listed among the allowed URLs.Dataset · publicThe original contributions presented in the study are publicly available. This data can be found here: https://github.com/wuqingsongwj/Peanut-seed .Open asset ↗wuqingsongwj/Peanut-seedlines:589-618Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 8 Sept 2026
In this paper, we present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs. The procedure relies on opensource tools with the only expensive item being one-off purchase of a drone. It includes acquiring images of the field of interest, stitching the images to get the entire field in one image, calculating and extracting the vegetation indices of the individual plots, and analyzing the extracted indices according to the experimental design. Two populations of groundnut genotypes with different maturities were evaluated for their reaction to early and late leaf spot (ELS, LLS) diseases under field conditions in 2020 and 2021. Each population was made up of 12 genotypes in 2020 and 18 genotypes in 2021. Evaluation of the genotypes was done in four locations in each year. We observed a strong correlation between the vegetation indices and the area under the disease progress curve (AUDPC) for ELS and LLS. However, the strength and direction of the correlation depended upon the time of disease onset, level of tolerance among the genotypes and the physiological traits the vegetation indices were associated with. In 2020, when the disease was observed to have set in late in medium duration population, at the beginning of the seed stage (R5), normalized green-red difference index (NGRDI) and variable atmospheric resistance index (VARI) derived at the beginning pod stage (R3) had a positive relationship with the AUDPC for ELS, and LLS. On the other hand, NGRDI and VARI derived from images taken at R5, and physiological maturity (R7) had negative relationships with AUDPC for ELS, and LLS. In 2021, when the disease was observed to have set in early (at R3) also in medium duration population, a negative relationship was observed between NGRDI and VARI and AUDPC for ELS and LLS, respectively. We found consistently negative relationships of NGRDI and VARI with AUDPC for ELS and LLS, respectively, within the short duration population in both years. Canopy cover (CaC), green area (GA), and greener area (GGA) only showed negative relationships with AUDPC for ELS and LLS when the disease caused yellowing and defoliation. The rankings of some genotypes changed for NGRDI, VARI, CaC, GA, GGA, and crop senescence index (CSI) when lesions caused by the infections of ELS and LLS became severe, although that did not affect groupings of genotypes when analyzed with principal component analysis. Notwithstanding, genotypes that consistently performed well across various reproductive stages with respect to the vegetation indices constituted the top performers when ELS, LLS, haulm, and pod yields were jointly considered.
Why it matches plant phenotyping methods圃場画像の取得・結合から植生指数やキャノピー形質の抽出、解析までを含むHTPP手順が研究の中心であり、病害状態の評価に技術的に適用している。
abstractwe present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs.
Peanut / groundnutField / plotRootClassificationObject detectionSegmentationRoot system architecture
Minirhizotron imagery can be used to assess plant root health, and the amount of data for analysis motivates automation of root detection through use of neural networks. Building upon previous work, we show that we can use transfer learning from our PRMI dataset to assess root health across twelve classes of a new dataset to answer questions regarding how root health is affected by access of a tree by large herbivores, site infestation by Pheidole megacephala, and location of the tree. This dataset was collected from three paired sites at the Ol Pejeta Conservancy in Laikipia, Kenya, and consists of 20,000 images collected between September 2021 and May 2022. Each paired site represents four locations based on all four possible combinations of site infestation by Pheidole megacephala for at least 20 years and existence of herbivore-exclusion fence to keep large herbivores out. 1,332 images across all twelve classes of site and treatment combination were labeled with respective ground truths for model training. Our work uses the UNet architecture using pretrained weights on the network encoder and decoder which were obtained in 2019 in work which achieved over 99% accuracy on a dataset of peanut and switchgrass imagery. In our work, we found that training the model with our new dataset resulted in consistent performance across all classes of our new dataset, with over 99% accuracy for each class.
Why it matches plant phenotyping methodsミニライゾトロン画像から植物根を自動セグメンテーションし、根の健全性を評価するUNet転移学習手法を開発・検証しており、表現型取得・抽出が研究の中心である。
abstractthe amount of data for analysis motivates automation of root detection through use of neural networks
For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short.
Why it matches plant phenotyping methodsピーナッツ種子のタンパク質含量を非破壊的に推定するためのハイパースペクトル画像・機械学習手法の提案と性能評価が研究の中心であり、植物器官の形質測定に該当する。
abstractFor rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed.
Peanut / groundnutRootClassificationCountingGrowth / development / phenology
To investigate the relationship between nodule category and plant growth characteristics in peanut, nodules were classified into three categories: large size nodule (LSN) ( greater than 2 mm), medium size nodule (MSN) (1-2 mm) and small size nodule (SSN) ( less than 1 mm) and their position on roots (primary and lateral roots). The number of LSNs on primary roots was found to be much higher than on lateral roots. The number of MSN developed on primary and lateral roots was comparable, whereas, lateral roots had a higher SSN number. In addition, when comparing the LSN and MSN numbers to SSN number, the plant growth parameters showed a high positive correlation. Multivariate regression analysis yielded similar results. This nodule classification technique aids in the identification of peanut nodules and their role in quantification of plant growth characteristics. Further, this technique can be used in identifying/screening of efficient nodulating peanut genotypes.
Why it matches plant phenotyping methods落花生根粒を大きさと根上の位置で分類・定量する手法が中心で、植物の生育特性との関連評価や効率的な根粒形成遺伝子型のスクリーニングに利用可能としているため、植物フェノタイピング手法に該当する。
abstractnodules were classified into three categories: large size nodule (LSN) ( greater than 2 mm), medium size nodule (MSN) (1-2 mm) and small size nodule (SSN) ( less than 1 mm) and their position on roots (primary and lateral roots).
Leaf spot (LS) caused by Cercosporidium personatum is one of the most harmful peanut diseases in the late growth stage and severely affects the yield of peanuts. Hyperspectral disease detection technology is efficient, objective, and accurate and is suitable for large-scale crop management practices. To establish a multi-scale spectral index (SI) with high accuracy and stability for the detection of peanut LS disease, the spectral reflectance of different disease severity levels at leaf, plant, and field scales was collected, and the difference in wavelength caused by disease severity was analyzed using the mean, variance, and dispersion matrix of hyperspectral reflectance. Meanwhile, the feature weights at different scales were obtained using Relief-F, and the average feature weights identified 540, 660, and 770 nm as multi-scale sensitive wavelengths. Three new SIs were constructed by combining single, ratiometric, and normalized wavelengths. The new SIs were compared and analyzed with 35 commonly used SIs by correlation analysis and M-statistic values, and 6 SIs were significantly correlated with disease severity levels and had good separability. Finally, k-nearest neighbor (KNN) and multinomial logistic regression (MLR) were used to evaluate the ability of the above SIs to detect LS severity. The results showed that the leaf spot multi-scale spectral index (LS-MSSI) constructed in this study was superior to the other SIs and obtained high accuracy at different scales simultaneously. At the leaf and plant scales, the MLR obtained high accuracy, with the overall accuracy (OA) reaching 93.77% and 92.50% and Kappa reaching 91.59% and 89.97%, respectively. At the field scale, the KNN obtained high accuracy, with the OA and Kappa reaching 90.29% and 87.04%, respectively. The LS-MSSI proposed in this study has high accuracy, stability, and robustness in the detection of LS severity at multiple scales, providing a technical basis and scientific guidance for the detection and precise management of peanuts.
Why it matches plant phenotyping methods落花生の葉斑病の重症度という植物状態を、ハイパースペクトル反射と新規スペクトル指標・分類モデルで多尺度推定する手法開発および精度評価が中心である。
abstractTo establish a multi-scale spectral index (SI) with high accuracy and stability for the detection of peanut LS disease, the spectral reflectance of different disease severity levels at leaf, plant, and field scales was collected
Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.
Why it matches plant phenotyping methods生のピーナッツ種子に含まれるフラボノイドおよびフェノール含量をNIR分光法と変数選択・PLSアルゴリズムで定量予測し、外部検証も行っている。種子形質の取得手法の開発・検証が中心であり、単なる生物学的実験のルーチン測定ではない。
abstractwe explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds.
Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. Identification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars. Our objective in 2017 and 2018 was to assess the U.S. mini-core collection for desirable traits, a valuable source for resistant germplasm under limited water conditions. Accessions were evaluated using traditional and high-throughput phenotyping (HTP) techniques, and the suitability of HTP methods as indirect selection tools was assessed. Traditional phenotyping methods included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), leaf wilting, fungal and viral disease, thrips rating, post-digging in-shell sprouting, and pod yield. The HTP method included 48 aerial vegetation indices (VIs), which were derived using red, blue, green, and near-infrared reflectance; color space indices were collected using an octocopter drone at the same time, with traditional phenotyping. Both phenotypings were done 10 times between 4 and 16 weeks after planting. Accessions had yields comparable to high yielding checks. Correlation coefficients up to 0.8 were identified for several Vis, with yield indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. VIs could be used successfully as surrogates for the physiological and agronomic trait selection in peanuts. Further, this study indicates that UAV-based sensors have potential for measuring physiologic and agronomic characteristics measured for peanut breeding, variable rate input application, real time decision making, and precision agriculture applications.
Why it matches plant phenotyping methodsUAVベースの高スループットセンシングを用いて植物形質を反復取得し、従来法との比較、収量との相関、遺伝率により選抜への有用性を評価しており、フェノタイピング手法が中心的である。
abstractIdentification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars.
Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction ( R p ), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA ( R p = 0.937, RPD = 3.38) and CP ( R p = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.
Why it matches plant phenotyping methodsピーナッツ種子の成分形質をNIRと変数選択・PLSモデルで非破壊推定し、複数モデルの予測性能を比較・検証しており、形質取得手法が研究の中心である。
abstractNear-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms
Abstract Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. A 3D reconstruction method based on dual RGB-D cameras was proposed for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits’ height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was carried out at the same time. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result shows that the synthetic accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. This approach should be useful for 3D modeling and phenotyping peanut breeding.
Why it matches plant phenotyping methodsピーナッツの3D形状・構造をRGB-Dカメラで再構成し、複数の植物形質を推定・精度検証する手法開発が研究の中心であるため。
abstractA 3D reconstruction method based on dual RGB-D cameras was proposed for the peanut plant 3D model quickly and accurately.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73-33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.
Why it matches plant phenotyping methodsRGB画像を用いた高スループット表現型解析法を開発・検証し、葉斑病評価と収量構成要素推定への有効性を比較検証しており、表現型取得・推定手法が研究の中心である。
abstractexplore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = –0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = –0.80), and b* (r = –0.75) were highly correlated with LLS visual scores. NDVI (r = –0.72), v* (r = –0.71), b* (r = –0.64), and GA (r = –0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.
Why it matches plant phenotyping methods複数のハンドヘルドセンサーによる病害症状の高スループット表現型取得を開発・評価し、目視スコアとの相関および予測精度を検証しているため、方法が研究の中心である。
abstractThe objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda.
Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties 1 . Both methods predicted the kernel mass with R 2 > 0.93 (XRT: R 2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R 2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R 2 = 0.91, MAE = 0.09) compared to XRT (R 2 = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.
Why it matches plant phenotyping methodsX線画像と画像変換・CNNを用いて、落花生のカーネル重量・殻重量という器官形質を推定し、重量測定で性能検証しているため、植物形質取得法が中心です。
abstractwe investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Main conclusion By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. Abstract One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.
Why it matches plant phenotyping methods葉の分光反射と回帰モデルにより、従来は時間のかかる光合成形質（Vc,max、Jmax）を非破壊・迅速に推定する手法を比較評価しており、表現型取得法が中心である。
abstractThis study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4.
Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.
Why it matches plant phenotyping methodsラマン分光法を用いた葉の非侵襲的表現型取得と、品種識別・線虫抵抗性判定の精度評価が研究の中心であるため。
abstractIn this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored.
The quality traits of peanuts (Arachis hypogaea L.) are fundamental to the whole peanut industry. However, many common analyses require the sample to be brought to the laboratory. Therefore, this research explores the feasibility of portable near-infrared spectroscopy combined with a single detection accessory to analyse the composition of peanuts in a single seed level quantitatively. The single detection accessory was specifically designed for spectral data collection considering the internal and external characteristics of single peanuts. Confocal laser scanning microscopy revealed that the oil body and protein body were randomly distributed at cell of single peanuts. The external characteristics of single peanuts were also determined and considered length (11.32–24.25 mm) and width (7.49–12.25 mm). The chemical compositional data (i.e. fat, sucrose, protein, and 16 amino acids) were determined by conventional wet-chemical methods and showed large variation. Principal component analysis on the compositional data showed that peanuts with higher fat contents usually have higher hydrophobic amino acids contents, lower sucrose contents, and lower protein contents. The composition prediction models of single peanuts were estimated using partial least squares regression models that were integrated with different spectral pre-treatments and validated by external sets. The results showed that the prediction models have good performance with a correlation coefficient above 0.88 (calibration) and 0.83 (prediction) and a residual prediction deviation above 1.5 except for a few indicators. Overall, the portable near-infrared spectroscopy offered reliable methods to assess the major components and amino acids quantitatively in a single peanut, which will improve the raw material quality in the peanut industry through the simultaneous and short-term determination of multiple indicators.
Why it matches plant phenotyping methods単一種子の成分形質を測定する携帯型NIR分光法と専用検出アクセサリを開発し、化学分析との比較・外部検証を行った研究であり、表現型取得法が中心です。
abstractthis research explores the feasibility of portable near-infrared spectroscopy combined with a single detection accessory to analyse the composition of peanuts in a single seed level quantitatively.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models' suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.
Why it matches plant phenotyping methodsUAV-RGB画像から植物形質（LAIと側方成長）を推定するモデルを開発し、別年・遺伝子型で検証しており、形質取得手法が研究の中心です。
abstractIn this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV).
One of the main problems in the peanut production process is to identify the pod maturity stage. Peanut plants have indeterminate growth, which leads to a high pod maturity variability within the same plant. Moreover, the actual method of determining maturity is destructive and highly subjectivity, which does not represent the overall variability in the field. Hence, the main goal of this study was to verify the possibility to estimate peanut maturity and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite images (~ 3 m) were obtained from the PlanetScope platform for two commercial peanut fields in São Paulo state, Brazil, during the reproductive stage of the peanut crop (89 to 118 days after sowing—DAS). The fields were divided into 54 plots (30 × 30 m). The maturity was obtained using the Hull Scrape method. All Vegetation Indices (VIs) used showed a high Pearson correlation (p < 0.001) between peanut maturity and the VIs, with values decreasing as maturity increased. Non-Linear Index (NLI) values from 0.561 to 0.465 suggested that pods reached greater maturity than 74% (inflection point). The results found in this study indicated a great potential to use high-resolution satellite images to predict peanut maturity variability in commercial field. In addition, the proposed method contributes to monitoring the dynamics spatio-temporal of maturity progression, allowing for more accurate in-season and inversion management strategies in peanut.
Why it matches plant phenotyping methods衛星画像と植生指数を用いて落花生の莢成熟度および圃場内変動を推定する方法が研究の中心であり、植物形質の非破壊取得・評価に該当する。
abstractthe main goal of this study was to verify the possibility to estimate peanut maturity and its in-field variability using an alternative non-destructive method based on orbital remote sensing.
Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)-based imaging and satellite-based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman-based diagnostics of water deficit and salinity stresses on two greenhouse-grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240-mM salt water per day for the duration of 5-day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand-held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman-based surveillance in commercial-scale agriculture and digital farming.
Why it matches plant phenotyping methods植物の水分欠乏・塩ストレス状態を携帯型ラマン分光とケモメトリクスで非侵襲的に診断し、精度を評価することが研究の中心であるため、ストレス状態のセンシング型フェノタイピング手法として採用。
abstractwe investigated accuracy of Raman-based diagnostics of water deficit and salinity stresses
Peanut ( Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a ∗ , b ∗ , u ∗ , v ∗ , green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a ∗ , u ∗ , GA, GGA, and CSI were significantly ( p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.
Why it matches plant phenotyping methodsRGB画像から落葉萎凋という植物状態を推定する手法の開発・評価が研究の中心であり、育種選抜への応用も検証しているため。
abstractThe objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production.
Indeterminate growth of peanut (Arachis hypogaea L.) creates indecision for best digging date for maturity and economic return. The current standard to determine peanut maturity is the Hull Scrape method. This method uses human observations to place hull scraped peanuts on a color maturity profile board. Human observations may lack precision and repeatability from individual to individual. X-ray technology has the capability of viewing peanut kernels through the hull to possibly ascertain density and maturity. The objective was to determine if x-ray could be used as a quick, non-destructive, and repeatable method to determine peanut maturity of runner, spanish, and virginia market types. Fresh dug peanut pods had 25 percent greater peanut area and gray scale values compared with hull scraped pods (runner and virginia only) and showed no difference in x-ray value between immature and fully mature peanut. Dried peanut showed a linear response of x-ray value versus peanut maturity (hull color). Virginia market type had much higher x-ray values followed by runners, then spanish. The relationship between peanut maturity and x-ray value peaked at the Orange class for runners (Georgia-06G, Georgia-13M), and Spanish (AT9899) while virginia (Georgia-11J) tended to peak at the Brown class. This research demonstrated that x-ray technology may be used to measure peanut density and possible maturity but needs further examination past Orange and Brown maturity class. Final x-ray values determined by this proprietary x-ray equipment may not be transferable due to specific x-ray power, detector precision, background color/scatter, and other electronic nuances.
Why it matches plant phenotyping methodsX線で落花生の成熟度・密度を非破壊かつ反復的に測定する方法の有効性と限界を検証しており、植物形質の取得法が研究の中心です。
abstractThe objective was to determine if x-ray could be used as a quick, non-destructive, and repeatable method to determine peanut maturity
This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.
Why it matches plant phenotyping methodsGPR取得プラットフォームと3Dデータの閾値処理・特徴抽出を開発し、ピーナッツ収量推定および高スループット表現型解析への適用を評価しており、表現型取得・抽出手法が中心である。
abstractThis study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield
Tomato spotted wilt virus (TSWV) has the potential to cause severe yield losses in peanut (Arachis hypogaea L.), an important annual legume grown around the world. The most effective approach to manage the disease caused by TSWV is to grow disease resistant peanut varieties. One of the key challenges to breeding for disease resistance is to develop an accurate, reproducible and efficient disease assessment method. Accurate field-based assessment of disease incidence and severity is technically challenging and time-consuming. To address this challenge, a field-based, high-throughput assessment tool was developed to quantify the spatial distribution of disease symptoms over experimental peanut plots using a Real Time Kinematic Global Positioning System (RTK-GPS), consumer-grade cameras, a microcontroller, and an open-source machine learning software. A field experiment was designed to establish a range of disease incidence and severity scenarios. This field experiment was imaged for two seasons to develop and validate the tool. Using transfer learning, an existing Convolutional Neural Network (CNN) was trained from supervised training imagery to classify and quantify areas within the plot-level imagery as, symptomatic, asymptomatic, or ground. Multiple images were assessed by the machine learning model and georeferenced to individual experimental plots using RTK-GPS data. The CNN model trained to detect the symptom, “stunting and mottling”, was evaluated using Receiver Operating Characteristic (ROC) curve analysis and yielded an Area Under the Curve (AUC) of 0.97, sensitivity of 0.77, and specificity of 0.98 on the test set. Results from the disease assessment tool were compared with results from visual disease assessments, conducted by a trained plant pathologist. Field plot level means from CNN-based assessment of stunting and mottling correlated with plot level means from visual assessment of severity (r = 0.78; P < 0.0001). To further validate the CNN-based method, the TSWV field experiment was analyzed using linear mixed models with both visual severity and CNN-based severity assessments used as responses. Both models (visual or CNN-based assessment) identified the same main effects as being significant and post hoc analysis resulted in the same separation of varieties for their severity of TSWV symptoms. The results of this study demonstrate the successful application of this tool for high-throughput disease severity assessment in peanut under field conditions.
Why it matches plant phenotyping methods圃場画像と機械学習を用いて植物病徴の重症度を定量化するツールを開発し、CNN性能と目視評価との相関によって検証しており、植物表現型取得が研究の中心である。
abstracta field-based, high-throughput assessment tool was developed to quantify the spatial distribution of disease symptoms over experimental peanut plots
This paper focuses on plant leaf image segmentation by considering the aspects of various unsupervised segmentation techniques for automatic plant leaf disease detection. The segmented plant leaves are crucial in the process of automatic disease detection, quantification, and classification of plant diseases. Accurate and efficient assessment of plant diseases is required to avoid economic, social, and ecological losses. This may not be easy to achieve in practice due to multiple factors. It is challenging to segment out the affected area from the images of complex background. Thus, a robust semantic segmentation for automatic recognition and analysis of plant leaf disease detection is extremely demanded in the area of precision agriculture. This breakthrough is expected to lead towards the demand for an accurate and reliable technique for plant leaf segmentation. We propose a hybrid variant that incorporates Graph Cut (GC) and Multi-Level Otsu (MOTSU) in this paper. We compare the segmentation performance implemented on rice, groundnut, and apple plant leaf images for various unsupervised segmentation algorithms. Boundary Displacement error (BDe), Global Consistency error (GCe), Variation of Information (VoI), and Probability Rand index (PRi), are the index metrics used to evaluate the performance of the proposed model. By comparing the outcomes of the simulation, it demonstrates that our proposed technique, Graph Cut based Multi-level Otsu (GCMO), provides better segmentation results as compared to other existing unsupervised algorithms.
Why it matches plant phenotyping methods植物葉画像から病変領域を抽出するセグメンテーション手法を提案し、複数手法との性能比較・指標評価を行っており、病害状態の画像ベース表現型取得が中心です。
abstractThis paper focuses on plant leaf image segmentation by considering the aspects of various unsupervised segmentation techniques for automatic plant leaf disease detection.
Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からピーナッツのLAIを推定するモデルを開発・較正し、精度比較しており、植物形質取得手法が研究の中心である。
abstractwe used a fixed-wing UAV with multispectral cameras for remote sensing monitoring
Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface designed and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistacia nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.
Why it matches plant phenotyping methodsCT画像取得法と3D解析ソフトウェアを開発し、種子・果実形態形質の自動抽出をベンチマークしており、フェノタイピング手法が中心である。
abstractHere, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · public3DPheno-Seed&Fruit software and CT image datasets used in this manuscript are free for academic purpose and can be downloaded from http://www.wutbiolab.com/resources/39/info/29 and https://github.com/whut-biolab-liuchang/projectOpen asset ↗lines:304-314Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Nov 2020Computers and Electronics in Agriculture.
One of the greatest key factors contributing to low yield and destruction of groundnut plant growth is leaf disease attack. The groundnut plants are prone to the diseases such as fungi, soil borne and viruses. The leaf disease detection and identification in the early stage is needed to control the spread of infection and also helps to get the maximum yield. The detection and classification of groundnut leaf diseases through naked eye observation by an expert is expensive that too developing countries. So, providing a software based solution for the above task is of great significance. In this paper, an image processing based approach that automatically identifies and categorizes the groundnut leaf diseases has been presented. The proposed method H2K is a fusion of Harris corner detector, HOG (Histogram on Oriented Gradient) and KNN classifier for accurate detection and classification of groundnut leaf diseases. It comprises of number of steps viz. image acquisition, image pre-processing by applying binary mask, HSV segmentation to segment the disease affected part, features detection and extraction using H2K (Harris, HOG and K-Nearest Neighbor) based classification of groundnut leaf diseases. The existing works concentrates on leaf diseases that are commonly occurring in any crops, but this paper proposed a robust and optimum approach for detection and classification of all major leaf diseases for Groundnut crop which is first of its kind. Thus the H2K method aids to improve the crop production and maximizes the yield. The proposed method H2K works well on sample images and detects and classifies 5 major groundnut leaf diseases including late spot which is difficult to control if it is not identified in the early stages. The existing Multiclass SVM is taken for comparison and the examined results shows that the H2K is robust and optimum in classifying groundnut leaf diseases with improved accuracy of 97.67%.
Why it matches plant phenotyping methods葉画像から植物病害状態を自動抽出・分類する画像解析手法が研究の中心であり、植物フェノタイピング手法として適格です。
abstractIn this paper, an image processing based approach that automatically identifies and categorizes the groundnut leaf diseases has been presented.
Bacterial wilt (BW) caused by Ralstonia solanacearum is the most serious peanut diseases in South China. Its timely and accurate detection is important to opportunely implement disease management practices. This study aimed to establish and select the most appropriate leaf-level reflectance-based vegetation indices for BW detection and to determine whether these new indices can be used in UAV multispectral imaging for peanut BW detection. ANOVA, multilayer perception, and the reduced sampling method were used to analyze the spectral data. The most effective detection wavelengths, 730 nm and 790 nm, were used for developing new peanut BW detection indices. The 15 hyperspectral indices with highest correlation coefficients (R > 0.80) were obtained based on 46 hyperspectral indices and the BW severity results from Experiment 1. By testing the above vegetation indices at the leaf level and in UAV images using different methods and the results from Experiment 2, it was found that four of the developed indices (BWI1, BWI3, BWI4, and BWI6) performed appropriately (P 1.0), as they could distinguish between healthy and BW infected peanut plants, even if the plant presented minimal external symptoms. Our findings confirmed the potential of hyperspectral remote sensing including leaf-level and UAV images for peanut BW detection at early disease stages and discrimination of different BW severity levels based on vegetation indices derived from leaf-level reflectance. Timely BW severity determination based on our results could provide farmers with useful information to control peanut BW disease.
Why it matches plant phenotyping methods落花生の病害症状・重症度を葉レベル分光およびUAV画像から推定する指標を開発・検証しており、植物表現型取得法が中心的です。
abstractThis study aimed to establish and select the most appropriate leaf-level reflectance-based vegetation indices for BW detection and to determine whether these new indices can be used in UAV multispectral imaging for peanut BW detection.
Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massive-scale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. We achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance. We presented a demo of plant root segmentation for all models under https://github.com/GatorSense/PlantRootSeg.
Why it matches plant phenotyping methods植物根画像から根を自動抽出するセグメンテーション手法を開発・比較検証しており、根形態の表現型取得が研究の中心である。
abstractMachine learning approaches can help to automate this task.
Identification of specific genotypes can be accomplished by visual recognition of their distinct phenotypical appearance, as well as DNA analysis. Visual identification (ID) of species is subjective and usually requires substantial taxonomic expertise. Genotyping and sequencing are destructive, time- and labor-consuming. In this study, we investigate the potential use of Raman spectroscopy (RS) as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of peanut genotypes. We show that chemometric analysis of peanut leaflet spectra provides accurate identification of different varieties. This same analysis can be used for prediction of nematode resistance and oleic-linoleic oil (O/L) ratio. Raman-based analysis of seeds provides accurate genotype identification in 95% of samples. Additionally, we present data on the identification of carbohydrates, proteins, fiber and other nutrients obtained from spectroscopic signatures of peanut seeds. These results demonstrate that RS allows for fast, accurate and non-invasive screening and selection of plants which can be used for precision breeding.
Why it matches plant phenotyping methodsラマン分光とケモメトリクスを用いて、ピーナッツの遺伝型や線虫抵抗性、油脂比などの植物形質を非破壊推定する手法が研究の中心であり、育種選抜への応用も示している。
abstractwe investigate the potential use of Raman spectroscopy (RS) as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of peanut genotypes.
Peanut / groundnutMultispectral / hyperspectralFruitClassificationGrowth / development / phenology
Seed maturity in peanut (Arachis hypogaea L.) determines economic return to a producer because of its impact on seed weight (yield), and critically influences seed vigour and other quality characteristics. During seed development, the inner mesocarp layer of the pericarp (hull) transitions in colour from white to black as the seed matures. The maturity assessment process involves the removal of the exocarp of the hull and visually categorizing the mesocarp colours into varying colour classes from immature (white, yellow, orange) to mature (brown, and black). This visual colour classification is time consuming because the exocarp must be manually removed. In addition, the visual classification process involves human assessment of colours, which leads to large variability of colour classification from observer to observer. A more objective, digital imaging approach to peanut maturity is needed, optimally without the requirement of removal of the hull's exocarp. This study examined the use of a hyperspectral imaging (HSI) process to determine pod maturity with intact pericarps. The HSI method leveraged spectral differences between mature and immature pods within a classification algorithm to identify the mature and immature pods. Therefore, there is no need to remove the exocarp nor is there a need for subjective colour assessment in the proposed process. The results showed a consistent high classification accuracy using samples from different years and cultivars. In addition, the proposed method was capable of estimating a continuous-valued, pixel-level maturity value for individual peanut pods, allowing for a valuable tool that can be utilized in seed quality research. This new method solves issues of labour intensity and subjective error that all current methods of peanut maturity determination have.
Why it matches plant phenotyping methodsハイパースペクトル画像からピーナッツ莢の成熟度を推定する手法を開発し、異なる年・品種で精度を検証しており、植物形質取得が研究の中心である。
abstractThis study examined the use of a hyperspectral imaging (HSI) process to determine pod maturity with intact pericarps.
Peanut allergy is one of the serious health concern and affects more than 1% of the world's population mainly in Americas, Australia, and Europe. Peanut allergy is sometimes life-threatening and adversely affect the life quality of allergic individuals and their families. Consumption of hypoallergen peanuts is the best solution, however, not much effort has been made in this direction for identifying or developing hypoallergen peanut varieties. A highly diverse peanut germplasm panel was phenotyped using a recently developed monoclonal antibody-based ELISA protocol to quantify five major allergens. Results revealed a wide phenotypic variation for all the five allergens studied i.e. , Ara h 1 (4-36,833 µg/g), Ara h 2 (41-77,041 µg/g), Ara h 3 (22-106,765 µg/g), Ara h 6 (829-103,892 µg/g), and Ara h 8 (0.01-70.12 µg/g). The hypoallergen peanut genotypes with low levels of allergen proteins for Ara h 1 (4 µg/g), Ara h 2 (41 µg/g), Ara h 3 (22 µg/g), Ara h 6 (829 µg/g), and Ara h 8 (0.01 µg/g) have paved the way for their use in breeding and genomics studies. In addition, these hypoallergen peanut genotypes are available for use in cultivation and industry, thus opened up new vistas for fighting against peanut allergy problem across the world.
Why it matches plant phenotyping methods多様性パネルを対象に、モノクローナル抗体ELISAで落花生種子の主要アレルゲン量を大規模に定量し、低アレルゲン遺伝子型を同定する測定ワークフローが研究の中心である。
abstractA highly diverse peanut germplasm panel was phenotyped using a recently developed monoclonal antibody-based ELISA protocol to quantify five major allergens.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:1096-1135Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
An Unmanned Autonomous Octocopter equipped with a multispectral camera was used to take imagery of peanut plant cover at different stages of maturity. Vegetation indexes (Normalized Difference Vegetation Index, Transformed Difference Vegetation Index, Modified Soil Adjusted Vegetation Index, Modified Chlorophyll Absorption Ratio Index and the Modified Triangular Vegetation Index) were stacked and used to mask out the peanut canopy cover from background soil, shadows and any other surficial materials. Masked peanut canopy was used to develop a peanut maturity-spectral reflectance prediction model. The model was built using partial least squares. Comparison between the model- predicted and real values showed that the model does not give an accurate estimate of maturity up to 60 days after planting, but the accuracy of the model increases with time. This may since the difference between chlorophyll a and become more significant in mature peanuts more than immature ones.The overall assessment of the model indicates that the model needs to be calibrated for more precise prediction of the peanut maturity. This could be achieved by expanding the peanut maturity, versus peanut leaf spectra, database by taking data more frequently, especially close to harvest. Data collection should be started two months after planting when the ratios between chlorophyll a and b become more detectable in the leaf reflectance spectra.
Why it matches plant phenotyping methodsマルチスペクトル画像からピーナッツの樹冠を抽出し、成熟度を推定する画像・スペクトル解析モデルが研究の中心であり、植物状態の定量的推定方法を開発・評価している。
abstractVegetation indexes (Normalized Difference Vegetation Index, Transformed Difference Vegetation Index, Modified Soil Adjusted Vegetation Index, Modified Chlorophyll Absorption Ratio Index and the Modified Triangular Vegetation Index) were stacked and used to mask out the peanut canopy cover from background soil, shadows and any other surficial materials.
Plant architecture characteristics contribute significantly to the microclimate within peanut canopies, affecting weed suppression as well as incidence and severity of foliar and soil-borne diseases. However, plant canopy architecture is difficult to measure and describe quantitatively. In this study, a ground-based LiDAR sensor was used to scan rows of peanut plants in the field, and a data processing and analysis algorithm was developed to extract feature indices to describe the peanut canopy architecture. A data acquisition platform was constructed to carry the ground-based LiDAR and an RGB camera during field tests. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May and the data collections were conducted once each month from July to September 2015. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100°, an angle resolution of 0.25°, and a scanning speed of 53 ms. The collected line-scanned data were processed using the developed image processing algorithm. The canopy height, width, and shape/density were evaluated. Euler number, entropy, cluster count, and mean number of connected objects were extracted from the image and used to describe the shape of the peanut canopies. The three peanut cultivars were then classified using the shape features and indices. A high correlation was also observed between the LiDAR and ground-truth measurements for plant height. This approach should be useful for phenotyping peanut germplasm for canopy architecture.
Why it matches plant phenotyping methodsLiDARによるキャノピー形状・密度・高さの取得と画像解析アルゴリズムを開発し、地上測定との相関で検証しており、植物表現型取得法が研究の中心である。
abstracta data processing and analysis algorithm was developed to extract feature indices to describe the peanut canopy architecture
Leaf spot is one of the most destructive diseases, which has a significant impact on the peanut production. Detecting leaf spot via spectral measurement and analysis is a possible alternative to traditional methods in detecting the spatial distribution of this disease. In this study, we identified sensitive bands and derived hyperspectral vegetation index specific to leaf spot detection. Hyperspectral canopy reflectance spectra of peanut cultivars susceptibilities to leaf spot were measured at two experimental sites in 2017. The normalized difference spectral index (NDSI) was derived based on their correlation with disease index (DI) in the leaf spectrum between 325 nm and 1075 nm. The results showed that canopy spectral reflectance decreased significantly in the near-infrared regions (NIR) as DI increased (r < −0.90). The spectral index for detecting leaf spot in peanut were LSI: (NDSI (R938, R761)) with R2 values of up to 0.68 for the regression model. The high fit between the observed and estimated values indicates that the DI detecting model based on the index could be used in peanut leaf spot detection in the absence of other stresses causing unhealthy symptoms. The results of this study show that it will provide a reliable, effective and accurate method for detecting leaf spot diseases in peanut through the analysis of hyperspectral data in the future.
Why it matches plant phenotyping methodsピーナッツ葉斑病の症状・重症度をキャノピー hyperspectral 反射から推定する測定・解析手法が研究の中心であり、植物病害状態の表現型取得に該当する。
abstractDetecting leaf spot via spectral measurement and analysis is a possible alternative to traditional methods in detecting the spatial distribution of this disease.
Why it matches plant phenotyping methodsピーナッツ莢画像からDUS形質を定量化し、特徴選択・認識・クラスタリングによる品種識別手法を開発・評価しており、表現型取得と解析が研究の中心である。
abstractIn order to verify the feasibility of variety identification for peanut DUS testing based on image processing, 2000 peanut pod images from 20 varieties were obtained by a scanner.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Near infrared (NIR) spectrophotometer offers rapid, noninvasive, nondestructive, and high‐throughput phenotyping of seed samples for use in agriculture and industry. In this study, a reflectance‐based NIR spectrophotometer was calibrated and used for the isolation of desirable higher‐oleic‐acid peanut recombinants from single‐seed‐derived segregating populations at F7 and F8 generations. A calibration model was developed through partial least‐square regression using wet chemistry data from 158 peanut genotypes. Desirable prediction for oil, palmitic acid, oleic acid, and linoleic acid in intact seed was obtained based on this calibration. It detected significant high correlations (r) and coefficient of determination (R2) between the actual gas chromatography values and NIR predicted values of fatty acid profile in another 123 peanut genotypes that were generated from crosses involving a high‐oleate mutant and Spanish bunch varieties with early maturity. From this recombinant single‐seed‐derived progenies, 15 higher‐oleate recombinants were isolated and later genotyped through an in‐house developed polymerase chain reaction‐based allele specific marker. The present study has generated high‐oleate peanut recombinants with early maturity in Spanish bunch background. The breeding materials generated here will be evaluated for yield attributing traits at different locations in future.
Why it matches plant phenotyping methodsピーナッツ種子の脂肪酸組成を非破壊NIRで推定する校正モデルを開発し、独立遺伝子型で検証して選抜に適用しており、表現型取得法が研究の中心である。
abstracta reflectance‐based NIR spectrophotometer was calibrated and used for the isolation of desirable higher‐oleic‐acid peanut recombinants
Aflatoxin contamination is a major economic and food safety concern for the peanut industry that largely could be mitigated by genetic resistance. To screen peanut for aflatoxin resistance, ten genotypes were infected with a green fluorescent protein (GFP)-expressing Aspergillus flavus strain. Percentages of fungal infected area and fungal GFP signal intensity were documented by visual ratings every 8 h for 72 h after inoculation. Significant genotypic differences in fungal growth rates were documented by repeated measures and area under the disease progress curve (AUDPC) analyses. SICIA (Seed Infection Coverage and Intensity Analyzer), an image processing software, was developed to digitize fungal GFP signals. Data from SICIA image analysis confirmed visual rating results validating its utility for quantifying fungal growth. Among the tested peanut genotypes, NC 3033 and GT-C20 supported the lowest and highest fungal growth on the surface of peanut seeds, respectively. Although differential fungal growth was observed on the surface of peanut seeds, total fungal growth in the seeds was not significantly different across genotypes based on a fluorometric GFP assay. Significant differences in aflatoxin B levels were detected across peanut genotypes. ICG 1471 had the lowest aflatoxin level whereas Florida-07 had the highest. Two-year aflatoxin tests under simulated late-season drought also showed that ICG 1471 had reduced aflatoxin production under pre-harvest field conditions. These results suggest that all peanut genotypes support A. flavus fungal growth yet differentially influence aflatoxin production.
Why it matches plant phenotyping methodsSICIA画像処理ソフトウェアを開発し、ピーナッツ種子上の菌類感染面積・GFPシグナルを定量化して目視評価と検証しており、感染状態の表現型取得が中心的である。
abstractSICIA (Seed Infection Coverage and Intensity Analyzer), an image processing software, was developed to digitize fungal GFP signals.
The amount of visible and near infrared light reflected by plants varies depending on their health. In this study, multispectral images were acquired by a quadcopter for high throughput phenotyping of tomato spot wilt disease resistance among twenty genotypes of peanuts. The plants were visually assessed to acquire ground truth ratings of disease incidence. Multispectral images were processed into several vegetation indices. The vegetation index image of each plot has a unique distribution of pixel intensities. The percentage and number of pixels above and below varying thresholds were extracted. These features were correlated with manually acquired data to develop a model for assessing the percentage of each plot diseased. Ultimately, the best vegetation indices and pixel distribution feature for disease detection were determined and correlated with manual ratings and yield. The relative resistance of each genotype was then compared. Image-based disease ratings effectively ranked genotype resistance as early as 93 days from seeding.
Why it matches plant phenotyping methodsマルチスペクトル画像と植生指数から、落花生の病害率という植物状態を推定する画像ベースの表現型計測モデルを開発・検証しており、手法が研究の中心である。
abstractmultispectral images were acquired by a quadcopter for high throughput phenotyping of tomato spot wilt disease resistance among twenty genotypes of peanuts.
High-throughput phenotyping using spectral reflectance measurements offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time. The aim of this study was to investigate the suitability of hyperspectral reflectance measurements of the crop canopy for the assessment of biomass, nitrogen concentration, nitrogen uptake, relative chlorophyll contents, and yield in 2 peanut cultivars, Giza 5 and Giza 6. Peanuts were grown under field conditions and subjected to 3 doses of nitrogen fertilizer with or without the application of 2 bio-fertilizers, Bradyrhizobium spp. or plant growth-promoting rhizobacteria. Simple linear regression of normalized difference spectral indices and partial least square regression (PLSR) were employed to develop predictive models to estimate the measured parameters. The tested spectral reflectance indices were significantly related to all measured parameters with R2 of up to 0.89. The spectral reflectance index values differed at the same level of nitrogen fertilizer, as well as among the 3 levels of nitrogen fertilizer application for inoculation with Bradyrhizobium and co-inoculation with Bradyrhizobium and plant growth-promoting rhizobacteria. The calibration models of PLSR data analysis further improved the results, with R2 values reaching 0.95. The overall results of this study indicate that hyperspectral reflectance measurements monitoring peanut plants enable rapid and non-destructive assessment of biomass, nitrogen status, and yield parameters of peanut cultivars subjected to various agronomic treatments.
Why it matches plant phenotyping methods作物キャノピーのハイパースペクトル反射測定と回帰モデルにより、バイオマス・窒素状態・収量を非破壊推定する方法が研究の中心であるため。
abstractHigh-throughput phenotyping using spectral reflectance measurements offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time.
Background and aims Intercropping systems contain two or more species simultaneously in close proximity. Due to contrasting features of the component crops, quantification of the light environment and photosynthetic productivity is extremely difficult. However it is an essential component of productivity. Here, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment. Different row layouts of an intercrop consisting of Bambara groundnut ( Vigna subterranea ) and proso millet ( Panicum miliaceum ) have been used as an example and the new opportunities presented by this approach have been analysed. Methods Three-dimensional plant reconstruction, based on stereo cameras, combined with ray tracing was implemented to explore the light environment within the Bambara groundnut-proso millet intercropping system and associated monocrops. Gas exchange data were used to predict the total carbon gain of each component crop. Key results The shading influence of the tall proso millet on the shorter Bambara groundnut results in a reduction in total canopy light interception and carbon gain. However, the increased leaf area index (LAI) of proso millet, higher photosynthetic potential due to the C4 pathway and sub-optimal photosynthetic acclimation of Bambara groundnut to shade means that increasing the number of rows of millet will lead to greater light interception and carbon gain per unit ground area, despite Bambara groundnut intercepting more light per unit leaf area. Conclusions Three-dimensional reconstruction combined with ray tracing provides a novel, accurate method of exploring the light environment within an intercrop that does not require difficult measurements of light interception and data-intensive manual reconstruction, especially for such systems with inherently high spatial possibilities. It provides new opportunities for calculating potential productivity within multi-species cropping systems, enables the quantification of dynamic physiological differences between crops grown as monoculture and those within intercrops, and enables the prediction of new productive combinations of previously untested crops.
Why it matches plant phenotyping methodsステレオカメラによる3次元植物再構成とレイトレーシングを開発・適用し、作物キャノピーの光環境や生産性を定量化する手法が研究の中心である。
abstractHere, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment.
The study aims to explore the potential of hyperspectral imaging (HSI) with chemometrics for rapidly and non-invasively visualizing the spatial distribution of protein content which can affect the quality of peanut products as a critical component of peanut. Spectral data contained in the region of interest (ROI) of the corrected hyperspectral images of peanut were extracted and protein contents were measured with conventional chemical method. By comparing different pretreatments and modeling algorithms, the second-order derivatives (2nd-der) on spectra is optimal pretreatment, and partial ceast square (PLS) is the best regression method. Based on the pretreatment spectra and the measured protein content model, a good performance model (RC=0.91, SEC=0.86; RP=0.86, SEP=0.69) was built with full wavelengths. The fourteen optimal wavelengths were carried out based on the regression coefficients (RC) of the established PLS model. Then, using optimal wavelengths built RC-PLS model which show resembling performance (RC=0.86, SEC=1.03; RP=0.80, SEP=0.77). At last, an imaging processing algorithm was developed to transfer each pixel in peanut to protein content with the 2nd-der-RC-PLS model. There was no significant difference between Kjeldahl and HSI method by the paired test. The result demonstrated the capacity of HSI in combination with chemometrics for fast and non- destructively determining protein content in peanut.
Why it matches plant phenotyping methodsピーナッツ種子のタンパク質含量という植物形質を、ハイパースペクトル画像とケモメトリクスで推定・可視化する手法の開発と化学分析による検証が中心である。
abstracthyperspectral imaging (HSI) with chemometrics for rapidly and non-invasively visualizing the spatial distribution of protein content
In this paper, we propose a simple, inexpensive optical device for remote measurement of various agricultural parameters. The sensor is based on temporal tracking of backreflected secondary speckle patterns generated when illuminating a plant with a laser and while applying periodic acoustic-based pressure stimulation. By analyzing different parameters using a support-vector-machine-based algorithm, peanut kernel abortion can be detected remotely. This paper presents experimental tests which are the first step toward an implementation of a noncontact device for the detection of agricultural parameters such as kernel abortion.
Why it matches plant phenotyping methodsピーナッツの莢内胚珠・種子の退化（kernel abortion）を非接触で推定する光学センサーとSVM解析法の開発・実験評価が中心であり、植物状態の取得手法に該当する。
titleOptical remote sensor for peanut kernel abortion classification.
Precision agriculture involves studying and managing crop variations within fields that can affect crop yield. In precision agriculture farmers are adapting technology and advanced remote sensing techniques with different software to relieve decision making. Hyperspectral ground measurements can be used for giving timely information about crops in specific areas and thereby providing valuable data for decision makers. In this paper field spectroscopy measurements measured by ASD field Spec4 spectroradiometer were used to monitor the spectral response and differences of peanut crop vegetation cover reflectance due to bio-physical plant variables. The results of Tukey’s HSD showed that blue, Red and NIR spectral zones are more sufficient in the monitoring differences between peanut growth stages than green, SWIR-1 and SWIR-2 spectral zones. The results of physiological spectral indices of growth stages showed significant correlations between varied classes productivity and spectral similarity measures, indicating that similarity between the samples' spectra decreases as the pigments concentration in the plant leaves increases. Furthermore, electromagnetic peanut crop mapping was successfully employed to simulate vegetation healthy effect on canopy structure and final yield.
Why it matches plant phenotyping methodsピーナッツの生育段階、植生被覆、葉色素、樹冠構造、収量を分光計測・スペクトル指標で推定するリモートセンシング手法が研究の中心であり、単なるルーチン測定ではない。
abstractHyperspectral ground measurements can be used for giving timely information about crops in specific areas and thereby providing valuable data for decision makers.
ABSTRACT Drought and heat stress can result in aflatoxin contamination of peanuts especially when this occurs during the last three to six wk of the growing season. Identifying drought-tolerant genotypes may aid in development of peanuts that are less susceptible to aflatoxin contamination. Research was conducted to phenotype seven peanut genotypes based on their response to drought stress. Six peanut genotypes that have exhibited lower aflatoxin and/or drought tolerance in previous researches (Tifguard, Tifrunner, Florida-07, PI 158839, NC 3033, C76-16) were compared to an aflatoxin-susceptible genotype, A72. The phenotyping methods included visual ratings, chlorophyll fluorescence (PIABS, ϕEO, and Fv/Fm), SPAD chlorophyll meter reading (SCMR), normalized difference vegetation index (NDVI), canopy temperature (CT), canopy temperature depression (CTD), and pod yield. Based on these traits, Tifguard and Tifrunner exhibited greater drought tolerance mechanisms than the other genotypes and may be good candidates to be incorporated in future drought tolerance studies. After the aflatoxin content of the different genotypes was measured, aflatoxin contamination showed high correlations with visual ratings (0.85), CTD (0.81), NDVI (0.79), and CT (0.73), and moderate correlations with Fv/Fm (0.62) and SCMR (0.57) (P ≥ 0.05). These easily measurable, rapid and cost-effective phenotyping methods may be used as alternative to more tedious and costly methods of identifying genotypes that are less susceptible to aflatoxin contamination. Using a combination of these methods is beneficial but not always practical. The combined use of visual ratings, CTD and NDVI is advised for initial evaluation of drought tolerance in peanut genotypes.
Why it matches plant phenotyping methods乾燥耐性の表現型評価手法を複数比較し、アフラトキシン汚染との相関を検証して、初期評価に有用な組合せを提案しているため、表現型取得法が研究の中心である。
abstractResearch was conducted to phenotype seven peanut genotypes based on their response to drought stress.