Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. We construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW, which is captured under controlled indoor scenarios, by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD- \(L_1\) and CD- \(L_2\) values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for 3D wheat phenotyping under occlusion and provides a systematic reference for applying 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下の単一画像から小麦器官を3D再構成し、形質推定精度を改善する手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractTo address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat.
Rice panicle blast detection is an important task in plant disease phenotyping. Field-based detection remains challenging because infected spike regions are often small, sparse, elongated, and affected by overlapping panicles, complex backgrounds, and variable illumination. In this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection. The model uses a Lightweight Ghost Backbone (LGB) to reduce redundant computation. It uses Anisotropic Axial Stripe Attention (A2SA) to represent elongated panicle structures. It also uses Focal Multi-Scale Attention (FMSA) for multi-scale feature refinement and Adaptive Geometric Shape IoU (AGS-IoU) for geometry-aware localization. The model was trained and evaluated on a rice panicle image dataset containing 1,055 training images, 69 validation images, and 169 test images. On the test set, RPB-YOLO11 achieved 76.09% mAP50, 45.44% mAP50-95, 73.98% precision, and 72.75% recall with 6.21 GFLOPs. Compared with the YOLO11n baseline, it improved mAP50, mAP50-95, precision, and recall by 2.73, 2.12, 1.64, and 2.11 percentage points, respectively. An Android-oriented inference application supports local image inference, detection visualization, class counting, and diseased-panicle incidence estimation. These results suggest that RPB-YOLO11 provides a practical approach for image-based rice panicle blast survey.
Why it matches plant phenotyping methodsイネ穂いもちの画像検出モデルを開発・比較検証し、罹病穂率を推定する実用アプリまで構築しており、植物病害状態の画像ベース表現型取得が中心である。
abstractIn this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection.
Reproduction assets foundThe paper links a public Hugging Face dataset used to establish the rice panicle blast detection dataset and a public GitHub release (data availability statement) containing the study's datasets/models.Dataset · publicsites, cultivars, growth stages, imaging conditions, and disease severities are still needed to evaluate generalization more fully.
Statements
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/XuzheYang2Doc/RPB-YOLO11/releases/tag/rpb-yolo11 .
Author contributions
XY: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. XZ: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. CX: Formal analysisOpen asset ↗XuzheYang2Doc/RPB-YOLO11 · rpb-yolo11lines:639-658Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 5 Sept 2026
Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.
Why it matches plant phenotyping methodsトウモロコシ雌穂の3D形態形質を抽出する低コスト画像計測パイプラインを開発し、手動測定および体積測定で技術検証しているため、方法が研究の中心である。
abstractWe developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear
Rice false smut is a major panicle disease that affects rice yield and grain quality and is an important target for resistance evaluation in breeding programs. Accurate field phenotyping is important for disease assessment and resistance screening, yet current assessment relies heavily on manual visual scoring and smut ball counting, which are laborious and subject to evaluator variation. In close-range single-panicle images, false smut balls are often small, dense, occluded, adhered, making automatic detection and severity grading difficult. To address these challenges, we developed a YOLOv12-RSLW detector by integrating RepGhost, SimAM, LSCD, and WIoU into YOLOv12. Detection boxes were then used to guide the Segment Anything Model for panicle and lesion mask extraction, allowing calculation of the lesion-to-panicle area ratio as a supplementary indicator for count-based severity grading. A total of 1911 original field images were collected. After augmentation, the dataset contained 5663 images, including 4531 training images, 566 validation images, and 566 test images. Detection performance was evaluated on the test set, while SAM segmentation was assessed using 80 manually annotated original images. YOLOv12-RSLW achieved 92.06% mAP@0.5, 91.46% precision, and 87.01% recall, with 3.45 M parameters and 6.0 GFLOPs. Compared with the baseline YOLOv12, mAP@0.5 and recall increased by 3.60 and 4.37 percentage points, respectively. Within the augmented dataset, 41.2% of samples initially assigned to Grade 1 and 28.1% of those assigned to Grade 2 met the area-ratio criteria for potential reassignment to higher grades. The framework provides a quantitative approach to rice false smut severity phenotyping and may support future resistance breeding after further validation.
Why it matches plant phenotyping methodsイネいもち病の病徴を画像から検出・分割し、病斑面積比に基づく重症度を定量化するフェノタイピング手法を開発・評価しており、方法が研究の中心である。
abstractTo address these challenges, we developed a YOLOv12-RSLW detector by integrating RepGhost, SimAM, LSCD, and WIoU into YOLOv12.
All living organisms rely on the movement of ions across cell membranes as the fundamental physical basis of their internal energy and signaling, and plants are no exception. Plants perceive, integrate, and respond to environmental stimuli through electrical signals, classified as action, variation, and system potentials, that are coupled with calcium waves, reactive oxygen species, and hydraulic and hormonal changes to coordinate whole-organism responses despite the absence of a nervous system. Yet most studies characterize these signals using a single feature, such as amplitude or spike duration, in a single tissue, an approach that cannot establish how such signals correspond to the underlying ionic activity, mobility, and structural complexity of the signaling environment, or how this correspondence varies across organs. Here, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato ( Solanum lycopersicum ) exposed to different stimulus. Electrical activity with increased stimulus strength, likely reflecting increased ionic flow, with the root showing the largest response. This suggests plant electrical signaling works as a distributed, ion-based information system, useful for stress monitoring and bio-inspired sensor design.
Why it matches plant phenotyping methods植物の電気生理シグナルを多面的に取得・解析する枠組みを中心に扱い、ストレスモニタリングへの応用可能性を示しているため、植物状態の測定方法として含める。
abstractHere, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato
Maize diseases affecting leaves, stalks, and ears can substantially reduce yield and quality; therefore, rapid and accurate recognition in complex field environments is important for intelligent agricultural monitoring. To address the large-scale variation, weak fine-grained texture, and strong background interference associated with multi-part maize diseases, this study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n. The method jointly improves spatial position awareness, shallow detail preservation, local-context modeling, key semantic-region enhancement, and lightweight detection-head reconstruction. RFCAConv, C3k2_RFCAConv, and Detect_LSDECD are introduced into the baseline network to strengthen directional texture modeling, multi-scale feature aggregation, and detection-head feature representation. FG-RFCAConv, HGD-C3k2, LCA-C3k2, and GRN-BiAttn are further designed for high-frequency differential gated detail compensation, P3 high-resolution detail enhancement, local-context fusion, and global-response-normalized attention regulation, respectively. Experimental results show that YOLOv11-MPD achieves Precision, Recall, mAP50, and mAP50-95 of 72.3%, 72.8%, 79.5%, and 50.2%, improving YOLOv11n by 2.4, 2.5, 2.9, and 2.4 percentage points, respectively, while reducing parameters from 2.6 M to 2.4 M. These results indicate that, within the scope of the dataset used in this study, YOLOv11-MPD improves multi-part maize disease detection under complex field conditions. However, the current conclusions are limited to the constructed dataset, and further validation using larger multi-region, multi-season, and multi-device datasets is required to evaluate its broader generalization ability.
Why it matches plant phenotyping methodsトウモロコシの葉・茎・穂における病害状態を画像から検出するアルゴリズムを開発し、性能比較・検証しており、植物表現型取得が研究の中心です。
abstractthis study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n.
Wheat grain number integrates fate of individual florets, strongly affected by environmental stress during sensitive stages like meiosis. Because development is asynchronous across tillers, spikelets, and florets, it is hard to distinguish stress tolerance from stress escape. Based on 2400 destructive measurements of spike and anther length together with non-destructive morphological measurements from 158 plants grown in four experiments in controlled-environment, we developed a framework to track individual floret developmental stages at plant level. We applied it in two case studies for connecting within-plant developmental asynchrony to reproductive success under favorable or heat conditions. All florets showed a common relative growth rate, producing additive delays across tillers (1-7 d), spikelets (1-5 d), and floret positions (1-6 d). This generated a developmental map for every floret based on external traits. Under control conditions, grain set probability at floret level combined both positional and developmental effects within a spike. Under heat stress, grain loss occurred only in florets at meiosis during the stress, allowing to quantify a true "stress response", while later florets escaped damage. This framework allows understanding and predicting floret development and linking it to grain set, clearly distinguishing timing effects from positional influences and separating tolerance from stress escape.
Why it matches plant phenotyping methods外部形態測定から個々の小花の発育段階を追跡する方法・発育マップを開発し、複数実験で適用しているため、表現型取得・推定が研究の中心である。
abstractwe developed a framework to track individual floret developmental stages at plant level
The inflorescence is a key yield-determining organ, yet its complex morphology makes manual pixel-level annotation time-consuming, leading to a scarcity of high-quality segmentation datasets. To address this bottleneck, we propose ZCAT (Zero-shot Cross-crop Annotation Transfer), a novel paradigm for zero-annotation cross-crop pseudo-mask screening. ZCAT completely eliminates pixel-level manual annotation of the target crop, requiring only holistic quality assessment of model-generated pseudo-masks (5-10 s per image). Specifically, we train a SegFormer model on public rice panicle datasets (CVRP and RiceSEG) and transfer it across crops to the wheat spike segmentation task. The key innovation is the introduction of a human-defined quality function Q, which circumvents the fundamental challenge in self-learning algorithms: the inability of computers to autonomously distinguish good masks from bad ones. Through iterative human-in-the-loop pseudo-label screening with a curriculum learning strategy, each round adds only a few high-quality pseudo-masks to the training set, continuously improving model performance. After four iterations, ZCAT produces pseudo-masks with an average Spike IoU of 0.7003, evaluated against the GWFSS manual annotations as ground truth. Moreover, the pseudo-mask dataset exhibited higher benchmark performance than the GWFSS manual annotations (Spike IoU 0.7612 vs. 0.7027; mIoU 0.8627 vs. 0.8247), suggesting stronger self-consistency. A generalization test on a strictly held-out set of 100 manually annotated wheat spike images showed that the model trained on ZCAT-generated pseudo-masks achieved marginally better performance than that trained on the GWFSS manual annotations (Spike IoU: 0.5112 vs. 0.4927; mIoU: 0.5627 vs. 0.5247). The time budget of the ZCAT pipeline was substantially lower than that of manual annotation. ZCAT opens a new pathway for rapid annotation of plant reproductive structures or other organs and significantly reduces data preparation costs in plant phenomics. The generated wheat spike pseudo-mask dataset and the mask quality screening tool (Mask Quality Screener) are open-sourced.
Why it matches plant phenotyping methods植物器官セグメンテーションのためのゼロショット転移、擬似マスク品質評価、反復学習パイプラインを開発・検証しており、表現型取得基盤が中心である。
abstractThe key innovation is the introduction of a human-defined quality function Q
Reproduction assets foundThe paper explicitly open-sources two paper-specific assets: the ZCAT-generated wheat spike pseudo-mask dataset and the Mask Quality Screener tool, both with public GitHub URLs in the Data availability statement.Dataset · publicThe wheat spike pseudo-mask dataset and Mask Quality Screener are available at https://github.com/zyxyes1/MaskQualityScreener and https://github.com/zyxyes1/Wheat-Spike-Semantic-Segmentation , respectively.Open asset ↗Wheat-Spike-Semantic-Segmentationlines:415-440Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.
Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。
abstractThis project develops a workable solution.
Reproduction assets foundThe paper builds its multimodal wheat phenotyping analysis on several explicitly cited public data assets: the Kaggle Wheat Plant Diseases image dataset (used for disease classification, Tables 2 and 9), the Global Wheat Head Detection dataset (used for head detection, Tables 1 and 6), FAOSTAT and India Open GovernmentDataset · publicAvailable online at: https://www.fao.org/faostat/ . FAOSTAT statistical database.Open asset ↗lines:1110-1162Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Spike count and spatial arrangement are among the strongest determinants of grain yield in wheat, yet reliable spike-level phenotyping under field conditions remains challenging. Field-acquired images are often affected by occlusion, heterogeneous illumination, and dense canopy overlap. In addition to these visual complexities, the effort required to generate large-scale instance-level annotations makes it difficult to build the extensive labeled datasets typically needed for robust segmentation models. This paper addresses both problems. The core methodological contribution is a semi-automated iterative annotation workflow: a YOLOv11x-seg model trained on a small manually annotated set is applied to unlabeled field images, and its predictions, after post-processing to remove duplicated, fragmented, and merged detections, are incorporated back into training. To prevent augmentation from distorting the training distribution, we introduce a distribution-aware augmentation strategy guided by Fréchet Inception Distance (FID), which retains only those augmented samples that remain within an acceptable distance from the original data distribution. Together, these components allowed us to build an effective training set from 3,899 high-resolution RGB images (4000 × 3000 pixels) of durum wheat collected at the CREA Research Centre for Cereal and Industrial Crops, with substantially reduced manual annotation effort. On the independent test set(242 images), the final YOLOv11x-seg model (M5 model) achieved a mask-level precision of 86.73%, recall of 83.02%, F1-score of 84.83%, mAP@50 of 89.42%, and mAP@50:95 of 60.51%. Spike masks were used to derive image-based traits including spike count, spike density, canopy coverage, spike area, spatial distribution, and vegetation indices. Their relationships with measured grain yield were explored through statistical analysis and machine-learning-based yield estimation.Both statistical and machine-learning analyses demonstrated that image-derived spike traits provided meaningful information for grain yield estimation, with canopy coverage showing the strongest positive association with yield. Using repeated nested cross-validation with out-of-fold (OOF) predictions, XGBoost achieved the highest yield estimation performance ( R OOF 2 = 0.312 , RMSE = 106.61 g/plot), supporting the potential of near-image phenotyping for late-stage yield estimation in wheat. These results show that semi-automated iterative annotation can enable practical wheat spike segmentation and image-based phenotyping under realistic open-field conditions. While grain yield estimation should be interpreted within the context of the experimental setting, the proposed framework highlights the value of image-derived spike traits for late-stage phenotyping and yield assessment in breeding experiments rather than for early-season yield forecasting.
Why it matches plant phenotyping methods半自動アノテーション、YOLOv11x-segによる小麦穂の画像セグメンテーション、分布認識型データ拡張、形質抽出と検証が中心的な方法論的貢献である。
abstractThe core methodological contribution is a semi-automated iterative annotation workflow
Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.
Why it matches plant phenotyping methods3D画像再構成・セグメンテーション・形質抽出を統合した植物フェノタイピング手法の開発が中心であり、収量関連形質の定量と予測まで技術的に評価している。
abstractthis study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images.
Rice panicle number per unit area is a key determinant of yield, but manual counting remains time-consuming and labor-intensive. This study proposes CEG-YOLO, a lightweight deep learning model for in-field rice panicle detection and counting using consumer-grade RGB imaging devices. The model introduces three improvements to YOLOv11s to address specific challenges in field scenarios: C2f-Fast replaces standard convolutions with depthwise convolutions to reduce computational cost for edge deployment; SPPF-ECA integrates an attention mechanism to suppress complex background interference; and GhostConv reduces feature redundancy to improve detection of dense panicles. A dataset of 5,175 images was collected from four rice cultivars planted at three densities using an iPhone 12. The proposed model achieved 93.9% average precision (AP) on the test set, outperforming YOLOv11s which achieved 89.1%, while reducing parameters to 7.8 million and floating-point operations (FLOPs) to 16.5 billion. Robustness evaluation yielded coefficients of determination (R²) values of 0.91 to 0.94 for lighting, 0.89 to 0.94 for planting density, and 0.90 to 0.94 for cultivar. A cross-year field test using an NVIDIA Jetson Orin NX edge device on 120 quadrats in 2025 achieved R² of 0.91, root mean square error (RMSE) of 4.0, and mean absolute error (MAE) of 3.3 at 20 frames per second, confirming practical deployability. This study demonstrates that smartphone-based proximal phenotyping with an optimized deep learning model can provide accurate, low-cost rice panicle counting for breeding and production applications.
Why it matches plant phenotyping methodsイネ穂数という植物形質をRGB画像から推定する深層学習モデルを開発し、精度・頑健性・実地展開性能を検証しており、フェノタイピング手法が研究の中心である。
abstractThis study proposes CEG-YOLO, a lightweight deep learning model for in-field rice panicle detection and counting using consumer-grade RGB imaging devices.
Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize ( Zea mays L . ). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years ( R 2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy ( R 2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.
Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから草丈と地上部バイオマスを推定する高スループット表現型測定モデルを開発・検証しており、フェノタイピング手法が研究の中心です。
abstractwe employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB
Wild rice (Oryza spp.) harbors abundant genetic variation and represents an important germplasm resource for improving yield-related traits in cultivated rice. Panicle number is a key phenotypic trait for evaluating tillering capacity and yield potential in wild rice. However, existing approaches for acquiring panicle-number phenotypes remain limited by low efficiency, high dependence on manual operation, and cumbersome matching between plant targets and accession identifiers. In this study, we proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation. The framework integrates field plant localization, accession identifier binding, flight route planning, plant-by-plant video acquisition, spatiotemporal registration, video slicing, and panicle detection and tracking, enabling structured panicle-number outputs indexed by accession identifier. To address the small scale, loose structure, morphological variation, and wind-induced swaying of wild rice panicles in UAV imagery, a wild rice panicle detection model was constructed, and WRPD-Tracker was developed for cross-frame identity association and non-redundant counting. The wild rice panicle detection model achieved an AP@50 of 91.56%, representing a 6.16-percentage-point improvement over the DEIM baseline, with 3.70 M parameters and 6.55 G FLOPs, while WRPD-Tracker achieved a HOTA of 65.1% and a MOTA of 79.0%, representing a 5.8-percentage-point improvement in HOTA over the baseline tracker. At the final counting level, UAV-based counts were highly consistent with manual ground counts, with an R² of 0.992 and an MAE of 0.37 panicles. This framework enables batch acquisition of panicle-number phenotypes in wild rice and provides quantitative support for germplasm evaluation, panicle-number trait comparison, and subsequent yield-related phenotypic studies.
Why it matches plant phenotyping methodsUAV画像とAIによるイネ穂数の取得・追跡・計数手法を開発し、手動計数と技術検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation.
Japanese agriculture faces pressing challenges, including a declining and aging farming population and the need to adapt to climate change. To address these issues, Smart Agriculture is being introduced to improve production efficiency. Among these, unmanned aerial vehicles (UAVs) have gained attention for their ability to rapidly monitor entire fields. We proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values. The experimental site consisted of five paddy fields within an 80 m × 50 m plot in Iwate Prefecture, Japan, equipped with weather and water sensors. Ground-truth data (overall length, culm length, panicle number, and stem number) were collected approximately one week before harvest. UAV monitoring was conducted four times using a multispectral camera, and growth analysis was performed with six VIs. Correlation analysis revealed a positive relationship between crop growth and the daily average water level during the drainage period, and a negative relationship with the daily temperature range in mid-June. A combined cluster-label representation, constructed from clustering results of all VIs for each mesh, enabled integrated analysis and visualization of multi-index patterns. Grid size optimization showed no significant differences in correlation trends between 1 m × 1 m and 5 m × 5 m resolutions. For non-crop area removal, a comparison of three image segmentation methods demonstrated that the Otsu Method achieved the highest performance. Finally, to facilitate practical use in the field, we prototyped a report interface for the diagnosis system. Future work will focus on developing a comprehensive field diagnosis system to clarify field environments, with the aim of addressing fragmentation and enclaves in Japanese farms.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数、画像分割、クラスタリングを統合した作物生育診断システムの開発・評価が中心であり、作物形質との相関検証や実用インターフェースも扱っている。
abstractWe proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values.
Above ground crop traits provide an early indication of a plant's capacity to tolerate stress, and are important for breeding programs aimed at improving stress tolerance. In this work, we present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time under control, drought, and saline conditions. We used daily sideview imaging of individual plants, followed by segmentation of the panicle, leaf and stem using the deep learning U-Net++ segmentation model. The resulting segmentations were used in regression models to estimate leaf area, fresh and dry biomass, and leaf dry weight. The regression models showed high predictive accuracy. Using these estimates, we could calculate specific leaf area and leaf weight ratio. In addition, radiation use efficiency for above-ground biomass production was calculated, providing an independent physiological check on the consistency of these predictions. Finally, using automated measurements of plant transpiration we were able to determine daily averages of whole plant stomatal conductance. The results show that image-derived morphological traits can be used to accurately estimate biomass-related traits and to derive physiologically meaningful indicators of plant performance over time. This method provides a framework for non-destructive monitoring of quinoa responses to drought and salinity.
Why it matches plant phenotyping methods画像取得、深層学習セグメンテーション、回帰による植物形質推定を中核とする高スループット表現型解析手法であり、ストレス実験での単なるルーチン測定ではない。
abstractwe present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time
Wheat head detection is a critical component in high-throughput phenotyping, holding significant application value for wheat yield estimation and breeding analysis. With the continuous advancement of general object detection models, state-of-the-art detectors achieve high accuracy in same-distribution wheat head detection scenarios. However, when applied to cross-distribution environments, their performance often degrades significantly. To investigate this issue, this paper adopts the official division of the GWHD2021 public dataset as the cross-distribution evaluation setting. Using the recently state-of-the-art object detection model YOLOv13 as the baseline, we systematically explore effective approaches to enhance cross-distribution generalization performance in wheat head detection. Specifically, we propose two lightweight modifications to YOLOv13’s Full-PAD architecture and HyperACE’s core modules, analyzing their potential mechanisms: (1) Replacing scalar gating in Full-PAD with channel-level gating enables finer-grained branch injection control. Concurrently, channel-level gating introduces equivalent stronger weight penalties during training, generating additional regularization effects. Through exploratory controlled experiments aligning weight-penalty strengths, we find that this gain depends on both channel decoupling and the accompanying implicit regularization rather than on decoupling alone. (2) Removing Batch Normalization from HyperACE’s core C3AH modules and adopting normalization strategies independent of batch statistics—such as Identity, Group Normalization, or Instance Normalization. Preliminary experiments show that while this results in a small, directionally positive change, the change remains within run-to-run variance; we therefore do not claim BN removal as a reliable standalone improvement. Furthermore, combining channel gating with BN removal does not yield further additive improvements compared to channel gating alone, indicating an interaction effect. To address this, we analyze relevant statistics between channel gating and HyperACE branches, providing an exploratory explanation for this non-additive phenomenon. In summary, this paper delivers empirical evidence and preliminary insights for enhancing generalization performance in the cross-distribution wheat head detection task of the advanced general-purpose detection model YOLOv13.
Why it matches plant phenotyping methods小麦穂の画像検出を対象に、YOLOv13の改良と異分布ロバスト性をGWHD2021で系統的に評価しており、植物表現型取得・解析手法が中心である。
abstractWheat head detection is a critical component in high-throughput phenotyping
Accurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping. However, unmanned aerial vehicle (UAV)-based panicle detection remains challenging because flight-altitude variation produces large target-scale changes. Flooded paddy backgrounds, leaf occlusion, and illumination fluctuations further obscure small panicle targets. To address these challenges, we constructed a composite multi-altitude dataset covering UAV imagery acquired from 3 to 20 m under varying field conditions. We then propose Panicle-DETR, a lightweight detection and counting framework based on a frequency-aware Cross Stage Partial (CSP) backbone. Rather than treating the Fast Fourier Transform (FFT) as a filter by itself, the proposed FasterFD module uses frequency-domain representations with learnable frequency-response reweighting to enhance panicle-related texture cues and reduce redundant background responses. A Lossless Feature Encoder is designed to preserve fine spatial information for small targets across altitude-induced scale changes, while a composite metric loss based on Normalized Gaussian Wasserstein Distance (NWD) and Inner-IoU improves localization for adherent and overlapping panicle clusters. On the composite dataset, Panicle-DETR achieved a Precision of 90.97%, a Mean Absolute Error of 4.28, and an R2 of 0.957 for single-frame panicle counting. With 13.78 M parameters and 53.0 GFLOPs, the framework achieved 16.9 FPS with 1.96 GB peak GPU memory in a battery-powered notebook benchmark, supporting its potential for resource-constrained field-side UAV image analysis.
Why it matches plant phenotyping methodsUAV画像からイネ穂の検出・計数という植物形質推定を中心に、マルチ高度データセットと専用解析フレームワークを開発・評価しているため。
abstractAccurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping.
ArabidopsisMicroscopyFlowerFruitPanicle / ear / spikeVisualization / data management
Background Precise characterization of gene expression patterns across temporal, cellular, and tissue-specific contexts is fundamental to understanding plant development and function. Recent advances in ClearSee-based tissue clearing have enabled high-resolution visualization of internal structures and fluorescent reporter signals in plant tissues. Although hand sectioning can provide optical access to tissues that are not amenable to whole-mount clearing, its application to submillimeter-scale and fragile Arabidopsis organs and tissues, including developing inflorescence apices, flowers, fruits, and organ boundaries, has remained limited. Consequently, analysis of these tissues has largely depended on specialized microdissection techniques and labor-intensive histological workflows, such as wax- or resin-embedded microtomy, which restrict throughput, accessibility, and routine use. Results We developed and optimized a simple hand-sectioning and imaging method that enables routine visualization of anatomical organization and gene expression patterns at cellular resolution in small, fragile Arabidopsis tissues. This method relies only on gentle manual tissue processing under a stereomicroscope and readily available reagents, allowing reproducible preparation of delicate tissues without the need for embedding or specialized equipment. Combined with ClearSee-based clearing and fluorescent reporters, the approach enables high-resolution imaging of internal tissue architecture and gene expression, while preserving tissue integrity and fluorescence signals that are often compromised during conventional embedding and microtomy procedures. Conclusions Our method substantially reduces technical complexity, costs, preparation time, and labor associated with cellular-resolution imaging of small, fragile plant tissues. By providing a simple, scalable, and accessible alternative to conventional histological workflows, this approach facilitates routine analysis of internal developmental processes across diverse plant species.
Why it matches plant phenotyping methods小型・脆弱な植物組織の解剖学的構造と遺伝子発現を細胞解像度で取得する手法を開発・最適化しており、表現型取得法が研究の中心である。
abstractWe developed and optimized a simple hand-sectioning and imaging method that enables routine visualization of anatomical organization and gene expression patterns at cellular resolution in small, fragile Arabidopsis tissues.
Abstract. 3D instance segmentation for laser scanning (LiDAR) point clouds remains a challenge in many remote sensing-related domains. Successful solutions typically rely on supervised deep learning and manual annotations, and consequently focus on objects that can be well delineated through visual inspection and manual labeling of point clouds. However, for tasks with more complex and cluttered scenes, such as in-field plant phenotyping in agriculture, such approaches are often infeasible. In this study, we tackle the task of in-field wheat head instance segmentation directly from terrestrial laser scanning (TLS) point clouds. To address the problem and circumvent the need for manual annotations, we propose a novel two-stage pipeline. To obtain the initial 3D instance proposals, the first stage uses 3D-to-2D multi-view projections, the Grounded SAM pipeline for zero-shot 2D object-centric segmentation, and multi-view label fusion. The second stage uses these initial proposals as noisy pseudo-labels to train a supervised 3D panoptic-style segmentation neural network. Our results demonstrate the feasibility of the proposed approach and show performance improvements relative to Wheat3DGS, a recent alternative solution for in-field wheat head instance segmentation without manual 3D annotations based on multi-view RGB images and 3D Gaussian Splatting, showcasing TLS as a competitive sensing alternative. Moreover, the results show that both stages of the proposed pipeline can deliver usable 3D instance segmentation without manual annotations, indicating promising, low-effort transferability to other comparable TLS-based point cloud segmentation tasks.
Why it matches plant phenotyping methods圃場小麦穂の3D個体セグメンテーションを対象に、TLS点群とゼロショット・マルチビュー融合・擬似ラベル学習を組み合わせた表現型取得手法を開発・評価しており、方法が研究の中心である。
abstractsuch as in-field plant phenotyping in agriculture, such approaches are often infeasible.
The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping
Why it matches plant phenotyping methods植物器官の3D再構成と形態情報抽出を目的とする計算手法を開発し、既存法と形態忠実度・器官構造整合性・処理速度で比較評価しており、フェノタイピング手法が中心である。
abstractThe objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud.
PHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases, with two AI-powered applications now reaching proof-of-concept stage. The first, FUSASEYD, addresses Fusarium Head Blight (FHB), a major fungal disease in winter wheat. Using RGB field images and a deep learning instance segmentation model (YOLOv11), the application detects and quantifies FHB symptoms on wheat ears, offering an automated alternative to time-consuming expert visual scoring. GEVES has developed both a PC interface and a smartphone application to visualise model predictions in the field. Validation in French registration trials is planned for the 2026 campaign. A companion article by V. Cadot et al. is currently under review in the Journal of Experimental Botany special issue on Plant Phenomics & Enviromics Across Scales. The second, COYL (Counting Orange and Yellow Larvae), tackles a practical challenge faced by breeders, and rapidly counting wheat blossom midge larvae, both Sitodiplosis mosellana and Contarinia tritici, to characterise variety susceptibility. Using smartphone RGB images and YOLOv-based object detection, the best-performing model achieved high accuracy and successfully distinguished between the two visually similar species. An online counting application has been developed, currently accessible to Walloon Agricultural Research Centre members. The labelled COYL-1 dataset is publicly available at https://doi.org/10.5281/zenodo.19402333 for community use. A companion article by Antoine Deryck et al. is under submission at Plant Phenomics Journal.
Why it matches plant phenotyping methodsRGB画像と深層学習によるコムギ穂の病徴検出・定量化を開発し、専門家評点の自動化とセンサー／画像法の検証を目的とするため、植物フェノタイピング手法が中心である。
abstractPHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases
PHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases, with two AI-powered applications now reaching proof-of-concept stage. The first, FUSASEYD, addresses Fusarium Head Blight (FHB), a major fungal disease in winter wheat. Using RGB field images and a deep learning instance segmentation model (YOLOv11), the application detects and quantifies FHB symptoms on wheat ears, offering an automated alternative to time-consuming expert visual scoring. GEVES has developed both a PC interface and a smartphone application to visualise model predictions in the field. Validation in French registration trials is planned for the 2026 campaign. A companion article by V. Cadot et al. is currently under review in the Journal of Experimental Botany special issue on Plant Phenomics & Enviromics Across Scales. The second, COYL (Counting Orange and Yellow Larvae), tackles a practical challenge faced by breeders, and rapidly counting wheat blossom midge larvae, both Sitodiplosis mosellana and Contarinia tritici, to characterise variety susceptibility. Using smartphone RGB images and YOLOv-based object detection, the best-performing model achieved high accuracy and successfully distinguished between the two visually similar species. An online counting application has been developed, currently accessible to Walloon Agricultural Research Centre members. The labelled COYL-1 dataset is publicly available at https://doi.org/10.5281/zenodo.19402333 for community use. A companion article by Antoine Deryck et al. is under submission at Plant Phenomics Journal.
Why it matches plant phenotyping methodsRGB画像と深層学習によりコムギ穂の病徴を検出・定量する手法の開発とセンサー／画像手法の検証が中心であり、植物病害表現型の取得に該当する。
abstractPHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases
We evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions. To address model robustness, the multi-output neural network was compared with linear regression, elastic net, random forest, and XGBoost using repeated five-fold cross-validation, an 80/20 holdout split, and independent year-wise validation. Under repeated cross-validation, XGBoost provided the strongest average predictive performance for both traits, with R2 values of 0.57 for GY and 0.67 for LP. The multi-output neural network showed moderate performance, with R2 values of 0.49 for GY and 0.57 for LP. Final holdout performance for the neural network for GY and LP was R2 = 0.64 and R2 = 0.92, respectively. Year-wise validation showed weak temporal transferability because the two seasons differed not only in environmental conditions, but also in lodging mechanism. Repeated permutation importance identified ear width (EW), kernel row number (RNE), thousand kernel mass (KM1000), and kernel number per ear (KNE) as important predictors of GY, while LP prediction was most strongly associated with internode major diameter (IDmajor), ear length (EL), and the number of green leaves (NGL). Across both permutation importance and SHAP, only RNE and NGL were consistently shared between GY and LP. Supplementary ALE diagnostics indicated that RNE showed increasing model-estimated effects for both predicted GY and LP, whereas NGL showed a positive association with predicted GY but a decreasing or nonlinear association with predicted LP. These results show that joint modeling can support exploratory trait interpretation, but the predictive relationships remain environment-specific and should not be interpreted as causal or broadly transferable without further multi-environment validation.
Why it matches plant phenotyping methods穀粒収量と倒伏率という植物形質を推定する多出力ニューラルネットワーク等のモデルを開発・比較し、交差検証、ホールドアウト、年次外部検証で性能評価しており、計算的形質推定が中心である。
abstractWe evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions.
An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and cross-sectional area profile. New shape descriptors based on cross-sectional area profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態を3D画像取得と計算解析で定量化するパイプラインを開発し、形状記述子の抽出と遺伝子型間での検証を行う、植物フェノタイピング手法の中心的研究である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe paper's Data Availability and Code Availability sections point to the authors' public GitHub repository containing sample 3D spike data and the analysis code for the wheat spike morphology pipeline.Code · publicCode Availability
The codes are available at the following link: https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtractionOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionlines:316-410Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially advanced automatic wheat spike detection and counting. However, precise spikelet-level phenotyping remains largely underexplored. This study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations. For spike detection, the pre-trained YOLOv11 achieved superior accuracy (mAP@0.5 = 95.8%, Pearson r = 0.993) with shorter training and inference times compared to YOLOv12. For spikelet detection, the non-pretrained YOLOv11 demonstrated higher accuracy (mAP@0.5 = 99.0%), while counting performance was comparable across models. These results establish OBB-based YOLO detection as a robust and scalable approach for AI-driven wheat phenotyping.
Why it matches plant phenotyping methods小麦の穂・小穂という収量関連形質の画像ベース検出・計数手法を比較評価し、大規模ベンチマークデータセットも構築しているため、フェノタイピング手法が中心である。
abstractThis study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations.
Reproduction assets foundThe paper openly states its supporting data (spike/spikelet imagery with OBB annotations) is available on Zenodo, and the underlying models are deployed on the authors' public WheatAI cloud platform.Dataset · publicData availability
The data supporting the findings of this study are openly available at: https://doi.org/10.5281/zenodo.20215489 .Open asset ↗zenodo · 10.5281/zenodo.20215489lines:219-266Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The number of petals in an inflorescence is an important phenotypic indicator for quality evaluation and cultivar identification of cut chrysanthemums ( Chrysanthemum morifolium Ramat.). Current manual measurement methods are time-consuming, error-prone, and poorly suited to the complex geometry of chrysanthemum flowers, which limits their utility for large-scale phenotyping and breeding programs. Although image-based phenotyping has advanced rapidly, automated and reliable methods for petal counting in densely packed or partially obscured inflorescences remain underdeveloped. Here, we developed a deep learning-based framework for automatic extraction of petal number in cut chrysanthemums. Images from multiple varieties were collected to construct a representative dataset, and petal density maps were generated through manual annotation with Gaussian kernel function. We employed a Congested Scene Recognition Network (CSRNet) enhanced with a Squeeze-and-Excitation (SE) channel attention mechanism (SE-CSRNet) for petal density estimation. Spearman correlation analysis revealed strong agreement between visible and actual petal counts (Spearman’s r=0.953, p<0.0001). Compared with the original CSRNet, SE-CSRNet reduced mean absolute error (MAE) and root mean squared error (RMSE) by 5.2% and 7.4%, respectively. Further optimization using regression fitting revealed that random forest achieved the best performance (MAE = 4.24, RMSE = 5.06, R 2 = 0.967), indicating reliable stability and satisfactory generalization under the conditions evaluated in this work. Application of the optimized model to two cut chrysanthemum varieties confirmed its practicality by successfully detecting reductions in petal number under high-temperature stress. Our results demonstrate that integrating dataset construction, deep learning–based density estimation, and machine learning optimization enables efficient and accurate prediction of petal number in cut chrysanthemums.
Why it matches plant phenotyping methods花弁数という植物形質を画像から自動抽出する深層学習手法を開発し、データセット構築、性能比較、検証、実用適用まで行っており、表現型取得手法が研究の中心である。
abstractHere, we developed a deep learning-based framework for automatic extraction of petal number in cut chrysanthemums.
Reproduction assets foundThe article states that some data (the chrysanthemum petal-counting dataset and related materials) will be available at the authors' public GitHub repository (qwsdfgz/petalscount), with other data available from the corresponding author upon reasonable request. The repository URL is explicitly provided by the authors,但Dataset · publicnctional components of bud-leaves and flowers in edible chrysanthemum (Chrysanthemum morifolium Ramat)
Horticulturae 11 5 2025 448
10.3390/horticulturae11050448
Appendix A
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
Some data will be available at this URL: https://github.com/qwsdfgz/petalscount . Other data are openly available from the corresponding author upon reasonable request.
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100238 .Open asset ↗qwsdfgz/petalscountlines:602-636Plant phenotyping relevance match · UnverifiedOpenAlex · checked 5 Sept 2026
Accurate wheat spike detection is essential for crop phenotyping and yield estimation, but real-world field conditions—such as dense spike overlap, environmental domain shifts, and degradation-induced failures like motion blur—pose significant challenges. Achieving robust perception under these circumstances while maintaining a strict accuracy-efficiency trade-off for edge devices remains a pressing research problem. To overcome these limitations, we propose GG-YOLO, a unified lightweight detection framework specifically tailored for complex agricultural environments. Rather than a simple recombination of existing lightweight modules, GG-YOLO integrates three original structural adaptations: First, a Dual-path Attentive Ghost Mechanism (DAGM) introduces gradient-guided attention modulation to enhance feature discrimination and explicitly resolve feature confusion in dense, overlapping regions. Second, a C3Ghost module combines multi-branch aggregation with linear feature generation, mitigating parameter redundancy in the prediction head by approximately 31% compared to the standard YOLOv8s without sacrificing semantic capacity. Third, DSample, a dynamic upsampling operator featuring an original dual-mode adaptive mechanism, robustly recovers fine-grained spatial details during multi-scale feature pyramid fusion. Extensive cross-dataset experiments on the GlobalWheat2020 and HNKJXYwheat datasets validate the model’s exceptional resilience to domain shifts and varying growth stages. GG-YOLO achieves a precision of 94.35%, a recall of 91.93%, and a state-of-the-art mAP@50 of 96.47%. Furthermore, the model contains only 7.89 M parameters and requires 20.4 GFLOPs, reaching an inference speed of 165 FPS on a desktop GPU and a validated real-time speed of 64 FPS on an NVIDIA Jetson edge computing platform. These results demonstrate that GG-YOLO establishes a superior accuracy-efficiency frontier, making it highly reliable for real-time field deployment in precision agriculture.
Why it matches plant phenotyping methodsコムギ穂の検出を作物フェノタイピングおよび収量推定に用いる画像解析手法を開発し、複数データセットで性能検証しているため、フェノタイピング手法が中心である。
abstractAccurate wheat spike detection is essential for crop phenotyping and yield estimation
Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.
Why it matches plant phenotyping methods植物CT画像から3D形状を抽出する、アノテーション不要・学習不要の分割手法を開発し、複数作物データセットで性能評価しており、表現型取得手法が研究の中心である。
abstractwe introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT.
Reproduction assets foundThe paper open-sources the DiffPlantCT implementation code on GitHub and benchmarks on two public plant CT datasets (walnut fruit via figshare; barley spike via Plant Methods), all with explicit availability statements and matching allowed URLs.Code · publicThe datasets and implementation code of the DiffPlantCT framework are open-sourced on GitHub at https://github.com/WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentation .Open asset ↗WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentationlines:220-287Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Abstract This study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding. This research, conducted using 782 wheat genotypes in Rafsanjan, Iran, compares MLR and ANN methodologies. MLR, using seven traits selected via stepwise regression, achieved an R² of 0.90, the root of mean square of error (RMSE) of 14, Average absolute percentage error (MAPE) of 13.3, and Average deviation of prediction from the actual value (MAE) of 10. Key traits identified were biological weight (weight of total plant per line, WPP) and harvest index (HI). Conversely, the GA-ANN model, employing six selected traits, demonstrated superior performance with R² values of 0.94, 0.96, and 0.94 for training, testing, and combined datasets respectively. Validation metrics for the ANN model were MSE of 144.3, RMSE of 12, and MAE of 5.7. GA-ANN selected height, peduncle length, days to flowering, spike length, biological weight, and harvest index as significant predictors. The results underscore that ANN models, particularly when combined with genetic algorithms for feature selection and optimization, can improve prediction accuracy by modeling complex, non-linear relationships in agricultural data, therefore providing more precise yield prediction tools for plant breeders. This study emphasizes the need for advanced predictive techniques in achieving more accurate assessments of crop yield for sustainable agriculture.
Why it matches plant phenotyping methods小麦遺伝子型レベルの収量という植物形質を対象に、MLRとGA-ANNの予測精度を比較・検証しており、計算による形質推定手法が研究の中心です。
abstractThis study investigates the accuracy of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), specifically a hybrid Genetic Algorithm-ANN (GA-ANN), for predicting wheat yield in plant breeding.
Maize ear rot severely restricts maize yield and quality, making the breeding of disease-resistant varieties the core strategy for disease prevention and control. Due to the highly uneven spatial distribution of lesions on maize ears, precise full-surface detection is essential for objectively quantifying disease severity. However, traditional manual disease grading is highly subjective, and conventional RGB-based detection methods struggle to precisely identify lesion regions associated with maize ear rot. These limitations hinder the precise identification and quantitative analysis of maize ear rot infection regions, thereby limiting the reliability of phenotypic data used for resistance evaluation and subsequent genome-wide association studies (GWAS). To address these challenges, this study developed an integrated full-surface hyperspectral imaging system featuring line-scan imaging and synchronous rotation control. Non-redundant full-surface ear images were then generated using the oriented FAST and rotated BRIEF (ORB) algorithm combined with random sample consensus (RANSAC), hereafter referred to as ORB-RANSAC. Furthermore, after Savitzky-Golay (SG) preprocessing and feature selection using a genetic algorithm (GA), three machine learning models and three deep learning models were established, and their classification performance was compared. The results showed that the convolutional neural network-bidirectional long short-term memory network (CNN-Bi-LSTM) model achieved the best average performance, with an average overall accuracy (OA) of 95.61 ± 0.36%. It also achieved higher overall accuracy than traditional machine learning models such as random forest (RF), indicating that CNN-Bi-LSTM can achieve high-precision pixel-level detection of lesion regions showing Fusarium-associated maize ear rot symptoms. Additionally, this model was deployed in locally developed automatic analysis software, enabling an integrated analysis workflow from raw hyperspectral data input to the quantification of disease-related phenotypic parameters. This study not only fills the technical gap in the non-destructive full-surface detection of maize ear rot but also provides an efficient and reliable automated tool for high-throughput phenomics research, which holds great significance for accelerating the discovery of maize resistance genes and ensuring food security.
Why it matches plant phenotyping methodsトウモロコシ穂の病斑をハイパースペクトル画像と深層学習で定量し、全表面撮像システム、解析モデル、ソフトウェアを開発したため、植物表現型取得法が中心である。
abstractthis study developed an integrated full-surface hyperspectral imaging system featuring line-scan imaging and synchronous rotation control.
Wheat spike detection is essential for yield estimation in precision agriculture, yet it remains challenging due to the small size of targets, dense distribution, and complex field environments. In this study, we propose LiteMS-YOLO, a lightweight object detection framework based on YOLO26n. The model integrates a Feature Complementary Mapping (FCM) module to enhance spatial-semantic feature interaction and a Multi-Kernel Perception (MKP) unit to improve multi-scale feature representation. In addition, targeted redundancy reduction strategies are introduced to significantly lower model complexity. Experiments are conducted on a combined dataset comprising the public Global Wheat Head Detection (GWHD) dataset and 100 field images collected by the Tangshan Academy of Agricultural Sciences, with a total of 6,378 high-resolution images and over 44,000 annotated wheat spikes. LiteMS-YOLO achieves a mAP50 of 92.28% and a mAP50–95 of 52.56%, while using only 0.627 million parameters. Compared with YOLO26n and YOLOv8n, the proposed method reduces parameters by approximately 75% and 79%, respectively, while maintaining competitive accuracy. These results demonstrate that LiteMS-YOLO strikes an excellent balance between detection accuracy and efficiency, making it well-suited for real-time deployment in resource-constrained agricultural scenarios.
Why it matches plant phenotyping methods小麦穂の検出による収量推定を目的に、画像ベースの検出モデルを開発し、複数データセットで性能検証している。植物器官の検出・計数に基づく表現型取得が研究の中心である。
abstractWheat spike detection is essential for yield estimation in precision agriculture
Ear density ( ) and ear surface area in cereals are important traits for adaptation to low inputs and climate change. Here we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot. First, the YOLOv5 ear detection algorithm is applied to nadir RGB images to estimate . Second, an ear segmentation algorithm is applied to nadir and 45° RGB images to compute the ear gap fraction at different viewing angles. The Beer-Lambert law is then inverted to compute the ear area index (EAI) from the observed ear gap fraction. is finally derived as the ratio between EAI and . We applied the methodology to a panel of 10 commercial bread wheat varieties how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m -2 ) for and 18% (1.3 cm 2 ) for . For awned varieties, ground-truth observations of were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm 2 ). was strongly correlated with grain dry mass per ear at harvest ( r 2 = 0.80 across genotypes and environments, r 2 per genotype ranged between 0.80 and 0.95) and was strongly correlated with grain yield ( r 2 = 0.83). These results indicate that both EAI and can be interesting non-destructive proxies for yield and grain dry mass per ear.
Why it matches plant phenotyping methodsRGB画像と地上ロボット、物体検出・セグメンテーション・Beer–Lambert法を組み合わせ、コムギ穂の密度と表面積を推定・検証する手法が研究の中心であるため。
abstractHere we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot.
Reproduction assets foundThe authors publicly release their ear surface area estimation algorithm with an example dataset on an INRAE forge repository, and the Phenomobile-derived ear density/ear surface area estimations used in the multi-environment analysis are included as supplemental material with the open-access article. The YOLOv5 GWC_SoDataset · publicThe algorithm developed to estimate the EAI and the average ear surface using binary images from ear segmentation are publicly available in the repository https://forge.inrae.fr/raul.lopez-lozano/wheat-ear-surface , jointly with an example dataset from the Mauguio 2023 trial (4 treatments, 1 replicate). The Phenomobile estimations of ear surface area and ear density used in the multi-environmental mixed model presented in Section 2.5 are included as supplemental material.Open asset ↗lines:614-652Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Accurate prediction of nitrogen utilization efficiency (NUtE) is critical for breeding nitrogen-efficient crop cultivars and optimizing field nitrogen management. Traditional prediction methods are time-consuming and limited to resolving plant-level nitrogen dynamics, hindering effective phenotype acquisition at the field scale and understanding of nitrogen uptake and transport in crops. This study aims to couple proximal remote sensing (PRS) and a crop growth model (CGM, i.e., WheatGrow model) via high-throughput phenotyping (HTP) techniques to establish a non-destructive prediction framework for plot-level NUtE at the field scale. Firstly, the organ-level nitrogen submodule was developed and integrated into the WheatGrow model, improving the simulation accuracy of plant nitrogen accumulation dynamics. Second, proximal RGB data combined with a deep-shallow machine learning approach enabled high-precision estimation of organ-specific critical nitrogen concentrations (leaf: R 2 = 0.94, RMSE = 0.21%; spike: R 2 = 0.95, RMSE = 0.10%). Fitted parameters of critical nitrogen dilution curves (CNDCs) demonstrated variations between cultivar and management in both organs, with spike nitrogen dilution rates exhibiting greater sensitivity to management practices than leaves. Finally, coupling PRS-derived organ-specific CNDCs with the enhanced WheatGrow model through the ensemble Kalman filter (EnKF) algorithm, yielded precise NUtE predictions at a small spatial scale (RMSE = 4.54 kg kg −1 , Bias = 0.05). Validation across multi-year, multi-cultivar trials demonstrated robust performance, reducing NUtE prediction errors below 10% (RRMSE = 9.9% ± 0.8%). This framework bridges HTP techniques with crop modeling, may catalyze a paradigm shift in CGMs from empirical parameterization to real-time sensing, and advance scalable nitrogen use efficiency phenotyping in sustainable crop improvement and smart agriculture.
Why it matches plant phenotyping methods高スループット表現型計測、近接リモートセンシング、RGB画像、機械学習、作物モデルを統合し、器官別窒素形質と圃場スケールのNUtEを推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractThis study aims to couple proximal remote sensing (PRS) and a crop growth model (CGM, i.e., WheatGrow model) via high-throughput phenotyping (HTP) techniques to establish a non-destructive prediction framework for plot-level NUtE at the field scale.
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Why it matches plant phenotyping methodsUAS画像からキャノピー高、穂形質、植生指数を抽出し、機械学習でソルガムのプロット収量を推定するパイプラインが研究の中心であり、形質取得・推定手法の評価も行っている。
abstractthis study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Accurate timing of rice panicle anthesis is critical for quantifying sterility risk under heat and humidity, yet minute-scale field measurement remains challenging because anthesis is transient and spikelets are tiny and difficult to detect. To address this, we present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model. For slender panicle detection and panicle pose normalization, YOLO-SnakePanNet was introduced by using Dynamic Snake Convolution with a lightweight box-rotated head. Ablation experiments show that YOLO-SnakePanNet achieved mAP@50 of 94.4%, improving by 3.6% over the YOLOv11 while reducing computation by 0.7 GFLOPs. For panicle-level anthesis pinpointing, PanicleTimeMAE was proposed by incorporating a pyramid-dilated temporal convolutional network and a confidence-aware smoothing gate into the transformer, reaching Acc@±1 of 0.85 on 5-min sampled sequences (±1 frame = ±5 min), yielding a 40% decrease in MAE over VideoMAEv2. Finally, correlation analysis between variety-level anthesis start time (T start ) and peak time (T peak ) and same-day meteorology showed that higher photosynthetically active radiation (r = -0.543/-0.573 for T start /T peak ) and temperature (r = -0.288/-0.272) advanced anthesis, whereas higher relative humidity (r = 0.397/0.438) and rainfall (r = 0.428/0.502) delayed anthesis. The variance decomposition within fixed-effects model for Tstart ( R2 = 0.651) and Tpeak ( R2 = 0.648) prediction shows that variance mainly attributed to meteorological effects (64%) and variety effects (33.5%). Overall, OPAL-Flow enables variety selection for heat- and humidity-resilient anthesis in rice breeding and supports ecophysiological dissection of anthesis regulation.
Why it matches plant phenotyping methodsイネ穂の開花時刻という植物形質を圃場画像・動画から推定する検出、追跡、超解像、時刻推定パイプラインを開発し、性能評価も行っているため、植物フェノタイピング手法が研究の中心である。
abstractwe present OPAL-Flow, a pipeline that provides single-panicle anthesis start and peak times under field conditions, consisting of a detector for panicle detection and tracking, a super-resolution reconstruction model, and an event-time pinpointing model.
Reproduction assets foundThe paper's Data availability statement explicitly states that the source code and test samples for OPAL-Flow are publicly available on GitHub at the authors' repository. This is a paper-specific, publicly actionable code asset. The phenotype datasets (panicle detection dataset, start/peak annotation sequences) are notCode · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/OPAL-FLOW . Additional data can be made available upon reasonable request.Open asset ↗gfjiyue/OPAL-FLOWlines:578-590Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Abstract Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. To support model training and evaluation, we construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD-L 1 and CD-L 2 values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for robust 3D wheat phenotyping under occlusion and provides a systematic reference for applying general-purpose 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下小麦の単視点3D再構成とアモーダル補完を開発し、データセット構築、複数手法の系統評価、器官形質推定誤差の検証まで行っており、植物フェノタイピング手法が中心である。
abstractthis study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat
Rice false smut is an important fungal infection in the rice panicle stage, which occurs only in the panicle. Rice yield and quality will be seriously threatened after the occurrence of panicle disease. Early identification of disease is very important for precise prevention and control. However, in the actual field environments, complex light changes, the dense distribution of small disease spots, panicle overlapping shading, and other factors often result in the semantic attenuation of key discriminant information in the stage of visual feature extraction, which has brought great challenges to the early detection and prevention of the disease. To resolve the above problems, this study introduces a rice false smut detection model derived from an improved YOLOv11 framework, named Rice-Smut, to bolster the resilience and stability of the network regarding the identification of rice false smut disease under complex field backgrounds. Firstly, in order to enhance the feature capture capabilities for multi-scale and densely distributed lesions, the C3SC backbone feature extraction network combining the SCConv block is integrated. This architecture can significantly suppress the spatial and channel redundancy and augment the precise characterization of the texture features of the lesion. Then, the C2PSA-SE attention module is introduced to effectively filter the background interference and improve the precise positioning of dense small targets. Finally, to address the irregular structure of rice false smut lesions, the GIoU loss function serves as a substitute for the conventional CIoU, which enhances the network's proficiency in locating the irregular shape lesions. Experimental outcomes revealed that the Rice-Smut model yielded a precision of 79.3% and mAP@50 of 75.3%, which represented a 7.6 and 4.5 percentage point improvement over the baseline model YOLOv11. The model requires 2.41M parameters, with a model size of 4.9MB, which results in low computational complexity. The preliminary validation on mobile platforms shows that the method is viable for the potential to be applied to the real-time field detection and disease monitoring of rice false smut, and can provide support for disease control decision-making and field management.
Why it matches plant phenotyping methodsイネの病徴（病斑）を画像から検出・位置推定するYOLOベース手法を開発し、複雑な圃場条件で性能検証しているため、植物病害状態のフェノタイピング手法が中心である。
abstractthis study introduces a rice false smut detection model derived from an improved YOLOv11 framework, named Rice-Smut, to bolster the resilience and stability of the network regarding the identification of rice false smut disease under complex field backgrounds.
Introduction Nitrogen utilization efficiency (NUtE) directly reflects the efficiency of nitrogen remobilization to grains, serving as a key indicator of yield formation and environmental performance. However, conventional methods for assessing NUtE rely on destructive sampling and laboratory analysis, which are labor-intensive and time-consuming, whereas most existing remote sensing studies estimate NUtE by directly regressing spectral features against the final efficiency value without decomposing it into its underlying physiological components. Methods This study developed a remote-sensing-based indicator of rice NUtE based on chlorophyll-related vegetation indices at key rice growth stages, termed the Nitrogen Utilization Efficiency-Vegetation Index (NUtE-VI). NUtE showed a close and near-linear relationship with the ratio of panicle nitrogen accumulation from heading to dough stage (ΔPNA dough-heading , sink indicator) to leaf nitrogen accumulation at booting stage (LNA booting , source indicator). Therefore, with multi-site field experiments across different rice cultivars and nitrogen treatments, this study employed unmanned aerial vehicle imaging to accurately estimate rice leaf and panicle nitrogen accumulations, enabling rapid, large-scale evaluation of rice NUtE. Results This proposed index showed a strong correlation with measured NUtE (R 2 = 0.72, rRMSE = 10.84%) and effectively captured the distinct patterns of NUtE across different nitrogen treatments and cultivars. Discussion Our developed indicator is generalizable across diverse conditions for high-throughput selection of nitrogen-efficient cultivars and precision nitrogen management in sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像とスペクトル指標からイネの窒素利用効率を推定する手法を開発し、多地点・品種・施肥条件で精度評価しており、植物形質の取得・推定法が中心である。
abstractThis study developed a remote-sensing-based indicator of rice NUtE based on chlorophyll-related vegetation indices at key rice growth stages, termed the Nitrogen Utilization Efficiency-Vegetation Index (NUtE-VI).
A comprehensive description of plant phenotypes of certain taxa is an important task when describing genera and species, as well as when setting their natural taxonomies. The development of modern technologies of effective phenotyping makes it possible to obtain a large amount of data with a quantitative and/or qualitative description of various traits in plants, mainly based on the analysis of their digital images. The study compared the results of the F2 hybrids assessment - visually and using machine learning methods - of two endemic tetraploid (2n = 4x = 28) wheat species which are Ethiopian wheat (Triticum aethiopicum Jakubz.) and Kartalian or Dika wheat (T. carthlicum Nevski). In the latter case, it is proposed to use the method of a mixture of Gaussian (normal) distributions in plant morphometry in order to identify groups that differ in character values. Most taxonomically important (species-specific) traits are controlled oligogenically and have a clear phenotypic manifestation, so hybridological analysis was an indispensable and basic type of analysis for subsequent detailed phenotyping of wheat spikes using machine-learning methods. According to a number of criteria, the estimates of patterns of inheritance obtained by different methods coincide. Based on the conducted research, we can state that the trait "tetraaristatum" (the presence of awns on both flower and spike glumes) is species-specific (taxonomically important) for T. carthlicum and it can be effectively used for taxonomic purposes both in carrying out hybridological analysis and in experiments using machine learning. Such a species-specific character is the "character (type) of awnedness" for T. aethiopicum. Our study demonstrates that a combination of automatic phenotyping methods and a model of a mixture of Gaussian distributions can, in principle, lead to an automatic analysis of the allocation of classes in F2 hybrids. It allows, in turn, to detect the presence of genes associated with species-specific traits of wheat plants. Further, the improvement of the applied artificial intelligence (AI) algorithms is required.
Why it matches plant phenotyping methodsコムギ穂の形態形質を対象に、画像に基づく機械学習フェノタイピングとガウス混合モデルを提案・適用しており、表現型の自動抽出・分類が研究の中心である。
abstractThe study compared the results of the F2 hybrids assessment - visually and using machine learning methods
Reproduction assets foundThe paper's supplementary materials (Supplementary Tables S1–S3 and Figure S1) contain the paper-specific phenotyping data: species-specific trait descriptions, the 19 spike morphometric characters per projection, and the Gaussian mixture model splitting results (means, variances, group sizes, χ² values). The full textSupplement · publicof these traits
are controlled by oligogenes and have a clear phenotypic
manifestation, the hybridological method was an indispensable
and primary type of analysis for subsequent detailed
phenotyping spikes of wheat species using machine learning
methods.
Supplementary Materials are available in the online version of the paper:
https://vavilov.elpub.ru/jour/manager/files/Suppl_Kruch_Engl_30_3.pdf
Plant material. The object of study was interspecific
hybrids obtained by crossing two endemic tetraploid
wheat species ♀T. aethiopicum Jakubz. (k-19301/2) with
♂T. carthlicum
Nevski (k-32496). The experiment was produced
in spring sowing in the greenhouses of the Breeding
and Genetics Complex (BGC)Open asset ↗lines:111-200Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Abstract Plant breeding is essential for crop improvement, yet progress is often hindered by slow, laborious, and subjective field phenotyping methods. High‐throughput phenotyping (HTP), particularly image‐based methodologies powered by machine learning, offers a pathway to overcome these limitations. However, achieving robustness and generalization when analyzing diverse genotypes within a crop and across reproductive stages remains challenging and can affect model performance and the accurate extraction of phenotypic features. This study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages, using wheat ( Triticum aestivum L.), sorghum ( Sorghum bicolor L.), and corn ( Zea mays L.) as case studies. The primary objectives were to analyze (i) the overall prediction performance on the aggregated dataset for each crop, (ii) the stratified performance by genotype and collection date, and (iii) the temporal and genotypic transferability across growth stages and unseen genotypes. Four distinct smartphone cameras were used to collect images of the reproductive structure across crop growth stages (different collection dates) from 160 corn, 80 sorghum, and 40 wheat genotypes. The total number of images per crop was 2000 for wheat, 4000 for sorghum, and 3840 for corn. Five semantic segmentation models were tested in this study—DeepLabv3+, MaskFormer, SegFormer, SegNet, and U‐Net—using the images and respective binary masks for training and testing. The SegFormer model achieved the highest intersection over union (IoU) values for corn (0.90) and sorghum (0.92), while the U‐Net model performed best for wheat (0.89). A minor performance decline, with IoU differences up to 0.1, was observed when testing the same model across different genotypes. However, the temporal transferability drops up to 0.5 IoU when training and inferring on different crop growth stages. The main reason for those changes may lie in the natural color and organ architecture temporal changes between the trained and tested datasets when transferring the models across growth stages. These results highlight the urgent need to prioritize robustness and transferability when developing reliable in‐field HTP methodologies.
Why it matches plant phenotyping methods植物の生殖器官画像からの表現型抽出に用いるセマンティックセグメンテーション手法を、作物・遺伝子型・生育段階間で性能と転移性の観点から比較検証しており、方法論が研究の中心である。
abstractThis study evaluated the performance of semantic segmentation models across a diverse panel of genotypes and distinct crop reproductive stages
Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remains challenging. Active 3D sensing methods such as Light Detection and Ranging (LiDAR) or time-of-flight (ToF) are sensitive to plant motion or poorly suited to outdoor conditions, while 3D reconstructions are computationally expensive. Direct 2D image processing would offer computational advantages, but image-based models lack explicit geometric information. We therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference. First, we train a rigid-invariant point cloud network using distance-based histogram features to obtain pose-robust geometric representations. We then combine the 3D model with a proposed multi-view image-based regulated Transformer (RT) in an ensemble architecture. Finally, we distill the ensemble knowledge into a purely image-based student model using either feature-based or label-based distillation. The two distilled RTs reduce the mean absolute error (MAE) from 654.31 mm$^3$ of the non-distilled RT to 639.93 mm$^3$ and 644.62 mm$^3$, and increase correlation from 0.76 to 0.77 and 0.82, respectively. At the same time, inference time is reduced from 160 ms to 1.4 ms per spike. Distillation further mitigates volume-dependent bias and reshapes the latent representation of the image model toward a geometry-aware shape. Our results demonstrate that 3D-informed training of a 2D Transformer allows for scalable and efficient spike volume estimation for high-throughput field phenotyping.
Why it matches plant phenotyping methods小麦穂の体積を画像・3D再構成・知識蒸留で推定する手法の開発と性能評価が中心であり、高スループット植物フェノタイピングへの応用も明示されている。
abstractWe therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference.
Reproduction assets foundThe paper explicitly states that links to its wheat spike dataset (multi-view images and 3D scans) and its analysis code are available via the authors' project webpage, which is an allowed URL. Other URLs (pyrender, CORDIS projects) are generic libraries or unrelated funding projects, not paper-specific assets.Dataset · publictance of around 2.5 m with a ground sampling distance of 0.3 mm (Fig. S1 a). The tagged and imaged spikes (Fig. S1 b) were sampled and ground truth volumes were acquired with a 3D light scanner (Shining 3D Einscan-SE V2, SHINING3D, Hangzhou, China) following the protocol of [ 76 ] . Links to the dataset and code can be found at https://oliviazum.github.io/3DKD-wheat/ . Detailed information about the dataset can be found in Sec. A .
3.3 Data Pre-Processing
Field images contained approximately 300-500 spikes per genotype within a plot of about 1.5 m 2 m^{2} . To reduce background inference, spike detection was first performed, and all subsequent processing was restricted to the detected regioOpen asset ↗lines:91-104Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Grain number is a primary agronomic trait for targeted yield improvement, with the prospect of enhanced grain production leading to greater food security. Given the complex polygenic nature of the grain number trait, large sample sizes are essential for effective QTL identification. The implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation. In this study, we trained a grain detection model using Ultralytics' You Only Look Once (YOLOv11) framework. Training was completed on 1000 images of barley spikes, derived from a doubled haploid (DH) population descended from Hindmarsh and RGT Planet. The trained model, termed BarleyGC, achieved satisfactory accuracy metrics (mAP50-95 = 71.9%, recall = 96.7%, precision = 97.1%). Phenotypic characterisation of the DH population was completed with BarleyGC on a distinct collection of 973 images. The Pearson correlation coefficient (r) between model and manual-derived counts for the trait of grain number per spike was 0.895 ( p n = 153 DH lines), revealed a QTL peak at position 224.959 cM on the genetic map (LOD = 3.14), named qGN-2H. The QTL region contained 21 candidate genes-including HORVU2Hr1G092290 (HORVU.MOREX.r3.2HG0184740), encoding the six-rowed spike 1 ( Vrs1 ) gene-a well-characterised major regulator of row-type divergence and lateral spikelet development. Our study demonstrates the power of the YOLOv11 framework for grain quantification, with BarleyGC capable of grain detection directly from images of intact spikes in two-rowed barley varieties-thus achieving accelerated sample processing for the grain number trait.
Why it matches plant phenotyping methods大麦穂の画像から穀粒数を推定するYOLOv11モデルを開発・検証し、手動計数との比較およびQTL解析に応用しており、植物表現型取得法が研究の中心である。
abstractThe implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Efficient phenotyping monitoring of cauliflower is crucial for its breeding and production. However, traditional manual measurement methods are time-consuming and labor-intensive, and existing deep learning (DL) methods mostly focus on the seedling stage, lacking systematic research covering the entire growth period. In this study, RGB images of cauliflower from seedling to harvest were collected. Through systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes. Evaluation results showed that YOLO12s-seg was the optimal model. It can achieved a segmentation mask mAP 50 of 99.4% for plants and curds in sparsely planted images and showed an advantage in identifying partially occluded early curds beneath inner leaves. Traits such as plant canopy width and curd diameter automatically extracted from segmentation results were highly consistent with manual measurements (R 2 > 0.90). Furthermore, the Richards model and Sine model were used to accurately fit the growth dynamics of leaf area and curd area, respectively. Based on growth kinetics, curds were classified into three types: mature and compact type, peak-burst type, and steady-increase type. Cluster analysis of 47 germplasms based on high-throughput phenotyping data revealed four groups and their growth characteristics: comprehensively coordinated type, mid-maturity compact type, large high-yield type, and curd-dominant type. Integrating the above functions, a platform for cauliflower growth monitoring and phenotypic analysis was developed. It provided full-process support from automatic image processing to growth dynamic analysis. This work provides an effective automated solution for high-throughput phenotyping analysis and growth dynamic monitoring of cauliflower, and offers a referable analytical framework for crop growth pattern research and intelligent breeding decision-making.
Why it matches plant phenotyping methods植物のインスタンスセグメンテーションから葉面積・草冠幅・花蕾形質を自動抽出し、手動測定との技術検証と成長動態解析、統合プラットフォーム開発を行っており、フェノタイピング手法が研究の中心である。
abstractThrough systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes.
Crop attribute detection, as a key component of intelligent agricultural harvesting machinery, plays a crucial role in harvesting efficiency, loss reduction, and autonomous operation control. Compared with existing reviews on artificial intelligence and sensing technologies in agriculture, this review focuses on crop attribute detection scenarios oriented toward the intelligent decision-making and control requirements of agricultural harvesting machinery. It mainly analyzes crop attributes that affect harvesting operations, as well as the sensors and algorithms involved in detecting these attributes, and further clarifies the relationship between detection methods and control decisions in agricultural harvesting machinery. For grain crops, the key attributes relevant to harvesting operations include plant height, plant density, spike number, crop lodging, canopy structure, and crop position. For fruit and vegetable crops, the key attributes relevant to harvesting operations include maturity, position, and quality. From the perspectives of multi-source data acquisition, data analysis, and attribute detection algorithms, the key technologies in the field of crop attribute detection are systematically summarized and analyzed, including sensors used in crop attribute detection, such as RGB, spectral, near-infrared, and LiDAR sensors, as well as data analysis and recognition approaches, such as image classification, object detection, and point cloud analysis. The complexity of field environments and the dynamics of machine operation are analyzed, highlighting the technical bottlenecks of current detection systems in environmental adaptability, real-time responsiveness, and resistance to interference. To address these challenges, feasible optimization directions were proposed, including multi-sensor fusion, weakly supervised learning, and few-shot learning. This review aims to provide systematic references and theoretical support for the coordinated development of crop detection and control decision-making in intelligent agricultural harvesting systems.
Why it matches plant phenotyping methods収穫機械向けではあるが、草丈・密度・穂数・倒伏・群落構造・成熟度など植物の形態・状態を検出するセンサーと解析手法を体系的にレビューしており、表現型取得法が中心である。
titleA Review of Crop Attribute Detection for Agricultural Harvesting Machinery
Abstract This study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences under well‐watered and reduced watering conditions in genetically uniform wheat ( Triticum aestivum L.) populations. It aims to support the design of breeding populations by identifying parents with complementary coping mechanisms that can be combined in crosses to produce superior progeny. We used RGB imaging to monitor side‐projected area (SPA) in BC 2 F 6 wheat progenies under well‐watered, pre‐anthesis, and post‐anthesis reduced watering conditions. SPA was modeled with logistic growth curves per genotype to extract dynamic canopy traits, which, together with the area under the SPA‐based growth curve, were then correlated with yield, straw biomass, harvest index, and spike traits measured at maturity. Despite genetic similarity, RGB‐based imaging revealed distinct phenotypes under normal conditions and stress response strategies among wheat lines, highlighting the value of dynamic, non‐destructive phenotyping for identifying complementary response patterns. Under well‐watered conditions ( n = 36), area under the curve was strongly associated with grain weight ( R 2 = 0.76, 95% confidence interval [CI]: 0.59–0.87), but relationships weakened under reduced watering, especially post‐anthesis, indicating a reduced association of canopy size with reproductive output. The data revealed contrasting response patterns among breeding lines based on characteristics of the logistic growth curve under normal conditions, their recovery slope after pre‐flowering reduced watering, or conversion of their straw biomass into harvestable grains. RGB imaging enables real‐time, non‐destructive detection of reduced watering responses in genetically similar wheat lines and provides complementary in‐season data to design next‐generation breeding populations for climate‐resilient cultivars.
Why it matches plant phenotyping methodsRGB画像で動的なキャノピー形質を抽出し、育種利用に向けた非破壊・リアルタイム表現型解析を実質的に評価しているため。
abstractThis study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences
Abstract Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum . This disease can cause significant economic loss due to decreasing yield, reducing seed quality, and the production of deoxynivalenol (DON); therefore, resistance to the disease is a primary concern for breeders. Phenotyping methods largely depend on the resistance mechanism being evaluated, but traditional approaches are often time‐consuming, subjective, and largely inaccurate. This review explores and contrasts traditional and modern phenotypic methods for various FHB resistance components. Digital image‐based phenotyping spans low‐cost RGB (red, green, blue) (i.e., Bayer array) sensors, multispectral sensors, and hyperspectral sensors. Across these sensors, approaches using spectral indices or deep learning have shown strong promise for detecting and classifying infection in both wheat spikes and kernels. Hyperspectral imaging has been largely explored and can be used to accurately estimate infection in spikes and kernels, as well as estimate DON content in the grain, using spectral indices or models input with specific wavebands. However, waveband‐specific approaches do not generalize well to new data, and hyperspectral imaging is significantly more resource‐intensive than RGB or multispectral cameras, limiting its practicality for most breeding programs. Phenotypic approaches using spectral indices and/or deep learning on digital images show the most potential for use in wheat breeding, due to their scalability and low cost. However, the widespread adoption of these techniques will depend on standardized imaging protocols, robust generalization across diverse genotypes, and effective integration into breeding pipelines.
Why it matches plant phenotyping methodsコムギ赤かび病抵抗性の表現型取得手法を、従来法からRGB・マルチスペクトル・ハイパースペクトル画像解析まで比較・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 explores and contrasts traditional and modern phenotypic methods for various FHB resistance components.
Rice ( Oryza sativa ) production underpins food security in many rice-consuming nations. As a critical developmental transition that directly determines yield and grain quality, flowering dates and timing are genetically complex and highly sensitive to environmental fluctuations. This complexity requires new methods to quantify diurnal floral characteristics, which are essential to hybrid breeding in cereals. Here, we present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice. After monitoring 172 rice accessions selected from the Chinese Rice Mini-Core Collection using cost-effective time-lapse imaging platforms for 16 days, we acquired over 530,000 accession-level images and established the Open Rice Flowering Training (ORFT) dataset, with over 39,000 panicles and 350,000 anthers annotated. Next, a two-stage customised DL model (i.e. YOLACT-Panicle for panicle segmentation and UNet-Anther for anther identification) was trained using the ORFT set, enabling ultra-high-frequency measures of anther extrusion at the minute level. Based on trait analysis, we further fitted curves to dynamically identify diurnal flowering patterns, including key timepoints such as the initial flowering timepoint ( T Ini. ), quickest flowering timepoint ( T Qck. ), and peak flowering time ( T Peak ), and novel traits such as the duration of rapid flowering phase ( P Rpd. ) and flowering density across key phases. After validating BloomSight-derived traits against manual observations, we classified the japonica and indica accessions into three patterns: Slow, Moderate, and Fast, all of which had distinct flowering windows. These analyses helped us integrate phenotypic variations into a genome-wide association study (GWAS), revealing many significant single nucleotide polymorphisms (SNPs) associated with known (e.g. EMF1 , OsMYB8 , and PME42 ) and several repeatedly identified unknown loci (one of these loci has been recently verified by other groups), demonstrating the value of the BloomSight framework. Taken together, we believe that BloomSight provides an ultra-high-frequency framework for diurnal flowering phenotyping, enabling the measurement of biological meaningful floral traits with minute-level resolution that can enable flowering-related developmental studies and hybrid-breeding applications in rice and more broadly benefit the plant and crop research community.
Why it matches plant phenotyping methodsイネの開花動態を高頻度画像と深層学習で抽出するフェノタイピング基盤を開発し、データセット構築と手動観測による検証も行っているため、方法が研究の中心である。
abstractwe present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice
Reproduction assets foundThe paper's Data and code availability statement explicitly provides public access to the ORFT annotated image dataset (BioStudies S-BSST2157), Python source code for floral trait analysis (GitHub The-Zhou-Lab/BloomSight), and trained DL models (GitHub releases). SRA accessions are molecular sequencing data, not phenotCode · publicPython-based source codes for automating floral trait analysis using the above data are accessible via our GitHub repository ( https://github.com/The-Zhou-Lab/BloomSight ).Open asset ↗The-Zhou-Lab/BloomSightlines:336-349Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Accurate, high-throughput quantification of rice panicles plays a vital role in advancing precision yield prediction. However, transitioning to real-time, edge-deployable unmanned aerial vehicle phenotyping is often impeded by extreme spatial scale variations from altitude fluctuations and complex unstructured backgrounds. To address this, we constructed a comprehensive composite dataset specifically capturing multi-altitude and varying illumination field conditions. We then propose Panicle-DETR, a highly optimized precision phenotyping framework incorporating a frequency-aware CSP backbone. By projecting visual perception into the frequency domain, the architecture inherently suppresses low-frequency environmental noise and minimizes computational redundancy. Furthermore, a Lossless Feature Encoder prevents the irreversible pixel decimation of micro-targets across varying operational altitudes, while a composite metric loss explicitly disentangles heavily adhered panicle clusters. Evaluated on our composite dataset, Panicle-DETR achieved an outstanding detection Precision of 90.97% alongside robust agronomic counting stability, demonstrated by a Mean Absolute Error of 4.28 and an \( R^2 \) of 0.957. With a compact footprint of only 13.78 M parameters, this framework fundamentally overcomes the computational and spatial limitations of traditional vision models, establishing a highly reliable paradigm for autonomous, onboard agricultural monitoring.
Why it matches plant phenotyping methods米の穂を画像から検出・計数する軽量なUAV表現型解析フレームワークを開発し、複合データセット上で性能評価しており、表現型取得・抽出手法が中心である。
abstractwe constructed a comprehensive composite dataset specifically capturing multi-altitude and varying illumination field conditions.
Self-incompatibility (SI) significantly reduces crop yield, often far below its genetic potential. Developing self-compatible varieties is the most effective strategy for overcoming SI in crops. Most macadamia ( Macadamia SPP.) species exhibit SI or partial self-incompatibility (PSI), so the efficient identification of self-compatible germplasms has emerged as a crucial topic. To characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility. That is, the degree of SI was based on the final self-incompatibility index (F_SI), which was calculated based on the open-pollination final nut set per raceme (OP_FNS) and self-pollination final nut set per raceme (SP_FNS) values (strong SI: F_SI ≥ 0.7, medium SI: 0.4 ≤ F_SI 35%). Through comprehensive analysis of the field-bagging and fluorescence-microscopy observations, thirteen varieties with strong SI (816, 778, 842, Special, 812, D, 820, 246, 772, A16, A4, 951, and 695), six varieties with moderate SI (851, 828, 508, 936, O.C, and D4), and four varieties with weak SI (915, HY, 836, and 814) were identified. Our study provides a theoretical foundation and technical support for advancing germplasm resource innovation, and the genetic improvement and breeding of self-compatible macadamia varieties.
Why it matches plant phenotyping methods自家不和合性という植物状態を対象に、圃場袋掛けと蛍光顕微鏡観察を用いた標準化分類体系を確立しており、表現型の取得・評価法が研究の中心である。
abstractTo characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility.
We introduce ``Primitive Indian Paddy Panicle Images,'' a benchmark image dataset of 22 primitive Indian rice panicle varieties (Sethy, Prabira; Pamerelli, Ranjith, 2026; Mendeley Data, V1, doi:10.17632/khfd7pzskd.1) and present an identification approach based on deep residual transfer learning. Using a transfer-learned ResNet-50 with image augmentation and an 80/10/10 train/validation/test split, the model attains 100.0% validation accuracy and 98.74% accuracy on the held-out test set. Per-class one-vs-rest AUCs on validation are 1.000 for all 22 classes; test AUCs range from 0.9924 to 1.000 (mean ≈ 0.999), with separate confusion matrices and ROC curves provided for validation and test partitions. These results demonstrate that deep residual transfer learning can robustly discriminate closely related panicle morphotypes when trained on a carefully curated dataset. We release the dataset to support reproducible research in germplasm identification, varietal purity assessment, and automated phenotyping.
Why it matches plant phenotyping methodsイネ穂画像のベンチマークデータセットと、深層学習による穂形態の自動識別手法が研究の中心であり、再現可能な植物表現型解析基盤として明示されている。
abstractWe introduce ``Primitive Indian Paddy Panicle Images,'' a benchmark image dataset of 22 primitive Indian rice panicle varieties
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 · publicDirect URL to data: https://data.mendeley.com/datasets/khfd7pzskd/1Open asset ↗Mendeleyhtml-lines:1-116Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
It has been repeatedly shown that spike productivity is the main component of wheat yield. The main spike parameters related to productivity are size, the number of grains and spikelets per spike, and the presence or absence of awns. In modern genetic research, morphometric analysis of hundreds and thousands of spikes is required to determine the loci that control spike productivity traits. On the other hand, thousands of accessions in modern collections of wheat genetic resources need detailed description. These considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods. These methods allow for automated acquisition of trait values that can serve as the basis for digital plant collections. Here we propose an extended set of spike characteristics obtained both manually and through digital image analysis and present plant characterization. These data form the basis of the updated version of the SpikeDroidDB database (http://spikedroid.biores.cytogen.ru/). The digital description of the spike consists of two blocks. The block of uploaded data includes a description of the plant and contains five tables: collection; variety sample (year of cultivation (vegetation), sowing identifier, taxonomic information, etc.), planting site, and characteristics of the spike determined manually (length, width of frontal and lateral views, type and color of the spike, etc.) The block of extracted features includes spike characteristics obtained by digital phenotyping and contains six tables: characteristics of the spike outline in the image; characteristics of the quadrangle model, values of the color components of the spike, dominant colors of the spike, and texture characteristics of the spike in the image. The most illustrative and significant features of the spike have been identified, allowing for the formation of the spike digital certificate, which includes size, shape, and color features derived from the digital images. The features forming the digital certificate have been compared between two wheat species, T. aethiopicum and T. carthlicum. It is shown that the features of the digital certificate allow for a clear representation of the spike model and the identification of distinct parameters: colors of the spike and awns and roundness of the frontal view of the spike. The database interface has been supplemented with the ability to upload data on plant and spike characteristics, as well as their images, in the batch mode.
Why it matches plant phenotyping methods小麦穂の画像解析による形態形質の自動取得、デジタル表現、データベース基盤を中心に開発・提示しており、植物フェノタイピング手法が研究の中核である。
abstractThese considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods.
Abstract Fusarium Head Blight (FHB) is one of the most destructive fungal diseases in global wheat production. Traditional methods for FHB detection face limitations such as high technical expertise requirements, limited coverage scope, and insufficient timeliness, making them inadequate for modern precision agriculture management demands. To address this challenge, this study proposes a lightweight MSA-YOLO detection model based on the YOLO11 deep learning framework. The proposed model achieves a favorable balance between performance and efficiency through three innovative design aspects: first, it replaces the original backbone network with the MobileOne network, establishing a foundation for model lightweight design; second, it substitutes the multi-head attention mechanism in the C2PSA module's PSABlock with a more computationally efficient SE module, further reducing model complexity while maintaining detection performance; finally, it introduces an Adaptive Threshold Focal Loss (ATFL) function to address class imbalance issues, enhancing the model's recognition capability for minority classes. The experimental data comprise 629 photographs of wheat spikelets covering various growth and development stages. Results demonstrate that the improved MSA-YOLO model reduces parameter count from 2.58M to 1.65M and computational complexity from 6.4 GFLOPs to 3.9 GFLOPs. Furthermore, comparative analysis with YOLOv10, YOLOv9, YOLOv8, and YOLOv5 models shows that MSA-YOLO exhibits an exceptional balance between speed and accuracy, making it well suited for practical applications in precision agriculture monitoring systems.
Why it matches plant phenotyping methods小麦穂のFHB症状を画像から検出する軽量深層学習モデルを開発・比較評価しており、植物病害状態の取得手法が研究の中心である。
abstractcomparative analysis with YOLOv10, YOLOv9, YOLOv8, and YOLOv5 models shows that MSA-YOLO exhibits an exceptional balance between speed and accuracy
High-density planting is an effective strategy to increase maize yield but imposes greater demands on plant architectural adaptability. To elucidate the structural response mechanisms of maize under varying planting densities, we developed a high-throughput 3D phenotyping system tailored to complex field conditions. High-precision point clouds of field-sampled plants were obtained via multi-view 3D reconstruction. Using a deep learning network, stem and leaf organs were semantically segmented (95.6% accuracy), while leaves were individually separated via clustering (94.8% accuracy). From these data, 31 plant architectural traits and 14 ear-leaf traits were extracted, establishing a hierarchical trait characterization system. Results showed that increased planting density significantly influenced plant architecture reshaping and structural coordination, leading to more compact plant forms and ear height position centralization. Ear leaves exhibited heightened sensitivity to density variation, particularly in leaf area, vertical distribution, and leaf inclination angle, suggesting an early-response role. Principal component analysis and clustering further revealed patterns of structural differentiation and key traits driving these changes under density treatments. The integrated workflow-comprising data acquisition, modeling, segmentation, clustering, trait extraction, and analysis-offers a robust approach for structural phenotyping and intelligent breeding selection in maize and other tall crops. This pipeline provides valuable technical support and data resources for optimizing dense planting strategies and advancing digital agriculture.
Why it matches plant phenotyping methods高スループット3D表現型システムを開発し、点群再構成・器官分割・クラスタリングから多数の植物構造形質を抽出することが中心であるため。
abstractwe developed a high-throughput 3D phenotyping system tailored to complex field conditions
Reproduction assets foundThe paper's authors provide a public GitHub repository for the study's source code (segmentation/trait-extraction pipeline). The phenotype point-cloud dataset itself is only available on request from the corresponding author, so it is not a public asset.Code · publicThe code of this study will be made publicly available upon publication. The source code is available at https://github.com/CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responses .Open asset ↗CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responseslines:330-415Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Doubled haploid (DH) technology can fast-track crop breeding. Haploid induction yields haploids with only one set of genomes, which are usually sterile. Haploid fertility (HF) is the ability of haploid plants to set seed, and it is a critical bottleneck in DH pipelines. Genetic mechanisms to restore HF hold immense potential in DH crop breeding, yet its phenotyping remains manual, destructive, and inconsistent. While recent advances in imaging and machine learning have improved throughput for general plant traits, no curated image dataset exists for Arabidopsis thaliana that explicitly represents HF. Here, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification. AutoSiQ includes high-resolution scanned inflorescences annotated with a seven-class ontology encompassing green siliques, green fertile siliques, mature siliques, fertile siliques, cracked fertile siliques, cracked siliques, and flowers. This multi-class annotation scheme preserves biologically meaningful information beyond binary fertile/non-fertile distinctions, enabling reliable fertility estimation and future phenotyping applications. We release baseline object detection models (YOLOv5), trained using the AutoSiQ dataset, and evaluate their performance across confidence thresholds. Model predictions strongly correlate with manual counts, achieving R² up to 0.94 for total silique number estimation. We further demonstrate AutoSiQ's utility for automated haploid fertility rate (HFR) estimation and genotype discrimination between two contrasting genotypes (WT and bmf2 mutant). A longitudinal analysis identifies ~60 days after sowing (DAS) as the optimal harvest time for maximizing mature silique counts by balancing between the number of immature buds and silique shattering. By releasing both the dataset and baseline code, AutoSiQ provides a reproducible and extensible foundation for high-throughput fertility phenotyping in haploid Arabidopsis .
Why it matches plant phenotyping methodsハプロイド稔性を画像から定量するデータセットと深層学習パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification.
Reproduction assets foundThe paper's AutoSiQ dataset (annotated scanned Arabidopsis inflorescence images with seven-class silique ontology and manual fertility counts) is publicly deposited on Zenodo per the data availability statement. The YOLOv5 GitHub repository is a generic third-party library, not an authors' code asset.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/17905566 .Open asset ↗zenodo · 17905566lines:367-402Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
OzBarley is a comprehensive genotype-to-phenotype resource to support research and enhance barley breeding by integrating genotypic and phenotypic data for gene discovery. This publicly available dataset comprises genotypic data from historical and modern elite barley cultivars of significance to Australian barley breeding. The phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology. Users can leverage genome-wide association studies (GWAS) and genomic selection to identify genetic variants associated with agronomically important traits in the OzBarley datasets, thereby accelerating targeted breeding strategies. The dataset is accessible for download under CC-BY 4.0 license and users are invited to contribute new data when using OzBarley plant material in their research. Through its FAIR-compliant design (Findable, Accessible, Interoperable, Reusable), OzBarley represents a resource to protect genotypes of historical relevance, explore the genetic architecture of adaptation to dryland environments, and to enhance knowledge of the resilience, yield, and quality of barley cultivars under diverse environmental conditions, contributing to global food security and agricultural sustainability.
Why it matches plant phenotyping methods高スループット画像およびX線CTによる形質取得を含む、再利用可能な遺伝型・表現型データ資源であり、植物フェノタイピング手法とデータセットが中心です。
abstractThe phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology.
3D instance segmentation for laser scanning (LiDAR) point clouds remains a challenge in many remote sensing-related domains. Successful solutions typically rely on supervised deep learning and manual annotations, and consequently focus on objects that can be well delineated through visual inspection and manual labeling of point clouds. However, for tasks with more complex and cluttered scenes, such as in-field plant phenotyping in agriculture, such approaches are often infeasible. In this study, we tackle the task of in-field wheat head instance segmentation directly from terrestrial laser scanning (TLS) point clouds. To address the problem and circumvent the need for manual annotations, we propose a novel two-stage pipeline. To obtain the initial 3D instance proposals, the first stage uses 3D-to-2D multi-view projections, the Grounded SAM pipeline for zero-shot 2D object-centric segmentation, and multi-view label fusion. The second stage uses these initial proposals as noisy pseudo-labels to train a supervised 3D panoptic-style segmentation neural network. Our results demonstrate the feasibility of the proposed approach and show performance improvementsrelative to Wheat3DGS, a recent alternative solution for in-field wheat head instance segmentation without manual 3D annotations based on multi-view RGB images and 3D Gaussian Splatting, showcasing TLS as a competitive sensing alternative. Moreover, the results show that both stages of the proposed pipeline can deliver usable 3D instance segmentation without manual annotations, indicating promising, low-effort transferability to other comparable TLS-based point cloud segmentation tasks.
Why it matches plant phenotyping methodsTLS点群と深層学習によるコムギ穂の3D個体分割パイプラインを開発・評価しており、植物表現型取得手法が中心である。
abstractsuch as in-field plant phenotyping in agriculture
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
This study addresses the challenge of automated high-throughput phenotyping of wheat spike characteristics using modern computer vision and deep learning methods. Accurate estimation of spikelet number is a key indicator of plant productivity, yet traditional manual counting approaches are labor-intensive, slow, and difficult to scale to large breeding datasets. To overcome these limitations, we propose a spikelet detection strategy based on simplified point annotations, where an expert marks only the centers of spikelets rather than drawing detailed segmentation masks or bounding boxes. This significantly reduces annotation time and lowers the overall cost of preparing training datasets for machine learning models. To determine the most effective way of utilizing such simplified annotations, three computational methods were explored: segmentation of binary masks using a U-Net architecture, density regression based on two-dimensional Gaussian distributions optimized via Kullback-Leibler divergence, and detection of fixed-size bounding regions using the YOLOv8 object detection framework. The models were evaluated on dedicated test datasets using both quantitative metrics (MAE, MAPE) and spatial localization metrics (Precision, Recall, F1 score). The results demonstrate that U-Net-based approaches provide consistently high accuracy in spikelet localization and counting while maintaining robustness to annotation imperfections. In contrast, the YOLOv8-based method showed reduced performance, likely due to the geometric mismatch between fixed-size boxes and the natural elongated shape of spikelets. Overall, the proposed methodology highlights the effectiveness of combining minimalistic point-level annotation with advanced segmentation models for automating phenotyping workflows. This approach has the potential to accelerate breeding programs, enhance the efficiency of large-scale phenotypic data collection, and support further development of robust computer-vision tools for plant science applications.
Why it matches plant phenotyping methodsコムギの穂の小穂数をRGB画像から自動推定する画像解析・深層学習手法を開発し、複数モデルを定量評価しており、フェノタイピング手法が研究の中心である。
abstractThis study addresses the challenge of automated high-throughput phenotyping of wheat spike characteristics using modern computer vision and deep learning methods.
Maize phenotyping remains a major bottleneck in genetic analysis and breeding. Despite advances in drones, field robots, and gantry phenotyping systems, ultra-affordable, high-throughput, field-based maize phenotyping at single-plant resolution is still lacking, largely due to the high cost, complex deployment, and limited flexibility of existing platforms under heterogeneous field conditions. To address these challenges, we propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe ( G iraffe + Li zard M aize P henotyping S yst e m), including two end-to-end phenotyping modules for maize plant architecture (the Giraffe module) and leaf traits (the Lizard module). The imaging device in the Giraffe module are built from modular electronics and 3D-printed parts from local retailers to achieve high-quality image acquisition. The Giraffe and the Lizard modules operate at speeds of 15 seconds and 8 seconds per sample, with costs of $379.1 and $241.1, respectively. Both modules feature fine-tuned YOLOv11x segmentation models for reliable and robust target segmentation, followed by customized Python-based analytical pipelines that enable precise extraction and quantification of phenotypic traits. This methodology achieves high accuracies ( R² ) for five key traits, including plant height (0.928), heights of above-ear leaves (0.87∼0.958), ear height (0.925), above-ear leaf number (0.837), and leaf width (0.937). To enhance accessibility, we developed user-friendly graphical interfaces and publicly released manually annotated datasets and source code to support broader adoption and further innovation. This work provides a practical and accessible solution for high-throughput field phenotyping and offers new opportunities for democratizing crop phenomics through affordable, open-source technologies.
Why it matches plant phenotyping methods低コストな撮像装置、コンピュータビジョン解析、形質抽出パイプライン、GUI、データセットとコードを統合したトウモロコシ表現型測定システムの開発・検証が研究の中心である。
abstractwe propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe
Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize (Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps fluorescently marked mutant (Ds-GFP) kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). Our dataset (1384 ears) represents 58 Ds-GFP insertion alleles. None of the 48 alleles with Mendelian inheritance showed any significant spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertional mutant of the gene encoding a putative actin-binding protein, base-to-apex gradient1* (bag1*), is associated with decreased mutant transmission at the ear base relative to the apex. Surprisingly, a mutant allele of another pollen-expressed gene (Zm00001eb236740) generates the opposite trend, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm cell attachment factor gamete expressed2 (gex2) can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants cause unexpectedly diverse spatial patterns of progeny genotypes.
Why it matches plant phenotyping methodsトウモロコシ穂上のカーネル表現型を画像認識でマッピングする更新版フェノタイピング基盤と統計解析パイプラインが中心的に開発・適用されているため。
abstractwe developed an updated phenotyping platform that maps fluorescently marked mutant (Ds-GFP) kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape).
Abstract Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To address these limitations, TomatoMAP is introduced as a comprehensive dataset for Solanum lycopersicum . The dataset contains 68,080 RGB images: 3,616 high-resolution macrophotographs (3648 × 5472) with semantic annotations, and 64,464 moderate-resolution images (1080 × 1440) captured from 12 plant poses at four camera elevations. Each image is accompanied by manually annotated bounding boxes for seven regions of interest (leaves, panicle, flower clusters, fruit clusters, axillary shoot, shoot, and whole-plant area) and by labels spanning 50 BBCH classes representing phenologically growth stages. A general cascading structure is proposed. For real-time applicability, models emphasizing the accuracy-efficiency trade-off (MobileNetv3, YOLOv11, and Mask R-CNN) are prioritized and benchmarked against multiple state-of-the-art models. Performance is assessed using accuracy, mAP, inference FPS, and normalized confusion matrices. In a study involving five domain experts, AI models trained on TomatoMAP achieves comparable accuracy levels. Reliability of automated fine-grained phenotyping is supported by Cohen’s Kappa statistics and inter-rater agreement heatmaps.
Why it matches plant phenotyping methodsトマトの多視点画像、器官領域・生育ステージ注釈を備えたデータセットを構築し、画像モデルの精度・効率・専門家一致度をベンチマークしており、植物フェノタイピング手法が中心である。
titleTomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping.
Reproduction assets foundThe paper's authors publicly release their analysis code (dataset construction scripts for TomatoMAP-Cls/Det and model training/evaluation code) on GitHub. The TomatoMAP phenotype image dataset itself is deposited at e!DAL (10.5447/ipk/2025/14), but no matching URL is present in the allowed list, so only the code assetCode · publicThe scripts for constructing TomatoMAP-Cls and TomatoMAP-Det, as well as the code used for model evaluation, are available at: https://github.com/0YJ/TomatoMAP.Open asset ↗https://github.com/0YJ/TomatoMAPhtml-lines:423-479Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Abstract An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and thickness profile. New shape descriptors based on thickness profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態形質を3D画像から抽出するパイプラインを開発し、形質の相関・遺伝子型間比較で検証しており、表現型取得法が研究の中心である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe preprint explicitly shares sample 3D spike scan data and the trait-extraction analysis code in the authors' public GitHub repository, with separate Data and code availability statements.Dataset · public1003/1)
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Key rice panicle traits (grain number, panicle length, grain dimensions, maturity) determine yield and quality, and high-precision/high-throughput measurement is critical for rice breeding. Traditional methods are. Methods A dataset of 5300 rice panicle images (loose/normal/dense types; milk/dough/full maturity/over-ripe stages) was constructed, with 3290 for training, 940 for validation, and 470 for testing. A deep learning pipeline integrating. Results The panicle length extraction achieved R²=0.9583, RMSE=5.69 mm. Grain counting R² values were 0.9799 (loose), 0.9551 (normal), 0.9278 (dense). Grain length R²=0.8823, grain width MAPE=6.64%. OPG-YOLOv8. Discussion This study provides a comprehensive, automated tool for rice panicle phenotyping, addressing occlusion challenges and bridging the gap between advanced models and breeding applications.
Why it matches plant phenotyping methodsイネ穂の画像から粒数・穂長・粒形などの形質を抽出する深層学習パイプラインを開発・評価しており、フェノタイピング手法が研究の中心です。
titleDeep learning-based methods for phenotypic trait extraction in rice panicles.
Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length ( R 2 = 0.972), ear diameter ( R 2 = 0.905), ear volume ( R 2 = 0.976), ear weight ( R 2 = 0.878), kernel number ( R 2 = 0.98), kernel row number ( R 2 = 0.888), kernel number per row ( R 2 = 0.852), kernel thickness ( R 2 = 0.705), kernel width ( R 2 = 0.515), and thousand kernel weight ( R 2 = 0.605). A user-friendly graphical interface is developed for manual inspection of ears after computer annotation. Manually annotated ear videos and images are publicly released as a resource for the crop phenomics community. Our study highlights the potential of DIY-based low-cost solutions to make phenotyping more accessible in crop genetic analysis and breeding.
Why it matches plant phenotyping methodsトウモロコシ穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との一致を検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll codes and the manual of command line interface and GUI can be found at the GitHub repository: https://github.com/Chimaco37/OpenEar.Open asset ↗Chimaco37/OpenEarhtml-lines:294-325Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Hybrid maize seed production relies on detasseling, a critical process to ensure genetic purity by removing male pre-tassels from female plants. However, missed pre-tassels, which are immature tassels partially enclosed by leaves and similar in color to maize foliage, remain difficult to detect and typically require labor-intensive manual inspection. This study proposes an improved UAV-based detection framework, YOLO for Missed Pre-Tassel (YOLO-MPT), built upon YOLOv7 for precise identification and geolocation of missed pre-tassels in hybrid maize fields. YOLO-MPT integrates deformable convolutions (DCNv2) for adaptive feature extraction, the S²-MLPv2 attention mechanism for enhanced spatial representation, and an additional small-object detection head to increase sensitivity to tiny or occluded targets. A comprehensive UAV-derived pre-tassel dataset was constructed under diverse agronomic and lighting conditions to support model training and validation. The impact of input image size on detection performance was systematically analyzed to identify the optimal training resolution. Experimental results show that YOLO-MPT achieved an average precision (AP) of 93.8 %, precision (P) of 93.3 %, recall (R) of 90.2 %, and an F1-score of 91.7 %, outperforming baseline models. Furthermore, a geographic coordinate extraction method was developed and integrated into a standalone “Missed Pre-Tassel Detection and Localization Software,” enabling automatic conversion of pixel detections into precise geospatial locations. Field experiments verified the workflow’s robustness and positioning accuracy, demonstrating the system’s potential to improve post-detasseling efficiency and quality assurance in hybrid maize seed production.
Why it matches plant phenotyping methodsUAV画像からトウモロコシの未抽苔を検出・地理定位する手法を開発し、データセット、性能検証、ソフトウェア化まで行っており、植物状態の取得・抽出が研究の中心である。
abstractThis study proposes an improved UAV-based detection framework, YOLO for Missed Pre-Tassel (YOLO-MPT), built upon YOLOv7 for precise identification and geolocation of missed pre-tassels in hybrid maize fields.
The center-pivot irrigation system is a highly efficient water-saving agricultural system. However, it typically operates solely based on predefined paths, speeds, and water volumes, and cannot assess the true needs of the crops due to its inability to monitor their growth status. To address this limitation, we deployed cameras on the center-pivot irrigation system to establish a mobile phenotyping platform, and developed a maize tassel detection model based on the YOLOv11 architecture. Several key improvements were implemented to address the complex morphology and challenging feature extraction of maize tassels: The ODConv (Omni-Dimensional Convolution) module was introduced to more comprehensively capture dynamic tassel features through a dynamic convolution strategy; To suppress interference from complex field environments on detection results, the SE (Squeeze-and-Excitation) attention mechanism was embedded to enhance effective feature responses and reduce noise impact; The BiFPN (Weighted Bi-directional Feature Pyramid Network) structure was adopted to strengthen multi-scale feature fusion capabilities, further improving the model’s detection performance for tassels at various scales; To tackle the difficulty of tassel recognition and localization in field environments, the C3K2 module was fused with the CSAM (Cross-Slice Attention Mechanism) to construct a C3K2-CSAM module, achieving more precise tassel identification and localization. Experimental results demonstrate that the OSBC-YOLO model achieved a precision of 91.8 % and a mean average precision (mAP) of 85.7 %, while reducing the number of parameters, FLOPs, and memory usage by 8.1 %, 28.1 %, and 7.3 % respectively compared to the original model. Field tests conducted on the center-pivot irrigation system revealed an error rate of 5.87 % between the number of tassels detected by the model and the ground truth count. These results verify that the OSBC-YOLO model can effectively perform tassel counting in practical operating environments and possesses the capability for recognition of crop phenotypic traits. This system provides critical data support for intelligent variable-rate irrigation decisions, promoting the transformation of center-pivot irrigation systems from traditional “single-function operation” to an integrated “perception-decision-execution” mode.
Why it matches plant phenotyping methodsトウモロコシ雄穂の検出・計数を行う移動型画像表現型解析プラットフォームとモデルを開発・検証しており、植物形態形質の取得が中心的貢献である。
abstractwe deployed cameras on the center-pivot irrigation system to establish a mobile phenotyping platform, and developed a maize tassel detection model based on the YOLOv11 architecture
WheatField / plotRGB / grayscaleThermalPanicle / ear / spikeSeed / grainPhysiological trait estimationSegmentationGrowth / development / phenologyWater status / transpiration
Grain filling plays a vital role in determining both the yield and quality of wheat. Therefore, timely and accurate monitoring of the grain filling course (GFC) is essential for assessing the feasibility of harvest timing optimization. Traditional methods based on field sampling are time-consuming and destructive. This study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery. Wheat ears were first segmented using a temperature-threshold approach, after which colour and temperature features were extracted. Grain water content (GWC) was then estimated using a Normalised Relative Ear Temperature (NRET) index, while days after anthesis (DAA) were retrieved using a piecewise linear model derived from ear colour features. Finally, a grain filling index (Kf) was developed using DAA corresponding to 25 % moisture content (DAA25%) to quantify the GFC. Results showed that thermal images acquired at 17:00 showed the greatest separability between ears and background canopy and the highest sensitivity to irrigation differences. Both NRET and DAA based models provided accurate GWC estimates (R² = 0.86 and 0.91; RMSE = 3.13 % and 4.21 %; rRMSE = 0.07 and 0.09, respectively). The Kf index effectively captured differences in GFC under different irrigation treatments and detected early maturity under water stress (p < 0.05). This study demonstrates the potential of combining thermal and RGB imagery for high-resolution, non-destructive monitoring of wheat grain filling and for supporting timely harvest management.
Why it matches plant phenotyping methodsRGB画像と熱赤外画像を統合し、穂の分割・特徴抽出から穀粒水分含量と登熟進行を推定する非破壊フェノタイピング手法が研究の中心である。
abstractThis study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery.
Aiming at the hysteresis problem of traditional contact-type mechanical throughput detection method during combine harvester operation, this study proposes a multimodal data-driven online throughput prediction method. By building a multimodal sensor system that integrates vehicle-mounted cameras, GPS, grain moisture content, and feeding auger power sensors, a throughput prediction framework based on wheat ear biomass characteristics was established: Firstly, the WEC-MVFF wheat ear online counting density map estimation model is designed, and MobileViT is used to build a feature extraction backbone network. The multi-scale fusion module and the centralized conversion module are combined to realize the collaborative extraction of shallow texture features and deep semantic features of dense wheat ears in the field. Secondly, divide the image into regions of interest and establish a throughput prediction model based on multimodal information of wheat ear number (image) − moisture content (sensor) − travel speed. At the same time, a detection model based on feeding auger power is constructed as a comparison benchmark. Field tests show that the WEC-MVFF model maintains an average counting accuracy of more than 90 % under different wheat ear density, travel speed and light intensity conditions. The model’s online counting advantage is verified through ablation study and comparative tests with other counting models. The throughput prediction method achieved an MAE of 0.70 kg/s and 0.77 kg/s in test area 1 and 2 respectively, the first-order difference fluctuation was stable in the range of ±0.50 kg/s, and the prediction frame rate of 10-13fps met the real-time requirements. Compared with the single-modal image prediction method, the accuracy of the multimodal method considering moisture content was improved by 0.80 kg/s and 0.75 kg/s in the two test areas, respectively. Compared with traditional mechanical quantity detection methods, it has higher accuracy and stability while achieving early prediction, providing reliable feedforward information support for the intelligent control of harvesters.
Why it matches plant phenotyping methods小麦穂の画像計数とマルチモーダルセンサを用いて、穂密度・バイオマス特性および収量流量を推定する手法を開発・検証しており、植物形質の取得・推定が研究の中心である。
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 15 Sept 2026
High-throughput, low-cost phenotyping remains a critical bottleneck in wheat breeding, genetics, and crop management. This is particularly evident in the measurement of complex yield components (i.e., spike and spikelet counts), disease and grain-quality traits related to Fusarium Head Blight (FHB) and Fusarium-Damaged Kernels (FDK), and microscale physiological traits such as density and size of stomata and aperture. We introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications. WheatAI v1.0 provides an accessible, browser-based interface that supports multiscale data ingestion from smartphones, Unmanned Aerial Vehicles (UAVs), and portable microscopes. The core functionalities of the platform include plot- and field-scale assessment via UAV- and smartphone-based wheat spike detection and counting, as well as smartphone-based spikelet counting. Additionally, it offers grain quality assessment through FDK ratio estimation and kernel morphometric measurements, such as length, width, and area, derived from smartphone images of kernel samples. For leaf-level analysis, WheatAI provides microscale phenotyping through automated stomatal counting, size, and aperture measurement from digital microscopy images. The system supports both single-image and bulk processing via a guided upload-and-run workflow. This platform is designed to reduce labor costs and rater subjectivity while accelerating field-to-lab decision cycles. By providing standardized, image-based outputs, WheatAI enables breeders, agronomists, and producers to implement high-throughput selection and precision scouting at scale.
Why it matches plant phenotyping methodsWheatAIは、画像から収量構成要素、病害関連形質、穀粒形態、気孔形質を抽出する高スループット植物フェノタイピング基盤そのものであり、方法・ソフトウェアの開発が中心です。
abstractWe introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications.
Fusarium head blight (FHB), a frequent disease in wheat cultivation, can lead to substantial yield losses and the production of mycotoxins in grains. Therefore, the development of wheat varieties resistant to FHB is an important strategy to reduce related losses. In this respect, manual surveys of FHB are time-consuming and labor-intensive. To overcome this issue, this paper proposes a method for detecting and evaluating wheat FHB using color imaging and deep learning. Initially, a lightweight convolutional neural network model based on the You Only Look Once (YOLO) v8s artificial intelligence (AI) model was designed to detect wheat spikes from color images. Testing revealed that the model's mean average precision in spike detection reached 0.964. Moreover, another lightweight model was developed for detecting wheat spikelet and FHB. To enhance the detection capability of the model for small objects, space-to-depth convolution (SPD-Conv) and BiFormer attention modules were integrated. The results indicated that the model can accurately detect spikelet and FHB, with a mean average precision of 0.936. Finally, based on the wheat spikelet detection results, the rate of diseased wheat spikes (RD_S) and the disease index for wheat (DI_W) were calculated to evaluate the severity of wheat FHB. For RD_S and DI_W, the coefficients of determination between phytologists' evaluations and the estimates derived from the proposed method were 0.71 and 0.93, respectively. These results demonstrate that the proposed method facilitates the accurate and efficient detection of wheat FHB and contributes to the quantitative evaluation of FHB in the field.
Why it matches plant phenotyping methods小麦のFHB症状をカラー画像と深層学習から検出・定量し、専門家評価との一致で検証する手法開発が中心であるため含める。
abstractthis paper proposes a method for detecting and evaluating wheat FHB using color imaging and deep learning.
The distribution of spikelets significantly affects wheat (Triticum aestivum L.) spike architecture. However, traditional methods lack the precision to study spikelet distribution effectively. We developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images. In addition to traditional spikelet number per spike (SNS), rachis length (RL), and spikelet density (SD, SNS/RL), we introduced spikelet distribution traits based on rachis internode lengths, providing quantitative insights into spike architecture. RachisSeg showed high consistency with manual measurements for SNS and RL, with the R2 values of 0.975 and 0.998, respectively. Using RachisSeg, we analyzed spikelet distribution patterns across wheat germplasm and found that traits such as spikelet distribution index (SDI) and apical-to-basal spikelet number ratio (AVB_SNS) were moderately correlated with grain yield per spike (GYPS) (r = 0.57 and 0.53, respectively), while internode width (IW) showed a strong positive correlation with GYPS (r = 0.75). Specifically, a denser spikelet arrangement in the upper spike negatively impacted grain number and weight in that section. Furthermore, comparative analysis revealed distinct spikelet distribution patterns among landraces, American cultivars, and Chinese cultivars. In a recombinant inbred line population, we identified 46 quantitative trait loci (QTLs) associated with rachis traits. A major QTL controlling SDI was detected on chromosome 6B, explaining up to 24.8% of the phenotypic variance. Candidate gene analysis suggested TraesCS6B02G417000 as a potential gene, whose mutant exhibited significant changes in RL and SDI. RachisSeg is a powerful tool for quantifying spikelet distribution, facilitating wheat genetic analysis, gene discovery, and breeding.
Why it matches plant phenotyping methodsRachisSegは、スキャン画像からコムギ穂軸・小穂分布形質を自動抽出する深層学習フェノタイピング手法として開発・検証されており、方法が研究の中心です。
abstractWe developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images.
Reproduction assets foundThe paper's authors publicly released the RachisSeg phenotyping pipeline (deep learning node detection and internode segmentation code) together with sample rachis images via their GitHub repository, explicitly stated in the Implementation and Data availability sections.Dataset · publicRachisSeg and sample rachis images is freely available online ( https://github.com/Jiang-Phenomics-Lab/RachisSeg ).Open asset ↗Jiang-Phenomics-Lab/RachisSeglines:514-549Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jan 2026Computers and Electronics in Agriculture.
Maize ear traits are critical indicators for elucidating yield formation mechanisms and are widely used in genetic studies. Traditional two-dimensional (2D) phenotyping suffers from planar analysis constraints, occlusions in single-view imaging, and limited robustness to mixed textures or curved ears. To address these issues and support germplasm archiving and breeding research, we developed MaizeEar3DPheno (MEP3D), a 3D point cloud-based method for quantifying maize ear phenotypic traits. A structured-light 3D scanning system equipped with a motorized rotary platform was designed to acquire point clouds of 30 maize ears from three different varieties. Preprocessing involved axis alignment via PCA, uniform downsampling, and removal of non-kernel regions. Following preprocessing, key phenotypic traits, including ear length, diameter, and barren tip length, were calculated from the processed point clouds. MEP3D integrated directional erosion with density-based clustering to achieve robust kernel segmentation and counting. A spatial analysis algorithm was further developed to locate kernel row arrangements from geometric features. The results demonstrated that the proposed method achieved high-precision cross-variety kernel counting, with a mean absolute percentage error (MAPE) of 0.91%, and a coefficient of determination (R²) of 0.9917 across all maize ears. Kernel row quantification was fully consistent with manual measurements, allowing extraction of row inclination and average kernel number per row. Ear length, diameter, and barren tip length estimation achieved R² values of 0.9864, 0.9871, and 0.9670, respectively, demonstrating robustness. The generated high-fidelity 3D phenotypic data supports automated evaluation of ear and kernel traits and facilitates in-depth analysis of spatial morphological characteristics.
Why it matches plant phenotyping methodsトウモロコシ雌穂の3D点群取得・処理・形質抽出法を開発し、カーネル数や穂長などを手動測定と比較検証しており、フェノタイピング手法が研究の中心である。
abstractwe developed MaizeEar3DPheno (MEP3D), a 3D point cloud-based method for quantifying maize ear phenotypic traits.
Understanding the relatedness of angiosperms and the evolution of their inflorescence remains challenging, as these structures are highly modified and prone to convergent evolution. The current research describing inflorescence architecture, whether genetically or morphologically focused, typically relies on text based explanations, 2D images, or 3D based models created with CAD modeling software. This creates a disparity in the reader's understanding of these models since descriptions rely heavily on the author's interpretation of the inflorescence. The goal of our study is to bridge this disconnection by producing anatomically and color-correct 3D inflorescence models using Solanaceae flowers that readers can explore directly, which will allow readers to view and rotate the inflorescence structure in real time. The Solanaceae clade serves as an excellent platform to demonstrate this concept, as it is an active area of floral research and exhibits high inflorescence diversity within the clade. Its ancestral scorpioid cyme-like morphology is presently thought to have been the result of convergent evolution from a currently unknown driving force. Developing 3D models of extant Solanaceae species could provide valuable insights into these evolutionary patterns and help clarify the mechanisms underlying inflorescence diversification. Here, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade. This process will involve taking high-resolution 360° photos at various angles using a camera. Photos will be processed with Agisoft Metashape software, which generates 3D models using photographs. It is expected that the rendered 3D images will accurately reflect specimen dimensions with precise color. This will serve as a way to study and provide a larger 3D inflorescence library that can bridge the gap between authors and readers within the literature.
Why it matches plant phenotyping methodsSolanaceaeの花序形態を対象に、フォトグラメトリと3D再構成による植物形質の取得・可視化手法を開発しており、方法が研究の中心である。
abstractHere, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade.
Timely and accurate crop yield estimation is important for sustainable agricultural planning and resource optimization. This study is motivated by the need for a scalable, non-destructive, phenology-aware yield estimation pipeline that can outperform spectral index-based methods. A novel in-season crop yield estimation framework is presented that uses high-resolution UAV-based multispectral imagery and deep neural networks. The pipeline integrates automated phenological stage mapping using a custom Spatial Phenology Attention and Feature Cross (SPARC) Network, canopy structure modeling, and wheat head segmentation via a U-Net model fine-tuned on masks generated with SAM 2. UAV imagery is collected across 18 timestamps, processed to produce reflectance maps, vegetation indices (VIs), canopy height models (CHMs), and fractional cover maps. Plot-level phenological and morphological features are extracted to train multiple regression models for in-season yield estimation. Results show that combining temporal phenological features with structural head metrics significantly improve estimation accuracy, with Gradient Boosting Regression achieving an R2 of 0.89. The proposed approach not only improves the granularity and timeliness of in-season yield estimations but also enables scalable, non-destructive crop monitoring solutions, providing practical information for both farmers and breeders alike.
Why it matches plant phenotyping methodsUAV画像と深層学習を用いて、作物のフェノロジー、形態特徴、穂形状を抽出し、圃場区画レベルの収量を推定する技術パイプラインが研究の中心である。
abstractA novel in-season crop yield estimation framework is presented that uses high-resolution UAV-based multispectral imagery and deep neural networks.
Field / plotFruitPanicle / ear / spikeLeafGrowth / time-series analysisGrowth / development / phenology
Amorphophallus paeoniifolius (elephant foot yam) is a tropical geophytic crop of significant agricultural and ethnobotanical value in Southeast Asia. Despite the relevance of the species, the life cycle and phenology of A. paeoniifolius remain poorly documented. This study presents the first comprehensive characterization of its phenological development using an extended Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale. Fieldwork was conducted from June 2024 to May 2025 in Dimiao, Bohol, Philippines. A species‐specific BBCH scale coding was developed, capturing key phenological stages: corm dormancy, leaf emergence, inflorescence development, anthesis, fruiting and senescence. A bimodal life cycle synchronized with the Northeast and Southwest Monsoon systems was observed, with a dormancy phase from October to May and an active reproductive–vegetative phase from April to September, primarily regulated by rainfall and rising temperatures. Observations support a resource allocation trade‐off, where corms alternate between reproductive and vegetative investment in response to environmental cues, particularly the onset of the Southwest Monsoon with rising precipitation, consistently high relative humidity and increasing temperatures that signal the shift from dormancy to active growth. Within this framework, the BBCH codes developed encompass dormancy (00), leaf development (10–19), pseudostem elongation (31–39), inflorescence and fruit development (51–59, 60–69, 71–79, 81–89) and senescence with return to dormancy (91–97). This baseline phenological model lays the groundwork for future long‐term ecological studies for sustainable cultivation and conservation of A. paeoniifolius under changing climatic conditions.
Why it matches plant phenotyping methods種特異的なBBCHスケールを開発し、植物の生育・繁殖フェノロジーを体系的にコード化することが研究の中心であり、植物状態の測定手法に該当する。
abstractThis study presents the first comprehensive characterization of its phenological development using an extended Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale.
This study explores the conceptual framework and evaluation methods of grape berry uniformity, elucidating its multidimensional nature arising from the coordinated contributions of berry size, shape, and cluster structure. Quantitative evaluation approaches based on the coefficient of variation, composite multi-trait indices, and high-throughput phenotyping technologies are systematically summarized. On this basis, key factors influencing berry uniformity are further analyzed, including genetic background, pollination and fertilization processes, berry developmental dynamics, plant growth regulator treatments, and water-nutrient environmental conditions. Integrating breeding strategies with production practices, a framework for improving berry uniformity is proposed, centered on “multi-trait selection, marker-assisted selection, and cultivation regulation.” Meanwhile, with the advancement of machine vision, high-throughput phenotyping, and multi-source data integration technologies, the evaluation of berry uniformity is shifting toward automation, precision, and intelligence. However, challenges remain in the standardization of evaluation systems, elucidation of molecular mechanisms, and integration of multi-source data. Future research directions toward data-driven precision improvement are discussed. This study aims to provide theoretical foundations and technical support for enhancing the quality and standardized production of table grapes.
Why it matches plant phenotyping methodsブドウ果実の均一性を対象に、評価指標、高スループットフェノタイピング、機械ビジョンによる自動評価を体系的に扱うレビューであり、フェノタイピング手法が中心です。
abstractQuantitative evaluation approaches based on the coefficient of variation, composite multi-trait indices, and high-throughput phenotyping technologies are systematically summarized.
Monitoring wheat growth, as one of the most important food grain sources for human nutrition, and forecasting yields are done through different phenological phases. Reliable estimates on yields play a crucial role in securing sufficient food supplies for the world's growing population. Currently, farmers estimate a wheat yield during the later stages of growth and are often biased in this process. Plant breeding scientists use a more accurate approach that collects data on the number of wheat ears manually counted at various locations throughout the field. A sufficiently precise count of wheat ears is one of the most important parameters for reliable early-stage prediction of wheat yield. To support the development of an affordable and trustworthy automated wheat ear detection approach, this work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties. Additionally, it evaluates six deep learning models for wheat ear detection. Among the F-RCNN-based models, RetinaNet, YOLOv8, and a Vision Transformer-based detector, RT-DETR, achieved the highest mean Average Precision (mAP@50) of 91%, with significantly higher computational complexity. BioS-Wheat complements Global Wheat Head Detection datasets, introducing a meaningful shift in data complexity with high sowing density and minimal row spacing, emphasizing the impact of agronomic diversity on model performance by an increased object occlusion and dense spatial arrangements. Enriched and agronomically diverse datasets support model robustness at different varieties, growth stages, and locations. This work offers a good baseline for establishing the procedure for image crowdsourcing, further dataset expansions, and model improvements.
Why it matches plant phenotyping methods小麦穂の画像検出による個体群形質推定を対象とし、注釈付きデータセットの構築と複数モデルの評価が中心であるため、表現型計測手法として収載する。
abstractthis work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties.
The operational effect of the reverse ear picking device for fresh corn is affected by stem diameter and ear orientation angle. The existing devices lack the ability to sense these parameters in real-time, making it difficult to dynamically adjust operating parameters, which leads to a high damage rate and harvest loss. To this end, this study focuses on the visual perception aspect and proposes a recognition method based on a depth camera and an improved D3-YOLOv11 segmentation model, which provides reliable visual input for subsequent adaptive regulation. Specifically, this study proposes Dual-Domain Dynamic Gate Conv (D3GConv) to enhance the multi-scale feature extraction ability of the model. In the neck network, a bidirectional weighted pyramid structure with semantic detail injection is designed to improve the segmentation accuracy of small objects. Generalized Focal Loss V2 was used to optimize the detection head to enhance the accuracy of boundary localization in dense stem scenes. Finally, the depth information is fused to realize the real-time measurement of stem diameter and ear orientation angle. Experimental results show that the Mask-mAP50 of the D3-YOLOv11 model reaches 99.3% and 94.6% in stem and ear instance segmentation tasks, respectively. The Mean Absolute Error of stem diameter measurement based on depth information is only 0.16 cm, and the Coefficient of Determination of ear orientation angle reaches 0.95, which verifies the reliability and practicability of this method in the adaptive control of the ear harvesting device. It provides an effective visual perception basis for improving the intelligence level of equipment.
Why it matches plant phenotyping methods深度カメラと改良YOLOによってトウモロコシの茎径・穂の向き角をリアルタイム測定する手法を開発・検証しており、植物形質の取得法が中心である。
abstractproposes a recognition method based on a depth camera and an improved D3-YOLOv11 segmentation model
Abstract Background Accurate assessment of plant nitrogen status is essential for optimizing fertilizer inputs, increasing productivity, and ensuring environmental quality. This study compared three nitrogen status assessment in corn ( Zea mays L.): visual assessment method, a novel real-time nutrient estimation using the Picketa LENS™ system, and the conventional laboratory tissue analysis as a reference method. To evaluate the accuracy of the Picketa LENS™ system, a field experiment with four nitrogen treatments (0% nitrogen (control), 80% nitrogen, 100% nitrogen, and 100% nitrogen + stabilizer) and four replications was conducted in York County, Nebraska. Results Visual assessment detected treatment differences, with the 0% nitrogen plots showing severe chlorosis and a progressive decline (63.4% reduction) in healthy leaves below the ear over five weeks, whereas 100% nitrogen maintained consistently higher healthy leaf counts (only a 7.4% reduction). However, visual assessment showed limited ability to distinguish between 80% nitrogen and 100% nitrogen + stabilizer treatments until weeks four and five. Both quantitative methods did not detect treatment differences due to the sampled leaf position. The Picketa system (2024 corn model) reported higher absolute nitrogen concentrations (approximately 4.4–4.6%) than laboratory analysis (2.9–3.2%) across all treatments and did not detect significant treatment effects. Conventional laboratory analysis detected only a modest increase in the 100% nitrogen treatment compared with the 0% control. For macronutrients, the Picketa system measured concentrations higher than conventional tissue sampling for phosphorus, potassium, and calcium, with potassium showing approximately 40% higher values and calcium showing 40–50% higher values, while magnesium and sulfur showed close agreement between methods. Micronutrient analysis revealed that the Picketa system consistently reported higher concentrations than conventional tissue sampling for iron (45% higher), manganese (approximately 4-fold higher), copper (90% higher), and zinc (33% higher), but reported significantly lower boron concentrations (67% lower). Despite these absolute value differences, both methods demonstrated similar patterns of detection across treatments. Conclusions Visual assessment effectively detected treatment differences, while the Picketa System and the conventional method did not, but maintained similar patterns. These findings highlight the promise of Picketa LENS and the importance of matching sample positions and timing to diagnostic objectives. Integrating real-time sensing with conventional methods can improve nitrogen management decisions.
Why it matches plant phenotyping methodsトウモロコシの窒素状態を測定するリアルタイムセンサー法を従来法と比較し、精度や処理差の検出性能を評価しているため、植物形質測定法の技術検証が中心である。
abstractThis study compared three nitrogen status assessment in corn ( Zea mays L.): visual assessment method, a novel real-time nutrient estimation using the Picketa LENS™ system, and the conventional laboratory tissue analysis as a reference method.
Bacterial Leaf Streak (BLS) and Fusarium Head Blight (FHB) are among the most damaging diseases of wheat (Triticum aestivum), with severe consequences for grain yield, quality, and ultimately food safety and security. Rapid and precise assessment of disease severity in the fields is crucial for effective field management, potential yield loss evaluation, and high-throughput phenotyping. This research examined the utility of UAV-based multispectral imagery in combination with both traditional machine learning and modern deep learning approaches to estimate wheat disease severity under field conditions. Data collection was carried out at two wheat experimental fields in South Dakota, USA, where Unmanned Aerial Vehicle (UAV) multispectral imagery was acquired in parallel with plot-level measurements of BLS and FHB severity. Spectral and textural metrics extracted from the UAV imagery served as inputs for machine/deep learning-based regression analyses. Regression models evaluated in this work comprised traditional machine learning methods Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and three deep learning architectures: Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and multi-head self-attention (MHSA)-enhanced CNN (Att-CNN). In addition, a deep transfer learning framework was tested by transferring an Att-CNN model trained on BLS to FHB severity estimation. The results showed that deep learning methods, particularly CNN-based architectures, consistently outperformed conventional machine learning approaches. Incorporation of a MHSA mechanism into the CNN architecture further enhanced performance, especially for BLS severity estimation. Att-CNN achieved the best results for both diseases, with R² = 0.83 and RRMSE = 30.55 % for BLS, and R² = 0.70 and RRMSE = 37.05 % for FHB. While estimation of FHB severity remained more challenging, transfer learning from BLS substantially improved prediction accuracy, raising R² from 0.70 to 0.79 and reducing RRMSE from 37.05 % to 31.31 %. The study highlights the considerable potential of UAV multispectral imagery, though with notable limitations, for monitoring crop diseases. This work also demonstrates the added value of attention-based deep learning and transfer learning techniques in addressing complex applications in agricultural remote sensing.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて、圃場のコムギ病害重症度という植物状態を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractThis research examined the utility of UAV-based multispectral imagery in combination with both traditional machine learning and modern deep learning approaches to estimate wheat disease severity under field conditions.
The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from imagery. Such advancements have led to phenotypic digitization and made rapid yield forecasting possible. Yield predictions are critical to assess the merit of genotypes to propel cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 m above using a DJI M300 drone at 90° nadir and 45° oblique angles. This research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN in detecting sorghum panicles, achieving a mean average precision at 50 % IoU (mAP@0.50) scores of 0.92-0.98, compared to 0.61-0.89 for Faster R-CNN. Panicle detection from field imagery showed a linear correlation of 0.86 with ground truth field panicle counts. Lab imagery analyses measured panicle area, seed counts, and seed area with correlation coefficients of 0.79, 0.94, and 0.25 with respective ground truth observations. Support Vector Regression (SVR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) were used to predict yield with correlation coefficients of 0.74, 0.71, and 0.78, respectively, and SHapley Additive exPlanation (SHAP) analysis revealed panicle seed count as the primary driver of yield prediction. We observed YOLO models are well-suited for extracting yield-predictive features from pertinent images. Such features can then be incorporated into ML regression models to predict yield per se performance with greater accuracy. The GitHub link is provided in the Data availability section.
Why it matches plant phenotyping methodsUAS・実験室画像から穂数、穂面積、種子数・面積などの植物形質を深層学習で抽出し、検出精度を検証して収量予測へ利用する方法が研究の中心である。
abstractThis research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images.
Reproduction assets foundThe authors explicitly state that scripts, fine-tuned models, datasets, and sample images for this sorghum yield-forecasting study are publicly available on GitHub, matching the allowed URL exactly.Code · publicThe scripts, fine-tuned models, datasets, and sample images pertinent to this manuscript are available on GitHub at https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.git .Open asset ↗https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.gitlines:244-299Plant phenotyping relevance match · UnverifiedarXiv · checked 6 Sept 2026
This extended abstract details our solution for the Global Wheat Full Semantic Segmentation Competition. We developed a systematic self-training framework. This framework combines a two-stage hybrid training strategy with extensive data augmentation. Our core model is SegFormer with a Mix Transformer (MiT-B4) backbone. We employ an iterative teacher-student loop. This loop progressively refines model accuracy. It also maximizes data utilization. Our method achieved competitive performance. This was evident on both the Development and Testing Phase datasets.
Why it matches plant phenotyping methods小麦穂を画像から分割する手法の開発が中心であり、植物器官の表現型取得に直接関係する。
titlePseudo-Label Refinement for Robust Wheat Head Segmentation via Two-Stage Hybrid Training
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Grape cluster compactness is a key trait that influences fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling × Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years, with marker effects ranging from 7.6% to 22.1%. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.
Why it matches plant phenotyping methodsSAMを用いた画像解析パイプラインで個々の果粒を分割し、サイズ・形状と房のコンパクトネスを算出する方法が、研究の主要な技術的要素として明示されている。
abstractAdditionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention.
RiceWheatField / plotMesh / voxelNeRF / 3D Gaussian SplattingLiDAR / point cloudPanicle / ear / spikeLeafRootSeed / grain
Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. It utilizes radiance field information to lift 2D masks, segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction. On a rice dataset, IPENS achieves a grain-level segmentation mean Intersection over Union (mIoU) of 63.72%. For phenotypic trait estimation, it achieves a grain voxel volume coefficient of determination R 2 = 0.7697 (Root Mean Square Error, RMSE = 0.0025), leaf surface area R 2 = 0.84 (RMSE = 18.93), and leaf length and width prediction accuracies of R 2 = 0.97 and R 2 = 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset, IPENS further improves segmentation performance to a mIoU of 89.68%, with exceptional phenotypic estimation results: panicle voxel volume R 2 = 0.9956 (RMSE = 0.0055), leaf surface area R 2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reaching R 2 = 0.99 and R 2 = 0.92 (RMSE = 0.23 and 0.15). Without requiring annotated data, IPENS rapidly extracts grain-level point clouds for multiple targets within three minutes using single-round image interactions. These features make IPENS a high-quality, non-invasive phenotypic extraction solution for rice and wheat, offering significant potential to enhance intelligent breeding.
Why it matches plant phenotyping methods植物形質抽出のためのNeRF-SAM2融合手法を開発し、作物データセットで分割性能と形質推定精度を検証しているため、方法開発・検証が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub. The rice/wheat MMR/MMW phenotype datasets (multi-view images, point clouds, annotations) are only available upon reasonable request, so they are not public.Code · publicCode is available at https://github.com/Vincent-Songwentao/IPENS-Code.git .Open asset ↗https://github.com/Vincent-Songwentao/IPENS-Code.gitlines:472-496Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Computers and Electronics in Agriculture.
Three-dimensional high-throughput plant phenotyping technology offers an opportunity for simultaneous acquisition of plant organ traits at the scale of plant breeders. Wheat, as a multi-tiller crop with narrow leaves and diverse spikes, poses challenges for organ segmentation and measurement due to issues such as occlusion and adhesion. Therefore, building on previous research, this paper establishes a phenotyping pipeline and develops a 3D phenotypic automated analysis system for individual wheat plants at different growth stages. This system enables automated and precise three-dimensional phenotypic acquisition and analysis of wheat plant architecture, spike morphology, and flag leaf traits. To address the challenges posed by the significant structural differences among wheat spikes, leaves, and stems, as well as their compact spatial distribution, we propose a point cloud segmentation model based on deep learning called ICFMNet. ICFMNet relies on an instance center feature matching module, which extracts features from each instance’s central region and matches them with global point-wise features by computing feature similarity. This approach enables precise instance mask generation independent of the spatial structure of the point cloud. In the analysis of wheat phenotypes, we introduce a contour-based method to accurately extract the barren segment from 3D-scale wheat spikes. Furthermore, we perform the analysis of a total of 19 phenotypes, including flag leaf phenotypes and whole-plant phenotypes. In the organ point cloud segmentation tests for wheat spikes, stems, and leaves, the semantic segmentation achieves mPrec, mRec, and mIoU values of 95.9 %, 96.0 %, and 92.3 %, respectively. The instance segmentation attains mAP and mAR scores of 81.7 % and 83.0 %, respectively. Moreover, in comparison to five other segmentation network models, ICFMNet demonstrates superior segmentation performance. To better assess barren segment localization accuracy, additional evaluations are conducted using two metrics: interval overlap and interval error, achieving values of 92.33 % and 0.1123 cm, respectively. Experimental results indicate that our method excels in terms of accuracy, efficiency, and robustness, providing a reliable systematic platform for precise identification and breeding research of wheat plant types. The source code and trained models for ICFMNet are available at https://github.com/xiao-pl/ICFMNet.
Why it matches plant phenotyping methods小麦個体・器官の3D形質を自動取得・抽出するセグメンテーションおよび解析パイプラインの開発と技術評価が研究の中心である。
abstractthis paper establishes a phenotyping pipeline and develops a 3D phenotypic automated analysis system for individual wheat plants at different growth stages.
Accurate monitoring of sunflower heads is critical for yield prediction, yet traditional methods are labor-intensive. This study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge. The proposed method used a Dual-Branch YOLOv10n model, leveraging multi-modal data for precise detection of sunflower heads at various growth stages. Feature indices were designed and a two-step clustering technique was applied to extract sunflower head point clouds, from which geometric parameters such as diameter and volume are computed. The detection model achieved high accuracy (precision: 0.9, recall: 0.894, mAP@50: 0.932) across growth stages. A strong correlation (R² = 0.80) was found between diameter measurements from point cloud and ground-truth data, while volume showed good alignment with biomass (R² = 0.61). This method offers an innovative, efficient solution for field-scale crop monitoring and yield estimation, advancing agricultural practices.
Why it matches plant phenotyping methodsUAV画像・深層学習・点群解析を統合し、ヒマワリ頭部の検出から直径・体積という植物器官形質を抽出・検証する方法が研究の中心であるため。
abstractThis study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge.
Accurate maize tassel counting on individual ridge plays a critical role in advancing maize breeding programs by providing insights into crop adaptability and agronomic performance. The current manual method of processing male inflorescences has low accuracy and high labor intensity. Moreover, the existing algorithms cannot be directly applied to the counting of an individual ridge maize tassels. An automated system with UAV-based RGB imagery is presented for maize tassel counting. The object detection model was developed based on You Only Look Once version 8-medium (YOLOv8m), which detects tassels. A double-step Otsu threshold algorithm (DSOTSUTA) was designed to extract individual maize tassel ridge, which eliminated the interference of two adjacent ridges on both sides. Individual ridge tassel counting was implemented by Deep learning based Simple Online and Realtime Tracking (Deep SORT). It assigned identifiers (IDs) to each tassel and filtered abnormal IDs by analyzing the displacement increments of IDs in consecutive frames eliminating errors caused by ID switching. The object detection model achieved a mean Average Precision (mAP) of 91.6 %. The DSOTSUTA was tested on 5340 images and effectively extracted individual maize tassel ridges. The system achieved a root mean square error (RMSE) of 22.14 tassels per video, a mean absolute percentage error (MAPE) of 8.43 %, and an accuracy of 92.23 %, signifying a mean absolute percentage error (MAPE) of 8.43 % between the predicted tassel counts and ground truth observations. These results indicate that this automated system has the ability to enhance the accuracy of individual ridge maize tassel counting in breeding programs.
Why it matches plant phenotyping methodsUAV画像からトウモロコシ雄穂を自動検出・追跡・計数する手法を開発し、精度評価まで行っており、植物形質取得が研究の中心である。
abstractAn automated system with UAV-based RGB imagery is presented for maize tassel counting.
Precision agriculture increasingly relies on unmanned aerial vehicle (UAV) imagery for high-throughput crop phenotyping, yet existing deep learning detection models face critical constraints limiting practical deployment: computational demands incompatible with edge computing platforms and insufficient accuracy for multi-scale object detection across diverse environmental conditions. We present LSM-YOLO, a lightweight detection framework specifically designed for aerial wheat head monitoring that achieves state-of-the-art performance while maintaining minimal computational requirements. The architecture integrates three synergistic innovations: a Lightweight Adaptive Extraction (LAE) module that reduces parameters by 87.3% through efficient spatial rearrangement and adaptive feature weighting while preserving critical boundary information; a P2-level high-resolution detection head that substantially improves small object recall in high-altitude imagery; and a Dynamic Head mechanism employing unified multi-dimensional attention across scale, spatial, and task dimensions. Comprehensive evaluation on the Global Wheat Head Detection dataset demonstrates that LSM-YOLO achieves 91.4% mAP@0.5 and 51.0% mAP@0.5:0.95—representing 21.1% and 37.1% improvements over baseline YOLO11n—while requiring only 1.29 M parameters and 3.4 GFLOPs, constituting 50.0% parameter reduction and 46.0% computational cost reduction compared to the baseline.
Why it matches plant phenotyping methodsUAV画像からコムギ穂を検出する軽量深層学習フレームワークを開発・評価しており、植物器官の画像ベース表現型取得が中心である。
abstractPrecision agriculture increasingly relies on unmanned aerial vehicle (UAV) imagery for high-throughput crop phenotyping
• FSFF improves UAV-based semantic segmentation for rice phenotyping. • LFFE builds the frequency awareness and mitigates the panicle-leaf similarity. • ASCE restores phase-aware local context and addresses the mutual occlusion. • The proposed method was evaluated in practical applications of rice breeding. UAV-based semantic segmentation offers new insights to accelerate breeding better varieties in rice breeding applications. However, the morphological similarity and mutual occlusion between the panicles and leaves still pose severe challenges for efficient rice phenotyping. To address these problems, this paper proposed a flexible spatial-frequency feature fusion (FSFF) method for high-throughput UAV-based semantic segmentation. The FSFF method consists of three key components: Learnable frequency feature extraction (LFFE), Adaptive spatial context enhancement (ASCE), and hierarchical feature fusion (HFF). LFFE is employed to build the foundation of frequency awareness, addressing the challenges from the morphological similarity between the panicles and leaves; ASCE is introduced to enhance boundary information and mitigate the negative effects of mutual occlusion. After that, the LFFE and ASCE modules are integrated in the HFF mode through a series of transformations. Ablation study was conducted to confirm the effectiveness of the proposed modules, and visualized explanation for performance improvement was explored by transforming the learned kernels to frequency spectrums. Later, the FSFF method was compared with the mainstream semantic segmentation approaches. Experimental results demonstrate that the FSFF method outperformed other counterparts in mIoU (+2.12%), pixel accuracy (+0.8%), and SSIM (+1.09%) with the best inference speed (0.7311 ms/image). Finally, the FSFF method was evaluated on the public dataset and practical rice breeding applications. The experimental results prove the generalization and potential of FSFF method in rice phenotyping, which may build a foundation to accelerate breeding cycles and ensure food security. Relevant codes will be available at https://github.com/ZZZ-bbb/FSFF/tree/master .
Why it matches plant phenotyping methodsUAV画像によるイネの穂・葉の形態を対象としたセマンティックセグメンテーション手法を開発し、アブレーション、比較評価、公開データセットおよび育種実環境で検証しているため、フェノタイピング手法が中心である。
abstractFSFF improves UAV-based semantic segmentation for rice phenotyping.
As precision agriculture advances, UAV-based aerial image object detection has emerged as a pivotal technology for maize-phenotyping perception operations. Complex backgrounds reduce the model’s performance in extracting features of maize tassels, while sacrificing model computation complexity to improve feature expression is detrimental to deployment on UAVs. To achieve a balance between the model size and deploy ability, an enhanced model incorporating spatial-channel convolution is proposed. First, a maize-breeding UAV was built, and the collection of maize tassel image data was realized. Second, Spatial and Channel Reconstruction Convolution (SCConv) was integrated into the neck network of the YOLOv8 baseline model, reducing the model computation complexity while maintaining the detection accuracy. Finally, the constructed maize tassel dataset and public Maize Tasseling Stage (MTS) dataset were used for the training and evaluation of the enhanced model. The results showed that the enhanced model achieved a precision of 92.2%, recall of 84.3%, and mAP@0.5 of 91.7%, with 7.3 G floating-point operations (FLOPs) and a model size of 5.16 MB. Compared with the original model, the enhanced model exhibited respective increases of 3.2%, 3.4%, and 3.4% in precision, recall, and mAP@0.5, along with respective reductions of 0.8 G FLOPs in computation complexity and 0.79 MB in model size. Compared with YOLOv10n, the precision, recall, and mAP@0.5 of the enhanced model are increased by 1.8%, 3.1%, and 2.9%, respectively, and the model computation is reduced by 0.3 G FLOPs, and the model size is reduced by 0.42 MB. The improved model is accurate, performs better on UAV aerial images in complex scenarios, and provides a methodological basis for deployment. It also supports maize tassel detection and holds potential for application in maize breeding.
Why it matches plant phenotyping methodsトウモロコシ雄穂のUAV画像からの検出を目的に、軽量化したYOLOv8ベースの画像解析手法を開発・評価しており、植物形質取得が研究の中心である。
abstractUAV-based aerial image object detection has emerged as a pivotal technology for maize-phenotyping perception operations.
Fusarium head blight (FHB) poses a significant threat to global wheat yields and food security, underscoring the importance of timely detection and severity assessment. Although existing approaches based on semantic segmentation and stereo vision have shown promise, their scalability is constrained by limited training datasets and the high maintenance cost and complexity of visual sensor systems. In this study, AR glasses were employed for image acquisition, and wheat spike segmentation was performed using Depth Anything V2, a monocular depth estimation model. Through geometric localization methods—such as identifying abrupt changes in stem width—redundant elements (e.g., awns and stems) were effectively excluded, yielding high-precision spike masks (Precision: 0.945; IoU: 0.878) that outperformed leading semantic segmentation models including Mask R-CNN and DeepLabv3+. The study further conducted a comprehensive analysis of differences between diseased and healthy spikelets across RGB, HSV, and Lab color spaces, as well as three color indices: Excess Green–Excess Red (ExGR), Normalized Difference Index (NDI), and Visible Atmospherically Resistant Index (VARI). A dynamic fusion weighting strategy was developed by combining the Lab-a* component with the ExGR index, thereby enhancing visual contrast between symptomatic and asymptomatic regions. This fused index enabled quantitative assessment of FHB severity, achieving an R2 of 0.815 and an RMSE of 8.91%, indicating strong predictive accuracy. The proposed framework offers an intelligent, cost-effective solution for FHB detection, and its core methodologies—depth-guided segmentation, geometric refinement, and multi-feature fusion—present a transferable model for similar tasks in other crop segmentation applications.
Why it matches plant phenotyping methods深度推定、幾何補正、特徴融合によってコムギ穂の病徴領域を抽出し、赤かび病重症度を定量推定する方法が研究の中心である。
abstractwheat spike segmentation was performed using Depth Anything V2, a monocular depth estimation model.
Wheat grain number integrates fate of individual florets, strongly affected by environmental stress during sensitive stages like meiosis. Because development is asynchronous across tillers, spikelets, and florets, it is hard to distinguish true stress tolerance from stress escape. Based on morphological and destructive measurements of spike and anther length in four controlled-environment experiments, we developed a framework to track all floret developmental stages at plant level, and particularly meiosis. We applied this framework in two case studies for connecting within-plant developmental asynchrony to reproductive success under favorable and heat stress conditions. All florets showed a common relative growth rate, producing stable and additive developmental delays across tillers, spikelets, and floret positions. This generated a developmental map for every floret based on simple external traits. Under favorable conditions, grain set probability at floret level combined both positional and developmental effects within a spike. Under heat stress, grain loss occurred only in florets at meiosis during the stress, allowing to quantify a true "stress response", while later florets escaped damage. This framework offers a quantitative tool to understand and predict floret development and link it to grain set, clearly distinguishing timing effects from positional influences and separating true tolerance from stress escape.
Why it matches plant phenotyping methods小花の発達段階を形態測定から追跡・推定する定量的フレームワークを開発し、植物体レベルで検証・適用しているため、表現型取得法が中心的です。
abstractwe developed a framework to track all floret developmental stages at plant level, and particularly meiosis.
Double haploid (DH) technology can significantly shorten the breeding cycle and improve the breeding efficiency, and it is favored by breeders. The metrics for evaluating the effect of haploid genome doubling mainly include anther emergence and ear seed setting. The evaluation of fertility restoration ability is mainly conducted through visual inspection at present, which is time-consuming, and easy to be affected by fatigue, resulting in errors and inconsistencies. Therefore, it is urgent to develop efficient and accurate evaluation technology to reduce the field work burden of researchers. In this work, we propose a grading evaluation model (Maize-IRNet) of haploid anther emergence and ear seed setting based on Inception-ResNet. Firstly, the modules of Stem and Inception-ResNet are utilized for image feature extraction and multi-scale feature learning. Then, the Reduction module is used for spatial downsampling and feature compression, and the global attention mechanism (GAM) is used to enhance the recognition of key regions of the image. The experimental results show that the Maize-IRNet's classification accuracy of haploid ear seed setting and anther emergence is 84.2 % and 84.0 %, which is higher than six baseline methods (VGG11_bn, ResNet50, ResNet101, ViT-Base-16, gMLP, MLP-Mixer). In order to facilitate the practical application for breeding researchers, we have developed a mobile application that integrates the Maize-IRNet model. This study helps to achieve high-throughput collection of fertility restoration phenotypes, improves the evaluation efficiency of fertility restoration, reduces breeding costs, and provides technical support for the promotion of engineering breeding of DH technology.
Why it matches plant phenotyping methodsトウモロコシの葯出現と穂の種子着生という生殖形質を画像から自動評価する深層学習モデルを開発・比較し、モバイルアプリにも実装しており、表現型取得法が中心である。
abstractTherefore, it is urgent to develop efficient and accurate evaluation technology to reduce the field work burden of researchers.
Reproduction assets foundThe paper's data availability statement explicitly provides the maize haploid fertility image dataset (1897 ear images, 6443 tassel images), the Maize-IRNet source code, and the Android APK, all hosted on the authors' public GitHub repository.Dataset · publicThe maize haploid fertility image dataset collected by smartphones is available at https://github.com/wyzwyz666/maize-haploid-fertility/blob/main/datasetOpen asset ↗wyzwyz666/maize-haploid-fertilitylines:506-531Code · publicThe source code: https://github.com/wyzwyz666/maize-haploid-fertility/blob/main/sourcecodeOpen asset ↗wyzwyz666/maize-haploid-fertilitylines:506-531Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Nov 2025Computers and Electronics in Agriculture.
High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and thier growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective methodologies. To address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were acquired from multiple viewpoints and represent different plant structures at different growth rates. Wheat3D PartNet samples are manually labeled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems) and that captured in drought and watered conditions. Wheat3D PartNet is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection-facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset's utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest.
Why it matches plant phenotyping methods植物の3D点群を用いた部位セグメンテーション用データセットの構築・検証が中心で、植物形質の定量化を直接支援するため。
abstractTo address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.)
Fusarium head blight (FHB), which is triggered by fusarium graminearum, drastically reduces wheat yield and quality levels while generating harmful mycotoxins, compromising food security and the health of humans and livestock. Effective real-time detection of wheat FHB in field scenarios remains a critical challenge. Consequently, we present SCS-YOLO, an innovative real-time agricultural disease detection model for wheat FHB detection and severity assessment. Moreover, we successfully deployed it on the low-cost, low-power NVIDIA Jetson Nano embedded platform, achieving low-resource real-time detection. First, we restructured the YOLOv5s backbone network using StarNet, maintaining computational efficiency while obtaining richer and more expressive feature representations. Then, we proposed a novel lightweight CB module to replace the C3 module, further reducing the number of model parameters and the computational scale. Finally, we incorporated a weighted Shape-NWD function, which considers the shapes and sizes of bounding boxes, effectively improving the ability of the model to detect small objects. The results demonstrated that the SCS-YOLO model attained a mean average precision (mAP) of 90.51 % while reducing model parameters and giga floating-point operations (GFLOPs) by 39.97 % and 42.13 %, respectively, outperforming the existing models. Subsequently, the diseased spike rate was calculated, and its coefficient of determination (R²) and root mean square error (RMSE) were 0.90 and 3.48, respectively, effectively quantifying the severity of wheat FHB. Additionally, with an average inference time of only 0.26 s on NVIDIA Jetson Nano, SCS-YOLO exhibited strong potential for rapid detection of wheat FHB on edge devices. In summary, this study offers a dependable, efficient, and accurate solution for wheat FHB detection and assessment. Moreover, SCS-YOLO is designed to be flexible, enabling potential extension to the analysis of other crop diseases or crop types.
Why it matches plant phenotyping methodsコムギ赤かび病の画像検出モデルを開発し、病穂率による病勢を定量評価しており、植物の病害状態を取得・推定する方法が研究の中心である。
abstractwe present SCS-YOLO, an innovative real-time agricultural disease detection model for wheat FHB detection and severity assessment.
Accurate, non-destructive prediction of cannabinoid concentrations in Cannabis sativa is critical for optimising crop value and ensuring regulatory compliance in the industrial and medicinal cannabis sectors. In this study, we demonstrate for the first time that fan leaf hyperspectral reflectance (FLHR) measurements taken across the canopy, performed both early and late in the flowering period, can reliably predict final cannabinoid content in mature inflorescences in two Cannabis cultivars under seven distinct lighting regimes. Machine learning models trained on FLHR spectra achieved high predictive accuracy, with R² values up to 0.89 for CBD, 0.77 for THC, and 0.8 for total cannabinoids, outperforming previous approaches. Importantly, our method utilises a hand-held, non-destructive hyperspectral device, enabling rapid, in situ assessment of intact fan leaves without the need for sacrificial sampling or laboratory analysis (e.g., for HPLC or GC-MS). FLHR measurements were able to differentiate cultivars and lighting treatments, offering a tool for germplasm classification. The capacity to predict cannabinoid profiles weeks before harvest has significant implications for cannabis production, enabling growers and breeders to enhance product quality, reduce costs, and ensure regulatory compliance, particularly for industrial hemp crops subject to strict THC limits, or to track and predict yields for medicinal cannabis operations.
Why it matches plant phenotyping methods手持ちハイパースペクトル測定と機械学習によるカンナビノイド含量予測が研究の中心であり、非破壊的な植物表現型取得・推定法として評価されている。
abstractfan leaf hyperspectral reflectance (FLHR) measurements taken across the canopy, performed both early and late in the flowering period, can reliably predict final cannabinoid content in mature inflorescences
The segmentation of wheat spike images is a prerequisite for conducting research on wheat spike diseases and yield estimation. To address issues such as small color differences in the background of wheat spike images in the field and low segmentation accuracy, this article proposes a wheat spike segmentation method called SAU-Net (Striped Pooling and Attention Mechanism optimized U-Net). Firstly, based on the U-Net model, residual network 50 (ResNet50) is selected as the backbone network of U-Net to reduce feature loss. Then, the stripe pooling block (SPB) and multi-scale dilated convolution (MSDC) are used to obtain local and global features, enhancing the accuracy of wheat spike feature extraction. Meanwhile, the convolutional block attention module (CBAM) is adopted to capture the dependency between channels and space, strengthen the focus on important features, and reduce the influence of background on segmentation results. Finally, a joint loss function is employed to further optimize the network performance. The results show that the average Intersection over Union (IoU) of the improved SAU-Net model is 88.57%, which is 5.29 percentage points higher than the improved U-Net model before. Compared with Pyramid Scene Parsing Network (PSPNet), Deep Convolutional Lab v3 (DeepLabv3), fully convolutional network (FCN), and Lite Residual Atrous Spatial Pyramid Pooling (Lraspp) network models, the SAU-Net model has the best segmentation accuracy. This study achieves wheat spike segmentation under complex backgrounds, providing technical support for wheat spike disease diagnosis and crop phenotype analysis.
Why it matches plant phenotyping methodsコムギ穂画像から穂を抽出する画像セグメンテーション手法を開発・比較評価しており、植物器官の表現型取得が中心である。
abstractThe segmentation of wheat spike images is a prerequisite for conducting research on wheat spike diseases and yield estimation.
Fusarium head blight (FHB), caused by the Fusarium species complex, significantly endangers wheat yield and safety. Accurate and timely assessment of FHB epidemic level in the field is crucial for effective disease management. However, the complex environment and indistinct edges of diseased areas present substantial challenges in distinguishing between healthy and diseased ears, thereby impacting the accuracy of FHB epidemic level detection. This study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level. The Focal-Edge Selection Module (FSM) within the backbone replaces original C2f module to enhance edge feature representation and facilitate multi-scale feature extraction. Furthermore, the Dual Spatial-Connection Feature Pyramid Network (DSCFPN), integrating Global-to-Local Spatial Aggregation (GLSA) with bidirectional pyramid interaction, balances global and local feature acquisition while optimizing the feature fusion mechanism. This design enables the model to effectively handle occlusions, scale variations, and complex environments. Experimental results demonstrate substantial improvements over eight comparative models in detecting healthy and diseased wheat ears, achieving mean Average Precision (mAP) of 86.1% and 82.9%, respectively. Notably, the model achieved a mean accuracy of 94.7% in detecting FHB epidemic levels through rigorous spatiotemporal validation using datasets collected from independent fields across different years, underscoring its robust generalization capability. Characterized by its low complexity and lightweight design, EBS-YOLO features a parameter count of 2.05 M, 7.4 GFLOPs, and a model size of 5.0 MB, making it an efficient approach for real-time FHB epidemic level detection.
Why it matches plant phenotyping methods小麦穂の健全・罹病状態と赤かび病の流行レベルを圃場画像から推定する深層学習手法を開発し、独立圃場・異なる年のデータで検証しているため、植物病害フェノタイピング手法が中心である。
abstractThis study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level.
Reproduction assets foundThe paper's wheat FHB image dataset (1152 field images used for EBS-YOLO training/evaluation) is explicitly stated as publicly available on the authors' GitHub repository.Dataset · publicam Development Project [2025QCY-KXJ-070]; the Science and Technology Partnership Program, Ministry of Science and Technology of China [KY202002018] and the National Natural Science Foundation of China [32081330501].
Data availability
The datasets supporting the conclusions of this article are available in the GitHub repository, https://github.com/yuanYuan8686/wheat-FHB-dataset.
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Ethics approval and consent to participate
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Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliationOpen asset ↗yuanYuan8686/wheat-FHB-datasetlines:369-441Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Corn is a globally important economic crop. Certain trait parameters of corn ears kernels per ear are essential indicators for corn breeding. However, acquiring these parameters faces two challenges: i) manual measurement is labor-intensive and error-prone, and ii) vision-based corn phenotyping machines require fixed image capturing environment and are cost-prohibitive. To address these limitations, we introduce CornPheno, a user-friendly, low-end, smartphone-based approach capable of executing corn ear phenotyping in the wild. CornPheno highlights three corn ear parameters: kernels per ear, rows per ear, and kernels per row. Technically, inspired by crowd localization in computer vision, we first extract kernels per ear based on a Corn data-trained Point quEry Transformer (CornPET). CornPET generates interpretable per-kernel point predictions and supports subsequent row detection. To detect rows, we introduce a novel point-based corn row detection approach, termed unicorn, featured by sqUeezed clusteriNg and bI-direCtional pOint seaRchiNg, to phenotype rows per ear and kernels per row. With adaptive geometric modeling, our approach is robust to partial rows, curved rows, and missing kernels. To promote the use of CornPheno, we have integrated it into OpenPheno, a WeChat-based mini-program, and made it open-access for corn breeders. We hope our approach can provide the community with a user-friendly and cost-effective way to facilitate corn breeding.
Why it matches plant phenotyping methodsトウモロコシ穂の粒数・列数などの形質を、スマートフォン画像から抽出する手法とソフトウェアを開発しており、フェノタイピング手法が研究の中心である。
abstractwe introduce CornPheno, a user-friendly, low-end, smartphone-based approach capable of executing corn ear phenotyping in the wild.
Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and dynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors. A temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (classification accuracy ≥0.97) even when relying solely on predictors from the early drought response phase. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage emerged as key classification features. A temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Importantly, prediction accuracy for these traits remained high (R 2 ≥ 0.84) even when restricted to early developmental phase data, including the stem elongation stage. Models trained on pooled drought and control data outperformed single-treatment models and maintained high predictive power across treatments. Together, these findings highlight the value of integrating high-throughput phenotyping with temporal modeling to enable earlier, more cost-effective selection of drought-resilient genotypes and demonstrate the broader potential of phenomics-driven strategies for accelerating crop improvement under stress-prone environments.
Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる時系列表現型取得と、収穫形質予測モデルの構築・評価が研究の中心であるため。
abstractdynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors
Reproduction assets foundThe authors explicitly state that the data and analysis pipeline code for this barley phenotyping study is publicly available on GitHub at https://github.com/hatiez/barley-TPP-pipeline. This is a paper-specific computational asset (the temporal phenomic classification/prediction pipeline) with an authors' public URL. DCode · publicThe data and analysis pipeline code is available on https://github.com/hatiez/barley-TPP-pipeline .Open asset ↗https://github.com/hatiez/barley-TPP-pipelinelines:390-415Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Accurate and early disease detection in paddy crops is essential for maximizing crop yield which ensures food security. Traditional methods are often labor-intensive, time-consuming, and domain-specific expertise. Feed-forward deep-learning models will perform accurate disease detection through the identification of spatial patterns. However, they cannot predict the diseases at the early stages due to the lack of temporal information. Temporal observations will help perform continuous monitoring and detect minute changes in the crops at the early times. To tackle this problem, we proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively. The SSDHR network uses multi-branch convolution kernels to extract distinct discriminative characteristics rather than conventional leaf-based indicators. It incorporates spatial, and temporal-based attention mechanism Symmetric Fusion Attention (SFA) to improve feature selection and XGBoost (XGB) classifier for better stability. According to experimental findings, the suggested framework achieves a 99.25% accuracy rate in identifying and classifying 13 paddy classes, including normal, blast, hispa, tungro, white stem borer, brown spot, leaf roller, downy mildew, yellow stem borer, bacterial leaf blight, bacterial leaf streak, black stem borer, and bacterial panicle blight.
Why it matches plant phenotyping methodsイネ病害の症状を空間・時間画像データから検出・分類する深層学習フレームワークを提案し、その性能を評価しているため、植物フェノタイピング手法が中心です。
abstractwe proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively.
Reproduction assets foundThe paper's phenotyping inputs are the publicly available Paddy Doctor image dataset (16,225 annotated paddy disease images) hosted on IEEE DataPort, with an explicit dataset link and data availability statement. No author analysis code or trained models are shared.Dataset · publicThe dataset used in our study was obtained from the publicly available repository titled “Paddy Disease and Pest Image Dataset” on IEEE Data Port. The dataset comprises 16,225 high-quality images across 13 classes, including 12 paddy disease and pest categories along with healthy samples.Open asset ↗IEEE Data Porthtml-lines:118-200Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R 2 of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms.
Why it matches plant phenotyping methodsコムギ穂の検出・計数という植物器官形質の画像ベース推定手法を開発し、データセット上で精度・計算効率・堅牢性を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices.
Reproduction assets foundThe paper uses the public GWHD2021 wheat spike detection dataset (available on Kaggle) as part of its hybrid dataset, with an explicit availability statement and URL. The self-collected Xinjiang field dataset is private and available only on request. No author analysis code, trained model checkpoints, or other paper-特定Dataset · publiciting, W.W., S.L. and Y.L.; supervision, J.C.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The public part of the dataset used in this study is available from the Global Wheat Head Detection (GWHD2021) dataset at https://www.kaggle.com/competitions/global-wheat-detection , accessed on 30 September 2025. The remaining part of the dataset is private and cannot be shared due to institutional or privacy restrictions. Requests for access to the private dataset may be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest. The funderOpen asset ↗GWHD2021lines:854-929Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Fusarium head blight (FHB) is a serious fungal disease that affect small grain cereals, causing significant wheat ( Triticum aestivum L.) yield and quality losses globally. Breeding disease-resistant wheat varieties is key to address FHB-related challenges, but its progress is delayed by traditional methods due to the small-scale, laborious and relatively subjective nature of manual assessment. This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once (YOLO) model to examine FHB in wheat, enabling us to perform large-scale and automated symptomatic analysis of this disease. We first established an Open FHB (OFHB) training dataset, consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression. Then, a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes, followed by an adaptive Excess Green method to identify symptomatic regions and thus FHB-related traits on spikes. To study resistance levels, we employed an unsupervised SHapley Additive exPlanations (SHAP) method to pinpoint key traits between 10 and 20 d after inoculation (DAIs), resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels (i.e., highly and moderately susceptible, and moderately and highly resistant), which were highly correlated with field specialists’ evaluations. Finally, we derived disease developmental curves based on measures of key traits during 10–20 DAI, quantifying varietal disease progression patterns over time. To our knowledge, this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat, providing a valuable toolkit for breeders and plant researchers to assess resistance levels, select disease-resistant varieties, and understand dynamics of the fungal disease.
Why it matches plant phenotyping methodsドローン画像、深層学習、症状領域抽出を組み合わせ、コムギ穂のFHB症状・関連形質と病害進展を自動定量する手法が研究の中心である。
abstractThis study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once (YOLO) model to examine FHB in wheat, enabling us to perform large-scale and automated symptomatic analysis of this disease.
Fusarium head blight (FHB) is one of the most serious wheat diseases and mainly infects the ear, affecting the yield and quality of wheat worldwide. Segmentation of FHB infection in wheat ear based on unmanned aerial vehicle (UAV) images is feasible and significant in ensuring timely control measures and maintaining food security. The high flight altitude of UAV allows for rapid image acquisition but results in blurred textures and details, and the variability of field environment leads to missed and false segmentation. To address these problems, we first executed the super-resolution (SR) of high-altitude UAV images, and then FHB infection was segmented using a deep gate network. Specifically, an SR network called hierarchical context aggregation network (HCAN) was developed to generate clear textures and detailed characteristics of wheat efficiently through the successive fusion of various contexts. HCAN was superior to the current state-of-the-art methods with a peak signal-to-noise ratio of 29.056 dB and a structural similarity index of 0.9142. Meanwhile, a reception enrichment gate network (REGN) was applied to segment FHB infection in wheat ear through the integration of dual-gate mechanism and multi-scale convolution. REGN gained superior results to those of other segmentation networks with a mean intersection over union of 77.93 %, mean pixel accuracy of 87.43 %, and mean Dice coefficient of 87.06 %. Indistinct edges, missed segmentation, and false segmentation were dramatically alleviated in high-density, overlapping, shaded and overexposed wheat because local and neighboring gate operations enhanced the representation and reception field, and multi-scale convolution could enrich the reception diversity. In sum, the proposed approach provided a reliable, efficient, and accurate determination of FHB infection in wheat on the basis of UAV images and could be extended to the analysis of other diseases or crops.
Why it matches plant phenotyping methodsUAV画像からコムギ穂のFHB感染状態を抽出する超解像・セグメンテーション手法を開発し、性能評価しており、植物病害表現型の取得方法が中心である。
abstractREGN gained superior results to those of other segmentation networks with a mean intersection over union of 77.93 %, mean pixel accuracy of 87.43 %, and mean Dice coefficient of 87.06 %.
Ethyl methanesulfonate (EMS) mutants are widely used for genetic analysis; however, EMS-derived mutant populations are not amenable to traditional genome-wide association studies (GWAS) because the EMS mutations are present at extremely low frequencies. To address this challenge, this work develops the GeneHunter-Gene-Level Association (GH-GLA) pipeline using an EMS-generated population of wheat (Triticum aestivum) mutants and an image-based phenotyping platform. GH-GLA enables comprehensive exploration of phenotypic variation induced by genome-wide saturation mutagenesis. Using GH-GLA to quantify 83 traits in the wheat population reveals that variation in spikelet geometry is significantly associated with key agronomic traits, including thousand-kernel weight. Using this indexed wheat EMS population and phenotype data, GH-GLA identified 5905 genes that are significantly associated with specific traits. Analysis of knockouts generated by gene editing, together with haplotypes affected by selection during breeding and genetic variation in 262 wheat accessions, confirm the roles of TaAN-1, TaBAM5L, and TaXTH28L in regulating thousand-kernel weight and spikelet angle. Furthermore, this work establishes an epistatic interaction network between gene pairs to elucidate their combined effects on the phenotype. Overall, GH-GLA provides a powerful strategy for functional gene identification, and the alleles discovered here offer valuable genetic resources for crop improvement.
Why it matches plant phenotyping methods画像ベースの表現型解析プラットフォームとGH-GLAパイプラインを開発・適用し、多数の小麦形質を定量して遺伝子同定に用いた研究であり、表現型取得・解析法が中心的です。
abstractthis work develops the GeneHunter-Gene-Level Association (GH-GLA) pipeline using an EMS-generated population of wheat (Triticum aestivum) mutants and an image-based phenotyping platform.
Reproduction assets foundThe paper's GH-GLA analysis code is publicly available on GitHub with explicit availability language. The phenotypic data (OMIX010498) and VCF data (GVM000963) are deposited in repositories whose URLs are not in the allowed list, so they cannot be cited as assets here.Code · publicAll scripts and codes associated with this project are available via GitHub at https://github.com/gaze‐abyss/GH‐GLA.Open asset ↗gaze‐abyss/GH‐GLAhtml-lines:434-491Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction Wheat is a vital global staple crop, where accurate ear detection and counting are essential for yield prediction and field management. However, the complexity of field environments poses significant challenges to achieving lightweight yet high-precision detection. Methods This study proposes YOLOv8-FDA, a lightweight detection and counting method based on YOLOv8. The approach integrates RFAConv for enhanced feature extraction, DySample for efficient multi-scale upsampling, HWD for compressed and accelerated model training, and the SDL loss for improved bounding box regression. Results Experimental results on the GWHD dataset show that YOLOv8-FDA achieves a precision of 86.3%, recall of 77.5%, and mAP@0.5 of 84.9%, outperforming the original YOLOv8n by significant margins. The model size is 2.96MB with a computational cost of 8.3 GFLOPs, and it operates at 19.2 FPS, enabling real-time counting with over 97.5% accuracy using cross-row segmentation. Discussion The proposed YOLOv8-FDA model demonstrates strong detection performance, lightweight characteristics, and efficient real-time capability, indicating its high practicality and suitability for deployment in real-world agricultural applications.
Why it matches plant phenotyping methodsコムギ穂の検出・計数という植物器官形質の画像ベース抽出法をYOLOv8改良モデルとして開発し、データセット上で性能検証しているため、方法が研究の中心である。
abstractThis study proposes YOLOv8-FDA, a lightweight detection and counting method based on YOLOv8.
Reproduction assets foundThe paper's wheat ear detection/counting experiments were run on the public 2021 GWHD dataset, which the authors explicitly link via a Zenodo DOI in the data availability statement. No author-specific code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: Zenodo at https://doi.org/10.5281/zenodo.5092309 .Open asset ↗Zenodo · 10.5281/zenodo.5092309lines:698-734Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Significance Statement Early studies noting uneven spatial distribution of progeny genotypes after pollination support a hypothesis where differences in pollen tube growth rate can bias inheritance. We used computer vision and statistical analysis to show alleles reducing maize pollen fitness are likely to produce statistically significant increasing, decreasing, or curvilinear spatial patterns from the apex of the inflorescence to the base, suggesting that differential pollen tube growth is not the only mechanism at play. Summary Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize ( Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than those at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). In our dataset (1384 ears) representing 58 Ds-GFP alleles, none with Mendelian inheritance (0/48) showed any significant pollen-conditioned spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertion into a gene encoding a putative actin-binding protein, base-to-apex gradient1* ( bag1* ), conditions increased mutant transmission at the ear apex relative to the base. Surprisingly, mutant alleles of two other pollen-expressed genes can generate the opposite pattern, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm-cell attachment factor, gamete expressed2 ( gex2 ), can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants have relatively common but heterogenous effects on the spatial distribution of progeny genotypes.
Why it matches plant phenotyping methodsトウモロコシ穂上のカーネル表現型を画像認識でマッピングし、空間的位置と遺伝子型伝達比を解析するプラットフォームおよび統計パイプラインを開発しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape).
Reproduction assets foundThe paper's maize ear phenotyping assets are publicly available: the EarVision.v2 repo contains the training images with bounding-box annotations and the trained Faster R-CNN model, and the EarScannerUtilities repo contains the ear-scanning/projection code. The EarScape spatial-analysis repo (with coordinate .xml filesCode · publica license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
540 Varifocal Lens 1080P USB Camera with H.264 High DeYinition Sony IMX323 Webcam. The
541 code for scanning ears, generating projections, and uploading those into cloud storage was
542 also updated and is available at https://github.com/fowler-lab-osu/EarScannerUtilities.
543 The set of ear projections used for the training set included 409 examples from the
544 summer Yield seasons of 2018, 2019 and 2022, encompassing images generated from three
545 different digital cameras and two different versions of the MES. For this training set,
546 projections were manually annotated usingOpen asset ↗fowler-lab-osu/EarScannerUtilitiespdf-layout-page:20 lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
A leading concern for global wheat production, Fusarium head blight (FHB) can cause yield losses of up to 50% during severe epidemics. The cultivation of FHB-resistant wheat varieties is widely acknowledged as a highly effective and economical approach to disease management. The disease resistance breeding task depends on accurately evaluating the severity level of FHB. However, existing approaches may fail to distinguish among healthy and slightly infected wheats due to insufficient fine-grained feature learning, resulting in unreliable predictions. To tackle these challenges, this paper proposed the FHBNet model for evaluating the severity level of FHB under an end-to-end manner by simply using image-level annotated RGB images. In total, 6035 RGB aerial images taken from the wheat field were used to construct the dataset and each image was labelled by the light, moderate, or severe category. In FHBNet, we first utilized the multi-scale criss-cross attention (MSCCA) block to capture the global contextual relationships from each pixel, thereby modelling the spatial context of wheat ears. Furthermore, in order to accurately locate small lesions in wheat ears, we applied the bi-level routing attention (BRA) module, which suppressed the most irrelevant key-value pairs and only retained a small portion of interested regions. The experimental results demonstrated that FHBNet achieved an accuracy of 79.49% on the test se5t, surpassing the mainstream neural networks like MobileViT, MobileNet, EfficientNet, RepLkNet, ViT, and ConvNext. Moreover, visualization heatmaps revealed that FHBNet can accurately locate the FHB lesions under complex conditions, e.g., varying severity levels and illuminations. This study validated the feasibility of rapid and nondestructive FHB severity level evaluation with only image-level annotated aerial RGB images as an input, and the research result of this study can potentially accelerate the disease resistance breeding task by providing high-throughput and accurate phenotype analysis.
Why it matches plant phenotyping methods小麦個体のFHB病斑・重症度を航空RGB画像から推定するモデルとデータセットを開発・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す。
abstractthis paper proposed the FHBNet model for evaluating the severity level of FHB under an end-to-end manner by simply using image-level annotated RGB images.
Introduction As a major food crop, accurate detection and counting of wheat ears in the field are of great significance for yield estimation. Aiming at the problems of low detection accuracy and large computational load of existing detection and counting methods in complex farmland environments, this study proposes a lightweight wheat ear detection model, YOLOv11-EDS. Methods First, the Dysample dynamic upsampling operator is introduced to optimize the upsampling process of feature maps and enhance feature information transmission. Second, the Direction-aware Oriented Efficient Channel Attention mechanism is introduced to make the model focus more on key features and improve the ability to capture wheat ear features. Finally, the Slim-Neck module is introduced to optimize the feature fusion structure and enhance the model's processing capability for features of different scales. Results Experimental results show that the performance of the improved YOLOv11-EDS model is significantly improved on the global wheat ear dataset. The precision is increased by 2.0 percentage points, the recall by 3.5 percentage points, mAP@0.5 by 1.5 percentage points, and mAP@0.5:0.95 by 2.5percentage points compared with the baseline model YOLOv11. Meanwhile, the model parameters are reduced to 2.5 M, and the floating-point operations are reduced to 5.8 G, which are 0. 1 M and 0.5 G lower than the baseline model, respectively, achieving dual optimization of accuracy and efficiency. The model still demonstrates excellent detection performance on a self-built iPhone-view wheat ear datasets, fully verifying its robustness and environmental adaptability. Discussion This study provides an efficient solution for the automated analysis of wheat phenotypic parameters in complex farmland environments, which is of great value for promoting the development of smart agriculture.
Why it matches plant phenotyping methods小麦穂の検出・計数という植物器官の表現型取得を目的に、YOLOモデルを開発し、公開・自作データセットで精度と頑健性を検証しているため、方法が中心である。
abstractthis study proposes a lightweight wheat ear detection model, YOLOv11-EDS.
Accurate and automated detection of wheat spikes is essential for high-throughput phenotyping and yield prediction, yet traditional manual counting is labor-intensive and error-prone. This study compared two deep learning models, U-Net and FasterViT, for wheat spike segmentation using pseudo-RGB images derived from hyperspectral data (400–1000 nm). A dataset of 400 wheat plants was collected at physiological maturity and annotated pseudo-RGB images were used for model training and testing. U-Net achieved a pixel accuracy of 0.893, a recall of 0.834, and a Dice score of 0.761. FasterViT outperformed U-Net with a pixel accuracy of 0.922, Intersection over Union (IoU) of 0.836, and a Dice score of 0.860, demonstrating better generalization and sharper segmentation of spikes. In terms of computational efficiency, U-Net required 2.5 seconds per image, whereas FasterViT required 6.85 seconds per image, reflecting a trade-off between speed and accuracy. Although the controlled dataset size was limited, the findings highlight the feasibility of low-resolution hyperspectral imagery for spike trait analysis. Future extensions could focus on field-based validation and integration into yield prediction pipelines to advance scalable precision agriculture.
Why it matches plant phenotyping methods小麦穂のセグメンテーション手法を比較・評価し、穂形質解析を目的とするため、植物フェノタイピング手法が中心である。
titleEvaluation of Deep Learning Models for Wheat Spike Segmentation Using Hyperspectral-derived Pseudo- RGB Images
The development of computer vision-based rice phenotyping techniques is crucial for precision field management and accelerated breeding, which facilitate continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into ecophysiological processes. However, owing to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both because of a lack of large, representative collections of rice field images and because of the time-intensive nature of the annotation. To address this gap, we created the first comprehensive multiclass rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing more than 6000 genotypes across all growth stages. From these original images, 3078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the subdataset from China spans all major genotypes and rice-growing environments from northeastern to southern regions. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and when multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at www.global-rice.com.
Why it matches plant phenotyping methodsイネの器官レベル表現型抽出を目的とした大規模画像セグメンテーションデータセットを作成し、複数モデルでベンチマークしているため、方法論が中心である。
abstractThe development of computer vision-based rice phenotyping techniques is crucial for precision field management and accelerated breeding
) disease that threatens global food security, requires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding. Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting, which is inefficient and destructive. Although deep learning offers great promise for automated DSR measurement, existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data, insufficient feature representation for diseased spikelets, and weak spatial encoding of densely arranged spikelets. To address these challenges, we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field. We designed FHBDSR-Net, a light framework for automated DSR measurement centered on diseased spikelet detection, which features (1) multi-scale feature enhancement architecture that dynamically combines lesion textures, morphological features, and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise; (2) the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts; and (3) a scale-aware attention module using dilated convolutions and self-attention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution. FHBDSR-Net detected diseased spikelets with an average precision of 93.8% with a lightweight design of 7.2 M parameters. The results were strongly correlated with expert evaluations, with a Pearson correlation coefficient of 0.901. Our method is suitable for deployment on resource-constrained mobile devices, facilitating portable plant phenotyping and smart breeding.
Why it matches plant phenotyping methodsコムギ穂の罹病小穂率という植物病害形質を画像から自動推定する手法を開発し、データセット構築と専門家評価による検証を行っており、フェノタイピング手法が中心である。
abstractrequires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding.
Reproduction assets foundThe paper's Data availability statement explicitly deposits both the spikelet-level annotated wheat spike image dataset (620 RGB images, 5,222 annotations) and the FHBDSR-Net analysis code in a public GitHub repository under the authors' account, matching an allowed URL.Dataset · publicThe dataset and code generated in this study are available at https://github.com/WeizhenLiuBioinform/Wheat-FHB-DSR-Measurement .Open asset ↗WeizhenLiuBioinform/Wheat-FHB-DSR-Measurementlines:901-961Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 6 Sept 2026
Rice serves as the staple food for over 50% of the world's population, making its yield prediction crucial for food security. The number of panicles per unit area is a core parameter for estimating rice yield. However, traditional manual counting methods suffer from low efficiency and significant subjective bias, while unmanned aerial vehicle (UAV) images used for panicle detection face challenges such as densely distributed panicles, large scale variations, and severe occlusion. To address the above challenges, this paper proposes a rice panicle detection model based on an improved You Only Look Once version 11x (YOLOv11x) architecture. The main improvements include: 1) Introducing a Bi-level Routing Attention (BRA) mechanism into the backbone network to improve the feature representation capability for small objects; 2) Adopting a Transformer-based detection head (TransHead) to capture long-term spatial dependencies; 3) Integrating a Selective Kernel (SK) Attention module to achieve dynamic multi-scale feature fusion; 4) Designing a multi-level feature fusion architecture to enhance multi-scale adaptability. Experimental results demonstrate that the improved model achieves an mAP@0.5 of 89.4% on our self-built dataset, representing a 3% improvement over the baseline YOLOv11x model. It also achieves a Precision of 87.3% and an F1-score of 84.1%, significantly outperforming mainstream algorithms such as YOLOv8 and Faster R-CNN. Additionally, panicle counting tests conducted on 300 rice panicle images show that the improved model achieves R 2 = 0.85, RMSE = 2.33, and rRMSE = 0.13, indicating a good fitting effect. The proposed model provides a reliable solution for intelligent in-field rice panicle detection using UAV images and holds significant importance for precise rice yield estimation.
Why it matches plant phenotyping methodsUAV画像からイネ穂数を検出・計数する深層学習手法を開発し、データセット上で性能検証しており、植物形質取得が研究の中心である。
abstractthis paper proposes a rice panicle detection model based on an improved You Only Look Once version 11x (YOLOv11x) architecture.
Understanding the genetic basis of quantitative traits related to crop growth, yield, and stress response requires the acquisition of large-scale, high-quality phenotypic datasets. High-throughput phenotyping platforms have become effective tools for meeting this requirement. Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads, their operational flexibility, and their proximity to crops, which allows for higher imaging resolution. In this study, we introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions. The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field phenotyping, with integrated visual and satellite navigation systems enabling autonomous operation. We validated the performance of the robot through a series of experiments involving various crop canopies. By capturing RGB images of rice and wheat, we independently performed wheat ear detection and rice panicle segmentation. For wheat ear detection, we achieve a precision of 0.783, a recall of 0.822, and a mean average precision (mAP) of 0.853 when the YOLOv8m model is used. For rice panicle segmentation, the SegFormer_B0 model yielded a mean intersection over union (mIoU) of 0.949 and an accuracy of 0.987. Additionally, by capturing RGB-D data of maize canopies, we performed 3D reconstructions to calculate plant height, achieving an R 2 of 0.99 compared with manual measurements. Similar experiments with rapeseed yielded an R 2 of 0.97. Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories, with classification accuracies ranging from 0.977 to 0.996. Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.
Why it matches plant phenotyping methods圃場用自律ロボットと複数の画像・分光センシング、形質抽出手法を開発し、作物キャノピーで性能検証しているため、植物フェノタイピング手法が研究の中心である。
abstractwe introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions.
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the data and code supporting this PhenoRob-F phenotyping study; remaining data are available on request.Code · publicPart of the data and code supporting this study are openly available with the following link: https://github.com/balloonhaha/PhenoRob-F. All other reasonable requests for data and research materials will be fulfilled upon contacting the corresponding authors.Open asset ↗balloonhaha/PhenoRob-Fhtml-lines:193-220Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Accurate identification and counting of wheat ear-heads are critical for reliable crop yield estimation. This study presents a novel pipeline utilizing the YOLOv8 model specifically designed for detecting wheat ear-heads in challenging agricultural environments. Leveraging orthomosaic imagery captured by drones, our approach integrates several advanced techniques to enhance detection accuracy. The pipeline begins with the identification of plots from orthomosaic images, followed by extraction and tiling of these plots for detailed analysis. The detection model is applied to the tiled images, and the results are stitched together to reconstruct the original image annotated with detected wheat ear-heads. To improve image quality—addressing issues like brightness, contrast, exposure, and blur—we employed the Real ESR GAN technique alongside a random cut out method for effective occlusion handling. Evaluations on Mahyco's dataset demonstrated a mean average precision (mAP) of 99.2% for plot detection and an accuracy of 86.7% for wheat ear-head detection against ground truth. Our model exhibited robust performance across varying growth stages and adverse conditions, underscoring its potential for practical agricultural applications. This research introduces an end-to-end pipeline that automates wheat ear-head detection, enabling scalable in-season yield prediction and providing a valuable tool for farmers and agronomists to enhance crop management decisions. Furthermore, our pipeline can be adapted into a desktop app or web portal, allowing farmers to upload orthomosaic images and receive an output Excel file with plot numbers and corresponding wheat ear-head counts, thus enabling comprehensive wheat yield estimation for their farmland.
Why it matches plant phenotyping methodsドローン画像から小麦穂数を自動検出・計数し、収量推定に用いる画像解析パイプラインを開発・評価しており、植物形質の取得方法が中心である。
abstractThis study presents a novel pipeline utilizing the YOLOv8 model specifically designed for detecting wheat ear-heads in challenging agricultural environments.
To tackle the challenges posed by substantial variations in target scale, intricate background interference, and the likelihood of missing small targets in multi-temporal UAV maize tassel imagery, an optimized lightweight detection model derived from YOLOv11 is introduced, named OTB-YOLO. Here, "OTB" is an acronym derived from the initials of the model's core improved modules: Omni-dimensional dynamic convolution (ODConv), Triplet Attention, and Bi-directional Feature Pyramid Network (BiFPN). This model integrates the PaddlePaddle open-source maize tassel recognition benchmark dataset with the public Multi-Temporal Drone Corn Dataset (MTDC). Traditional convolutional layers are substituted with omni-dimensional dynamic convolution (ODConv) to mitigate computational redundancy. A triplet attention module is incorporated to refine feature extraction within the backbone network, while a bidirectional feature pyramid network (BiFPN) is engineered to enhance accuracy via multi-level feature pyramids and bidirectional information flow. Empirical analysis demonstrates that the enhanced model achieves a precision of 95.6%, recall of 92.1%, and mAP@0.5 of 96.6%, marking improvements of 3.2%, 2.5%, and 3.1%, respectively, over the baseline model. Concurrently, the model's computational complexity is reduced to 6.0 GFLOPs, rendering it appropriate for deployment on UAV edge computing platforms.
Why it matches plant phenotyping methodsUAV画像からトウモロコシ雄穂を検出する軽量モデルを開発し、ベンチマークデータセットで性能比較しているため、植物器官の表現型取得手法が中心である。
abstractThis model integrates the PaddlePaddle open-source maize tassel recognition benchmark dataset with the public Multi-Temporal Drone Corn Dataset (MTDC).
Background Accurate sorghum spike detection is critical for monitoring growth conditions, accurately predicting yield, and ensuring food security. Deep learning models have improved the accuracy of spike detection thanks to advances in artificial intelligence. However, the dense distribution of sorghum spikes, variable sizes and complex background information in UAV images make detection and counting difficult. Methods We propose a multiscale and oriented sorghum spike detection and counting model in UAV images (MOSSNet). The model creates a Deformable Convolution Spatial Attention (DCSA) module to improve the network's ability to capture small sorghum spike features. It also integrated Circular Smooth Labels (CSL) to effectively represent morphological features. The model also employs a Wise IoU-based localization loss function to improve network loss. Results Results show that MOSSNet accurately counts sorghum spike under field conditions, achieving mAP of 90.3%. MOSSNet shows excellent performance in predicting spike orientation, with RMSEa and MAEa of 14.6 and 12.5 respectively, outperforming other directional detection algorithms. Compared to general object detection algorithms which output horizonal detection boxes, MOSSNet also demonstrates high efficiency in counting sorghum spikes, with RMSE and MAE values of 9.3 and 8.1, respectively. Discussion Sorghum spikes have a slender morphology and their orientation angles tend to be highly variable in natural environments. MOSSNet 's ability has been proved to handle complex scenes with dense distribution, strong occlusion, and complicated background information. This highlights its robustness and generalizability, making it an effective tool for sorghum spike detection and counting. In the future, we plan to further explore the detection capabilities of MOSSNet at different stages of sorghum growth. This will involve implementing object model improvements tailored to each stage and developing a real-time workflow for accurate sorghum spike detection and counting.
Why it matches plant phenotyping methodsUAV画像からソルガム穂の検出・計数・向き推定を行う深層学習手法を開発し、精度評価も実施しており、植物形態形質の取得手法が中心である。
abstractWe propose a multiscale and oriented sorghum spike detection and counting model in UAV images (MOSSNet).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
BACKGROUND: Spikelet number, a core phenotypic parameter for wheat yield composition, requires precise estimation through accurate spike contour extraction and differentiation between grain surfaces and spikelet surfaces. However, technical challenges persist in precise spike segmentation under complex field backgrounds and morphological differentiation between grain/spikelet surfaces. METHOD: Building on two-year multi-angle wheat spike imagery, we propose an enhanced YOLOv9-LDS multi-scale object detection framework. The algorithm innovatively constructs a lightweight depthwise separable network (LDSNet) as backbone, balancing computational efficiency and accuracy through channel re-parameterization strategy; incorporates an Efficient Local Attention (ELA) module to build feature enhancement networks, and employs dual-path feature fusion mechanisms to strengthen edge texture responses, significantly improving discrimination of overlapping spikes and complex backgrounds. Further optimizes the loss function system by replacing traditional IoU with Scylla Intersection over Union (SIoU) metric, enhancing bounding box regression through dynamic focus factors, and adding high-resolution small-object detection layers to mitigate dense spikelet feature loss. RESULTS: Independent test set validation shows the improved model achieves 83.9% contour integrity recognition rate and 92.4% mAP@0.5, exceeding baseline by 3.2 and 5.3% points respectively. Ablation studies confirm LDSNet-ELA integration reduces false positives by 27.6%, while the enhanced loss function system improves small-object recall by 19.4%. CONCLUSIONS: The proposed framework demonstrates superior performance in complex field scenarios with dense targets and dynamic illumination. The multi-scale feature synergy enhancement mechanism overcomes traditional models' limitations in detecting overlapping spikes. This method not only enables precise spike phenotyping but also provides robust algorithmic support for intelligent field spikelet counting systems, advancing translational applications in crop phenomics.
Why it matches plant phenotyping methodsコムギ穂の輪郭抽出・検出による穂形質測定法を開発し、独立テストとアブレーションで性能検証しており、植物フェノタイピング手法が中心である。
abstractSpikelet number, a core phenotypic parameter for wheat yield composition, requires precise estimation through accurate spike contour extraction
Introduction Rising global populations and climate change necessitate increased agricultural productivity. Most studies on rice panicle detection using imaging technologies rely on single-time-point analyses, failing to capture the dynamic changes in panicle coverage and their effects on yield. Therefore, this study presents a novel temporal framework for rice phenotyping and yield prediction by integrating high-resolution RGB imagery with deep learning-based semantic segmentation. Methods High-resolution RGB images of rice canopies were acquired over two growing seasons. We evaluated five semantic segmentation models (DeepLabv3+, U-Net, PSPNet, FPN, LinkNet) to effectively delineate rice panicles. Time-series panicle coverage data, extracted from the segmented images, were fitted to a piecewise function to model their growth and decline dynamics. This process distilled key predictive parameters: K (maximum panicle coverage), g (growth rate), d0 (time of maximum growth rate), a (decline rate), and d1 (transition point). These parameters served as predictors in four machine learning regression models (PLSR, RFR, GBR, and XGBR) to estimate yield and its components. Results In panicle segmentation, DeepLabv3+ and LinkNet achieved superior performance (mIoU > 0.81). Among the piecewise function parameters, K showed the strongest positive correlation with Yield and Grain Number (GN) ( r = 0.87 and r = 0.85, respectively), while d0 was strongly negatively correlated with the Filled Grain Ratio (FGR) ( r = -0.71). For yield prediction, the RFR and XGBR models demonstrated the highest performance (R 2 = 0.89). SHAP analysis quantified the relative importance of each parameter for predicting yield components. Discussion This framework proves to be a powerful tool for quantifying rice developmental dynamics and accurately predicting yield using readily available RGB imagery. It holds significant potential for advancing both precision agriculture and crop breeding efforts.
Why it matches plant phenotyping methodsRGB画像と深層学習セグメンテーションによりイネ穂の被覆率を時系列で抽出し、成長動態と収量を推定するフェノタイピング手法が中心である。
abstractTherefore, this study presents a novel temporal framework for rice phenotyping and yield prediction by integrating high-resolution RGB imagery with deep learning-based semantic segmentation.
Wheat spike morphology plays a critical role in determining grain yield and has garnered significant interest in genetics and breeding research. However, traditional measurement methods are limited to simple traits and fail to capture complex spike phenotypes with high precision, thus limiting progress in yield-related trait analysis. In this study, a deep learning pipeline, called Speakerphone, for acquiring precise wheat spike phenotypes was developed. Our pipeline achieved a mean intersection over union (mIoU) of 0.948 in spike segmentation. Additionally, the spike traits measured by our method strongly agreed with the manually measured values, with Pearson correlation coefficients of 0.9865 for spike length, 0.9753 for the number of spikelets per spike, and 0.9635 for fertile spikelets. Using experimental data of 221 wheat cultivars from various regions of Zhao County, Hebei Province, China, our pipeline extracted 45 phenotypes and analyzed their correlations with thousand-grain weight (TGW) and spike yield. Our findings indicate that precise measurements of spike area, spikelet area, and other phenotypic traits clarify the correlation between spike morphology and wheat yield. Through hierarchical clustering on the basis of spike morphology, we categorized wheat spikes into six classes and identified the phenotypic differences among these classes and their effects on TGW and yield. Furthermore, phenotypic differences among wheat cultivars from different geographical regions and over decades were revealed in this study, with an increase in the number of large-spike cultivars over time, especially in southern China. This research may help breeders understand the relationship between wheat spike morphology and yield, thus providing an important basis for future wheat breeding efforts.
Why it matches plant phenotyping methods小麦穂の画像から形態形質を抽出する深層学習パイプラインを開発し、セグメンテーション性能と手動測定との一致を検証しているため、フェノタイピング手法が研究の中心です。
abstracta deep learning pipeline, called Speakerphone, for acquiring precise wheat spike phenotypes was developed.
Reproduction assets foundThe paper's SpikePheno phenotyping pipeline (deep learning segmentation and trait extraction for wheat spikes) is explicitly stated to be publicly available on GitHub. No public dataset of the 2198 spike images or annotations is stated; the labelme link is a generic third-party tool, not a paper-specific asset.Code · publicThe full implementation of the spikePheno pipeline is available in GitHub at the following URL: https://github.com/Jiang-Phenomics-Lab/spikePheno .Open asset ↗Jiang-Phenomics-Lab/spikePhenolines:210-330Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat ( Triticum aestivum L.) during 2022-2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic-inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R 2 = 0.88 ( p 2 ) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water-fertilizer management (70% ETc irrigation with 3:7 organic-inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development.
Why it matches plant phenotyping methodsUAV画像からコムギ穂を認識・計数する画像解析手法を開発し、データセット、モデル改良、比較検証、圃場性能評価まで行っており、植物形質取得が研究の中心です。
abstractThis study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting.
Spike morphology is crucial for wheat (Triticum aestivum L.) yield and environmental adaptation. We developed a high-throughput phenotyping platform to dissect spike morphology traits based on 54 traits in 1,359 wheat accessions. These 54 spike morphology traits exhibited clear geographical differences among 306 worldwide accessions and breeding selection trend across different time windows for 1,053 accessions released from 1900 to 2020 in China. Based on geographical distribution and breeding selection of haplotypes, we attribute the differences in spike morphology to variable haplotype combinations. Wheat breeding breaks the trade-off between spike length and width/thickness, resulting in increased spike volume. A large proportion of genomic regions has been identified across wheat varieties and utilized as a fixed group to facilitate the targeted improvement and selection of desirable traits during wheat breeding programs. Overall, we provide a resource for the molecular design of spike morphology to facilitate future wheat breeding.
Why it matches plant phenotyping methodsコムギ穂の形態形質を多数個体から取得するハイスループット表現型解析プラットフォームの開発と適用が研究の中心である。
abstractWe developed a high-throughput phenotyping platform to dissect spike morphology traits based on 54 traits in 1,359 wheat accessions.
Reproduction assets foundThe paper's high-resolution spike phenotyping platform software is explicitly released as public code by the authors on GitHub. The genotype datasets (GVM000272/GVM000720) are molecular omics deposits and do not qualify as phenotype/trait data; other listed tools are generic third-party libraries.Code · publicn/gvm) under accession number GVM00027239
or GVM000720.
• The genotype data for 1053 Chinese accessions (1900–2020) are pub
licly available at the Genome Variation Map (https://bigd.big.ac.cn/gvm)
under accession number GVM000720.
• The software for the high-resolution phenotyping platform is publicly avail
able with the link https://github.com/ShenKC-hub/wheat_platform1.0.
• Any additional information required to reanalyze the data reported in this
paper is available from the lead contact upon request.
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation
of China (32272122, 32401876, and 32225038)，the Strategic Priority
Research Program of Chinese Academy of Open asset ↗ShenKC-hub/wheat_platform1.0 · wheat_platform1.0pdf-raw-page:15 lines:1-81Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Accurate identification of floral morphological traits, such as flower type and the diameters of ligulate and bisexual flowers, is essential for the quality evaluation and varietal improvement of cut chrysanthemums ( Chrysanthemum morifolium Ramat.). Traditional manual or rule-based image processing methods are inefficient and struggle with complex floral structures. To address these limitations, we developed a lightweight deep learning and machine learning pipeline for automated trait extraction in over 30 chrysanthemum cultivars. A ShuffleNet V2 model achieved 95.24% accuracy in flower type classification, with lightweight characteristics (1.26M parameters, 0.15 GFLOPs), fast inference time (14.78 ms per image), and 67.65 FPS. Ligulate and bisexual flowers were segmented using an optimized U-Net achieving a reduction of over 95% in parameters, achieving an average Dice similarity coefficient (DSC) of 0.934. For diameter estimation, mean squared errors (MSE) of 6.605 mm (ligulate) and 2.034 mm (bisexual) were obtained, with coefficients of determination (R2) approaching 0.98. Fine-grained classifications—Single-petals vs. Repeating-petals and Incurve vs. Honeycomb—were achieved using geometric and texture features with F1-scores above 0.87. These results demonstrate a scalable and efficient solution for floral trait analysis, supporting high-throughput phenotyping in ornamental horticulture.
Why it matches plant phenotyping methodsキクの花器官形態を画像から自動抽出・推定する深層学習パイプラインを開発しており、植物表現型取得が研究の中心である。
abstractwe developed a lightweight deep learning and machine learning pipeline for automated trait extraction in over 30 chrysanthemum cultivars.
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 %. However, the precision for stems with 54 % was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
Why it matches plant phenotyping methods小麦器官の画素レベルセグメンテーション用データセットを構築し、モデル性能を検証する研究であり、植物形質抽出のための画像解析手法が中心です。
abstractThe labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.
Reproduction assets foundThe paper's GWFSS wheat organ segmentation dataset (1096 pixel-labelled images plus 52,078 unlabelled images, subset/imaging-setup metadata) and the benchmark segmentation model are publicly deposited in the ETH Research Collection and mirrored on Hugging Face, with links also listed on the Global Wheat site.Dataset · publicThe full dataset (GWFSS_v1.0_full) including the 1096 ground-truth labelled images (GWFSS_v1.0_labelled), the descriptions of the datasets (GWFSS_v1.0_subsets.csv) and imaging setups (GWFSS_v1.0_imaging_setups.csv) is available in the ETH research collection (https://doi.org/10.3929/ethz-b-000734546)Open asset ↗ETH research collection · 10.3929/ethz-b-000734546html-lines:1006-1041Dataset · publicTo facilitate access, the labelled data and the benchmark model will also be available at (https://huggingface.co/datasets/GlobalWheat/GWFSS_v1.0).Open asset ↗huggingface · GlobalWheat/GWFSS_v1.0html-lines:1129-1192Dataset · publicLinks to these datasets can be found at: https://www.global-wheat.com/gwfss.html.Open asset ↗html-lines:1006-1041Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
This study utilized plant phenomics image analysis technology to explore the agronomic characteristics of rice cultivars, aiming to enhance growth stability, yield potential, and digital data for rice breeding. RGB images were captured at three lateral angles during the growth period of the plants using ScanLyzer, LemnaTec. A total of 42 agronomic traits were analyzed across 102 rice cultivars, categorized into three maturing groups. In addition, to evaluate the measurement accuracy, 9 phenotypic traits, the panicle length (Pl), panicle count (Pc), and number of seeds were also measured destructively after harvest. Parameter estimated revealed that the Pl trait exerted the strongest positive effect on seed production across all groups analyzed, with coefficients (β) of 0.459 for the entire population, 0.456 in the early-maturing group, 0.537 in the medium-maturing group, and 0.574 in the medium-late maturing group (p < 0.05). Other traits, such as maximum area (Am), and maximum height (Hm), also positively influenced seed production but to a lesser extent. Notably, duration of maximum value of rice plant width had a significant negative effect in the early-maturing group (β = -0.369, p < 0.05). Correlation analyses revealed strong positive relationships between seed production and various traits across maturity classes, notably with days to maximum height, Pl, Pc, and seed count. Additionally, panicle length and count emerged as pivotal factors influencing seed numbers. These findings underscore the varying impacts of agronomic traits on seed yield depending on cultivars and maturity groups, offering valuable insights for the selection of rice cultivars aimed at optimizing seed production.
Why it matches plant phenotyping methods自動画像解析システムによる多数の植物形質抽出が研究の中心であり、破壊測定による測定精度評価も実施しているため、植物フェノタイピング手法の実質的な適用・検証に該当する。
abstractThis study utilized plant phenomics image analysis technology to explore the agronomic characteristics of rice cultivars
High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and thier growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective methodologies. To address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were acquired from multiple viewpoints and represent different plant structures at different growth rates. Wheat3D PartNet samples are manually labeled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems) and that captured in drought and watered conditions. Wheat3D PartNet is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection—facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset’s utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest.
Why it matches plant phenotyping methods植物の3D形態解析と形質定量を目的とする注釈付きデータセットを構築し、複数の3Dモデルで有用性を検証しており、フェノタイピング手法・データ資源が中心である。
abstractThe development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits
The architecture of rice tillers plays a pivotal role in yield potential, yet conventional phenotyping methods have struggled to capture these intricate three-dimensional (3D) structures with high fidelity. In this study, a 3D model reconstruction method was developed specifically for rice tillers to overcome the challenges posed by their slender, feature-poor morphology in multi-view stereo-based 3D reconstruction. By applying strategically designed colorful reference markers, high-resolution 3D tiller models of 231 rice landraces were reconstructed. Accurate phenotyping was achieved by introducing ScaleCalculator, a software tool that integrated depth images from a depth camera to calibrate the physical sizes of the 3D models. The high efficiency of the 3D model-based phenotyping pipeline was demonstrated by extracting the following seven key agronomic traits: flag leaf length, panicle length, first internode length below the panicle, stem length, flag leaf angle, second leaf angle from the panicle, and third leaf angle. Genome-wide association studies (GWAS) performed with these 3D traits identified numerous candidate genes, nine of which had been previously confirmed in the literature. This work provides a 3D phenomics solution tailored for slender organs and offers novel insights into the genetic regulation of complex morphological traits in rice.
Why it matches plant phenotyping methodsイネ分げつの3D再構成とScaleCalculatorによるスケール校正を開発し、7つの形態形質を抽出するフェノタイピング手法が研究の中心であるため。
abstracta 3D model reconstruction method was developed specifically for rice tillers
Reproduction assets foundThe paper's 3D tiller models for 231 rice landraces are publicly deposited on Zenodo, and the authors' ScaleCalculator phenotyping source code is publicly available on GitHub, both explicitly stated in the Data Availability Statement. SNP genotype data are unpublished and excluded.Code · publicvelopment Co. LTD, and
Jiangsu Collaborative Innovation Center for Modern Crop Production.
Data Availability Statement: The 3D tiller models created in this study are available for research pur-
poses at https://zenodo.org/records/16080993 (accessed on 18 July 2025).The source code of ScaleCal-
culator is available on GitHub at https://github.com/ganlab/OSTRA/tree/master/ScaleCalculator
(accessed on 18 July 2025).
Acknowledgments: We thank Jianmin Wan for their valuable suggestions and Jiaqi Deng for their
technical help.
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this article.
References
1. Food and Agriculture OrganizatOpen asset ↗github · ganlab/OSTRApdf-raw-page:16 lines:1-50Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Trait-based breeding has been shown to enhance and sustain yield potential of various crops under current and future changing climate. To be successful, trait-based breeding requires extensive phenotyping of plants in large-scale field trials that may include hundreds of genotypes. Computer vision approaches have been used extensively for image-based high-throughput phenotyping of diverse traits. However, studies focused on estimating grain count, a trait that directly influences the overall yield, are limited due to a lack of benchmark grain image datasets. In this work, we focus on grain count estimation in sorghum, a crop that holds immense significance for both food and energy production. We introduce a large Sorghum-Grain-Count (SGC) dataset consisting of (front and back, with and without flash) images of 1264 panicles from 316 genotypes (i.e., 4 panicles per genotype), for a total of approximately 5000 images, as well as approximately 12,500 images containing the corresponding threshed grains, together with machine counts per panicle. To develop baseline models, we manually annotated grains in images from 100 genotypes using bounding boxes. We used the manually annotated images to train baseline models for small object detection and counting. We also trained regression-based models for grain count estimation. The best overall model for count estimation from panicle images was a regression model, which achieved a mean absolute percent error of 29.97 and an R 2 value of 0.75. We make our dataset and baselines publicly available to facilitate further research on grain count estimation in sorghum and other crops. • We curated the Sorghum-Grain-Count dataset, which includes ∼17,500 panicle and threshed grain images covering 316 genotypes. • This is the largest dataset for grain count estimation and can help advance research on small object count estimation. • We trained strong baseline object detection models, as well as regression-based models for grain count estimation. • Our best model for grain count estimation from panicle images was a regression model that achieved an R 2 value of 0.75. • Our models provide a foundation for non-destructive yield estimation tools, which are greatly needed by breeding programs.
Why it matches plant phenotyping methodsソルガムの穂・粒画像から粒数という植物収量関連形質を推定する大規模データセット、ベンチマーク、検出・回帰モデルを開発・公開しており、表現型取得・推定手法が研究の中心です。
abstractWe introduce a large Sorghum-Grain-Count (SGC) dataset consisting of (front and back, with and without flash) images of 1264 panicles from 316 genotypes
The wheat planted at the end of the rainy season in the Cerrado suffers from a strong water deficit. A selection of genetic material with drought tolerance is necessary. In improvement programs that evaluate a large number of materials, efficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors. The experiment was conducted in 2016 using a phenotyping platform, where irrigation gradients ranging from 184 (WR4) to 601 mm (WR1) were created, allowing for the comparison of four genotypes. In addition to productivity, we evaluated plant height, hectoliter weight, the number of spikes per square meter, ear length, photosynthesis, and the indices calculated by the sensors. For most morphophysiological parameters, extreme stress makes it difficult to discriminate materials. WR1 (601 mm) and WR2 (501 mm) showed similar trends in almost all variables. The data validated the phenotyping platform, which creates an irrigation gradient, considering that the results obtained, in general, were proportional to the water levels. The similar trend between sensors (NDVI, PRI, and LIFT) and morphophysiological, plant growth, and crop yield evaluations validated the use of sensors as a tool in selecting drought-tolerant wheat genotypes using a non-invasive methodology. Considering that only four genotypes were used, none showed absolute and unequivocal tolerance to drought; however, each genotype exhibited some desirable characteristics related to drought tolerance mechanisms.
Why it matches plant phenotyping methodsセンサーを用いた非破壊フェノタイピングと灌漑勾配プラットフォームを検証し、センサー指標と植物形質・収量を比較しているため、方法が中心的である。
abstractefficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors.
Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To overcome these challenges, we developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols. Our dataset contains 64,464 RGB images that capture 12 different plant poses from four camera elevation angles. Each image includes manually annotated bounding boxes for seven regions of interest (ROIs), including leaves, panicle, batch of flowers, batch of fruits, axillary shoot, shoot and whole plant area, along with 50 fine-grained growth stage classifications based on the BBCH scale. Additionally, we provide 3,616 high-resolution image subset with pixel-wise semantic and instance segmentation annotations for fine-grained phenotyping. We validated our dataset using a cascading model deep learning framework combining MobileNetv3 for classification, YOLOv11 for object detection, and MaskRCNN for segmentation. Through AI vs. Human analysis involving five domain experts, we demonstrate that the models trained on our dataset achieve accuracy and speed comparable to the experts. Cohen's Kappa and inter-rater agreement heatmap confirm the reliability of automated fine-grained phenotyping using our approach.
Why it matches plant phenotyping methods植物の多視点画像取得、アノテーション付きデータセット、深層学習による分類・検出・セグメンテーションを中心に開発・検証した、明確な植物フェノタイピング手法研究です。
abstractwe developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols.
Reproduction assets foundThe paper's TomatoMAP dataset (images, annotations) is publicly deposited in e!DAL at IPK with an explicit DOI URL given in the Data Records section.Dataset · publicDataset is deposited in e!DAL (electronic data archive library) of IPK (Leibniz Institute of Plant Genetics
and Crop Plant Research): https://doi.ipk-gatersleben.de/DOI/10bb9f14-ce90-4747-836f-cf61dfb5eea1/Open asset ↗e!DAL · 10bb9f14-ce90-4747-836f-cf61dfb5eea1pdf-page:7 lines:1-73Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 13 Sept 2026
The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from Uncrewed Aerial System (UAS)-derived imagery. Such advancement leads to phenotypic digitization and sorghum yield forecasting. Yield analytics are critical for breeding programs to assess the genetics and breeding potential of genotypes to enhance cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 meters above using a DJI M300 drone equipped with the P1 sensor at nadir (90 degrees) and oblique (45 degrees) angles. This research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN model in detecting sorghum panicles, achieving a mean average precision at 50% Intersection over Union (IoU) ranging from 0.92 to 0.98, compared to 0.61 to 0.89. Panicle detection from field imagery correlated at 0.86 with ground truth. Lab imagery analyses measured panicle size, seed counts, and seed area with correlation coefficients of 0.71, 0.95, and 0.25, respectively. Three machine learning models: Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) are used to predict yield with correlation coefficients of 0.58, 0.76, and 0.70, respectively. We observed that YOLO models are well-suited for extracting yield-attributing traits from images, which are then incorporated into ML regression models to improve yield prediction performance.
Why it matches plant phenotyping methodsUAS・実験室画像からソルガム穂の検出、サイズ・種子数・面積などの形質抽出と収量予測を行い、複数の物体検出モデルを比較検証しているため、表現型取得手法が中心である。
abstractThis research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images.
Developing disease-resistant crops is a critical strategy for reducing chemical treatments and mitigating plant disease outbreaks, particularly amid global environmental changes. Fusarium head blight (FHB), caused by a complex of Fusarium species, is one of the most devastating cereal diseases, leading to significant economic losses and contamination of grain with harmful mycotoxins that threaten global cereal production and human health. The high variability in virulence within the complex of Fusarium spp and the lack of efficient high-throughput screening methods have impeded the development of resistant cultivars and made large-scale virulence testing labor-intensive and time-consuming. This study evaluates the efficacy of detached leaf, coleoptile, and seedling assays as high-throughput alternatives to the standard head infection assay for assessing the virulence of Fusarium species and differentiating wheat genotypes by resistance or susceptibility. Two near-isogenic wheat lines, one carrying FHB resistance loci and the other without, were used to assess the virulence of four Fusarium species. The seedling and coleoptile assays showed strong concordance with the traditional head infection assay, accurately reflecting differences in disease severity across Fusarium species and between wheat lines. Conversely, the detached leaf assay provided some differentiation among species but was inconsistent in identifying differences between plant genotypes. Across all assays, F. graminearum consistently exhibited the highest virulence, causing severe disease in leaves, stems, seedlings, and heads, while F. poae was the least virulent. Interestingly, F. culmorum and F. avenaceum displayed tissue-specific variability. These findings establish the coleoptile and seedling assays as rapid, high-throughput alternatives for breeding programs, accelerating the identification of FHB-resistant genotypes and reducing the reliance on the labor-intensive head assay.
Why it matches plant phenotyping methodsコムギ赤かび病抵抗性・病原性を評価する複数の病徴アッセイを比較し、標準穂感染試験との一致性と高スループット代替法としての有効性を検証しており、植物表現型取得法が研究の中心である。
abstractThis study evaluates the efficacy of detached leaf, coleoptile, and seedling assays as high-throughput alternatives to the standard head infection assay for assessing the virulence of Fusarium species and differentiating wheat genotypes by resistance or susceptibility.
The morphological structure of wheat spikes plays a central role in wheat yield. Wheat spike morphology, closely associated with crop yield, has attracted considerable attention in the fields of genetics and breeding. However, traditional measurement methods can only measure simple traits, and precise phenotypes remain difficult to obtain, constraining the study and improvement of complex spike-related traits. This study utilized deep learning technologies to develop a pipeline, called SpikePheno, for the acquisition of precise wheat spike phenotypes. Our pipeline demonstrated high accuracy in spike segmentation, achieving a mean Intersection over Union (mIoU) of 0.948. Additionally, our method accurately identified spikelet counts, achieving an R 2 of 0.9923. Using experimental data of 221 wheat cultivars from various regions of China grown in Zhao County, Hebei Province, our pipeline extracted 45 different phenotypes and studied their correlations with thousand grain weight (TGW) and spike yield. Our findings indicate that precise measurement of spike area, spikelet area, and other phenotypic traits enables a clearer understanding of the correlation between spike morphology and wheat yield. Through hierarchical clustering based on spike morphology, we categorized wheat spikes into six classes and identified phenotypic differences between these classes and their impact on TGW and yield. Furthermore, this study revealed phenotypic differences between wheat cultivars from different geographical regions and over different decades, with an increase in large-spike cultivars over time, especially in southern China. This research may help breeders understand the relationship between wheat spike morphology and yield, providing an important basis for future wheat breeding efforts.
Why it matches plant phenotyping methodsSpikePhenoという深層学習画像解析パイプラインを開発し、コムギ穂の分割・小穂数同定・複数形質抽出を中心に評価しているため、植物フェノタイピング手法論文に該当する。
abstractThis study utilized deep learning technologies to develop a pipeline, called SpikePheno, for the acquisition of precise wheat spike phenotypes.
Rice panicle detection is a key technology for improving rice yield and agricultural management levels. Traditional manual counting methods are labor-intensive and inefficient, making them unsuitable for large-scale farmlands. This paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images. The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling with multi-branch dilated convolutions to preserve fine-grained spatial information; and a Dynamic Bidirectional Feature Pyramid Network (DynamicBiFPN) employing input-adaptive kernels to optimize cross-scale feature fusion. The model was trained and evaluated on the open-access Dense Rice Panicle Detection (DRPD) dataset, which comprises UAV images captured at 7 m, 12 m, and 20 m altitudes. Experimental results demonstrate that our method significantly outperforms existing advanced models, achieving an AP50 of 0.8931 and an F2 score of 0.8377 on the test set. While ensuring model accuracy, the parameters of the proposed model decreased by 42.87% and the GFLOPs by 48.95% compared to Panicle-AI. Grad-CAM visualizations reveal that FRPNet exhibits superior background noise suppression in 20 m altitude images compared to mainstream models. This work establishes an accuracy-efficiency balanced solution for UAV-based field phenotyping.
Why it matches plant phenotyping methodsUAV画像からイネ穂の検出・計数という植物形質を抽出する軽量深層学習手法を開発し、公開データセットで評価しており、表現型取得手法が中心である。
abstractThis paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images.
Reproduction assets foundThe paper states its data (the DRPD rice panicle UAV dataset used for training/evaluation) is publicly available via a GitHub release URL, which matches an allowed URL.Dataset · publicData Availability Statement: Data is available at https://github.com/changcaiyang/Panicle-AI/
releases/tag/v1.0.Open asset ↗Panicle-AI · v1.0pdf-page:22 lines:1-59Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: Plant phenotyping has become increasingly important for advancing plant science, agriculture, and biotechnology. Classic manual methods are labor-intensive and time-consuming, while existing computational tools often require advanced coding skills, high-performance hardware, or PC-based environments, making them inaccessible to non-experts, to resource-constrained users, and to field technicians. RESULTS: To respond to these challenges, we introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping. The platform is designed for ease of use, enabling users to phenotype plant traits quickly and efficiently with only a smartphone at hand. We currently instantiate the use of the platform with tools such as SeedPheno, WheatHeadPheno, LeafAnglePheno, SpikeletPheno, CanopyPheno, TomatoPheno, and CornPheno; each offering specific functionalities such as seed size and count analysis, wheat head detection, leaf angle measurement, spikelet counting, canopy structure analysis, and tomato fruit measurement. In particular, OpenPheno allows developers to contribute new algorithmic tools, further expanding its capabilities to continuously facilitate the plant phenotyping community. CONCLUSIONS: By leveraging cloud computing and a widely accessible interface, OpenPheno democratizes plant phenotyping, making advanced tools available to a broader audience, including plant scientists, breeders, and even amateurs. It can function as a role in AI-driven breeding by providing the necessary data for genotype-phenotype analysis, thereby accelerating breeding programs. Its integration with smartphones also positions OpenPheno as a powerful tool in the growing field of mobile-based agricultural technologies, paving the way for more efficient, scalable, and accessible agricultural research and breeding.
Why it matches plant phenotyping methodsスマートフォンで植物形質を取得・解析するソフトウェアプラットフォームの開発が中心であり、複数の具体的な表現型解析ツールを提供している。
abstractwe introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping.
Reproduction assets foundThe paper's authors publicly release the OpenPheno platform code (GitHub repository) and the evaluation sample data used for algorithm validation and demonstration (dataset subdirectory). Both are paper-specific, public, and actionable.Dataset · publicEvaluation sample data used for algorithm validation and demonstration has been made publicly available at out GitHub repository: https://github.com/openpheno/OpenPheno/tree/main/dataset .Open asset ↗openpheno/OpenPheno · tree/main/datasetlines:171-191Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R 2 ≥ 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R 2 ≥ 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.
Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる高スループット表現型取得と、時系列モデルによる干ばつ状態および収穫形質の予測が研究の中心である。
abstractwe investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants
Sorghum canopy architecture in field trials is determined by various phenotypic traits, such plant and panicle count, leaf density and angle and panicle morphology, and canopy height. These traits together affect light capture and biomass production as well as conversion of photosynthates to grain yield. Panicle morphology exhibits considerable variation as influenced by genetics, environmental conditions and management practices. This study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology. First, we developed a scalable, low-altitude Unmanned Aerial Vehicle (UAV)-based protocol that leverages videos for efficient data acquisition, combined with Neural Radiance Fields (NeRF)s to generate high-quality 3D point cloud reconstructions of sorghum canopies. Next, a 3D model was built to simulate 3D sorghum canopies to create annotated datasets for training deep learning-based semantic segmentation and panicle detection algorithms. Finally, we propose SegVoteNet, a novel multi-task deep learning model that integrates VoteNet and PointNet++ within a shared backbone architecture. Designed for semantic segmentation and 3D detection on pure point cloud data, SegVoteNet incorporates a voting and sampling module that leverages segmentation results to optimize object proposal generation. SegVoteNet is robust, achieving 0.986 Mean Average Precision (mAP) @ 0.5 Intersection Over Union (IOU) on synthetic datasets, and 0.850 mAP @ 0.5 IOU on real point cloud datasets for sorghum panicle detection, without fine-tuning. This set of pipelines provides a robust scalable method for phenotyping sorghum panicles in field trials in breeding and commercial applications. Further work is developing a capability to estimate grain number per panicle, which would provide breeders with additional phenotypes to select.
Why it matches plant phenotyping methodsUAV・NeRF・点群再構成と深層学習によるソルガム穂形態の取得・推定パイプラインを開発し、実データで性能評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Sorghum canopy architecture in field trials is determined by various phenotypic traits, such plant and panicle count, leaf density and angle and panicle morphology, and canopy height. These traits together affect light capture and biomass production as well as conversion of photosynthates to grain yield. Panicle morphology exhibits considerable variation as influenced by genetics, environmental conditions and management practices. This study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology. First, we developed a scalable, low-altitude Unmanned Aerial Vehicle (UAV)-based protocol that leverages videos for efficient data acquisition, combined with Neural Radiance Fields (NeRF)s to generate high-quality 3D point cloud reconstructions of sorghum canopies. Next, a 3D model was built to simulate 3D sorghum canopies to create annotated datasets for training deep learning-based semantic segmentation and panicle detection algorithms. Finally, we propose SegVoteNet, a novel multi-task deep learning model that integrates VoteNet and PointNet++ within a shared backbone architecture. Designed for semantic segmentation and 3D detection on pure point cloud data, SegVoteNet incorporates a voting and sampling module that leverages segmentation results to optimize object proposal generation. SegVoteNet is robust, achieving 0.986 Mean Average Precision (mAP) @ 0.5 Intersection Over Union (IOU) on synthetic datasets, and 0.850 mAP @ 0.5 IOU on real point cloud datasets for sorghum panicle detection, without fine-tuning. This set of pipelines provides a robust scalable method for phenotyping sorghum panicles in field trials in breeding and commercial applications. Further work is developing a capability to estimate grain number per panicle, which would provide breeders with additional phenotypes to select.
Why it matches plant phenotyping methodsUAV、3D点群、NeRF、深層学習を統合したソルガム穂形態の取得・抽出パイプラインを開発し、実データで性能評価しており、植物表現型計測手法が研究の中心である。
abstractThis study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology.
The spike number (SN) is an important trait that significantly impacts grain yield in wheat. Manual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting. A YOLOX algorithm was used to determine the optimal growth stage for developing wheat spike detection models among recombinant inbred lines (RILs) across Zhongmai 175 × Lunxuan 987 and a diverse panel of 166 cultivars. We subsequently increased the precision of spike identification by developing a new YOLOX-P algorithm that incorporates the convolutional block attention module and increasing the resolution of the input images. We also used these SN data to identify underlying loci in the Zhongmai 578 × Jimai 22 RIL population. The results revealed that the late grain-filling stage presented the highest precision among the SN detection models, with accuracies ranging from 91.8 to 95.02 %. The improved YOLOX-P algorithm demonstrated higher mean average precision scores (5.30−5.99 %) and F1 scores (0.06) than did the YOLOX algorithm when it was applied to the same subsets. Three new SN loci, namely, QSN.caas-4A2, QSN.caas-4D and QSN.caas-5B2, were identified using the 50k SNP arrays. Two kompetitive allele-specific PCR markers linked with QSN.caas-4A2 and QSN.caas-5B2 were developed, and their genetic effects were validated in a diverse panel of 166 cultivars. These findings provide useful tools for high-throughput identification of SNs and novel loci in wheat.
Why it matches plant phenotyping methodsRGB画像とYOLOX系コンピュータビジョンによりコムギの穂数を自動推定する手法の開発・比較が中心であり、遺伝子座同定への応用も行っているため、植物表現型手法論文として採用。
abstractManual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting.
Modern plant phenomics leverages advanced digital technologies to derive qualitative and quantitative traits that define plant phenotypes, offering crucial insights for breeders and farmers in precision agriculture.However, real-field conditions, with their complexity and lack of flexibility, pose significant challenges for machine vision algorithm initially developed in controlled laboratory settings.The objective of this study was to explore the potential of human aided 3D point cloud analysis for phenotyping crop under near real-field conditions using a custom-built desktop application, with sorghum and soybean plants as the case study.Light detection and ranging (LiDAR) data acquisition was performed using a Leica BLK360 imaging laser scanner (Leica Geosystems AG, USA).Coordinate difference measurements were employed in extracting various phenotypic traits from plant point clouds.The sphere outlier removal (SOR) was fundamental in macro-noise reduction, while color-based scatter plot matrix were used for micro-noise isolation.The correlation between point cloud-derived traits and manually measured values was strong, with root mean square error (RMSE) of 17.84 mm for sorghum plant height, 16.28 mm for soybean plant height, 11.65 mm for sorghum panicle height, and 0.967 mm for sorghum stem diameter, and corresponding R-squared values between 0.7334 and 0.9492.However, measuring more complex traits like crown diameter, which are influenced by overlap and occlusion, was less accurate, with an RMSE of 102.4 mm and an R-squared value of 0.3702.While 3D phenotyping in near real-field environment reliably captures linear plant structures, complex morphological traits require improved occlusionhandling algorithms.Future work should prioritize high resolution sensors to capture finer details.Likewise, automated workflows are poised to improve not only throughput but the reliability and reproducibility of the 3D phenotyping approach.
Why it matches plant phenotyping methodsLiDAR点群とカスタムアプリケーションを用いた3D植物形態表現型の取得・解析手法を開発し、手動測定との精度検証も行っており、表現型測定法が研究の中心である。
abstractThe objective of this study was to explore the potential of human aided 3D point cloud analysis for phenotyping crop under near real-field conditions using a custom-built desktop application
Developing disease-resistant crops is a critical strategy for reducing chemical treatments and mitigating plant disease outbreaks, particularly amid global environmental changes. Fusarium head blight (FHB), caused by a complex of Fusarium species, is one of the most devastating cereal diseases, leading to significant economic losses and contamination of grain with harmful mycotoxins that threaten global cereal production and human health. The high variability in virulence within the complex of Fusarium spp and the lack of efficient high-throughput screening methods have impeded the development of resistant cultivars and made large-scale virulence testing labor-intensive and time-consuming. This study evaluates the efficacy of detached leaf, coleoptile, and seedling assays as high-throughput alternatives to the standard head infection assay for assessing the virulence of Fusarium species and differentiating wheat genotypes by resistance or susceptibility. Two near-isogenic wheat lines, one carrying FHB resistance loci and the other without, were used to assess the virulence of four Fusarium species. The seedling and coleoptile assays showed strong concordance with the traditional head infection assay, accurately reflecting differences in disease severity across Fusarium species and between wheat lines. Conversely, the detached leaf assay provided some differentiation among species but was inconsistent in identifying differences between plant genotypes. Across all assays, F. graminearum consistently exhibited the highest virulence, causing severe disease in leaves, stems, seedlings, and heads, while F. poae was the least virulent. Interestingly, F. culmorum and F. avenaceum displayed tissue-specific variability. These findings establish the coleoptile and seedling assays as rapid, high-throughput alternatives for breeding programs, accelerating the identification of FHB-resistant genotypes and reducing the reliance on the labor-intensive head assay.
Why it matches plant phenotyping methodsコムギ赤かび病抵抗性・病原性を評価する複数の表現型測定法を比較し、標準穂感染法との一致性とハイスループット代替法としての有効性を検証しているため、方法中心の研究である。
abstractThis study evaluates the efficacy of detached leaf, coleoptile, and seedling assays as high-throughput alternatives to the standard head infection assay for assessing the virulence of Fusarium species and differentiating wheat genotypes by resistance or susceptibility.
Abstract Barley head detection is a crucial task for agricultural applications such as yield estimation and crop monitoring. Unlike wheat, automated barley head detection has not been extensively studied due to challenges posed by its complex head structures and the lack of annotated datasets. In this paper, we leverage YOLOv10, a state-of-the-art object detection framework, to detect barley heads from high-resolution images captured using UAVs. Our dataset, consisting of UAV-captured images and supplemented with the Global Wheat Head Dataset, provides a robust foundation for model training. The proposed approach achieves a mean Average Precision of 0.83 at Intersection of Union 0.5, setting a new benchmark for barley head detection. This work contributes to advancing automated crop monitoring systems in precision agriculture.
Why it matches plant phenotyping methodsUAV画像からオオムギ穂を検出するYOLOv10手法とデータセットを中心に開発・評価しており、植物器官の画像ベース表現型取得に該当する。
abstractIn this paper, we leverage YOLOv10, a state-of-the-art object detection framework, to detect barley heads from high-resolution images captured using UAVs.
Advanced plant phenotyping technologies play a crucial role in targeted trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. The method utilizes radiance field information to lift 2D masks, which are segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy is designed to effectively resolve the single-interaction multi-target segmentation challenge. Experimental validation demonstrates that IPENS achieves a grain-level segmentation accuracy (mIoU) of 63.72% on a rice dataset, with strong phenotypic estimation capabilities: grain volume prediction yields R2 = 0.7697 (RMSE = 0.0025), leaf surface area R2 = 0.84 (RMSE = 18.93), and leaf length and width predictions achieve R2 = 0.97 and 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset,IPENS further improves segmentation accuracy to 89.68% (mIoU), with equally outstanding phenotypic estimation performance: spike volume prediction achieves R2 = 0.9956 (RMSE = 0.0055), leaf surface area R2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reach R2 = 0.99 and 0.92 (RMSE = 0.23 and 0.15). This method provides a non-invasive, high-quality phenotyping extraction solution for rice and wheat. Without requiring annotated data, it rapidly extracts grain-level point clouds within 3 minutes through simple single-round interactions on images for multiple targets, demonstrating significant potential to accelerate intelligent breeding efficiency.
Why it matches plant phenotyping methods植物の3D画像から穀粒・葉・穂の形態形質を抽出する手法を開発し、複数作物データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
The spike number (SN) is an important trait that significantly impacts grain yield in wheat. Manual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting. A YOLOX algorithm was used to determine the optimal growth stage for developing wheat spike detection models among recombinant inbred lines (RILs) across Zhongmai 175 × Lunxuan 987 and a diverse panel of 166 cultivars. We subsequently increased the precision of spike identification by developing a new YOLOX-P algorithm that incorporates the convolutional block attention module and increasing the resolution of the input images. We also used these SN data to identify underlying loci in the Zhongmai 578 × Jimai 22 RIL population. The results revealed that the late grain-filling stage presented the highest precision among the SN detection models, with accuracies ranging from 91.8 to 95.02 %. The improved YOLOX-P algorithm demonstrated higher mean average precision scores (5.30-5.99 %) and F1 scores (0.06) than did the YOLOX algorithm when it was applied to the same subsets. Three new SN loci, namely, QSN . caas-4A2, QSN . caas-4D and QSN . caas-5B2 , were identified using the 50k SNP arrays. Two kompetitive allele-specific PCR markers linked with QSN . caas-4A2 and QSN . caas-5B2 were developed, and their genetic effects were validated in a diverse panel of 166 cultivars. These findings provide useful tools for high-throughput identification of SNs and novel loci in wheat.
Why it matches plant phenotyping methodsRGB画像とコンピュータビジョンによりコムギの穂数を自動推定する手法を開発・比較し、精度を評価しているため、植物フェノタイピング手法が中心である。
abstractHence, there is an urgent need to develop efficient and accurate methodologies for SN counting.
Reproduction assets foundThe paper publicly releases two paper-specific assets: (1) a wheat spike number image dataset (subsets CD&DD&XX) on GitHub, and (2) the YOLOX-P analysis code on Google Drive. Both have explicit availability statements with author-provided URLs.Dataset · publicThe image set for CD&DD&XX is publicly available on GitHub ( https://github.com/lileimax/YOLOXP-wheat-spike-identification ).Open asset ↗https://github.com/lileimax/YOLOXP-wheat-spike-identificationlines:223-246Code · publicThe code for YOLOX-P is publicly available on Google Drive ( https://drive.google.com/drive/folders/1urCDUdyrq14FuwG2I3YwCZraUGEEl_8X?usp=sharing ).Open asset ↗lines:247-250Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract Flowering time is a critical phenological trait in maize ( Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi‐subjective and labor‐intensive manual observation, limiting the scale and efficiency of genetics and breeding improvement. Leveraging unoccupied aerial system (UAS, also known as unoccupied aerial vehicles or drones) technology coupled with convolutional neural networks (CNNs) presents a promising approach for high‐throughput detection of flowered plots in maize. Most CNN image analysis is overly complicated for simple tasks relevant to plant scientists. Here, a methodology for extracting tasseling from UAS red/green/blue imagery using a CNN‐based approach was applied to 220 hybrids and 30 test lines grown in eight diverse environments (Wisconsin and Texas) and then validated through an unrelated set of hybrids. Overall accuracies of 0.946, 0.911, 0.985, and 0.988 were obtained for classifying maize images with or without tassels from College Station, TX, in 2020; College Station, TX, in 2021; Arlington, WI, in 2021; and Madison, WI, in 2021, respectively. By employing deep learning techniques, larger volumes of phenotypic data can be processed enabling high‐throughput phenotyping in breeding programs. Although large datasets are required to train CNN models, the proposed methodology prioritizes simplicity in computational architecture while maintaining effectiveness in identifying flowered maize across diverse genotypes and environments.
Why it matches plant phenotyping methodsUAS画像とCNNを用いてトウモロコシの抽だい（開花期）を抽出する方法を開発し、複数環境および独立データで精度検証しており、植物フェノタイピング手法が研究の中心です。
abstractHere, a methodology for extracting tasseling from UAS red/green/blue imagery using a CNN‐based approach was applied to 220 hybrids and 30 test lines grown in eight diverse environments (Wisconsin and Texas) and then validated through an unrelated set of hybrids.
Accurately detecting rice panicles in complex field environments remains challenging due to their small size, dense distribution, diverse growth directions, and easy confusion with the background. To accurately detect rice panicles, this study proposes OE-YOLO, an enhanced framework derived from YOLOv11, incorporating three synergistic innovations. First, oriented bounding boxes (OBB) replace horizontal bounding boxes (HBB) to precisely capture features of rice panicles across different heights and growth stages. Second, the backbone network is redesigned with EfficientNetV2, leveraging its compound scaling strategy to balance multi-scale feature extraction and computational efficiency. Third, a C3k2_DConv module improved by dynamic convolution is introduced, enabling input-adaptive kernel fusion to amplify discriminative features while suppressing background interference. Extensive experiments on rice Unmanned Aerial Vehicle (UAV) imagery demonstrate OE-YOLO's superiority, achieving 86.9% mAP50 and surpassing YOLOv8-obb and YOLOv11 by 2.8% and 8.3%, respectively, with only 2.45 M parameters and 4.8 GFLOPs. The model has also been validated at flight heights of 3 m and 10 m and during the heading and filling stages, achieving mAP50 improvements of 8.3%, 6.9%, 6.7%, and 16.6% compared to YOLOv11, respectively, demonstrating the generalization capability of the model. These advancements demonstrated OE-YOLO as a computationally frugal yet highly accurate solution for real-time crop monitoring, addressing critical needs in precision agriculture for robust, oriented detection under resource constraints.
Why it matches plant phenotyping methodsイネ穂の画像検出を目的としたYOLOベースの手法開発と性能検証が研究の中心であり、植物器官の観測・抽出法に該当する。
abstractTo accurately detect rice panicles, this study proposes OE-YOLO, an enhanced framework derived from YOLOv11, incorporating three synergistic innovations.
Field / plotRGB / grayscalePanicle / ear / spikeClassificationObject detectionGrowth / development / phenology
This dataset is an expanded version of a previously published collection of high-resolution RGB images of Urochloa spp. genotypes, initially designed to facilitate automated classification of phenological stages and raceme identification in forage breeding trials. The original dataset included 2400 images of 200 genotypes captured under controlled conditions, supporting the development of computer vision models for High-Throughput Phenotyping (HTP). In this updated release, 139 additional images and 24,983 new annotations have been added, bringing the dataset to a total of 2539 images and 47,323 raceme annotations. This version introduces increased diversity in image-capture conditions, with data collected from two geographic locations (Palmira, Colombia, and Ocozocoautla de Espinosa, Mexico) and a range of image-capture devices, including smartphones (e.g. Realme C53 and Oppo Reno 11), a Nikon D5600 camera, and a Phantom 4 Pro V2 drone. Images now vary in perspective (nadir, high-angle, and frontal) and capture distance (1-3 meters), enhancing the dataset applicability for robust Deep Learning (DL) models. Compared to the original dataset, raceme density per plant has nearly doubled in some samples, offering higher raceme overlap for advanced instance segmentation tasks. This expanded dataset supports deeper exploration of phenotypic variation in Urochloa spp. and offers greater potential for developing adaptable models in crop phenotyping.
Why it matches plant phenotyping methods植物の生育ステージ分類と総状花序の同定を目的とする画像データセットで、注釈付き画像の拡張、撮影条件の多様化、インスタンスセグメンテーション用途が中心であり、再利用可能な表現型解析基盤に該当する。
abstractThis dataset is an expanded version of a previously published collection of high-resolution RGB images of Urochloa spp. genotypes, initially designed to facilitate automated classification of phenological stages and raceme identification in forage breeding trials.
Reproduction assets foundThe paper is a Data in Brief describing a public Harvard Dataverse deposit of the paper's own Urochloa spp. hybrid RGB images and COCO raceme annotations, with a direct DOI URL listed in allowed_urls.Dataset · publicupo Papalotla
City 1: Palmira, Valle del Cauca.
City 2: Ocozocoautla de Espinosa, Chiapas.
Country 1: Colombia.
Country 2: Mexico.
Geolocalization 1: 3°29’N, 76°21’W
Geolocalization 2: 16°45′N 93°28′W
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi.org/10.7910/DVN/X4LM19
Direct URL to data: https://doi.org/10.7910/DVN/X4LM19Open asset ↗Harvard Dataverse · 10.7910/DVN/X4LM19lines:1-51Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Bunch compactness (BC) is a complex, multi-trait characteristic that has been studied mostly in the context of wine grapes, with table grapes being scarcely considered. As these groups have marked phenotypic and genetic differences, including BC, the study of this trait is reported here using a genetically diverse collection of 116 Vitis vinifera L. cultivars and lines enriched for table grapes over two seasons. For this, 3D scanning-based morphological data were combined with ground measurements of 14 BC-related traits, observing high correlations among both approaches (R 2 > 0.90-0.97). The multivariate analysis suggests that the attributes 'berries per bunch', 'berry weight and width', and 'bunch weight and length' could be considered as the main descriptors for BC, optimizing evaluation times. Then, GWASs based on a set of 70,335 SNPs revealed that GBS analysis in this same population enabled the detection of several SNPs associated with different sub-traits, with a locus for 'berries per bunch' in chromosome (chr) 18 being the most prominent. Enrichment analysis of significant and frequent SNPs found simultaneously in several traits and seasons revealed the over-representation of discrete functions such as alpha-linolenic acid metabolism and glycan degradation. In summary, the utility of 3D automated phenotyping was validated for table grape backgrounds, and new SNPs and candidate genes associated with the BC trait were detected. The latter could eventually become a selection tool for grapevine breeding programs.
Why it matches plant phenotyping methodsブドウ房のコンパクトネスを対象に、3Dスキャンによる自動形態計測を地上測定と比較・検証し、育種利用可能な表現型評価法として実証しているため、方法が中心的です。
abstract3D scanning-based morphological data were combined with ground measurements of 14 BC-related traits, observing high correlations among both approaches (R 2 > 0.90-0.97).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Background: Spikelet number, a core phenotypic parameter for wheat yield composition, requires precise estimation through accurate spike contour extraction and differentiation between grain surfaces and spikelet surfaces. However, technical challenges persist in precise spike segmentation under complex field backgrounds and morphological differentiation between grain/spikelet surfaces. Method : Building on two-year multi-angle wheat spike imagery, we propose an enhanced YOLOv9-LDS multi-scale object detection framework. The algorithm innovatively constructs a lightweight depthwise separable network (LDSNet) as backbone, balancing computational efficiency and accuracy through channel re-parameterization strategy; incorporates an ELA local attention module to build feature enhancement networks, and employs dual-path feature fusion mechanisms to strengthen edge texture responses, significantly improving discrimination of overlapping spikes and complex backgrounds. Further optimizes the loss function system by replacing traditional IoU with SIoU metric, enhancing bounding box regression through dynamic focus factors, and adding high-resolution small-object detection layers to mitigate dense spikelet feature loss. Results: Independent test set validation shows the improved model achieves 83.9% contour integrity recognition rate and 92.4% mAP@0.5, exceeding baseline by 3.2 and 5.3 percentage points respectively. Ablation studies confirm LDSNet-ELA integration reduces false positives by 27.6%, while the enhanced loss function system improves small-object recall by 19.4%. Conclusions: The proposed framework demonstrates superior performance in complex field scenarios with dense targets and dynamic illumination. The multi-scale feature synergy enhancement mechanism overcomes traditional models' limitations in detecting overlapping spikes. This method not only enables precise spike phenotyping but also provides robust algorithmic support for intelligent field spikelet counting systems, advancing translational applications in crop phenomics.
Why it matches plant phenotyping methods小麦穂の輪郭抽出・認識と穂粒数推定のための画像解析手法を開発し、独立テストとアブレーションで性能検証しており、植物表現型取得が中心である。
abstractSpikelet number, a core phenotypic parameter for wheat yield composition, requires precise estimation through accurate spike contour extraction
Wheat spike detection plays an important role in phenotyping, as it provides an approach for direct yield estimation and serves as an indicator of yield potential. Traditional method of phenotyping involved manual counting of wheat heads which is a time-consuming, and error-prone process. However, the advent of Deep Learning (DL) techniques has revolutionized this process by allowing automated detection and counting of wheat heads using high-resolution imagery, hence facilitating large-scale, High Throughput Phenotyping (HTP) of wheat. Despite technological advancements, issues related to environmental variability, differences among cultivars, and overlapping heads continue to make automated detection task difficult and error-prone. To address these issues, researchers have focused on enhancing the robustness of DL models and increasing the diversity of wheat head datasets to improve detection accuracy and reliability of this approach. In this study, we have compared the performance of three state-of-the-art DL models, YOLOv10x, RetinaNet, and MM-Grounding DINO, for wheat head detection. To ensure diversity of the dataset, we have integrated two datasets, the Global Wheat Head Detection (GWHD) 2021 and the SPIKE dataset representing various wheat genotypes. This study aims to advance wheat head detection methods and offer a comparative evaluation of these three DL models.
Why it matches plant phenotyping methods小麦穂の画像検出・カウントによる表現型取得を中心に、複数の深層学習モデルとデータセットを比較評価しており、方法比較・ベンチマークとして適格。
abstractWheat spike detection plays an important role in phenotyping, as it provides an approach for direct yield estimation and serves as an indicator of yield potential.
Introduction Detection of rice panicles and recognition of rice growth stages can significantly improve precision field management, which is crucial for maximizing grain yield. This study explores the use of deep learning on mobile phones as a platform for rice phenotype applications. Methods An improved YOLOv8 model, named YOLO_Efficient Computation Optimization (YOLO_ECO), was proposed to detect rice panicles at the booting, heading, and filling stages, and to recognize growth stages. YOLO_ECO introduced key improvements, including the C2f-FasterBlock-Effective Multi-scale Attention (C2f-Faster-EMA) replacing the original C2f module in the backbone, adoption of Slim Neck to reduce neck complexity, and the use of a Lightweight Shared Convolutional Detection (LSCD) head to enhance efficiency. An Android application, YOLO-RPD, was developed to facilitate rice phenotype detection in complex field environments. Results and discussion The performance impact of YOLO-RPD using models with different backbone networks, quantitative models, and input image sizes was analyzed. Experimental results demonstrated that YOLO_ECO outperformed traditional deep learning models, achieving average precision values of 96.4%, 93.2%, and 81.5% at the booting, heading, and filling stages, respectively. Furthermore, YOLO_ECO exhibited advantages in detecting occlusion and small panicles, while significantly optimizing parameter count, computational demand, and model size. The YOLO_ECO FP32-1280 achieved a mean average precision (mAP) of 90.4%, with 1.8 million parameters and 4.1 billion floating-point operations (FLOPs). The YOLO-RPD application demonstrates the feasibility of deploying deep learning models on mobile devices for precision agriculture, providing rice growers with a practical, lightweight tool for real-time monitoring.
Why it matches plant phenotyping methodsイネの穂と生育段階を画像から推定する軽量深層学習モデルとAndroidアプリを開発・性能評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study explores the use of deep learning on mobile phones as a platform for rice phenotype applications.
Automated extraction of plant morphological traits is crucial for supporting crop breeding and agricultural management through high-throughput field phenotyping (HTFP). Solutions based on multi-view RGB images are attractive due to their scalability and affordability, enabling volumetric measurements that 2D approaches cannot directly capture. While advanced methods like Neural Radiance Fields (NeRFs) have shown promise, their application has been limited to counting or extracting traits from only a few plants or organs. Furthermore, accurately measuring complex structures like individual wheat heads-essential for studying crop yields-remains particularly challenging due to occlusions and the dense arrangement of crop canopies in field conditions. The recent development of 3D Gaussian Splatting (3DGS) offers a promising alternative for HTFP due to its high-quality reconstructions and explicit point-based representation. In this paper, we present Wheat3DGS, a novel approach that leverages 3DGS and the Segment Anything Model (SAM) for precise 3D instance segmentation and morphological measurement of hundreds of wheat heads automatically, representing the first application of 3DGS to HTFP. We validate the accuracy of wheat head extraction against high-resolution laser scan data, obtaining per-instance mean absolute percentage errors of 15.1%, 18.3%, and 40.2% for length, width, and volume. We provide additional comparisons to NeRF-based approaches and traditional Muti-View Stereo (MVS), demonstrating superior results. Our approach enables rapid, non-destructive measurements of key yield-related traits at scale, with significant implications for accelerating crop breeding and improving our understanding of wheat development.
Why it matches plant phenotyping methods3D Gaussian SplattingとSAMを用いて小麦穂の3Dセグメンテーションと形態形質抽出手法を開発し、レーザースキャン、NeRF、MVSと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ある。
abstractwe present Wheat3DGS, a novel approach that leverages 3DGS and the Segment Anything Model (SAM) for precise 3D instance segmentation and morphological measurement of hundreds of wheat heads automatically
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract Grape cluster compactness is a key trait that influence fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling × Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.
Why it matches plant phenotyping methodsSAMを用いた画像ベースの果粒セグメンテーションと形状・サイズ・房のコンパクトネス推定が、遺伝解析のための主要な表現型取得手法として明示されているため。
abstractAdditionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention.
Introduction Rice is one of the world's leading food crops, with nearly half of the world's population eating rice as their staple food. Rice yield is directly related to varieties, and the most intuitive agronomic trait of varietal yield is the number of grains per panicle. Methods In this study, rice panicles are taken as the research object, and images of the panicles are captured using a smartphone. The CSRNet counting model based on deep learning is then improved and applied to the problem of counting the number of grains per panicle in rice. Results and discussion The results show that the method of this study has a mean error value of 3.83% on the final validation set. On this basis, the development of rice per panicle counting APP based on Android terminal and batch counting software RiceGrainCounter based on PC terminal realizes real-time counting on Android terminal and batch counting on PC terminal, which can provide theoretical basis and technical support for rice per panicle counting.
Why it matches plant phenotyping methodsスマートフォン画像と改良CSRNetによりイネの穂当たり籾数を推定・計数する手法を開発し、検証するとともに、AndroidアプリとPCソフトウェアを実装しており、表現型取得が研究の中心である。
abstractimages of the panicles are captured using a smartphone. The CSRNet counting model based on deep learning is then improved and applied to the problem of counting the number of grains per panicle in rice.
Wheat spike detection holds significant importance for agricultural production as it enhances the efficiency of crop management and the precision of operations. This study aims to improve the accuracy and efficiency of wheat spike detection, enabling efficient crop monitoring under resource-constrained conditions. To this end, a wheat spike dataset encompassing multiple growth stages was constructed, leveraging the advantages of MobileNet and ShuffleNet to design a novel network module, SeCUIB. Building on this foundation, a new wheat spike detection network, LGWheatNet, was proposed by integrating a lightweight downsampling module (DWDown), spatial pyramid pooling (SPPF), and a lightweight detection head (LightDetect). The experimental results demonstrate that LGWheatNet excels in key performance metrics, including Precision, Recall, and Mean Average Precision (mAP50 and mAP50-95). Specifically, the model achieved a Precision of 0.956, a Recall of 0.921, an mAP50 of 0.967, and an mAP50-95 of 0.747, surpassing several YOLO models as well as EfficientDet and RetinaNet. Furthermore, LGWheatNet demonstrated superior resource efficiency with a parameter count of only 1,698,529 and GFLOPs of 5.0, significantly lower than those of competing models. Additionally, when combined with the Slicing Aided Hyper Inference strategy, LGWheatNet further improved the detection accuracy of wheat spikes, especially for small-scale targets and edge regions, when processing large-scale high-resolution images. This strategy significantly enhanced both inference efficiency and accuracy, making it particularly suitable for image analysis from drone-captured data. In wheat spike counting experiments, LGWheatNet also delivered exceptional performance, particularly in predictions during the filling and maturity stages, outperforming other models by a substantial margin. This study not only provides an efficient and reliable solution for wheat spike detection but also introduces innovative methods for lightweight object detection tasks in resource-constrained environments.
Why it matches plant phenotyping methods小麦穂の画像検出・計数という植物器官形質の抽出手法を開発し、データセット構築、モデル比較、精度・資源効率評価まで行っており、フェノタイピング手法が中心である。
abstractThis study aims to improve the accuracy and efficiency of wheat spike detection
Maize (Zea mays L.) stands as a pivotal grain crop, and hybrid seed production enables the combination of desirable traits from diverse parent varieties. Ensuring seed purity is fundamental in hybrid seed production, where accurate unreleased tassel detection and efficient detasseling are crucial. Specifically, in the context of maize field seed production, the detasseling of the female parent necessitates prior execution preceding pollen shedding. Despite concerted efforts encompassing both mechanical and manual detasseling methodologies, an estimated range of 420–480 tassels per acre evade removal, exerting a detrimental impact on the purity of hybrid seeds. While extant researches concerning maize tassel detection and enumeration primarily concentrate on post-pollen shedding stages, leaving pre-tassel identification largely unexplored. To address this gap, this study develops a novel object detection framework, YOLO-detassel, for pre-tassel identification during the tasseling stage. UAVs were deployed to capture high-resolution RGB imagery from a hybrid seed production field in western China, creating a dataset of pre-tassel maize plants. YOLO-detassel enhances the YOLOv5 model through several innovations: (1) MobileNetV3 is integrated into the backbone to reduce parameters and improve computational efficiency while maintaining high detection accuracy; (2) the Simple Attention Module (SimAM) incorporates three-dimensional weights to better capture spatial and channel features holistically; and (3) the Content-Aware Reassembly of Features module boosts context integration and spatial awareness for robust detection in complex field conditions. Empirical evaluations attest to the efficacy of the proposed YOLO-detassel algorithm, attaining an Average Precision (AP) of 96.8% for pre-tassel object detection, coupled with a commendable recall rate of 94.2%, precision of 98.4% and a notable detection speed of 32 frames per second. The elucidated object detection algorithm not only signifies an innovative stride towards addressing the challenge of identifying missed pre-tassels in maize seed production but also holds promise for extending its applicability towards the detection of pre-tassels on female parent maize plants leveraging UAV imagery automatically.
Why it matches plant phenotyping methodsトウモロコシのプレタッセルという植物器官・生育状態をUAV画像から検出する手法を開発し、精度・再現速度を評価しており、植物表現型の取得が中心である。
abstractthis study develops a novel object detection framework, YOLO-detassel, for pre-tassel identification during the tasseling stage.
Predicting spatial distribution of Fusarium Head Blight (FHB) is essential for precision preventive site-specific fungicide application in winter wheat cultivation. The current study presents a novel approach for the prediction of the within-field spatial distribution of FHB of winter wheat using random forest (RF), least squares support vector machines (LS-SVM), and multilayer perceptron (MLP), using high resolution data on soil characteristics, meteorological data, and remote sensing derived crop growth indices. The predictive performance of the models was assessed using two distinct training approaches based on data collected from three fields in Lithuania; a cross-field validation approach (Approach 1) and a field-specific model approach (Approach 2), using data attained from three experimental fields in Lithuania. In approach 1, MLPs achieved the highest performance with coefficient of determination (R²) values reaching up to 0.71 and residual prediction deviation (RPD) values reaching 1.87. In Approach 2, MLPs have demonstrated high performances with R² values reaching 1.00 and RPD values up to 25.63. Field-specific models significantly outperformed cross-field models, achieving Kappa coefficient values ranging from 0.91 to 0.97 across all investigated fields. The above findings indicate the potential of the effective combination of machine learning models with remote sensing and soil data for the accurate FHB prediction and for the adoption of more sustainable and targeted crop protection practices.
Why it matches plant phenotyping methods冬小麦のFHB（病害状態）の圃場内空間分布を、リモートセンシングデータと機械学習で推定し、複数の検証設計で性能評価しているため、病害フェノタイピング手法の適用・検証が中心です。
abstractThe current study presents a novel approach for the prediction of the within-field spatial distribution of FHB of winter wheat using random forest (RF), least squares support vector machines (LS-SVM), and multilayer perceptron (MLP), using high resolution data on soil characteristics, meteorological data, and remote sensing derived crop growth indices.
Abstract Background Rapeseed( Brassica napus L. ) inflorescence coverage is a crucial phenotypic parameter for assessing crop growth and estimating yield. Accurate crop cover assessment is typically performed using Unmanned Aerial Vehicles (UAVs) in combination with semantic segmentation methods. However, the irregular and variable morphology of rapeseed inflorescences presents significant challenges in segmentation. To address these challenges, advanced methods that can improve segmentation accuracy, particularly under limited data conditions, are needed. Results In this study, we propose a cost-effective and high-throughput approach using a semi-supervised learning framework, DM_CorrMatch. This method enhances input images through strong and weak data augmentation techniques, while leveraging the Denoising Diffusion Probabilistic Model (DDPM) to generate additional samples in data-scarce scenarios.We propose an automatic update strategy for labeled data to dilute the proportion of erroneous labels in manual segmentation. Furthermore, a novel network architecture, Mamba-Deeplabv3+, is proposed, combining the strengths of Mamba and Convolutional Neural Networks (CNNs) for both global and local feature extraction. This architecture effectively captures key inflorescence features, even under varying poses, while reducing the influence of complex backgrounds. The proposed method is validated on the Rapeseed Flower Segmentation Dataset (RFSD), which consists of 720 UAV images from the Yangluo experimental station of the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (CAAS). The experimental results showed that our method outperforms four traditional segmentation methods and eleven deep learning methods, achieving an Intersection over Union (IoU) of 0.886, Precision of 0.942, and Recall of 0.940. Conclusions The proposed semi-supervised learning-based method, combined with the Mamba-Deeplabv3+ architecture, demonstrates superior performance in accurately segmenting rapeseed inflorescences under challenging conditions. Our approach effectively handles complex backgrounds and various poses of inflorescences, providing a reliable tool for rapeseed flower cover estimation. This method can aid in the development of high-yield cultivars and improve crop monitoring through UAV-based technologies.
Why it matches plant phenotyping methodsUAV画像からナタネ花序被覆率という植物形質を推定する半教師ありセグメンテーション手法を開発し、データセット上で既存手法と比較検証しているため、方法が中心である。
abstractwe propose a cost-effective and high-throughput approach using a semi-supervised learning framework, DM_CorrMatch.
Accurate and efficient assessment of highland barley (Hordeum vulgare L.) density is crucial for optimizing cultivation and management practices. However, challenges such as overlapping spikes in unmanned aerial vehicle (UAV) images and the computational requirements for high-resolution image analysis hinder real-time detection capabilities. To address these issues, this study proposes an improved lightweight YOLOv5 model for highland barley spike detection. We chose depthwise separable convolution (DSConv) and ghost convolution (GhostConv) for the backbone and neck networks, respectively, to reduce the parameter and computational complexity. In addition, the integration of convolutional block attention module (CBAM) enhances the model's ability to focus on target object in complex backgrounds. The results show that the improved YOLOv5 model has a significant improvement in detection performance. Precision and recall increased by 3.1% to 92.2% and 86.2%, respectively, with an F1 score of 0.892. The AP0.5 reaches 92.7% and 93.5% for highland barley in the growth and maturation stages, respectively, and the overall mAP0.5 improved to 93.1%. Compared to the baseline YOLOv5n model, the number of parameters and floating-point operations (FLOPs) were reduced by 70.6% and 75.6%, respectively, enabling lightweight deployment without compromising accuracy. In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. Although this study also suffers from limitations such as insufficient generalization under varying lighting conditions and reliance on rectangular annotations, it provides valuable support and reference for the development of real-time highland barley spike detection systems, which can help to improve agricultural management.
Why it matches plant phenotyping methodsUAV画像からハダンオオムギの穂を検出し密度評価に用いる軽量化画像解析モデルを開発・比較しており、植物形質状態の取得手法が中心である。
abstractthis study proposes an improved lightweight YOLOv5 model for highland barley spike detection
Reproduction assets foundThe paper's Data availability statement explicitly provides the authors' highland barley UAV spike-detection dataset on ModelScope and their analysis code on GitHub, both paper-specific and publicly actionable.Dataset · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗Cai121/highland_barleylines:182-211Code · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗trangle666ddd/YOLOv5-highland-barley-detectionlines:182-211Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Computer vision is increasingly used in farmers’ fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimetre ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today’s AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of wheat organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90%. However, the precision for stems with 54% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
Why it matches plant phenotyping methodsコムギ器官の画素単位セグメンテーションデータセットを構築し、モデル性能を評価する研究であり、植物形態・健全性の定量化に用いる表現型取得手法が中心です。
abstractThe labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.
Rice is the world's most consumed staple food crop and there is a need to increase its yield in terms of food security. Understanding rice yields is important for farmers and national decision-making, and is critical for increasing yields. Remote sensing and machine learning have improved the accuracy and efficiency of yield monitoring. In particular, the combination of unmanned aerial vehicles (UAV) and convolutional neural networks (CNN), which is a type of deep learning, has been studied in recent years owing to its flexibility in data acquisition and high accuracy. Rice yield predictions using UAV and CNN have been reported to build more robust models after the mid-ripening stage. However, optimal input image conditions, such as the growth stage of image acquisition, spectral bands, and image cut-out areas, have not been studied, and there is room for improvement in this respect. In addition, recent efforts to find clues to improve the reliability and accuracy of advanced machine learning models have focused on explainable artificial intelligence (XAI), which attempts to reveal the basis of model inferences. However, there are almost no examples of using XAI for regression tasks with CNN in the research field of agricultural sciences. Therefore, in this study, the optimal input image conditions were investigated for the prediction of rice yield using a CNN based on UAV aerial images collected after the mid-ripening stage. An attempt was made to provide a rationale for the results by visualizing the region of interest in the CNN model. First, using red edge spectral bands at the maturity stage was more effective than at the mid-ripening stage. In addition, higher accuracy was achieved by allowing feature extraction from a slightly wider area than the actual harvested area, especially at the maturity stage. Furthermore, visualization of the region of interest showed that yield prediction was more focused on panicles at the maturity stage. This provided a relevant rationale for optimal input image conditions. In summary, this study identified the optimal input image conditions that enabled yield prediction with higher accuracy. Additionally, using XAI, which visualizes the region of interest, increases the trustworthiness of the model outputs. The results of this study will improve the accuracy and reliability of yield prediction models.
Why it matches plant phenotyping methodsUAV画像とCNNを用いたイネ収量推定について、撮影時期・スペクトル帯・画像範囲を比較最適化し、XAIで推定根拠を検証しており、植物形質（収量）の取得・推定手法が中心である。
abstractTherefore, in this study, the optimal input image conditions were investigated for the prediction of rice yield using a CNN based on UAV aerial images collected after the mid-ripening stage.
WheatLiDAR / point cloudPanicle / ear / spikeLeafSegmentation
Plant phenotyping is crucial for precisely measuring traits across diverse morphotypes in cereals such as wheat, where the variability in the proportion of ears to leaves poses significant challenges due to class imbalances and the granularity limitations of 3D point cloud data. Our study addresses these intra-class imbalances through two strategies using the PointNet++ network: use plant features i.e., ear ratio and ear count for weighted point cloud sampling and apply class weights in the loss function. We analyze datasets from three morphologically distinct wheat varieties: Paragon, Gladius, and Apogee. Introducing point cloud weights based on ear ratio and ear count significantly improves the network’s ability to recognize underrepresented parts in datasets with uneven distributions of ear and non-ear points. We observe a differential impact in all the categories in terms of segmentation accuracy and average mIoU. In comparison to the base method, all the wheat categories display enhancement in performance after applying both techniques. The best results are obtained with point cloud weights in the loss function for the Gladius dataset, showing a substantial improvement of 10% to 12% in average ear mIoU compared to base method (0.483). Although a similar trend is observed with weighted sampling, the enhanced results in the Gladius dataset range from 0.611 to 0.626 indicate model’s enhanced capability to identify different segments or parts precisely. This research demonstrates the efficacy of our methods in addressing class imbalance issues in point cloud segmentation and provides enhanced accuracy for particularly across different wheat genotypes. In conclusion, the techniques in this study can reliably handle intra class imbalance in diverse datasets, which offers a scalable solution to improve agricultural phenotyping.
Why it matches plant phenotyping methods小麦3D点云中穂・葉など植物器官のセグメンテーション精度を改善する手法を開発・評価しており、植物フェノタイピングの画像解析手法が中心である。
abstractOur study addresses these intra-class imbalances through two strategies using the PointNet++ network
Fusarium head blight (FHB) poses a substantial threat to cereal crop production, significantly affecting both grain yield and quality by producing harmful mycotoxins such as deoxynivalenol (DON), which is detrimental to human and animal health. To manage this threat effectively, precise detection and mapping of FHB spatial distribution at the field level are crucial. This study aimed to detect and map FHB in four commercial winter wheat fields in Belgium and Lithuania using a push-broom hyperspectral camera (400–1000 nm), mounted on a tractor. The on-line collected hyperspectral data were first subjected to a linear regression model to segment wheat ears from the background using a linear regression model, achieving a precision of 0.99. The segmented hyperspectral data were then correlated with FHB severity, assessed by means of groundtruth captured RGB images using two dataset. The first dataset (M1) combined data from both countries, whereas the second dataset (M2) used data from the three fields in Lithuania only. The two datasets were then subjected to four machine learning (ML) modelling techniques, namely, extra trees regression (ETR), random forest regression (RFR), support vector regression (SVR), and one-dimensional convolutional neural network (1DCNN). Once validated using an independent validation set, these models were used to predict and map FHB using the on-line collected spectra in the four fields. Additionally, recursive feature elimination (RFE) and mutual information (MI) approaches to select the optimal wavebands for FHB detection were employed. Results demonstrated the capability of ETR to predict FHB severity successfully, surpassing the other ML models, achieving coefficients of determination (R²) values of 0.68 and 0.79 for M1 and M2, respectively. The residual prediction deviation (RPD) values recorded were 1.77 for M1 and 2.18 for M2, and the ration of performance to inter-quartile range (RPIQ) values were 2.89 and 3.51, respectively. Moreover, M2 showed enhanced model accuracy for the used ML models, except for SVM. The application of MI on ETR significantly improved the predictive accuracy, with R² values of 0.75 for M1 and 0.82 for M2, In contrast, the application of RFE did not result in any improvement in the models effectiveness, as evidenced by R² values of 0.65 and 0.75 for M1 and M2, respectively. A comparison between the predicted points from the on-line scanning and ground truth maps shows varying levels of spatial similarity with a kappa value reaching 0.58. These results confirm the potential of integrating hyperspectral imaging with ML models for effective detection and spatial mapping of FHB in wheat fields.
Why it matches plant phenotyping methods小麦のFHB病害重症度をハイパースペクトル画像と機械学習で推定・空間マッピングし、独立検証とモデル比較を行う手法研究であり、植物状態の取得・推定が中心である。
titleField-based hyperspectral imaging for detection and spatial mapping of fusarium head blight in wheat
Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research. However, the lack of publicly available high-quality image datasets with detailed annotations has severely hindered the development of these models. In this work, we present a comprehensive multicultivar and multiview rice plant image dataset (CVRP) created from 231 landraces and 50 modern cultivars grown under dense planting in paddy fields. The dataset includes images capturing rice plants in their natural environment, as well as indoor images focusing specifically on panicles, allowing for a detailed investigation of cultivar-specific differences. A semiautomatic annotation process using deep learning models was designed for annotations, followed by rigorous manual curation. We demonstrated the utility of the CVRP by evaluating the performance of four state-of-the-art (SOTA) semantic segmentation models. We also conducted 3D plant reconstruction with organ segmentation via images and annotations. The database not only facilitates general-purpose image-based panicle identification and segmentation but also provides valuable resources for challenging tasks such as automatic rice cultivar identification, panicle and grain counting, and 3D plant reconstruction. The database and the model for image annotation are available at https://bic.njau.edu.cn/CVRP.html.
Why it matches plant phenotyping methodsイネ画像データセットとアノテーションモデルを開発・評価し、セグメンテーション、器官再構成、穂・粒数計測などの再利用可能な表現型解析を中心に扱っているため。
abstractwe present a comprehensive multicultivar and multiview rice plant image dataset (CVRP)
Reproduction assets foundThe paper's own CVRP rice image dataset (images + annotations), accompanying code, and trained Mask2Former annotation model are explicitly stated as publicly available on Hugging Face and the authors' NJAU site.Dataset · publicThe CVRP dataset is publicly available on Hugging Face at https://huggingface.co/datasets/CVRPDataset/CVRP for academic use under the specified license.Open asset ↗CVRPDataset/CVRPhtml-lines:236-252Code · publicThe accompanying code and trained models are available at https://huggingface.co/CVRPDataset/Model.Open asset ↗CVRPDataset/Modelhtml-lines:236-252Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Aboveground biomass (AGB) is important for monitoring crop growth and field management. Accurate estimation of AGB helps refine field strategies and advance precision agriculture. Remote sensing with Unmanned Aerial Vehicles (UAVs) has become an effective method for estimating key parameters of rice. This study involved four experiments conducted across varied locations and timeframes to collect field sampling data and UAV imagery. Feature extraction, including Vegetation Index (VI), textures, and canopy height, was performed. Key factors influencing biomass estimation across different rice organs were analyzed. Based on these insights, a Random Forest model was developed for AGB estimation. The VIS-Leaf factor-Spike factor-Stem factor (VIS-L-Sp-St) model proposed in this study outperformed traditional methods. The training set achieved an R² of 0.89 with a reduced RMSE of 191.30 g/m², surpassing the traditional VIS model (R²=0.64, RMSE=363.53 g/m²). Notably, in the validation set, the VIS-L-Sp-St model showed good transferability, with an R² of 0.85 and RMSE of 196.55 g/m², outperforming MLR (R²=0.02, RMSE=5944.09 g/m²), PLSR (R²=0.18, RMSE=934.27 g/m²) methods, BP (R²=0.14, RMSE=581.61 g/m²) method and SVM method((R²=0.45, RMSE=600.91 g/m²). Sensitivity analysis showed that different rice organs respond differently to specific features. This insight improves feature selection efficiency and enhances AGB estimation accuracy. The organ-specific AGB estimation model highlights its potential to support precision agriculture and field management, contributing to advancements in agricultural research and application.
Why it matches plant phenotyping methodsUAV画像から植生指数・テクスチャ・草高を抽出し、ランダムフォレストでイネの地上部バイオマスを推定する手法を開発・検証しており、表現型取得と推定が研究の中心である。
abstractFeature extraction, including Vegetation Index (VI), textures, and canopy height, was performed.
Location-based methods for counting rice panicles have often been underestimated, primarily due to their perceived inferior performance when compared to detection-based techniques. However, we argue that the potential of these location-based methods has not been fully realized, largely owing to the limitations of existing model architectures. In response to this challenge, we introduce LKNet, an innovative model developed on the foundation of the location-based framework P2Pnet. To enhance the performance of panicle counting across diverse types and growth stages, we implemented several key strategies. Firstly, we reconstructed the localization loss function as a predictive probability distribution to reduce the influence of manual labeling. Additionally, we dynamically adapted the receptive field to better accommodate different panicle types through the use of large kernel convolutional blocks. We evaluated LKNet on several publicly available counting task datasets and achieved state-of-the-art performance on the Diverse Rice Panicle Detection dataset. Furthermore, we employed a rice panicle dataset collected at an altitude of 7 m, which includes various panicle types and growth stages for model training and evaluation. The results showed that LKNet effectively accommodates variations in panicle morphology, with R 2 values ranging from 0.903 to 0.989. These findings highlight LKNet's potential to enhance precision in panicle counting in rice breeding programs.
Why it matches plant phenotyping methodsイネ穂の画像ベース計数モデルを開発し、複数データセットで評価しており、植物表現型取得・抽出手法が中心である。
abstractwe introduce LKNet, an innovative model developed on the foundation of the location-based framework P2Pnet.
Reproduction assets foundThe paper's authors explicitly state that the LKNet analysis code is publicly available on GitHub, matching an allowed URL. No separate phenotype dataset deposit by the authors is stated (the 7 m rice panicle dataset and public benchmarks like DPRD/MTC/SHTech are described but no authors' dataset URL is given).Code · publicCode is available at https://github.com/L129921/LKnet .Open asset ↗LKnetlines:297-319Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Aegilops tauschii Coss., a progenitor of bread wheat, is an important wild genetic resource for breeding. The species comprises three genetically defined lineages (TauL1, TauL2, and TauL3), each displaying distinctive phenotypes in various agronomic traits, including spike shape. In the present work, we studied the relationship between population structure and spike shape variation patterns using a collection of 249 accessions. f4-statistics-based ancestry profiling confirmed the previously identified lineages and revealed a genetic component derived from TauL3 in the genomes of some southern Caspian and Transcaucasus TauL1 and TauL2 accessions. Spike shape variation patterns were analyzed using a convolutional neural network-based approach, trained on green and dry spike image datasets. This analysis showed that spike shape diversity is structured according to lineages and demonstrated that the lineages can be distinguished based on spike shape. The implications of these findings for the origins of common wheat and the intraspecific taxonomy of Ae. tauschii are discussed. Plain Language SummaryWild wheat, Aegilops tauschii, represents a vast reservoir of alleles that have not yet been utilized in breeding. These alleles may confer beneficial phenotypes, such as drought tolerance and disease resistance, when introduced into bread wheat. To fully leverage this reservoir, it is essential to quickly identify strains with potentially useful alleles. In Ae. tauschii, which consists of strain groups (lineages) with unique genetic makeups, this can be done by determining a strains lineage based on spike shape. In this work, we trained machine learning models for this purpose and found that spike shape diversity reflects lineage structure. These models demonstrated potential for practical use in assigning strains to their respective lineages based on spike shape. Our work opens new avenues for the application of machine learning in wheat improvement, as well as in the genetic and evolutionary studies of wheat morphology.
Why it matches plant phenotyping methodsCNNで緑色および乾燥スパイク画像から穂形状を解析・分類する手法を開発し、系統識別への有用性を検証しており、植物表現型取得・抽出が研究の中心である。
abstractSpike shape variation patterns were analyzed using a convolutional neural network-based approach, trained on green and dry spike image datasets.
Accurate detection of missed tassels is crucial for maintaining the purity of hybrid maize seed production. This study introduces the MT-YOLO model, designed to replace or assist manual detection by leveraging deep learning and unmanned aerial systems (UASs). A comprehensive dataset was constructed, informed by an analysis of the agronomic characteristics of missed tassels during the detasseling period, including factors such as tassel visibility, plant height variability, and tassel development stages. The dataset captures diverse tassel images under varying lighting conditions, planting densities, and growth stages, with special attention to early tasseling stages when tassels are partially wrapped in leaves-a critical yet underexplored challenge for accurate detasseling. The MT-YOLO model demonstrates significant improvements in detection metrics, achieving an average precision (AP) of 93.1%, precision of 93.3%, recall of 91.6%, and an F1-score of 92.4%, outperforming Faster R-CNN, SSD, and various YOLO models. Compared to the baseline YOLO v5s, the MT-YOLO model increased recall by 1.1%, precision by 4.9%, and F1-score by 3.0%, while maintaining a detection speed of 124 fps. Field tests further validated its robustness, achieving a mean missed rate of 9.1%. These results highlight the potential of MT-YOLO as a reliable and efficient solution for enhancing detasseling efficiency in hybrid maize seed production.
Why it matches plant phenotyping methodsトウモロコシの雄穂という植物器官をUAS画像から検出する深層学習手法を開発・比較・圃場検証しており、植物状態の画像計測が研究の中心である。
abstractThis study introduces the MT-YOLO model, designed to replace or assist manual detection by leveraging deep learning and unmanned aerial systems (UASs).
Monitoring sorghum during the flowering stage is essential for effective fertilization management and improving yield quality, with spike identification serving as the core component of this process. Factors such as varying heights and weather conditions significantly influence the accuracy of sorghum spike detection models, and few comparative studies exist on model performance under different conditions. YOLO (You Only Look Once) is a deep learning object detection algorithm. In this research, images of sorghum during the flowering stage were captured at two heights (15 m and 30 m) in 2023 via a UAV and utilized to train and evaluate variants of YOLOv5, YOLOv8, YOLOv9, and YOLOv10. This investigation aimed to assess the impact of dataset size on model accuracy and predict sorghum flowering stages. The results indicated that YOLOv5, YOLOv8, YOLOv9, and YOLOv10 achieved mAP@50 values of 0.971, 0.968, 0.967, and 0.965, respectively, with dataset sizes ranging from 200 to 350. YOLOv8m performed best on 15 sunny and 15 cloudy clouds and, overall, exhibited superior adaptability and generalizability. The predictions of the flowering stage using YOLOv8m were more accurate at heights between 12 and 15 m, with R 2 values ranging from 0.88 to 0.957 and rRMSE values between 0.111 and 0.396. This research addresses a significant gap in the comparative evaluation of models for sorghum spike detection, identifies YOLOv8m as the most effective model, and advances flowering stage monitoring. These findings provide theoretical and technical foundations for the application of YOLO models in sorghum spike detection and flowering stage monitoring. These findings provide a technical means for the timely and efficient management of sorghum flowering.
Why it matches plant phenotyping methodsUAV画像とYOLOモデルを用いたソルガム穂の検出および開花期推定を中心に、複数モデルの精度比較・検証を行っており、植物状態の画像ベース計測手法として中核的です。
abstractThis research addresses a significant gap in the comparative evaluation of models for sorghum spike detection, identifies YOLOv8m as the most effective model, and advances flowering stage monitoring.
ABSTRACT Quinoa is a grain crop with excellent nutritional properties that has attracted global attention for its potential contribution to future food security in a changing climate. Despite its long history of cultivation, quinoa has been improved little by modern breeding and is a niche crop outside its native cultivation area. Grain yield is strongly affected by panicle traits, whose phenotypic analysis is time consuming and prone to error because of their complex architecture, and automated image analysis is an efficient alternative. We designed a panicle phenotyping pipeline implemented in Python via mask R‐convolutional neural networks for panicle segmentation and classification. After model training, we analysed 5151 images of quinoa panicles collected over three consecutive seasons from a breeding programme in the Peruvian highlands. The pipeline follows a stagewise approach, which first selects the optimal segmentation model and then another model that best classifies panicle shape. The best segmentation model achieved a mean average precision (mAP) score of 83.16 and successfully extracted the panicle length, width, area and RGB values. The classification model achieved 95% prediction accuracy for the amarantiform and glomerulate panicle types. A comparison with manual trait measurements using ImageJ revealed a high correlation for panicle traits (r > 0.94, p < 0.001). We used the pipeline with images from multilocation trials to estimate genetic variance components of an index on the basis of panicle length and width. We further updated the model for images that included metric scales taken in field trials to extract metric measurements of panicle traits. Our pipeline enables accurate and cost‐effective phenotyping of quinoa panicles. Using automated phenotyping based on deep learning, optimal panicle ideotypes can be selected in quinoa breeding and improve the competitiveness of this underutilized crop.
Why it matches plant phenotyping methodsキヌア穂の画像取得・セグメンテーション・形状分類・形質抽出を中核とする高スループット表現型解析パイプラインを開発し、手動測定との相関で検証しているため。
abstractWe designed a panicle phenotyping pipeline implemented in Python via mask R‐convolutional neural networks for panicle segmentation and classification.
Wheat ( Triticum aestivum L.) is one of the significant food crops in the world, and the number of wheat ears serves as a critical indicator of wheat yield. Accurate quantification of wheat ear counts is crucial for effective scientific management of wheat fields. To address the challenges of missed detections, false detections, and diminished detection accuracy arising from the dense distribution, small size, and high overlap of wheat ears in Unmanned Aerial Vehicle (UAV) imagery, we propose a lightweight model, PSDS-YOLOv8 (P2-SPD-DySample-SCAM-YOLOv8), on the basis of the improved YOLOv8 framework, for the accurate detection of wheat ears in UAV images. First, the high resolution micro-scale detection layer (P2) is introduced to enhance the model's ability to recognize and localize small targets, while the large-scale detection layer (P5) is eliminated to minimize computational redundancy. Then, the Spatial Pyramid Dilated Convolution (SPD-Conv) module is employed to improve the ability of the network to learn features, thereby enhancing the representation of weak features of small targets and preventing information loss caused by low image resolution or small target sizes. Additionally, a lightweight dynamic upsampler, Dynamic Sample (DySample), is introduced to decrease computational complexity of the upsampling process by dynamically adjusting interpolation positions. Finally, the lightweight module Spatial Context-Aware Module (SCAM) is utilized to accurately map the connection between small targets and global features, enhancing the discrimination of small targets from the background. Experimental results demonstrate that the improved PSDS-YOLOv8 model achieves Mean Average Precision(mAP) 50 and mAP50:95 scores of 96.5% and 55.2%, which increases by 2.8% and 4.4%, while the number of parameters is reduced by 40.6% in comparison with the baseline YOLOv8 model. Compared to YOLOv5, YOLOv7, YOLOv9, YOLOv10, YOLOv11, Faster RCNN, SSD, and RetinaNet, the improved model demonstrates superior accuracy and fewer parameters, exhibiting the best overall performance. The methodology proposed in this study enhances model accuracy while concurrently reducing resource consumption and effectively addressing the issues of missed and false detections of wheat ears, thereby providing technical support and theoretical guidance for intelligent counting of wheat ears in UAV imagery.
Why it matches plant phenotyping methodsUAV画像から小麦穂数という植物形態・収量関連形質を抽出する改良YOLOモデルを開発し、複数モデルとの性能比較で技術的に検証しているため、方法開発が中心である。
abstractwe propose a lightweight model, PSDS-YOLOv8 (P2-SPD-DySample-SCAM-YOLOv8), on the basis of the improved YOLOv8 framework, for the accurate detection of wheat ears in UAV images.
Background Rice blast is one of the most destructive diseases in rice cultivation, significantly threatening global food security. Timely and precise detection of rice panicle blast is crucial for effective disease management and prevention of crop losses. This study introduces ConvGAM, a novel semantic segmentation model leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM). This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. Furthermore, the model incorporates advanced loss functions to handle data imbalances effectively, supporting accurate classification across diverse disease severities. Results The ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. Quantitative evaluation demonstrates that the model achieves an overall accuracy of 91.4%, a mean IoU of 79%, and an F1 score of 82% on the test set. The incorporation of Focal Tversky Loss further enhances the model's ability to handle imbalanced datasets, improving detection accuracy for rare and severe disease categories. Correlation coefficient analysis across disease severity levels indicates high consistency between predictions and ground truth, with values ranging from 0.962 to 0.993. These results confirm the model's reliability and robustness, highlighting its effectiveness in rice panicle blast detection under challenging conditions. Conclusion The ConvGAM model demonstrates strong qualitative advantages in detecting rice panicle blast disease. By integrating advanced feature extraction with the ConvNeXt-Large backbone and GAM, the model achieves precise detection and classification across varying disease severities. The use of Focal Tversky Loss ensures robustness against dataset imbalances, enabling accurate identification of rare disease categories. Despite these strengths, future efforts should focus on improving classification accuracy and adapting the model to diverse environmental conditions. Additionally, optimizing model parameters and exploring advanced data augmentation techniques could further enhance its detection capabilities and expand its applicability to broader agricultural scenarios.
Why it matches plant phenotyping methodsUAV画像からイネ穂いもち病の病徴・重症度を推定するセマンティックセグメンテーション手法を開発し、精度評価しており、植物状態の取得・抽出が中心である。
abstractThis study introduces ConvGAM, a novel semantic segmentation model leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM).
Maize anthers emerge from male-only florets, a process that involves complex genetic programming and is affected by environmental factors. Quantifying anther exertion provides a key indicator of male fertility; however, traditional manual scoring methods are often subjective and labor-intensive. To address this limitation, we developed Tasselyzer - an accessible, cost-effective, and time-saving method for quantifying maize anther exertion. This image-based program uses the PlantCV platform to provide a quantitative assessment of anther exertion by capturing regional differences within the tassel based on the distinct color of anthers. We applied this method to 22 maize lines with six genotypes, showing high precision (F 1 score > 0.8). Furthermore, we demonstrate that customizing the parameters to assay a specific line is straightforward and practical for enhancing precision in additional genotypes. Tasselyzer is a valuable resource for maize research and breeding programs, enabling automated and efficient assessments of anther exertion.
Why it matches plant phenotyping methods画像解析と機械学習により、雄性不稔性に関わるトウモロコシ葯の突出度を定量化する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractwe developed Tasselyzer - an accessible, cost-effective, and time-saving method for quantifying maize anther exertion.
The maize tassel represents one of the most pivotal organs dictating maize yield and quality. Investigating its phenotypic information constitutes an exceedingly crucial task within the realm of breeding work, given that an optimal tassel structure is fundamental for attaining high maize yields. High-throughput phenotyping technologies furnish significant tools to augment the efficiency of analyzing maize tassel phenotypic information. Towards this end, we engineered a fully automated multi-angle digital imaging apparatus dedicated to maize tassels. This device was employed to capture images of tassels from 1227 inbred maize lines falling under three genotype classifications (NSS, TST, and SS). By leveraging the 3D reconstruction algorithm SFM (Structure from Motion), we promptly obtained point clouds of the maize tassels. Subsequently, we harnessed the TreeQSM algorithm, which is custom-designed for extracting tree topological structures, to extract 11 archetypal structural phenotypic parameters of the maize tassels. These encompassed main spike diameter, crown height, main spike length, stem length, stem diameter, the number of branches, total branch length, average crown diameter, maximum crown diameter, convex hull volume, and crown area. Finally, we compared the GFC (Gaussian Fuzzy Clustering algorithm) used in this study with commonly used algorithms, such as RF (Random Forest), SVM (Support Vector Machine), and BPNN (BP Neural Network), as well as k-Means, HCM (Hierarchical), and FCM (Fuzzy C-Means). We then conducted a correlation analysis between the extracted phenotypic parameters of the maize tassel structure and the genotypes of the maize materials. The research results showed that the Gaussian Fuzzy Clustering algorithm was the optimal choice for clustering maize genotypes. Specifically, its classification accuracies for the Non-Stiff Stalk (NSS) genotype and the Tropical and Subtropical (TST) genotype reached 67.7% and 78.5%, respectively. Moreover, among the materials with different maize genotypes, the number of branches, the total branch length, and the main spike length were the three indicators with the highest variability, while the crown volume, the average crown diameter, and the crown area were the three indicators with the lowest variability. This not only provided an important reference for the in-depth exploration of the variability of the phenotypic parameters of maize tassels but also opened up a new approach for screening breeding materials.
Why it matches plant phenotyping methodsトウモロコシ雄穂の3D画像取得、再構成、構造形質抽出を行う自動フェノタイピング装置と解析ワークフローが研究の中心であるため。
abstractwe engineered a fully automated multi-angle digital imaging apparatus dedicated to maize tassels.
Maize tassel is a crucial pollen producing organ that plays an important role in maize production. It is challenging to investigate the morphological and structural traits of maize tassels for breeding programs, since traditional manual measurements of organ-scale phenotypic traits are labor-intensive and prone to human errors. It is, therefore, urgent to design and develop new phenotyping systems for maize tassels to improve throughput and measurement accuracy. This study first introduced the TreeQSM in crop phenotyping at organ scale and developed TIPS (TreeQSM based Image Phenotyping System for maize tassels). The system mainly consists of three digital cameras for acquiring multi-view images of individual maize tassel. These cameras were vertically arranged with different angles of view. The acquired images were used to reconstruct the 3D point cloud data of individual maize tassel, which were fed into the TreeQSM to extract four tassel phenotypic parameters including trunk length, branch number, branch length, and branch angle. The performance analyses of the developed system were conducted on 52 tassel samples from 37 maize materials with different canopy geometric structures. The experimental results showed that the TIPS could achieve accurate tassel parameters estimation with the R² of 0.964, 0.973, 0.935, and 0.857, and the RMSE of 1.72, 1.28, 19.94, and 2.73 for the trunk length, branch number, total branch length, and first node branch angle, respectively. This study also investigated the advantages of the data acquisition with the TIPS and the effects of different shooting angles, number of images, lighting conditions and tassel types on the four tassel parameters estimation accuracy. The comparison results indicated that the four influencing factors reduced the estimation accuracy to varying degree. Compared with the other three parameters, the estimation accuracy of branch angles less than 20 were largely affected. The higher the degree of compactness, the worse the estimation accuracy. Compared with shooting angles, the reduction of the number of images in a certain range had less impact on the quality of 3D point cloud reconstruction. The influence of low light was obviously greater than that of strong light. This study provides a valuable guide to the collection and quantitative analysis of high-throughput phenotype information of maize tassels in breeding program.
Why it matches plant phenotyping methodsトウモロコシ雄穂の3次元画像計測システムを開発し、TreeQSMによる形態形質抽出、精度検証、撮影条件の影響評価を行っており、フェノタイピング手法が研究の中心である。
abstractThis study first introduced the TreeQSM in crop phenotyping at organ scale and developed TIPS (TreeQSM based Image Phenotyping System for maize tassels).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This Data Note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. FINDINGS: We provide time-series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across 6 years. Measurement data for 8 key wheat traits are included-namely, canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. CONCLUSIONS: This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.
Why it matches plant phenotyping methods4,000以上の小麦プロットについて高解像度画像時系列と複数の植物形質を統合した公開データセットが主題であり、植物表現型研究向けのデータ資源として中心的です。
abstractThis Data Note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data.
Numerous studies have reported a significant positive correlation between wheat yield and the quantity of wheat heads. However, collecting data on wheat heads in the field poses a challenge for several reasons, including the uncontrollable nature of the environment, inconsistent data quality, and ambiguous data truth. To address these challenges, we developed a simulation strategy to replicate the conditions of a real wheat field, which enabled the data collection process to be conducted indoors over a short period. After applying grayscale image processing to process the simulated wheat images, we trained and tested nine deep learning models: Faster-RCNN, YOLOv7, YOLOv8, CenterNet, SSD, RetinaNet, EfficientDet, Deformable-DETR and DINO. Our results indicated that YOLOv7 performed the best (R² = 0.963, RMSE = 2.463). We then compared our model trained on simulated wheat data to a model trained on real wheat data (R² = 0.963 vs 0.972, RMSE = 2.463 vs 2.692). We also achieved good model performance on five test sets: GWHD, SDAU2021-SDAU2024. The results demonstrated the efficacy of our simulation, which provides an efficient and convenient strategy for the precision agriculture community.
Why it matches plant phenotyping methods小麦穂数という植物形態形質の画像取得・推定手法を、シミュレーション画像、画像処理、複数検出モデルの比較、実画像および公開データセットでの評価を通じて開発・検証しており、フェノタイピング手法が中心である。
abstractwe developed a simulation strategy to replicate the conditions of a real wheat field, which enabled the data collection process to be conducted indoors over a short period.
Selective harvesting robots for broccoli face significant challenges in field operations, where occlusions by leaves and stems, varying maturity stages and lighting interferences greatly affect performance. Addressing the need for a robust network capable of maturity recognition and localisation under various occlusion conditions for spherical crops, OccluInst-a single-stage instance segmentation network based on RGB-D and CNN-Transformer architecture was proposed. The solution is to make full use of visible information and crop characteristics. This model builds a dual-branch cross-modal calibration framework to generate instance-aware kernels and segmentation mask features. The proposed Attention Weight Interactive Fusion Module (AWIF) enhances the fusion efficiency of multi-scale RGB and depth features in complex scenarios, while the designed Adaptive Fusion Ratio Module (AFR) filters out noisy depth data and extracts valuable information to achieve feature alignment. Additionally, the developed Material Awareness Module (MA) highlights critical areas, improving feature extraction for irregular, multi-scale targets. The improved circular boundary anchor box accurately localises broccoli under various levels of occlusion. Ablation studies confirm the effectiveness of each module. OccluInst can swiftly and accurately identify the maturity categories and coordinates of broccoli under different occlusion levels. It achieves a mAP₅₀ of 86.2% and mAR of 83.5%, with an average centre point deviation of 3.68 pixels on images with a resolution of 848×480, and a detection speed of 51.4 frames per second, providing a robust visual foundation for selective harvesting robots.
Why it matches plant phenotyping methodsRGB-D画像からブロッコリーの成熟カテゴリーという植物状態を推定するセグメンテーション手法を開発し、遮蔽条件下で性能評価している。単なる収穫対象の位置検出にとどまらず、成熟度推定が中心的である。
abstractOccluInst can swiftly and accurately identify the maturity categories and coordinates of broccoli under different occlusion levels.
Among the rice varieties developed for different purposes, Basmati varieties are unique for their morphological characters and quality. The origin, evolution and development of Basmati varieties has thrown challenges in terms of varietal classification and correct identification. Besides the classical method used in DUS testing for variety identification, new method consisting of whole plant images using deep learning algorithms was studied to identify basmati rice varieties. Classification of varieties by images of whole plant at different growth stages using deep learning algorithms was carried out to find the best algorithm and the best stage for effective discrimination of varieties. The ripening stage (terminal panicles ripened) was identified as the most suitable stage for effective classification of the varieties among the four stages namely, booting stage, 50% flowering, milk stage and ripening stage. The testing accuracy of all algorithms ranged between 60 to 73%. The testing accuracy at the ripening stage was found to be 73% using VGG 16, a deep learning model. Pusa Basmati 1609 and Pusa Basmati 1637 were identified with 100% accuracy. High testing accuracy was observed in identifying some other varieties namely, Pusa Basmati 1121, Pusa Basmati 1401, Pusa Basmati 1609, Pusa Sugandh 3. There was a high chance of misclassification among the genetically close varieties. Genetically close varieties that could not be differentiated using leaf and panicle characteristics, could be classified up to 90% accuracy using plant images and VGG 16. From this study it is concluded that plant image analysis by deep learning methods can be a viable alternative approach for identification of rice varieties.
Why it matches plant phenotyping methods水稲の全草画像と深層学習による品種識別手法を開発・比較しており、植物画像から品種状態を抽出する方法が研究の中心である。
abstractnew method consisting of whole plant images using deep learning algorithms was studied to identify basmati rice varieties.
WheatLiDAR / point cloudPanicle / ear / spikeLeafSegmentation
Plant phenotyping is crucial for precisely measuring traits across diverse morphotypes in cereals such as wheat, where the variability in the proportion of ears to leaves poses significant challenges due to class imbalances and the granularity limitations of 3D point cloud data. Our study addresses these intra-class imbalances through two strategies using the PointNet++ network: use plant features i.e., ear ratio and ear count for weighted point cloud sampling and apply class weights in the loss function. We analyze datasets from three morphologically distinct wheat varieties: Paragon, Gladius, and Apogee. Introducing point cloud weights based on ear ratio and ear count significantly improves the network’s ability to recognize underrepresented parts in datasets with uneven distributions of ear and non-ear points. We observe a differential impact in all the categories in terms of segmentation accuracy and average mIoU. In comparison to the base method, all the wheat categories display enhancement in performance after applying both techniques. The best results are obtained with point cloud weights in the loss function for the Gladius dataset, showing a substantial improvement of 10% to 12% in average ear mIoU compared to base method (0.483). Although a similar trend is observed with weighted sampling, the enhanced results in the Gladius dataset range from 0.611 to 0.626 indicate model’s enhanced capability to identify different segments or parts precisely. This research demonstrates the efficacy of our methods in addressing class imbalance issues in point cloud segmentation and provides enhanced accuracy for particularly across different wheat genotypes. In conclusion, the techniques in this study can reliably handle intra class imbalance in diverse datasets, which offers a scalable solution to improve agricultural phenotyping.
Why it matches plant phenotyping methods小麦の3D点群による器官セグメンテーションを対象に、クラス不均衡への重み付きサンプリングと損失関数を開発・評価しており、植物表現型取得の計算手法が研究の中心である。
abstractOur study addresses these intra-class imbalances through two strategies using the PointNet++ network: use plant features i.e., ear ratio and ear count for weighted point cloud sampling and apply class weights in the loss function.
The rice sector is facing the challenge of increasing rice yields while maintaining or improving input use efficiency. The purpose of this study was to determine the most effective vegetation indices for monitoring nitrogen uptake (N uptake) under different irrigation techniques. The study was conducted in Uruguay over two rice-growing seasons. A split plot experimental design featured two irrigation treatments (main plots): continuous flooding (C) and alternate wetting and drying (AWD). The nitrogen-rate (N-rate) treatments (split plots) included no nitrogen, the recommended N-rate based on soil analyses, and two additional doses (±50% of the recommendation). The plant N uptake relationships with selected drone-based vegetation indices (VIs) were assessed at panicle initiation. The presence or absence of standing water during image collection affected the VIs and their relationships with N uptake. The relationships estimated for traditional irrigation may not be applicable for AWD. The SCCCI was the top index with a significantly stronger relationship with N uptake under the C (R2 = 0.84) and AWD (R2 = 0.71) irrigation techniques in relation to all evaluated vegetation indices. The Clre, NDRE2, NDRE, and CLg also had a significant relationship with N uptake under both irrigation treatments in both seasons, though their average R2 values of 0.75, 0.74, 0.73, and 0.71, respectively, were lower than the SCCCI (average R2 = 0.78). The findings would assist rice growers for selecting effective VIs for remote crop monitoring.
Why it matches plant phenotyping methodsドローン画像由来の植生指数を比較・評価し、イネの窒素吸収量という植物生理状態の推定における有効性を検証しており、フェノタイピング手法が中心である。
abstractThe purpose of this study was to determine the most effective vegetation indices for monitoring nitrogen uptake (N uptake) under different irrigation techniques.
This study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. Through comparative experiments with various advanced models, we comprehensively evaluate the performance of our model. In the disease detection task, our model performs excellently, achieving a precision of 0.93, a recall of 0.89, an accuracy of 0.91, and an mAP of 0.90. By introducing the density loss function, we are able to effectively improve the detection accuracy when dealing with high-density regions. In the wheat spike counting task, the model similarly demonstrates a strong performance, with a precision of 0.91, a recall of 0.88, an accuracy of 0.90, and an mAP of 0.90, further validating its effectiveness. Furthermore, this paper also conducts ablation experiments on different loss functions. The results of this research provide a new method for wheat spike counting and disease detection, fully reflecting the application value of deep learning in precision agriculture. By combining the probability density attention mechanism and the density loss function, the proposed model significantly improves the detection accuracy and efficiency, offering important references for future related research.
Why it matches plant phenotyping methodsコムギ穂数の計数と植物病害の検出を対象に、深層学習モデルと特徴抽出・損失関数を開発し、比較およびアブレーション実験で評価しており、表現型取得手法が中心である。
abstractwe propose an advanced feature extraction strategy and a model based on the probability density attention mechanism
Dissecting the drought resistance (DR) mechanism and designing drought-resistant rice varieties are promising strategies to address the challenge of climate change. Here, we selected a typical drought-avoidant (DA) variety IRAT109 and drought-tolerant (DT) variety Hanhui15 as the parents to develop a stable recombinant inbred line (RIL) population (F 8 , 1,262 lines). The de novo assembled genomes of both parents were released. Through re-sequencing of the RIL population, a set of 1,189,216 reliable SNPs were obtained and used for constructing a dense genetic map. Using both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period and identified 32,586 drought-responsive quantitative trait loci (QTLs) including 2,097 unique QTLs. The QTLs related to panicle i-traits occurred on the middle of chromosome 8 over 600 times, while the QTLs related to leaf i-traits on the 5’ end of chromosome 3 over 800 times, indicating potential effect of these QTLs on plant phenotypes. We chose three candidate genes ( OsMADS50, OsGhd8, OsSAUR11 ) related to leaf, panicle, and root traits respectively and verified their functions in resisting drought. Gene OsMADS50 was found to negatively regulate DR by modulating leaf dehydration, grain size, and root downward growth. Furthermore, a total of 18 and 21 composite QTLs significantly related to grain weight and plant biomass were screened from 597 lines in RIL population under drought conditions in field experiments, and composite QTL region was highly overlapped (76.9%) with known DR gene region. Based on three candidate DR genes, we proposed the haplotype design suitable for different environments and breeding objectives. This study provides a valuable reference for multi-modal and time-series phenomic analyses, deciphers the genetic mechanism of DA and DT rice varieties, and offers a molecular navigation map for breeding DR variety.
Why it matches plant phenotyping methods地下・地上フェノミックプラットフォームとマルチモーダルカメラで全生育期間の画像形質を大量取得しており、フェノタイピング手法の適用と技術的ワークフローが研究の中核です。
abstractUsing both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period
Reproduction assets foundThe paper's phenome data (aboveground and belowground rice images/i-traits) and the authors' data-handling code and deep-learning model are explicitly deposited at public URLs listed in the Data Availability Statement. Genome data (riceome.hzau.edu.cn) is molecular omics and excluded.Code · publicAll the phenome data and core data-handling code have been deposited online.Open asset ↗lines:140-175Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
The rice panicle traits substantially influence grain yield, making them a primary target for rice phenotyping studies. However, most existing techniques are limited to controlled indoor environments and have difficulty in capturing the rice panicle traits under natural growth conditions. Here, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field based on the video acquired by the smartphone. The proposed method combined the large model Segment Anything Model (SAM) and the small model You Only Look Once version 8 (YOLOv8) to achieve high-precision segmentation of rice panicle images. The neural radiance fields (NeRF) technique was then employed for 3D reconstruction using the images with 2D segmentation. Finally, the resulting point clouds are processed to successfully extract panicle traits. The results show that PanicleNeRF effectively addressed the 2D image segmentation task, achieving a mean F1 score of 86.9% and a mean Intersection over Union (IoU) of 79.8%, with nearly double the boundary overlap (BO) performance compared to YOLOv8. As for point cloud quality, PanicleNeRF significantly outperformed traditional SfM-MVS (structure-from-motion and multi-view stereo) methods, such as COLMAP and Metashape. The panicle length was then accurately extracted with the rRMSE of 2.94% for indica and 1.75% for japonica rice. The panicle volume estimated from 3D point clouds strongly correlated with the grain number ( R 2 = 0.85 for indica and 0.82 for japonica ) and grain mass (0.80 for indica and 0.76 for japonica ). This method provides a low-cost solution for high-throughput in-field phenotyping of rice panicles, accelerating the efficiency of rice breeding.
Why it matches plant phenotyping methodsスマートフォン動画、画像セグメンテーション、NeRFによる3D再構成を統合し、イネ穂の形質を抽出する手法を開発・検証した研究であり、植物フェノタイピング手法が中心である。
abstractHere, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field based on the video acquired by the smartphone.
Wheat yield is positively correlated with the number of wheat spikes in the field, which is an essential index for plant breeders. To efficiently calculate this index, there is a high demand for precise and automatic plant phenotyping methods that quantify images. As a tool for estimating crop yields, machine vision is rapidly advancing. In the majority of crop phenotyping fields, however, annotating thousands of small objects using bounding boxes or polygons is extremely time- and labor-intensive. This study aims to develop a state-of-the-art framework for localizing and counting wheat spikes using dotted annotation datasets in conjunction with Gaussian and constant density map generation algorithms. In addition, we developed hybrid UNet architectures as the computational component, including VGG16-UNet, ResNet34-UNet, ResNet50-UNet, and ResNeXt-UNet. Furthermore, we improved the performance of wheat counting by employing a hybrid density map estimation-non-maximal supervision algorithm. Wheat images from the ACID and GWHD datasets are used to evaluate the proposed models. With F1 score and Mean Absolute Percentage Error (MAPE) of 0.96 and 1.68%, respectively, the results from the ACID dataset demonstrate a significant improvement in spike localization and counting compared to previous research studies. Additionally, to assess the proposed models’ generalizability in real-world modeling scenarios, the ACID-based pretrained models are used to predict the more complex in-field GWHD dataset. With an F1 score of 0.57 and a MAPE of 2.302%, the pretrained models were able to localize and count the wheat spikes in the new dataset. Following that, the ACID-based pretrained models are retrained for a few epochs using the GWHD dataset’s small training sample size. The results demonstrate a significant improvement in the proposed model’s performance, with an F1 score of 0.91 and a MAPE of 1.56%.
Why it matches plant phenotyping methods小麦穂数の画像ベース局在化・計数という植物形質推定法を開発し、複数データセットで性能と汎化性を評価しており、フェノタイピング手法が中心である。
abstractThis study aims to develop a state-of-the-art framework for localizing and counting wheat spikes using dotted annotation datasets in conjunction with Gaussian and constant density map generation algorithms.
Accurately counting the number of grains per panicle is crucial for evaluating rice yield and selecting superior germplasm resources. Traditional measurement methods are labor-intensive, time-consuming, and prone to errors. To address this challenge, computer vision-based methods have emerged as a promising approach for seed counting. However, achieving precise grain counting is particularly challenging due to their natural morphology, which involves occlusion and substantial variations in size, shape and orientation. This often requires additional steps, such as manual shaping or threshing. Therefore, we propose an innovative approach for precisely counting rice grains in their natural form by integrating object detection, image classification and regression equations. Initially, we trained the Yolov7-tiny model for grain counting. Subsequently, we introduced a classification system based on the variability in the natural morphology of rice panicles using the EfficientNetV2 network, enabling the classification of rice panicles into five distinct classes. Furthermore, we devised a set of univariate linear regression equations for the different classes of rice panicles, utilizing data from 2920 diverse rice germplasm to establish correlations predicted and actual values. Experimental findings demonstrated a counting accuracy of 92.60% with an average absolute percentage error of 7.69%. And, this study revealed that utilizing two-sided images of panicles did not significantly improve counting accuracy. This study represents a successful endeavor in achieving precise and efficient counting of rice panicles within their natural morphology, offering a novel solution for detecting and counting dense objects.
Why it matches plant phenotyping methodsイネ穂の粒数という植物形質を、画像分類・物体検出・回帰により非破壊推定する手法を開発し、精度評価も行っており、フェノタイピング手法が中心である。
abstractwe propose an innovative approach for precisely counting rice grains in their natural form by integrating object detection, image classification and regression equations.
Fusarium Head Blight (FHB) is a devastating disease of wheat worldwide. It is an explosive epidemic disease that can severely reduce or even fail wheat production. Estimating the disease ear rate and disease severity is crucial for effective plant protection. Manual assessment is labor-intensive and time-consuming. Accurately and quickly segmenting wheat ears and areas affected by Fusarium head blight (FHB) in complex field environments is essential for quantitative assessment of wheat trait phenotypes and FHB in wheat plants. This paper presents DeepFHB, an automated method for efficiently detecting, locating, and segmenting dense wheat spikes and diseased areas in digital images captured under natural field conditions. The experiment consists of three steps:Firstly, the process begins by generating initial coarse-grained mask predictions at lower resolutions to provide a rough segmentation. Secondly, a quadtree-based method is employed to identify and refine multi-scale inconsistent regions. Finally, a transformer-based refinement network is introduced to predict highly accurate instance segmentation masks. The results demonstrate that the DeepFHB algorithm outperforms traditional methods in detecting and segmenting diseased areas. Our DeepFHB model achieves state-of-the-art single-model results of 64.408 box AP and 64.966 mask AP on the FHB-SA dataset. This study is capable of rapidly and accurately segmenting wheat spikes and wheat scab lesions in agricultural scenarios with high field density, high crop occlusion, and high background interference. This provides a foundation for subsequent targeted research to assist agricultural workers in assessing the severity of wheat diseases.
Why it matches plant phenotyping methods小麦穂および赤かび病病斑を画像から自動検出・セグメンテーションし、病害率・重症度という植物形質を定量化する手法が研究の中心である。
abstractAccurately and quickly segmenting wheat ears and areas affected by Fusarium head blight (FHB) in complex field environments is essential for quantitative assessment of wheat trait phenotypes and FHB in wheat plants.
The dynamics of maize tassel area reflect the growth and development of maize plants, monitoring which facilitates crop breeding and management. At present, the monitoring of maize tassels mainly depends on manual work, which is very labor intensive and may be biased by human errors. The U-Net model has proved effective for crop segmentation using RGB imagery. However, there has not been a systematic study to test how the accuracy of U-Net model vary when applied to different maize varieties, at different tasseling stages, and on images of different spatial resolutions. Moreover, the capability of U-Net model for monitoring the dynamics of tassel area has not been explored. In this study, the potential of the U-Net model to provide an accurate segmentation of the tassels in complex situations from near-ground RGB images and UAV images were comprehensively studied. The results showed that the segmentation accuracy of U-Net model with Vgg16 as feature extraction network (IoU = 0.71) for tassels at the whole tasseling stages was better than that of U-Net model with MobileNet (IoU = 0.63). The U-Net model with Vgg16 as the feature extraction network maintained a good segmentation accuracy for maize tassels at different tasseling stages (IoU = 0.63–0.76), for different varieties (IoU = 0.65–0.79), and at different resolutions (IoU = 0.57–0.71), which proved the robustness of the model. Changes in the segmented area of tassels from images were basically consistent with the trends observed in the actual area of tassel measured manually. UAV RGB images with resolution of 3.06 mm showed a good segmentation accuracy (IoU = 0.54). In summary, the results showed that the U-Net model has a good segmentation accuracy of maize tassels under various complex situations. This study provides an effective method to monitor the maize tassel status in crop phenotyping experiments in the future.
Why it matches plant phenotyping methodsトウモロコシ雄穂面積という植物形質を、近接RGB画像およびUAV画像からU-Netで抽出・推定する手法を開発し、品種・生育段階・解像度間で精度と頑健性を検証しており、フェノタイピング手法が中心である。
abstractIn this study, the potential of the U-Net model to provide an accurate segmentation of the tassels in complex situations from near-ground RGB images and UAV images were comprehensively studied.
Wheat scab is a highly destructive disease that adversely impact wheat crops throughout their growth cycle. It is crucial to promptly evaluate the levels of wheat scab in the field to prevent its spread. Manual observation, however, is inefficient and time-consuming. Recent research has indicated that computer vision-based methods can enhance efficiency in this regard. This study proposed a method for predicting wheat scab levels using a rotation detector and Swin classifier. To minimise background interference, the study incorporated the rotation wheat detector (RWD) network for detecting wheat heads. The RWD network employed the Kalman filter Intersection over Union (KFIoU) to predict the angle, thereby improving accuracy. The Swin wheat classifier (SWC) network was employed to classify healthy and diseased wheat heads. The SWC network benefited from the shifted window self-attention module (SW-MSA), which enhanced feature extraction by establishing connections with other windows. The proposed method was evaluated using wheat field images collected over 3 years. The results demonstrate promising performance, achieving a 96% accuracy in predicting wheat scab levels. Furthermore, the R² and RMSE values for diseased wheat count were 97.62% and 3.61, respectively. This method offers an accurate means of predicting wheat scab levels through the analysis of wheat field images. Additionally, the introduction of the rotation detector presents a novel contribution to research on wheat scab detection.
Why it matches plant phenotyping methods小麦穂の画像から健全・罹病状態と赤かび病レベルを推定する画像解析手法を開発・評価しており、植物病害表現型の取得が中心である。
abstractThis study proposed a method for predicting wheat scab levels using a rotation detector and Swin classifier.
The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike. In order to solve the above problems and further improve the accuracy of wheat spike counting, an improved wheat spike counting model DMseg-Count was proposed by enhancing local contextual supervision information based on existing target object counting model DM-Count. Firstly, wheat spike local segmentation branch was introduced to improve the network architecture of DM-Count, so as to extract the local contextual supervision information of wheat spike. Secondly, an element-by-element point multiplication mechanism was designed to fuse global and local contextual supervision information of wheat spike. Finally, the total loss function was constructed to optimize the model. The test results showed that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed DMseg-Count model were 5.79 and 7.54, respectively, which were 9.76 and 10.91 higher than the standard distribution matching for crowd counting (DM-Count) model. Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. In summary, the proposed DMseg-Count model can effectively detect wheat spike and has good counting performance, which provides a new method for automatic counting of wheat spike and yield prediction in complex field environments.
Why it matches plant phenotyping methods小麦穂の画像から穂数を自動検出・計数するモデルを開発しており、植物形質（穂数）の取得手法が研究の中心であるため。
abstractan improved wheat spike counting model DMseg-Count was proposed
Real-time or pre-symptomatic wheat scab (WS) detection is inevitable for precision agriculture to secure yield and quality at the critical grain formation stage. For this, feature selection (FS) techniques and machine learning (ML) have demonstrated their capabilities. However, for the same type and size of dataset, all FS and ML techniques behave differently due to their diverse primary constituents. This study attempts to leverage ML for WS classification and prediction employing different FS techniques on hyperspectral data of wheat spikes. The spectral features were selected and assessed to regress and classify disease occurrence. Relief-F-neural net (NN) manifested the best results with classification accuracy (CA) of 67 % and 89 % at the pre-symptomatic scale and 3 days after inoculation (DAI), respectively. Followed by continuous wavelet transform (CWT)-NN with 63 % CA at the pre-symptomatic scale and CWT-Xgboost with 89 % CA at 3DAI. For prediction, random forest regression revealed best accuracy of R² = 0.94 and RMSE = 7.70, followed by partial least squares regression with R² = 0.90 and RMSE = 10.37. The results offer a precise quantitative benchmark for future investigations into the capacity of hyperspectral data and FS for the real-time quantification of plant diseases.
Why it matches plant phenotyping methods小麦穂のハイパースペクトル反射から病害の発生・重症度を機械学習で分類・予測し、特徴選択と性能を比較評価することが中心であり、植物病害表現型の測定手法に該当する。
abstractThis study attempts to leverage ML for WS classification and prediction employing different FS techniques on hyperspectral data of wheat spikes.
Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGBLₑₐf), stem (AGBSₜₑₘ), and reproductive organs (AGBR) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGBLₑₐf inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGBLₑₐf inversion hybrid model was coupled with the allometric model to estimate the AGBSₜₑₘ and AGBR in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGBLₑₐf datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R²) of wheat and maize AGBSₜₑₘ were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R² of AGBR was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.
Why it matches plant phenotyping methodsUAV画像、特徴選択、機械学習、PROSAIL、アロメトリーモデルを統合し、葉・茎・生殖器官のバイオマスを推定する手法の開発と検証が研究の中心である。
abstractWe aim to develop a novel model to precisely estimate the AGB of leaf (AGBLₑₐf), stem (AGBSₜₑₘ), and reproductive organs (AGBR) by UAV for film-mulched wheat and maize.
Wheat head or spike detection is significant for phenotyping because it can be directly correlated to yield and is an indicator of yield potential. Historically, wheat head counting was a labor-intensive and error prone process. Use of Deep Learning (DL) techniques has automated this process allowing automated wheat head detection and counting using high resolution imagery, allowing large-scale, High Throughput Phenotyping (HTP). Despite the use of advanced technologies, wheat head detection is a challenging task due to high environmental variability, cultivar differences, and head overlap. Several attempts have been made to make the DL models more robust and the wheat head datasets more diverse to improve the detection accuracy and reliability. With the introduction of advanced DL architectures, there has been continuous improvement in accuracy of head detection. In this study, we have evaluated the performance of three different cutting-edge DL models - YOLOv10x, RetinaNet, and MM-Grounding DINO for wheat head detection. We have also combined two different wheat datasets, Global Wheat Head Detection (GWHD) 2021 and SPIKE dataset to get a diverse dataset with a wide range of genotypes. This study aims to contribute to the ongoing evolution of wheat head detection techniques and provide an insight into how these three models perform for this task.
Why it matches plant phenotyping methodsコムギ穂の検出・計数という植物形態形質の画像解析手法を対象に、複数の深層学習モデルを評価し、異なるデータセットを統合して性能を比較しているため、フェノタイピング手法が中心である。
abstractUse of Deep Learning (DL) techniques has automated this process allowing automated wheat head detection and counting using high resolution imagery, allowing large-scale, High Throughput Phenotyping (HTP).
The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at an altitude of 15 m, followed by image stitching and plot boundary recognition via a mask region-based convolutional neural network (Mask R-CNN). The images were then segmented into plot-scale subgraphs, which were categorized into 3 growth stages. The panicle vision transformer (Panicle-ViT), which integrates a multipath vision transformer and replaces the Mask R-CNN backbone, accurately detects panicles. Additionally, the Res2Net50 architecture classified panicle types with 4 angles of 0°, 15°, 45°, and 90°. The results confirm that the performance of Plot-Seg is comparable to that of manual segmentation. Panicle-ViT outperforms the traditional Mask R-CNN across all the datasets, with the average precision at 50% intersection over union (AP 50 ) improved by 3.5% to 20.5%. The PNpA quantification for the full dataset achieved superior performance, with a coefficient of determination ( R 2 ) of 0.73 and a root mean square error (RMSE) of 28.3, and the overall panicle classification accuracy reached 94.8%. The proposed approach enhances operational efficiency and automates the process from plot cropping to PNpA prediction, which is promising for accelerating the selection of desired traits in rice breeding.
Why it matches plant phenotyping methodsイネの穂数・穂型という植物形質を、UAV画像と画像解析・深層学習で自動抽出する手法を開発し、精度検証しているため、方法が研究の中心である。
abstractThis study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Sunflower (Helianthus annuus) is a widely cultivated crop that exhibits a trait known as capitulum (or head) inclination at maturity. This trait is influenced by various structural factors, including head weight, stem traits, and plant height. A sunflower head should be at an angle at which the head faces the ground to avoid damage from the sun and birds. While this desired inclination range is known, current methods, including visual estimation and a model of measuring inclined length of the stem, fail to provide precise measurements of angle. This study introduces novel approaches to mathematically measure the head inclination angle. The research, which was conducted over the 2022 and 2023 growing seasons, involved an aluminum rod equipped with a ruler and a digital protractor to measure various height and angle components. Using the data collected, three methods were applied for measuring inclination: a previously published model as a control, a trigonometry‐based approach using angle and height measurements, and other model‐based approaches. A linear model resulted in a formula to calculate the head angle of any plant based solely on two height measurements, the highest point of the plant at both bloom (R5) and maturity (R9). Calculations of heritability and correlation suggest this method has created a precise alternative to existing estimation methods. The resulting formula has the potential to be paired with measurements from high‐throughput phenotyping methods, such as those facilitated with drones and ground robots, to fully automate the process of collecting head inclination data.
Why it matches plant phenotyping methodsヒマワリ頭花傾斜角という植物形質を対象に、既存法との比較を含む数学的測定法と計算式を開発・評価しており、表現型取得法が研究の中心である。
abstractThis study introduces novel approaches to mathematically measure the head inclination angle.
Wheat is one of the important food crops in the world, and the stability and growth of wheat production have a decisive impact on global food security and economic prosperity. Wheat counting is of great significance for agricultural management, yield prediction and resource allocation. Research shows that the wheat ear counting method based on deep learning has achieved remarkable results and the model accuracy is high. However, the complex background of wheat fields, dense wheat ears, small wheat ear targets, and different sizes of wheat ears make the accurate positioning and counting of wheat ears still face great challenges. To this end, an automatic positioning and counting method of wheat ears based on FIDMT-GhostNet (focal inverse distance transform maps - GhostNet) is proposed. Firstly, a lightweight wheat ear counting network using GhostNet as the feature extraction network is proposed, aiming to obtain multi-scale wheat ear features. Secondly, in view of the difficulty in counting caused by dense wheat ears, the point annotation-based network FIDMT (focal inverse distance transform maps) is introduced as a baseline network to improve counting accuracy. Furthermore, to address the problem of less feature information caused by the small ear of wheat target, a dense upsampling convolution module is introduced to improve the resolution of the image and extract more detailed information. Finally, to overcome background noise or wheat ear interference, a local maximum value detection strategy is designed to realize automatic processing of wheat ear counting. To verify the effectiveness of the FIDMT-GhostNet model, the constructed wheat image data sets including WEC, WEDD and GWHD were used for training and testing. Experimental results show that the accuracy of the wheat ear counting model reaches 0.9145, and the model parameters reach 8.42M, indicating that the model FIDMT-GhostNet proposed in this study has good performance.
Why it matches plant phenotyping methods小麦穂の画像から位置推定・個数計数を行う深層学習手法を開発し、複数データセットで性能検証しており、植物形質取得が研究の中心である。
abstractan automatic positioning and counting method of wheat ears based on FIDMT-GhostNet (focal inverse distance transform maps - GhostNet) is proposed.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Introduction Monitoring crop spike growth using low-altitude remote sensing images is essential for precision agriculture, as it enables accurate crop health assessment and yield estimation. Despite the advancements in deep learning-based visual recognition, existing crop spike detection methods struggle to balance computational efficiency with accuracy in complex multi-scale environments, particularly on resource-constrained low-altitude remote sensing platforms. Methods To address this gap, we propose FDRMNet, a novel feature diffusion reconstruction mechanism network designed to accurately detect crop spikes in challenging scenarios. The core innovation of FDRMNet lies in its multi-scale feature focus reconstruction and lightweight parameter-sharing detection head, which can effectively improve the computational efficiency of the model while enhancing the model's ability to perceive spike shape and texture.FDRMNet introduces a Multi-Scale Feature Focus Reconstruction module that integrates feature information across different scales and employs various convolutional kernels to capture global context effectively. Additionally, an Attention-Enhanced Feature Fusion Module is developed to improve the interaction between different feature map positions, leveraging adaptive average pooling and convolution operations to enhance the model's focus on critical features. To ensure suitability for low-altitude platforms with limited computational resources, we incorporate a Lightweight Parameter Sharing Detection Head, which reduces the model's parameter count by sharing weights across convolutional layers. Results According to the evaluation experiments on the global wheat head detection dataset and diverse rice panicle detection dataset, FDRMNet outperforms other state-of-the-art methods with mAP @.5 of 94.23%, 75.13% and R 2 value of 0.969, 0.963 between predicted values and ground truth values. In addition, the model's frames per second and parameters in the two datasets are 227.27,288 and 6.8M, respectively, which maintains the top three position among all the compared algorithms. Discussion Extensive qualitative and quantitative experiments demonstrate that FDRMNet significantly outperforms existing methods in spike detection and counting tasks, achieving higher detection accuracy with lower computational complexity.The results underscore the model's superior practicality and generalization capability in real-world applications. This research contributes a highly efficient and computationally effective solution for crop spike detection, offering substantial benefits to precision agriculture practices.
Why it matches plant phenotyping methods作物穂・穂首の画像検出とカウントを目的とする新規深層学習手法を開発し、複数データセットで精度・計算効率を評価しているため、植物表現型取得法が中心である。
abstractwe propose FDRMNet, a novel feature diffusion reconstruction mechanism network designed to accurately detect crop spikes in challenging scenarios.
The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2 n = 6 x = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis.
Why it matches plant phenotyping methods小麦穂の2D画像解析による形態計測法を実際に適用し、手動測定との相関検証とデジタル形質の有用性評価を行っており、植物フェノタイピング手法が中心である。
abstractRecently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis.
Reproduction assets foundThe paper's spike image dataset (the 2D images used for quadrangle-model phenotyping of 190 wheat plants) is publicly deposited on Zenodo, explicitly linked in the Data Availability Statement. The supplementary files contain statistical results (normality tests, ANOVA tables, confusion matrices, specimen descriptions)衍Dataset · publicThe spike image dataset is available at https://zenodo.org/records/13837454 , accessed on 27 September 2024.Open asset ↗Zenodo · 13837454lines:895-912Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract Seed shattering is a major economic problem in seed production of perennial ryegrass ( Lolium perenne L.). The objective was to identify potential relationships between phenotypic traits and seed retention in a 2‐year field trial with 21 diverse global accessions of perennial ryegrass. Accessions were grouped according to level of seed retention. Phenotypic traits examined were growth habit, spike length, curvature of the spike, spikelets per spike, spikelet length, internode length between spikelets, angle of spikelet insertion into the rachis, seeds per spike, and seed weight. Traits were captured and measured by use of novel two‐dimensional (2D) and three‐dimensional (3D) imaging tools. Among accessions, median seed retention values for the high and low seed retention groups were 61% and 36%, respectively. Four traits were found to significantly impact seed shattering: spike length ( p ≤ 0.01), seed weight ( p ≤ 0.001), seeds per spike ( p ≤ 0.05), and internode length between spikelets ( p ≤ 0.01). Seed retention was highest in accessions with short spikes. Most of the accessions that had high seed retention also had lower mean seed weight than the commercial plant materials. Accession (PI 231620) had both high levels of seed retention and a mean seed weight that is acceptable in the marketplace. These qualities may be used to improve seed retention in the breeding of perennial ryegrass cultivars. The 2D and 3D imaging methods have applicability in measurement of other plant morphological traits and across a broad range of plant species.
Why it matches plant phenotyping methods2D・3D画像解析を用いた植物形態形質の取得・測定が研究の中心的手法であり、種子保持性との関連評価に実質的に適用されているため。
abstractTraits were captured and measured by use of novel two‐dimensional (2D) and three‐dimensional (3D) imaging tools.
Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential.
Why it matches plant phenotyping methods植物のフェノロジー段階と穂状花序を画像から識別するための高解像度データセットであり、植物表現型取得・抽出を中心とする研究。
abstractThis work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages.
Reproduction assets foundThe paper is itself a data descriptor whose core asset is a public Harvard Dataverse dataset of 2,400 RGB images of Urochloa hybrids with phenological stage labels and COCO-format raceme polygon annotations, directly reproducing the paper's phenotyping data.Dataset · publict diffuser to ensure uniform lighting.
Data source location
Institution: Alliance Bioversity International & CIAT.
City: Palmira, Valle del Cauca.
Country: Colombia.
Geolocalization: 3°29′N, 76°21′W .
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi.org/10.7910/dvn/u0kl6y
Direct URL to data: https://doi.org/10.7910/dvn/u0kl6y
Instructions for accessing these data: The dataset [ 1 ] is licensed under the Creative Commons Attribution 4.0 International, which allows using, sharing, adapting, distribution and reproduction in any medium or format if attribution is given to the creator.
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Value of the Data
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The dataset offOpen asset ↗Harvard Dataverselines:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published13 Sept 2024Tenth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2024)Cited by 1 · OpenAlex ↗
Precision agriculture is the application of correct amount of fertilizers and water pesticide to achieve higher agricultural productivity. Furthermore, under the framework of precision agriculture is the automated estimation of yield with advanced technologies including Artificial Intelligence (AI) and Remote Sensing (RS). The use of RS has advanced crop yield estimations and predictions in recent years. However, to validate RS-based models it is important to perform in-situ exercises such as fruit counting, which is a time-consuming task that increases the production costs. Drones, robots, and in-situ cameras in combination with AI algorithms are widely used to efficiently address these issues. The recent advancement in computational resources and power available has enabled the utilization of Deep Learning AI models. One of the best-performing models for object detection is the You-Only-Look-Once (YOLO). In this study, the YOLOv5s is used for object detection, which is the second smallest and fastest YOLOv5 architecture, on two different benchmark datasets collected from AgML. The first dataset consists of 1730 images of mango trees in Australia during night, and the second dataset consists of 6512 images of wheat heads collected from different regions around the world. The main objective of this work is to demonstrate the capabilities of light AI models for object detection and to evaluate their performance, which will serve as a benchmark for future comparison with the on-board environment.
Why it matches plant phenotyping methods植物器官の検出・カウントによる収量推定を対象とし、YOLOv5sの性能評価とベンチマーク化が主目的であるため、計算画像フェノタイピング手法として採用。
abstractIn this study, the YOLOv5s is used for object detection, which is the second smallest and fastest YOLOv5 architecture, on two different benchmark datasets collected from AgML.
Reproduction assets foundThe paper evaluates YOLOv5s on two public benchmark datasets. The MangoYOLO dataset is explicitly cited with public access URLs and was directly used for the paper's mango yield-estimation experiments, qualifying as a paper-specific public asset. The Global Wheat Head Detection dataset is also used but its Zenodo URL (Dataset · publicAnand Koirala, C McCarthy, Kerry Walsh, and Z Wang, ‘MangoYOLO data set’. Central Queensland University,
2021. Accessed: May 23, 2024. [Online]. Available: http://hdl.handle.net/10018/1261224,
https://researchdata.edu.au/mangoyolo-setOpen asset ↗pdf-page:7 lines:1-50Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Sept 2024Computers and Electronics in Agriculture.
Low-light image enhancement poses a significant challenge in agricultural settings, particularly for time-series captured wheat images. The use of time-series captured image data can improve the accuracy of wheat yield prediction by analyzing the growth status and characteristics of wheat. Given the difficulty of capturing paired wheat images in various lighting conditions, a method based on the Global Wheat Head Detection (GWHD) dataset is presented to synthesize pairs of low-light/normal-light wheat images. This approach expands to create a new dataset, LN-GWHD, specifically for the task of enhancing wheat images in low-light conditions. Importantly, it enables a more extensive evaluation of wheat image restoration tasks and performance accuracy under more controlled and diverse conditions. It is worth noting that this method can be extended to a wide range of scenarios for the enhancement of low-light situations. Where low-light conditions are common due to various factors such as weather, existing methods based on the transformer architecture have achieved state-of-the-art (SOTA) enhancement performance by capturing relationships between low-light feature channels. However, these approaches perform the similarity computation on all tokens and may introduce redundant features, limiting their ability to focus on critical information for high-quality image reconstruction. To recover more high-quality wheat image information, the explicit inter-channel sparse transformer (EIST) network is proposed, which is specifically designed to recover wheat images under low-light conditions. EIST comprises multiple blocks, each featuring explicit sparse top-k attention (ESTA) and bilateral gated feed-forward network (BGFN). ESTA dynamically selects the most critical information by focusing on the top-k key labels in each query, while BGFN enhances valid information interaction through re-double filtering. The overall architecture employs transformers to learn correlations and features between different image channels, resulting in noise reduction, detail enhancement, and improved clarity. Additionally, the Fourier spectrum loss is introduced to constrain the image reconstruction in the frequency domain, allowing more detailed information to be recovered. Extensive experiments on the LN-GWHD datasets demonstrate that EIST surpasses SOTA methods. Furthermore, our approach achieves superior results in detecting low-light wheat ears during back-end evaluation.
Why it matches plant phenotyping methods低照度下の小麦穂画像を復元し、後段の穂検出性能まで評価する画像処理手法と専用データセットを開発しており、植物表現型取得の技術が中心です。
abstracta method based on the Global Wheat Head Detection (GWHD) dataset is presented to synthesize pairs of low-light/normal-light wheat images
High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops. Specifically for sorghum ( Sorghum bicolor L.), rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle. In this context, the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time. Therefore, this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions. A preharvest benchmark dataset was collected at field scale (2023 season, Kansas, USA), with 648 images of sorghum panicles retrieved via smartphone device, and grain number counted. Each sorghum panicle image was manually labeled, and the images were augmented. Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation, with an average precision of 75% and 89%, respectively. For the grain number, 3 models were trained: MCNN (multiscale convolutional neural network), TCNN-Seed (two-column CNN-Seed), and Sorghum-Net (developed in this study). The Sorghum-Net model showed a mean absolute percentage error of 17%, surpassing the other models. Lastly, a simple equation was presented to relate the count from the model (using images from only one side of the panicle) to the field-derived observed number of grains per sorghum panicle. The resulting framework obtained an estimation of grain number with a 17% error. The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level.
Why it matches plant phenotyping methodsスマートフォン画像からソルガム穂の検出・分割と粒数推定を開発・検証しており、植物形質取得手法が研究の中心である。
abstractthis study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions.
Reproduction assets foundThe paper's authors explicitly state that the code used to train, test, and analyze the data is publicly available on GitHub. The phenotype image datasets are only available upon request.Code · publicThe code used to train, test, and analyze the data is available at https://github.com/GustavoSantiago113/Sorghum_Grain_Counter .Open asset ↗GustavoSantiago113/Sorghum_Grain_Counterlines:169-296Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Fusarium head blight (FHB) is a plant disease caused by various species of the Fusarium fungus. One of the major concerns associated with Fusarium spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and animal health and leads to major economic losses. A reliable site-specific precise Fusarium spp. infection early warning model is, therefore, needed to ensure food and feed safety by the early detection of contamination hotspots, enabling effective and efficient fungicide applications, and providing FHB prevention management advice. Such precision farming techniques contribute to environmentally friendly production and sustainable agriculture. This study developed a predictive model, Sága, for on-site FHB detection in wheat using imaging spectroscopy and deep learning. Data were collected from an experimental field in 2021 including (1) an experimental field inoculated with Fusarium spp. (52.5 m × 3 m) and (2) a control field (52.5 m × 3 m) not inoculated with Fusarium spp. and sprayed with fungicides. Imaging spectroscopy data (hyperspectral images) were collected from both the experimental and control fields with the ground truth of Fusarium -infected ear and healthy ear, respectively. Deep learning approaches (pretrained YOLOv5 and DeepMAC on Global Wheat Head Detection (GWHD) dataset) were used to segment wheat ears and XGBoost was used to analyze the hyperspectral information related to the wheat ears and make predictions of Fusarium -infected wheat ear and healthy wheat ear. The results showed that deep learning methods can automatically detect and segment the ears of wheat by applying pretrained models. The predictive model can accurately detect infected areas in a wheat field, achieving mean accuracy and F1 scores exceeding 89%. The proposed model, Sága, could facilitate the early detection of Fusarium spp. to increase the fungicide use efficiency and limit mycotoxin contamination.
Why it matches plant phenotyping methods小麦穂のハイパースペクトル画像と深層学習を用いて、Fusarium感染穂という植物病害状態を直接検出・推定するモデルを開発し、性能評価しているため、フェノタイピング手法が中心である。
abstractThis study developed a predictive model, Sága, for on-site FHB detection in wheat using imaging spectroscopy and deep learning.
Published9 Aug 2024International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer EngineeringCited by 1 · OpenAlex ↗
Detection and calculation of wheat ears are critical for land management, yield estimation, and crop phenotype analysis. Most methods are based on superficial and color features extracted using machine learning. However, these methods cannot fulfill wheat ear detection and counting in the field due to the limitations of the generated features and their lack of robustness. Various detectors have been created to deal with this problem, but their accuracy and calculation precision still need to be improved. This research proposes a deep learning method using you only look once (YOLO), especially the YOLOv8 model with depth and channel width configuration, stochastic gradient descent (SGD) optimizer, structure modification, and convolution module along with hyperparameter tuning by transfer learning method. The results show that the model achieves a mean average precision (mAP) of 95.80%, precision of 99.90%, recall of 99.50%, and frame per second (FPS) of 22.08. The calculation performance of the wheat ear object achieved accurate performance with a coefficient of determination (R^2) value of 0.977, root mean square error (RMSE) of 2.765, and bias of 1.75.
Why it matches plant phenotyping methodsYOLOv8によるコムギ穂の検出・計数手法を開発し、精度指標で検証しており、植物形態・収量関連形質の取得が中心である。
abstractThis research proposes a deep learning method using you only look once (YOLO), especially the YOLOv8 model
The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method’s high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared.
Why it matches plant phenotyping methods圃場高スループット表現型プラットフォームと画像解析モデルを開発・評価し、トウモロコシの雄穂・絹糸を検出してASIという植物形質を推定することが研究の中心である。
abstractThis paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform.
The rice panicle traits significantly influence grain yield, making them a primary target for rice phenotyping studies. However, most existing techniques are limited to controlled indoor environments and difficult to capture the rice panicle traits under natural growth conditions. Here, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field using smartphone. The proposed method combined the large model Segment Anything Model (SAM) and the small model You Only Look Once version 8 (YOLOv8) to achieve high-precision segmentation of rice panicle images. The NeRF technique was then employed for 3D reconstruction using the images with 2D segmentation. Finally, the resulting point clouds are processed to successfully extract panicle traits. The results show that PanicleNeRF effectively addressed the 2D image segmentation task, achieving a mean F1 Score of 86.9% and a mean Intersection over Union (IoU) of 79.8%, with nearly double the boundary overlap (BO) performance compared to YOLOv8. As for point cloud quality, PanicleNeRF significantly outperformed traditional SfM-MVS (structure-from-motion and multi-view stereo) methods, such as COLMAP and Metashape. The panicle length was then accurately extracted with the rRMSE of 2.94% for indica and 1.75% for japonica rice. The panicle volume estimated from 3D point clouds strongly correlated with the grain number (R2 = 0.85 for indica and 0.82 for japonica) and grain mass (0.80 for indica and 0.76 for japonica). This method provides a low-cost solution for high-throughput in-field phenotyping of rice panicles, accelerating the efficiency of rice breeding.
Why it matches plant phenotyping methodsスマートフォン画像、セグメンテーション、NeRFによる3D再構成を統合し、イネ穂の形質抽出を開発・検証した中心的な手法研究である。
abstractHere, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field using smartphone.
The monitoring of the tassel number and tasseling time reflects the maize growth and is necessary for crop management. However, it mainly depends on field observations, which is very labor intensive and may be biased by human errors. Tassel detection remains challenging due to the varying appearance of tassels across maize varieties, tasseling stages, and spatial resolutions. Moreover, the capability of the deep learning model for monitoring tassel number change and the time of entering tasseling stage has not been explored. In this study, we propose a novel approach for fast tassel detection using PConv (Partial Convolution) within YoloV8 series, named PConv-YoloV8 series. Compared to seven state-of-the-art deep learning methods, PConv-YoloV8 × 6 best trades off detection accuracy with the number of parameters (Parameters = 52.50 MB, AP = 0.950, R² = 0.92, rRMSE = 9.08%). The potential of PConv-YoloV8 × 6 to provide an accurate detection of tassels in complex situations from near-ground and UAV images were comprehensively studied. PConv-YoloV8 × 6 maintained an excellent detection accuracy for maize at different tasseling stages (AP = 0.826–0.972, R² = 0.83–0.92, RMSE = 1.94–3.01, rRMSE = 21.06%-7.09%), for different varieties (AP = 0.901–0.978, R² = 0.77–0.97, RMSE = 1.39–3.16, rRMSE = 11.72%-5.06%), at different resolutions (AP = 0.921–0.956, R² = 0.84–0.93, rRMSE = 8.72%-17.71%), and on UAV images with different resolutions (AP = 0.918–0.968, R² = 0.98–0.99, rRMSE = 6.43%-12.76%), which proved the robustness of the model. The tasseling number and the time of entering tasseling stage detected from images were basically consistent with the trends observed in the manually labeled results. This study provides an effective method to monitor the tassel number and the time of entering the tasseling stage. A new maize tassel detection dataset (18260 tassels in 729 near-ground images and 20835 tassels in 144 UAV images) is created. Future studies will focus on making more lightweight models and achieving real-time detection capabilities.
Why it matches plant phenotyping methodsトウモロコシの雄穂数・抽雄期という植物形質をRGB画像から抽出する深層学習手法を開発し、複数条件で検証している。データセット作成も含み、表現型取得が研究の中心である。
abstractwe propose a novel approach for fast tassel detection using PConv (Partial Convolution) within YoloV8 series, named PConv-YoloV8 series.
Rice panicle traits serve as critical indicators of both yield potential and germplasm resource quality. However, traditional manual measurements of these traits, which typically involve threshing, are not only laborious and time-consuming but also prone to introducing measurement errors. This study introduces a high-throughput and nondestructive method, termed extraction of panicle traits (EOPT), along with the software Panicle Analyzer, which is designed to assess unshaped intact rice panicle traits, including the panicle grain number, grain length, grain width, and panicle length. To address the challenge of grain occlusion within an intact panicle, we define a panicle morphology index to quantify the occlusion levels among the rice grains within the panicle. By calibrating the grain number obtained directly from rice panicle images based on the panicle morphology index, we substantially improve the grain number detection accuracy. For measuring grain length and width, the EOPT selects rice grains using an intersection over union threshold of 0.8 and a confidence threshold of 0.7 during the grain detection process. The mean values of these grains were calculated to represent all the panicle grain lengths and widths. In addition, EOPT extracted the main path of the skeleton of the rice panicle using the Astar algorithm to determine panicle lengths. Validation on a dataset of 1,554 panicle images demonstrated the effectiveness of the proposed method, achieving 93.57% accuracy in panicle grain counting with a mean absolute percentage error of 6.62%. High accuracy rates were also recorded for grain length (96.83%) and panicle length (97.13%). Moreover, the utility of EOPT was confirmed across different years and scenes, both indoors and outdoors. A genome-wide association study was conducted, leveraging the phenotypic traits obtained via EOPT and genotypic data. This study identified single-nucleotide polymorphisms associated with grain length, width, number per panicle, and panicle length, further emphasizing the utility and potential of this method in advancing rice breeding.
Why it matches plant phenotyping methods画像からイネ穂の粒数・粒長・粒幅・穂長を高スループットかつ非破壊で抽出する手法とソフトウェアを開発し、データセットで精度検証しているため、植物表現型取得法が研究の中心である。
abstractThis study introduces a high-throughput and nondestructive method, termed extraction of panicle traits (EOPT), along with the software Panicle Analyzer, which is designed to assess unshaped intact rice panicle traits, including the panicle grain number, grain length, grain width, and panicle length.
Plant counting plays an important role in evaluating planter effectiveness, assessing seed quality, devising agricultural management plans, and estimating crop yields. Given its significance and the ease of acquiring agricultural images, the development of an end-to-end image-based plant counting model applicable across diverse agricultural settings is crucial. The proposed TasselNetV2++, an improved version of TasselNetV2+ for plant counting, introduces notable enhancements to its encoder and counter while maintaining the existing normalizer. In the encoder, we designed a dual-branch architecture, with one branch being a customized YOLOv5s backbone and the other branch being the original encoder equipped with an attention mechanism. It is precisely the branch-level transfer learning, coupled with multilayer fusion, within the dual-branch architecture that significantly enhances the feature extraction capability of the network across a wide range of scenarios. Moreover, the counter has been enhanced with an attention mechanism that recalibrates its focus on crucial spatial locations and channel-wise features following average pooling. Experimental results demonstrate that TasselNetV2++ outperforms its predecessor across multiple counting tasks. Compared to TasselNetV2+, TasselNetV2++ achieves a substantial reduction in relative root mean squared error (rRMSE). Specifically, it brings a 33.3% relative decrease of rRMSE on the soybean seedlings counting dataset, 8.4% on the wheat ears detection dataset, 28.6% on the maize tassels counting dataset, and 18.0% on the sorghum heads counting dataset. Notably, ablation experiment demonstrates the indispensability of the branch-level transfer learning in achieving precise plant counting. Branch-level transfer learning achieves a notable relative decrease in rRMSE of 31.4% for soybean seedlings, 7.9% for wheat tassels, 36.5% for maize tassels, and 2.0% for sorghum heads. The proposed TasselNetV2++ attains remarkable advancements and introduces a straightforward yet highly effective branch-level transfer learning strategy.
Why it matches plant phenotyping methods植物個体・器官の画像ベース計数モデルを開発し、複数データセットで性能比較とアブレーション評価を行っており、表現型取得・抽出法が中心である。
abstractThe proposed TasselNetV2++, an improved version of TasselNetV2+ for plant counting, introduces notable enhancements to its encoder and counter while maintaining the existing normalizer.
Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small-grain cereals worldwide. Developing FHB-resistant cultivars is critical but requires field and greenhouse disease assessment, which are typically laborious and time consuming. In this work, we developed automated applications based on three-dimensional (3D) convolutional neural networks (CNNs) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index. Such tools are an important step toward the creation of automated and efficient phenotyping methods. The data used to generate the results are 3D point clouds consisting of four colour channels—red, green, blue (RGB), and near-infrared (NIR)—collected using a multispectral 3D scanner. Our 3D CNN models for FHB detection achieved 100% accuracy. The influence of the multispectral information on performance was evaluated; the results showed the dominance of the RGB channels over both the NIR (720 nm peak wavelength) and the NIR plus RGB channels combined. Our best 3D CNN models for estimation of total and infected number of spikelets achieved mean absolute errors (MAEs) of 1.13 and 1.56, respectively. Our best 3D CNN models for FHB severity estimation achieved 8.6 MAE. A linear regression analysis between the visual FHB severity assessment and the FHB severity predicted by our 3D CNN showed a significant correlation.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャンと3D CNNを用いて、コムギのFHB症状、穂の小穂数、感染小穂数、病害重症度を自動推定する手法を開発・評価しており、植物表現型取得が中心である。
abstractwe developed automated applications based on three-dimensional (3D) convolutional neural networks (CNNs) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index.
To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.
Why it matches plant phenotyping methodsCT画像から花序内の小花と花托の接触点を自動検出し、3D位置を推定する手法の開発・精度評価が研究の中心であるため、植物フェノタイピング手法に該当する。
abstractTherefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques.
Reproduction assets foundThe authors publicly deposited the labeled CT slice-image dataset (contact point annotations and receptacle segmentation labels) on Figshare, and a 3D visualization video of the contact point estimation results is available on YouTube. Raw CT volumes are only available on request. No author analysis code repository is.Dataset · publicre task is to automate the clustering parameters, which are currently determined manually. We also plan to develop a mathematical model of the position of the contact point between the receptacle and florets based on the estimation results.
Data availability statement
The labeled data for this study can be found in the Figshare https://doi.org/10.6084/m9.figshare.25388434 . The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributionsOpen asset ↗Figshare · 10.6084/m9.figshare.25388434lines:513-540Plant phenotyping relevance match · UnverifiedCrossref · checked 8 Sept 2026
In complex field environments, wheat grows densely with overlapping organs and different plant weights. It is difficult to accurately predict feed quantity for wheat combine harvester using the existing YOLOv5s and uniform weight of a single wheat plant in a whole field. This paper proposes a feed quantity prediction method based on the improved YOLOv5s and weight of a single wheat plant without stubble. The improved YOLOv5s optimizes Backbone with compact bases to enhance wheat spike detection and reduce computational redundancy. The Neck incorporates a hierarchical residual module to enhance YOLOv5s’ representation of multi-scale features. The Head enhances the detection accuracy of small, dense wheat spikes in a large field of view. In addition, the height of a single wheat plant without stubble is estimated by the depth distribution of the wheat spike region and stubble height. The relationship model between the height and weight of a single wheat plant without stubble is fitted by experiments. Then, feed quantity can be predicted using the weight of a single wheat plant without stubble estimated by the relationship model and the number of wheat plants detected by the improved YOLOv5s. The proposed method was verified through experiments with the 4LZ-6A combine harvester. Compared with the existing YOLOv5s, YOLOv7, SSD, Faster R-CNN, and other enhancements in this paper, the mAP50 of wheat spikes detection by the improved YOLOv5s increased by over 6.8%. It achieved an average relative error of 4.19% with a prediction time of 1.34 s. The proposed method can accurately and rapidly predict feed quantity for wheat combine harvesters and further realize closed-loop control of intelligent harvesting operations.
Why it matches plant phenotyping methods小麦穂の検出、株高推定、および株重推定を組み合わせた画像・深度ベースの植物形質抽出が技術の中心であり、収穫量予測への応用だけでなく、植物の形態・重量推定手法を検証している。
abstractThe improved YOLOv5s optimizes Backbone with compact bases to enhance wheat spike detection and reduce computational redundancy.
Quantifying the effect of maize tassel on canopy reflectance is essential for creating a tasseling progress monitoring index, aiding precision agriculture monitoring, and understanding vegetation canopy radiative transfer. Traditional field measurements often struggle to detect the subtle reflectance differences caused by tassels due to complex environmental factors and challenges in controlling variables. The three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure. This study used the LESS (large-scale remote sensing data and image simulation framework) model to analyze the impact of maize tassels on visible and near-infrared reflectance in heterogeneous 3D scenes by modifying the structural and optical properties of canopy components. We also examined the anisotropic characteristics of tassel effects on canopy reflectance and explored the mechanisms behind these effects based on the quantified contributions of the optical properties of canopy components. The results showed that (1) the effect of tassels under different planting densities mainly manifests in the near-infrared band of the canopy spectrum, with a variation magnitude of ±0.04. In contrast, the impact of tassels on different leaf area index (LAI) shows a smaller response difference, with a magnitude of ±0.01. As tassels change from green to gray during growth, their effect on reducing canopy reflectance increases. (2) The effect of maize tassel on canopy reflectance varied with spectral bands and showed an obvious directional effect. In the red band at the same sun position, the difference in tassel effect caused by the observed zenith angle on canopy reflectance reaches 200%, while in the near-infrared band, the difference is as high as 400%. The hotspot effect of the canopy has a significant weakening effect on the shadow effect of the tassel. (3) The non-transmittance optical properties of maize tassels reduce canopy reflectance, while their high reflectance increases it. Thus, the dual effects of tassels create a game in canopy reflectance, with the final outcome mainly depending on the sensitivity of the canopy spectrum to transmittance. This study demonstrates the potential of using 3D radiative transfer models to quantify the effects of crop fine structure on canopy reflectance and provides some insights for optimizing crop structure and implementing precision agriculture management (such as selective breeding of crop optimal plant type).
Why it matches plant phenotyping methods3D放射伝達モデルを中核に、トウモロコシ雄穂という植物器官の構造・光学特性が群落反射率へ与える影響を定量化し、出穂進行モニタリングへの利用可能性を示しているため、単なる反射率のルーチン測定ではない。
abstractThe three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure.
The phenotypic analysis of wheat spikes plays an important role in wheat growth management, plant breeding, and yield estimation. However, the dense and tight arrangement of spikelets and grains on the spikes makes the phenotyping more challenging. This study proposed a rapid and accurate image-based method for in-field wheat spike phenotyping consisting of three steps: wheat spikelet segmentation, grain number classification, and total grain number counting. Wheat samples ranging from the early filling period to the mature period were involved in the study, including three varieties: Zhengmai 618, Yannong 19, and Sumai 8. In the first step, the in-field collected images of wheat spikes were optimized by perspective transformation, augmentation, and size reduction. The YOLOv8-seg instance segmentation model was used to segment spikelets from wheat spike images. In the second step, the number of grains in each spikelet was classified by a machine learning model like the Support Vector Machine (SVM) model, utilizing 52 image features extracted for each spikelet, involving shape, color, and texture features as the input. Finally, the total number of grains on each wheat spike was counted by adding the number of grains in the corresponding spikelets. The results showed that the YOLOv8-seg model achieved excellent segmentation performance, with an average precision (AP) @[0.50:0.95] and accuracy (A) of 0.858 and 100%. Meanwhile, the SVM model had good classification performance for the number of grains in spikelets, and the accuracy, precision, recall, and F1 score reached 0.855, 0.860, 0.865, and 0.863, respectively. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were as low as 1.04 and 5% when counting the total number of grains in the frontal view wheat spike images. The proposed method meets the practical application requirements of obtaining trait parameters of wheat spikes and contributes to intelligent and non-destructive spike phenotyping.
Why it matches plant phenotyping methods小麦穂の粒数などの形質を画像から抽出する手法を開発・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 proposed a rapid and accurate image-based method for in-field wheat spike phenotyping consisting of three steps: wheat spikelet segmentation, grain number classification, and total grain number counting.
Accurate wheat ear counting is one of the key indicators for wheat phenotyping. Convolutional neural network (CNN) algorithms for counting wheat have evolved into sophisticated tools, however because of the limitations of sensory fields, CNN is unable to simulate global context information, which has an impact on counting performance. In this study, we present a hybrid attention network (CTHNet) for wheat ear counting from RGB images that combines local features and global context information. On the one hand, to extract multi-scale local features, a convolutional neural network is built using the Cross Stage Partial framework. On the other hand, to acquire better global context information, tokenized image patches from convolutional neural network feature maps are encoded as input sequences using Pyramid Pooling Transformer. Then, the feature fusion module merges the local features with the global context information to significantly enhance the feature representation. The Global Wheat Head Detection Dataset and Wheat Ear Detection Dataset are used to assess the proposed model. There were 3.40 and 5.21 average absolute errors, respectively. The performance of the proposed model was significantly better than previous studies.
Why it matches plant phenotyping methods小麦穂数という植物形質をRGB画像から推定する深層学習手法を開発し、複数データセットで性能評価しており、表現型取得・抽出法が中心である。
abstractAccurate wheat ear counting is one of the key indicators for wheat phenotyping.
Reproduction assets foundThe paper uses two publicly available wheat ear image datasets (GWHD and WEDD) as its phenotyping inputs, with explicit public URLs in the data availability statement. No authors' analysis code or trained model is deposited.Dataset · publics generalization ability. This will provide real-time and accurate information for agricultural production, help farmers make scientific decisions, and improve crop management and yield.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: http://www.global-wheat.com/
https://github.com/simonMadec .
Author contributions
QH: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – review & editing. WL: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. YZ: Software, Writing – review & editing. TR: SoftwaOpen asset ↗https://github.com/simonMadeclines:388-410Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high-yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic-by-environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho-physiological and yield-related traits. The results indicated that a multi-environment model outperformed a single-environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral-based estimated spike number was utilized as a secondary trait in a multi-trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral-genomic chain-model workflow, which utilizes spectral-based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.
Why it matches plant phenotyping methodsUAV搭載ハイパースペクトル反射から小穂数という植物形質を機械学習で推定する手法と、そのスペクトル形質を用いた再利用可能なワークフローが研究の中心であるため。
abstractA machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle.
Phenotyping is an integral part of plant breeding operations. In many cases the trait measured is not identical to the target trait for reasons of speed and or cost. This is a form of indirect selection, where correlation between the trait measured and the target phenotype influences the rate of genetic gain. Low correlations lead to slow rates of genetic gain. In sub-Saharan African maize breeding programs, maize grain yield in breeding experimental plots is measured as a field weight (FW), which includes the grain and cob. The weight of grain from each plot is estimated as a standard proportion of grain to total ear weight using a shelling percentage of 80 %. This approach assumes that there is no genetic, environment or genetic by environment interaction in shelling percentage which, if present, would contribute to slower rates of genetic gain for grain yield. This study investigated the magnitude of genetic and environmental variation in shelling percentage and its impact on selection in six hybrid maize multi-environment yield trials in Ethiopia over two seasons. The data of shelled grain weight (SW) and cob weight (CW) from the trials were analyzed using a bivariate linear mixed model. Genetic variances for both traits varied across the six testing sites ranging from 0.199 to 2.975 for SW and from 0.029 to 0.245 for CW. The genetic correlations between pairs of sites for SW and CW also varied, indicating the existence of genotype by environment interaction for these traits. Additionally, the bivariate regressions between FW and SW indicated there was substantial genetic deviation around the 80 % shelling response, and this relationship was impacted by environmental influences. The use of a constant relationship of 80 % shelling biases grain yield prediction in multi-environment hybrid maize yield trials and thus reduces the rate of genetic gain in maize breeding programs. Taking into account the variations in the shelling percentage of the genotypes across sites in predicting grain yield from field weight improves the accuracy of genotype selection and the rate of genetic gain in maize breeding programs.
Why it matches plant phenotyping methods圃場重量から穀粒収量を推定する固定シェリング率の妥当性を検証し、遺伝型・環境変動を考慮した二変量モデルによる推定改善を中心に扱っているため、植物形質取得・推定法の技術的評価に該当する。
abstractThis study investigated the magnitude of genetic and environmental variation in shelling percentage and its impact on selection
Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson's r 2 = 0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R 2 = 0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM's potential integration into currently available pipelines for image generation and processing in vineyard conditions.
Why it matches plant phenotyping methodsSAMを用いたブドウ房画像からの個別果粒セグメンテーション、検証、補正、および房構造・コンパクトネス形質の算出が研究の中心であるため。
abstractThis study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images.
Reproduction assets foundThe authors state that all data and code to reproduce the study's grapevine cluster segmentation and architecture analysis are publicly available in their GitHub repository. Other URLs (SAM checkpoint, pycocotools, RMBG, arXiv refs) are generic third-party resources, not paper-specific assets.Code · publicAll the data and code to reproduce the results of this study are available at https://github.com/diazgarcialab/SAM-cluster-segmentation .Open asset ↗diazgarcialab/SAM-cluster-segmentationlines:105-230Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
A bstract Flowering time is a critical phenological trait in maize ( Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi-subjective and labor-intensive manual observation, limiting the scale and efficiency of genetics and breeding improvement. Leveraging unoccupied aerial system (UAS, also known as UAVs or drones) technology coupled with convolutional neural networks (CNNs) presents a promising approach for high-throughput phenotyping of tasseling in maize. Most CNN image analysis is overly complicated for simple tasks relevant to plant scientists. Here a methodology for extracting tasseling from RGB imagery using a CNN-based approach was applied to 220 hybrids and 30 test lines grown in eight diverse environments (Wisconsin and Texas, U.S.A.) then validated through an unrelated set of hybrids. Overall accuracies of .946, .911, .985, and .988 were obtained for classifying maize images with or without tassels from College Station, TX in 2020; College Station, TX in 2021; Arlington, WI in 2021; and Madison, WI in 2021 respectively. By employing deep learning techniques, larger volumes of phenotypic data can be processed enabling high-throughput phenotyping in breeding programs. Although large datasets are required to train CNN models, the proposed methodology prioritizes simplicity in computational architecture while maintaining effectiveness in identifying flowered maize across diverse genotypes and environments.
Why it matches plant phenotyping methodsトウモロコシの開花（抽だい）という植物形質をUAS RGB画像とCNNで抽出する方法を開発・検証しており、画像取得・解析手法が研究の中心である。
abstractLeveraging unoccupied aerial system (UAS, also known as UAVs or drones) technology coupled with convolutional neural networks (CNNs) presents a promising approach for high-throughput phenotyping of tasseling in maize.
The tassel state in maize hybridization fields not only reflects the growth stage of the maize but also reflects the performance of the detasseling operation. Existing tassel detection models are primarily used to identify mature tassels with obvious features, making it difficult to accurately identify small tassels or detasseled plants. This study presents a novel approach that utilizes unmanned aerial vehicles (UAVs) and deep learning techniques to accurately identify and assess tassel states, before and after manually detasseling in maize hybridization fields. The proposed method suggests that a specific tassel annotation and data augmentation strategy is valuable for substantial enhancing the quality of the tassel training data. This study also evaluates mainstream object detection models and proposes a series of highly accurate tassel detection models based on tassel categories with strong data adaptability. In addition, a strategy for blocking large UAV images, as well as improving tassel detection accuracy, is proposed to balance UAV image acquisition and computational cost. The experimental results demonstrate that the proposed method can accurately identify and classify tassels at various stages of detasseling. The tassel detection model optimized with the enhanced data achieves an average precision of 94.5% across all categories. An optimal model combination that uses blocking strategies for different development stages can improve the tassel detection accuracy to 98%. This could be useful in addressing the issue of missed tassel detections in maize hybridization fields. The data annotation strategy and image blocking strategy may also have broad applications in object detection and recognition in other agricultural scenarios.
Why it matches plant phenotyping methodsトウモロコシの雄穂状態という植物器官の状態を、UAV画像と深層学習で検出・分類する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis study presents a novel approach that utilizes unmanned aerial vehicles (UAVs) and deep learning techniques to accurately identify and assess tassel states, before and after manually detasseling in maize hybridization fields.
Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechanisms and changes in the characteristics of infected wheat. On this basis, the imaging scales are divided into microscopic, medium, submacroscopic, and macroscopic scales. Then, we outline the recent relevant articles, algorithms, and methodologies about wheat FHB from disease detection to qualitative analysis and summarize the potential difficulties in the practicalization of the corresponding technology. This paper could provide researchers with more targeted technical support and breakthrough directions. Additionally, this paper provides an overview of the ideal application mode of the FHB detection technologies based on multi-scale imaging and then examines the development trend of the all-scale detection system, which paved the way for the fusion of non-destructive detection technologies of wheat FHB based on multi-scale imaging.
Why it matches plant phenotyping methods小麦の赤かび病を画像から検出・定量化する技術を対象としたレビューであり、植物病害状態の画像ベース表現型計測が中心です。
titleWheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective.
Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and Dice scores of 79.6% and 83.6% on two external datasets from the Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery.
Why it matches plant phenotyping methodsコムギ穂の画像セグメンテーション手法と合成データセットを開発し、内部・外部データセットで性能検証しており、植物フェノタイピング手法が中心である。
abstractwe developed a realistic annotated synthetic dataset for wheat head segmentation.
Reproduction assets foundThe paper's wheat head segmentation datasets (synthetic, GAN-generated, and evaluation sets) are publicly available at the authors' stated URL; the analysis code is only available on request.Dataset · publicPublicly available datasets were utilized in this study. These data can be found here: https://www.cs.usask.ca/ftp/pub/whs/ (accessed on 1 June 2023). The code used to generate synthetic data presented in this study are available on request.Open asset ↗lines:74-261Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
The number of wheat spikes has an important influence on wheat yield, and the rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation and food security. Computer vision and machine learning have been widely studied as potential alternatives to human detection. However, models with high accuracy are computationally intensive and time consuming, and lightweight models tend to have lower precision. To address these concerns, YOLO-FastestV2 was selected as the base model for the comprehensive study and analysis of wheat sheaf detection. In this study, we constructed a wheat target detection dataset comprising 11,451 images and 496,974 bounding boxes. The dataset for this study was constructed based on the Global Wheat Detection Dataset and the Wheat Sheaf Detection Dataset, which was published by PP Flying Paddle. We selected three attention mechanisms, Large Separable Kernel Attention (LSKA), Efficient Channel Attention (ECA), and Efficient Multi-Scale Attention (EMA), to enhance the feature extraction capability of the backbone network and improve the accuracy of the underlying model. First, the attention mechanism was added after the base and output phases of the backbone network. Second, the attention mechanism that further improved the model accuracy after the base and output phases was selected to construct the model with a two-phase added attention mechanism. On the other hand, we constructed SimLightFPN to improve the model accuracy by introducing SimConv to improve the LightFPN module. The results of the study showed that the YOLO-FastestV2-SimLightFPN-ECA-EMA hybrid model, which incorporates the ECA attention mechanism in the base stage and introduces the EMA attention mechanism and the combination of SimLightFPN modules in the output stage, has the best overall performance. The accuracy of the model was P=83.91%, R=78.35%, AP= 81.52%, and F1 = 81.03%, and it ranked first in the GPI (0.84) in the overall evaluation. The research examines the deployment of wheat ear detection and counting models on devices with constrained resources, delivering novel solutions for the evolution of agricultural automation and precision agriculture.
Why it matches plant phenotyping methods小麦穂の検出・計数という植物器官形質の抽出を目的に、検出モデル、注意機構、軽量FPN、データセットを開発・評価しており、表現型取得手法が中心である。
abstractthe rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation
Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R 2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.
Why it matches plant phenotyping methods小麦穂の画像検出・追跡による計数手法を開発し、精度・相関・リアルタイム性能を評価しており、植物形質（穂数）の取得が研究の中心である。
abstractIn order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x.
Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.
Why it matches plant phenotyping methods小麦の葉・穂・茎・根の病害や損傷を画像から認識するAIモバイルアプリの開発・性能評価が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。害虫・雑草識別も含むが、病害認識の技術的評価が明示されている。
abstractThis paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region.
Reproduction assets foundThe article's data availability statement explicitly states that the authors' weed phenotyping image dataset is publicly available on Zenodo (DOI 10.5281/zenodo.7598372), a paper-specific public asset. No author analysis code or trained model checkpoints are described with a public URL.Dataset · publicThe original contributions presented in the study are publicly available (see the weed phenotyping image dataset). This data can be found here: https://doi.org/10.5281/zenodo.7598372 .Open asset ↗Zenodo · 10.5281/zenodo.7598372lines:460-508Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Wheat ear counting is crucial for calculating wheat phenotypic parameters and scientifically managing fields, which is essential for estimating wheat field yield. In wheat fields, detecting wheat ears can be challenging due to factors such as changes in illumination, wheat ear growth posture, and the appearance color of wheat ears. To improve the accuracy and efficiency of wheat ear detection and meet the demands of intelligent yield estimation, this study proposes an efficient model, Generalized Focal Loss WheatNet (GFLWheatNet), for wheat ear detection. This model precisely counts small, dense, and overlapping wheat ears. Firstly, in the feature extraction stage, we discarded the C4 feature layer of the ResNet50 and added the Convolutional block attention module (CBAM) to this location. This step maintains strong feature extraction capabilities while reducing redundant feature information. Secondly, in the reinforcement layer, we designed a skip connection module to replace the multi-scale feature fusion network, expanding the receptive field to adapt to various scales of wheat ears. Thirdly, leveraging the concept of distribution-guided localization, we constructed a detection head network to address the challenge of low accuracy in detecting dense and overlapping targets. Validation on the publicly available Global Wheat Head Detection dataset (GWHD-2021) demonstrates that GFLWheatNet achieves detection accuracies of 43.3% and 93.7% in terms of mean Average Precision (mAP) and AP50 (Intersection over Union (IOU) = 0.5), respectively. Compared to other models, it exhibits strong performance in terms of detection accuracy and efficiency. This model can serve as a reference for intelligent wheat ear counting during wheat yield estimation and provide theoretical insights for the detection of ears in other grain crops.
Why it matches plant phenotyping methodsコムギ穂を画像から検出・計数するモデルを開発し、公開データセットで精度検証している。穂数は植物形質および収量推定に関わるため、フェノタイピング手法が中心である。
abstractthis study proposes an efficient model, Generalized Focal Loss WheatNet (GFLWheatNet), for wheat ear detection.
Spikelet diseases pose severe threats to crop production and crop protection requires timely evaluation of disease severity (DS). However, most studies have only investigated the spikelet diseases within a short period of crop growth. Few have examined the consistency in DS monitoring accuracy across growth stages. This study aimed to investigate the differences in spectral responses among growth stages and to develop a spectral index (SI), rice spikelet rot index (RSRI), for multi-stage monitoring of the rice spikelet rot disease. Proximal hyperspectral images were collected over spikelets with various levels of DS at heading, anthesis, and grain filling stages. The reflectance was related to the DS extracted from concurrent high-resolution RGB images. The proposed RSRI was evaluated for the DS estimation and lesion mapping across growth stages in comparison with existing SIs. The results demonstrated that the spectral responses to DS in the green and near-infrared regions for filling were weaker than those for anthesis, and blue bands were necessary in DS quantification for early infection. The RSRI-based models exhibited the best validation accuracy for heading and the most consistent performance across growth stages as comparison to other SIs (Heading: R² = 0.65; anthesis: R² = 0.84; filling: R² = 0.78). Moreover, RSRI-based DS maps exhibited the best lesion identification for slightly, mildly, and severely infected spikelets. This study suggests that RSRI could be promising in breeding and crop protection as a novel index for DS estimation regardless of the spikelet ripening effect.
Why it matches plant phenotyping methodsイネ穂の病害重症度を近接ハイパースペクトル画像とRGB画像から推定・マッピングする指標を開発し、複数生育段階で検証しており、植物表現型取得法が中心である。
abstractto develop a spectral index (SI), rice spikelet rot index (RSRI), for multi-stage monitoring of the rice spikelet rot disease.
Maize tassels is an important organ of maize plant, and has a very important impact on yield prediction and variety breeding. Therefore, realizing the efficient and accurate detection of maize tassels in the natural environment is the key to obtain maize phenotypes in large quantities and accurately predict maize yield. This study constructed a dataset of tassels from different growth stages, proposed a YOLOv5-Tassel (YOLOv5-T) maize tassels detection model based on UAV remote sensing platform, combined with attention mechanism, YOLOv5_l network, spatial pyramid pooling structure and multi-scale extraction advantages of Atrous convolution. The experimental results showed that the Average Precision (AP) of the model for maize tassels detection could reach 98.70 %, and the detection speed was 42.2f/s. And the method in this paper had strong robustness to changes in light intensity and maize tassels at different growth stages. It was feasible to use YOLOv5-T to detect large areas of maize tassels in real time, which provided a useful reference for the estimation of maize yield and the selection of maize varieties.
Why it matches plant phenotyping methodsトウモロコシ雄穂をUAV画像から検出するモデルを開発・検証しており、植物形質取得手法が研究の中心である。
abstractproposed a YOLOv5-Tassel (YOLOv5-T) maize tassels detection model based on UAV remote sensing platform
Abstract Agriculture is an essential sector that plays a necessary role in the economic improvement of a country. Prediction of plant diseases at the earliest stage may result in better yield and sustainable for growing population. The conventional method necessitates highly skilled inspectors to identify the phenotypic expression of different diseases. Alternatively, biochemical technologies offer more precise means of obtaining crop disease information by analyzing susceptible rice. However, these methods are time-consuming, expensive, reliant on laboratories, and require skilled professionals, rendering them unaffordable for most farmers. The paper aims to propose a solution to prevent infection at the earliest stage for the benefit of farmers. A novel crop disease detection model deploying a deep convolutional generative adversarial network (DC-GAN) and with multidimensional feature compensation Residual Neural Network (MDFC-ResNet) and named as DC-GAN-MDFC–ResNet, which aims at fine grained disease identification system detects from three aspects, bacterial leaf blight, leaf streak and panicle blight. Initially the input data undergone preprocessing using the several processes like data improvement, data normalization, and Singular value decomposition (SVD) to reduce the negative influence that the data set has on the training of the model. When compared to traditional convolution models, the suggested DC-GAN-MDFC–ResNet architecture exhibits in terms of highest classification accuracy, Segmentation free methodology and training stability. The experiments done in this work using Plant Village dataset which show the proposed technique offering improved recognition with the rate of 95.99% accuracy and generating higher quality samples compared to other well-known deep learning models.
Why it matches plant phenotyping methodsイネ葉・穂の病徴を画像から分類する深層学習モデルの開発が中心であり、植物病害状態という表現型を直接推定している。
abstractA novel crop disease detection model deploying a deep convolutional generative adversarial network (DC-GAN) and with multidimensional feature compensation Residual Neural Network (MDFC-ResNet)
Why it matches plant phenotyping methodsイネいもち病の植物症状を定量化する0〜6段階の病害スケールを開発し、複数レース・品種および圃場で評価しているため、病害表現型測定法が研究の中心である。
abstractHere, we develop a 0 to 6 scale for blast disease that allows proper assignment of rice breeding lines and varieties into six resistance levels
Spike grain number is a crucial parameter when estimating wheat yield. However, most methods predominantly focus on a single period, lacking universality. To achieve rapid and intelligent wheat spike grain counting from the filling stage to the mature stage, this study used different smartphones to obtain wheat ear images of different varieties and constructs the spike grain dataset required for deep learning segmentation model through image normalization and data enhancement. Then we propose a segmentation model called Multi-Attention TransUNet (MATransUNet) and a spike grain counting model (SGCountM) to achieve wheat spike grain counting. Our results indicate that, comparing with other models, MATransUNet achieves the best segmentation performance with strong robustness and generalization capabilities, achieving a mean intersection over union(mIoU) of 85.93%. Through the comparative analysis with manual counting results, our Method I, doubling the number of grains on one side of the ears, results in a root mean squared error (RMSE) of 4.38 and a coefficient of determination(R²) of 0.85; while the Method II, adding the grains on both sides of the ears, yields a RMSE of 3.13 and a R² of 0.93. The counting accuracy significantly improves compared to not using the SGCountM. Moreover, transfer learning notably enhances the accuracy of the segmentation and counting model, enabling accurate spike grain counting from the filling stage to the mature stage. Finally, we integrate MATransUNet and SGCountM to design and implement a wheat spike grain segmentation and counting system (WeChat mini program). In practical tests, the mini program can effectively, accurately segments and counts wheat spike grain, demonstrating its feasibility and effectiveness. This research provides valuable insights into the intelligent application of wheat spike grain counting.
Why it matches plant phenotyping methods小麦穂粒数という植物形質を画像からセグメンテーション・計数する手法を開発し、比較検証と実装まで行っており、フェノタイピング手法が研究の中心である。
abstractwe propose a segmentation model called Multi-Attention TransUNet (MATransUNet) and a spike grain counting model (SGCountM) to achieve wheat spike grain counting.
In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study was to estimate the correlation between vegetation indices (VIs) and grain yield and to identify the optimal timing for accurately estimating yield. Furthermore, this study aims to employ photographic quantification to measure the characteristics of corn ears and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in a Complete Randomized Block (CRB) design with three replications across three locations. Vegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield. The experiments consistently exhibited high levels of experimental quality across different locations, characterized by both high accuracy and low coefficients of variation. The experimental quality was consistently significant across all sites, with accuracy ranging from 79.07% to 95.94%. UAV flights conducted at the beginning of the crop cycle revealed a positive correlation between grain yield and the evaluated vegetation indices. However, a positive correlation with yield was observed at the V5 vegetative growth stage in Lavras and Ijaci, as well as at the V8 stage in Nazareno. In terms of corn ear phenotyping, the regression coefficients for ear width, length, and total number of grains (TNG) were 0.92, 0.88, and 0.62, respectively, demonstrating a strong association with manual measurements. The use of imaging for ear phenotyping is promising as a method for measuring corn components. It also enables the identification of the optimal timing to accurately estimate corn grain yield, leading to advancements in the agricultural imaging sector by streamlining the process of estimating corn production.
Why it matches plant phenotyping methodsUAVによる植生指標測定と画像によるトウモロコシ穂形質の定量化を中心に、手測定との一致を検証しており、植物表現型取得法が主要な貢献である。
abstractVegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield.
Background In vivo solid-phase microextraction (SPME) is a minimally invasive, non-exhaustive sample-preparation technique that facilitates the direct isolation of low molecular weight compounds from biological matrices in living systems. This technique is especially useful for the analysis of phytocannabinoids (PCs) in plant material, both for forensic purposes and for monitoring the PC content in growing Cannabis spp. plants. In contrast to traditional extraction techniques, in vivo SPME enables continuous tracking of the changes in the level of PCs during plant growth without the need for plant material collection. In this study, in vivo SPME utilizing biocompatible C18 probes and liquid-chromatography coupled to quadrupole time-of flight mass spectrometry (LC-Q-TOF-MS) is proposed as a novel strategy for the extraction and analysis of the acidic forms of five PCs in growing medicinal cannabis plants. Results The SPME method was optimized by testing various parameters, including the extraction phase (coating), extraction and desorption times, and the extraction temperature. The proposed method was validated with satisfactory analytical performance regarding linearity (10-3000 ng/mL), limits of quantification, and precision (relative standard deviations below 5.5 %). The proposed method was then successfully applied for the isolation of five acidic forms of PCs, which are main components of growing medicinal cannabis plants. As a proof-of-concept, SPME probes were statically inserted into the inflorescences of two varieties of Cannabis spp. plants (i.e., CBD-dominant and Δ9-THC-dominant) cultivated under controlled conditions for 30 min extraction of tetrahydrocannabinolic acid (Δ9-THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), cannabiviarinic acid (CBVA), and tetrahydrocannabivarinic acid (THCVA). Significance and novelty The results confirmed that the developed SPME-LC-Q-TOF-MS method is a precise and efficient tool that enables direct and rapid isolation and analysis of PCs under in vivo conditions. The proposed methodology is highly appealing option for monitoring the metabolic pathways and compositions of multiple PCs in medicinal cannabis at different stages of plant growth.
Why it matches plant phenotyping methods生育中の植物体からフィトカンナビノイド組成を非破壊・経時的に取得するSPME-LC-MS法を開発、最適化、分析性能検証し、実植物で適用しているため、植物フェノタイピング手法が中心である。
abstractThe SPME method was optimized by testing various parameters, including the extraction phase (coating), extraction and desorption times, and the extraction temperature.
The rice spike, a crucial part of rice plants, plays a vital role in yield estimation, pest detection, and growth stage management in rice cultivation. When using drones to capture photos of rice fields, the high shooting angle and wide coverage area can cause rice spikes to appear small in the captured images and can cause angular distortion of objects at the edges of images, resulting in significant occlusions and dense arrangements of rice spikes. These factors are unique challenges during drone image acquisition that may affect the accuracy of rice spike detection. This study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives. Initially, based on an enhanced version of YOLOv5, the EMA (efficient multiscale attention) attention mechanism is introduced, a novel neck network structure is designed, and SIoU (SCYLLA intersection over union) is integrated. Experimental results demonstrate that RICE-YOLO achieves a mAP@0.5 of 94.8% and a recall of 87.6% on the rice spike dataset. During different growth stages, it attains an AP@0.5 of 96.1% and a recall rate of 93.1% during the heading stage, and a AP@0.5 of 86.2% with a recall rate of 82.6% during the filling stage. Overall, the results indicate that the proposed method enables real-time, efficient, and accurate detection and counting of rice spikes in field environments, offering a theoretical foundation and technical support for real-time and efficient spike detection in the management of rice growth processes.
Why it matches plant phenotyping methodsイネ穂の検出・計数という植物器官形質の画像ベース推定手法を、ドローン画像と改良YOLOv5により開発・評価しており、手法が研究の中心である。
abstractThis study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives.
Abstract Background The increasing ambient temperature significantly impacts plant growth, development, and reproduction. Uncovering the temperature-regulating mechanisms in plants is of high importance, not only for boosting our plant biology knowledge but also for assisting plant breeders in improving plant resilience to these stress conditions. Numerous studies on the molecular mechanisms by which plants regulate temperature responses revealed that plants employ distinct transcription factors to regulate thermomorphogenesis specific to each tissue type. A significant discovery in this field was the identification of PHYTOCHROME-INTERACTING FACTORs (PIFs) as key regulators of thermomorphogenesis during vegetative growth. PIF4, a regulator of auxin-mediated signaling pathways, is crucial in controlling high-temperature responses. Results In this study, we screened the temperature responses of the wild type and several PhyB-PIF4 pathway Arabidopsis mutant lines in combined and integrative phenotyping platforms for root in soil, shoot, inflorescence, and seed. We demonstrated that high ambient temperature differentially impacts vegetative and reproductive organs through this pathway. Suppression of the PhyB-PIF4 components mimics the response to a high ambient temperature in wild-type plants. We also identified correlative responses to high ambient temperature between shoot and root tissues. This integrative and automated phenotyping was complemented by monitoring the changes in transcript levels in reproductive organs. Transcriptomic profiling of the pistils from plants grown under high ambient temperature identified key elements that may provide clues to the molecular mechanisms behind temperature-induced reduced fertilization rate, such as a downregulation of auxin metabolism, upregulation of genes involved auxin signalling, miRNA156 and miRN160 pathways, pollen tube attractants. Conclusions Thermomorphogenesis is uniquely controlled in the different plant tissues at different developmental stages. We have identified key elements that may help to determine the response to high ambient temperatures during reproduction processes.
Why it matches plant phenotyping methods複数器官を対象とする統合・自動フェノタイピングプラットフォームを用いた大規模な表現型取得が研究の主要な構成要素であり、単なるルーチン測定を超える実質的な方法適用と判断する。
abstractwe screened the temperature responses of the wild type and several PhyB-PIF4 pathway Arabidopsis mutant lines in combined and integrative phenotyping platforms for root in soil, shoot, inflorescence, and seed.
Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.
Why it matches plant phenotyping methodsトウモロコシ穂の生理状態・成長に関係する電気インピーダンス分布を3次元再構成する計算手法を開発し、シミュレーション、模型、実穂で性能評価しているため、植物フェノタイピング手法が中心である。
abstractTherefore, an effective method for 3D conductivity reconstruction is necessary.
Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.
Why it matches plant phenotyping methods小麦穂の検出・計数という植物器官形質の画像ベース推定手法を開発し、専用データセットを構築して性能評価しているため、方法が中心的である。
abstractwe propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes.
Reproduction assets foundThe paper introduces the RGWHD dataset (oriented-bounding-box annotations of GWHD wheat images), publicly released via Baidu pan with extraction code, and makes its experiment scripts publicly available on GitHub. The underlying GWHD image dataset (public on Kaggle) is the image source used for the paper's phenotyping.Dataset · publiction, Validation, Writing—original draft preparation. N.L.: Formal analysis, Resources, Project ad-ministration.C .F.: Investigation, Data curation. All authors have read and agreed to the published version of the manuscript.
Data availability
The datasets generated and analysed during the current study are available in RGWHD ( https://pan.baidu.com/s/1Fy3HpIfAeQhRef_ZuKu4iw ) and the extraction code is vbiy. The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request. The scripts to run all experiments are publicly available through our GitHub page https://github.com/cwr0821/OFPN .
Competing interests
The authors deOpen asset ↗RGWHDlines:445-520Code · publicdy are available in RGWHD ( https://pan.baidu.com/s/1Fy3HpIfAeQhRef_ZuKu4iw ) and the extraction code is vbiy. The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request. The scripts to run all experiments are publicly available through our GitHub page https://github.com/cwr0821/OFPN .
Competing interests
The authors declare no competing interests.
Footnotes
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References
1. Sharma, S., Kooner, R., Arora, R., Insect pests and crop losses. Breeding insect resistant crops for suOpen asset ↗cwr0821/OFPNlines:445-520Dataset · public. The GWHD dataset is a comprehensive collection of well-annotated wheat head images, compiled by nine research institutions across seven countries. It serves as a valuable resource for training robust models to accurately estimate the location and density of wheat heads in seven categories. The GWHD dataset can be accessed at https://www.kaggle.com/competitions/global-wheat-detection/data . The SPIKE dataset comprises 335 images captured at three distinct growth stages, covering ten different wheat varieties. The UWHD dataset consists of 550 images captured by a drone at an altitude of 10 m. The ACID dataset consists of 520 images taken in controlled greenhouse conditions, featuring 4158 laOpen asset ↗lines:52-149Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Apr 2024Computers and Electronics in Agriculture.
Unmanned aerial vehicles (UAVs) have the potential to reduce manual interventions in digitizing farmland and improve the accuracy and efficiency of data collection. Measuring crop yields with UAVs is a critical step in achieving precision agriculture on unmanned farms. The key to estimating rice yield is to distinguish the panicle region from the non-panicle region based on semantic segmentation. However, the traditional semantic segmentation model, such as the UNet, has an inferior segmentation performance on UAV images. In addition, UAVs are unable to segment the rice panicle region in real time due to the limited edge computing capabilities for some improved UNet models. To address this issue, this study proposes a new method for augmenting UAV rice panicle image segmentation called weighted skip-connection feature fusion (WSFF). Furthermore, a novel model WSUNet is constructed by combining WSFF and UNet model, which aims to enhance the performance of rice panicle segmentation without additional computational cost. Two datasets of rice panicle images taken with UAV are constructed. These two and cell nuclei dataset are used to compare the performance of UNet, WSUNet and UNet++. Mean intersection over union (mIOU) and mean pixel accuracy (mPA) are adopted as evaluation metrics. In terms of mIOU, the results indicate that WSUNet outperforms the UNet on all datasets, with a maximum increase of 2.84 on the rice panicle dataset. And the average inferencing speed (AIS) of WSUNet on CPU is 2.1 times that of UNet++. Additionally, in order to verify the role of WSFF, ¹WSUNet and ²WSUNet are constructed based on WSFF with two different skip-connection modes. By observing the training scalars of ¹WSUNet, ²WSUNet, WSUNet, and UNet, it can be seen that the model set with WSFF has a more competitive learning ability than UNet, and the segmentation performance of the model could be further improved with the increase of the amount of skip-connection.
Why it matches plant phenotyping methodsUAV画像からイネ穂領域を抽出するセグメンテーション手法を開発し、複数データセットで性能比較・検証しているため、植物表現型取得法が中心である。
abstractthis study proposes a new method for augmenting UAV rice panicle image segmentation called weighted skip-connection feature fusion (WSFF).
Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy ( r 2 ) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.
Why it matches plant phenotyping methodsトウモロコシ雄穂を画像から自動計数し、画像セグメンテーションと2種類の機械学習手法の精度を検証する研究であり、植物表現型取得法が中心である。
abstractwe developed machine learning methods for obtaining automated tassel counts at the plot level.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24072172/s1 , Data S1 containing training images and annotations, Figures S1–S7.Open asset ↗10.3390/s24072172/s1lines:127-146Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 10 6 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.
Why it matches plant phenotyping methods小麦穂のFHB症状を画像から検出する軽量化手法を開発・評価しており、植物病害状態の取得・推定が中心的な方法論的貢献である。
abstractthis article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB.
The Burkholderia glumae bacterium causes bacterial grain rot in rice, posing significant threats to the crop's yield, particularly thriving during the rice flowering and grain filling stages. This disease is especially evident in rice grains before harvest, presenting challenges in the detection and classification of rice panicles. Firstly, diseased grains may mix with healthy ones, complicating their separation. Secondly, the size of grains on a panicle varies from small to large, which can be problematic when detected using object detection methods. Thirdly, disease classification can be conducted by evaluating the extent of infection on rice panicles to assess its impact on yield. Finally, the challenges in detection, classification, and preprocessing for disease identification and management necessitate the adoption of diverse approaches in machine learning and deep learning to develop optimal methods and support smart agriculture.
Why it matches plant phenotyping methodsイネ穂・粒の病徴を画像で検出・分類するデータセットであり、植物の病害状態を推定するフェノタイピング用途が中心です。
titleGrain rot dataset caused by Burkholderia Glumae Bacteria.
Reproduction assets foundThis Data in Brief article describes its own publicly deposited rice grain rot image dataset (1528 annotated images, YOLO format) on Zenodo, with explicit direct URL and DOI, qualifying as a paper-specific public phenotyping image dataset.Dataset · publicrice fields in the Mekong Delta region using a mobile phone.
Data source location
Provinces in the Mekong Delta
Latitude: 10.063363, Longitude: 105.594339
Data accessibility
Repository name: Bacterial Grain Rot Dataset Caused by Burkholderia Glumae Bacteria
Data identification number: 10.5281/zenodo.10805462
Direct URL to data: https://zenodo.org/records/10805462
Guidance on retrieving this dataset: Individuals may obtain the dataset by downloading it from the provided link and then unzipping the files for use.
Related research article
Quach, Luyl-Da, et al. “Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small ObjectsOpen asset ↗zenodo · 10.5281/zenodo.10805462lines:1-57Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Abstract Rice grain size, grain length and grain width, are very important traits directly related to rice yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice. Traditional measurement methods still mainly rely on manual labor, which is time-consuming, labor-intensive, and error-prone. In this study, a novel method, dubbed “GSM-Method”, based on convolutional neural network and traditional image processing technology was developed for efficient and precise measurement of rice grain size parameters on rice panicle structure. Firstly, primary branch images of rice panicles were collected at the same height to build image database. Then, the grain detection model using convolutional neural network was established for grain recognition and localization. Subsequently, the calibration value was obtained through traditional image processing technology. Finally, the “GSM-Method” integrated with grain detection model and calibration value was developed for automatic measurement of grain size. The performance of the developed GS-Method was evaluated through testing 60 primary branch images. The test results showed that the root mean square error (RMSE) of grain length for two rice varieties (Huahang15 and Qingyang) were respectively 0.26 mm and 0.30 mm, while the corresponding RMSE of grain width was 0.27mm and 0.31mm, respectively. The proposed algorithm can provide an effective, convenient and low-cost tool for yield evaluation and breeding research.
Why it matches plant phenotyping methodsイネ穎果の長さ・幅という植物形質を画像と深層学習で自動計測する手法を開発し、精度評価まで行っており、フェノタイピング手法が中心である。
abstracta novel method, dubbed “GSM-Method”, based on convolutional neural network and traditional image processing technology was developed for efficient and precise measurement of rice grain size parameters on rice panicle structure.
Detection of spikes is the first important step toward image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leaves that exhibit similar colors to spike regions. Consequently, accurate detection of grain spikes renders, in general, a non-trivial task even for advanced, state-of-the-art deep neural networks (DNNs). To improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module. The performance of our extended FRCNN-A vs. conventional FRCNN was compared on images of different European wheat cultivars, including "difficult" bushy phenotypes from 2 different phenotyping facilities and optical setups. Our experimental results show that introduced architectural adaptations in FRCNN-A helped to improve spike detection accuracy in inner regions. The mean average precision (mAP) of FRCNN and FRCNN-A on inner spikes is 76.0% and 81.0%, respectively, while on the state-of-the-art detection DNNs, Swin Transformer mAP is 83.0%. As a lightweight network, FRCNN-A is faster than FRCNN and Swin Transformer on both baseline and augmented training datasets. On the FastGAN augmented dataset, FRCNN achieved a mAP of 84.24%, FRCNN-A attained a mAP of 85.0%, and the Swin Transformer achieved a mAP of 89.45%. The increase in mAP of DNNs on the augmented datasets is proportional to the amount of the IPK original and augmented images. Overall, this study indicates a superior performance of attention mechanisms-based deep learning models in detecting small and subtle features of grain spikes.
Why it matches plant phenotyping methods穂を画像から検出し収量の定量評価につなげる深層学習手法を開発・比較評価しており、植物表現型取得が研究の中心である。
abstractTo improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module.
Introduction: ) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive. Method: Here we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector. Results: Applying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date. Discussion: Our optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.
Why it matches plant phenotyping methodsUAV画像と深層学習によるイネ穂検出・計数を中核とし、出穂関連形質を高スループットに定量するフェノタイピング手法およびワークフローを評価・適用している。
abstractaerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits
Reproduction assets foundThe article states that all relevant code for the UAV phenotyping and panicle detection pipeline is publicly available in the authors' GitHub repository r1cheu/phenocv. Other URLs (Ultralytics, COCO, WinQTLCart) are generic third-party tools, not paper-specific assets.Code · publicAll relevant code can be accessed at https://github.com/r1cheu/phenocv .Open asset ↗r1cheu/phenocvlines:317-328Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2024Computers and Electronics in Agriculture.
Fusarium head blight (FHB) pathogen jeopardizes the quality and yield of wheat crops at critical grain formation stages. Given these, the capability of near-infrared hyperspectral imaging and non-imaging data was explored to develop wheat fusarium spectral indices (WFSI) and wheat fusarium texture indices (WFTI) on independent three-year datasets using consistently selected wavelet features (WFs) and texture features (TFs). Subsequently, five classifiers – Knn, RF, SVM, NN and Xgboost – were employed to fully explore the selected features and evaluate the newly developed indices for FHB classification accuracy. The frequent measurement results indicated that the biochemical and spectral changes were consistent for whole spike-pathogen interaction with the development of FHB. At the pre-symptomatic scale, the WFSI₁, WFSI₂, WFTI₁, and WFTI₂ individually demonstrated the average classification accuracy (ACA) in all classifiers of 78.90 %, 72.0 %, 73.60 %, and 71.0 %, respectively. At the disease scale (DS) two (3–5 % disease prevalence), the ACA of WFSI₁, WFSI₂, WFTI₁, and WFTI₂ increased to 90.10 %, 90.30 %, 88.10 %, and 85.40 %, respectively, for imaging data. Furthermore, the fusion of all four developed indices in 2019, 2020, and 2021 showed enhanced ACA of 79.05 %, 76.75 % and 78.59 %, respectively. The ACA of the fused four developed indices in the three years increased to 98.06 %, 89.68 % and 94.83 % at DS2, respectively, and for higher DS (10–100 % disease prevalence) also exhibited higher accuracy. Among all the classifiers, neural net (NN) performed better after Xgboost. The developed indices were also employed and successfully proved on independent datasets acquired from imaging and non-imaging sensors. This work reveals the promising implementation of hyperspectral information in improving FHB early monitoring in precision agriculture applications.
Why it matches plant phenotyping methods小麦穂のFHB症状・病害状態を対象に、ハイパースペクトル画像、テクスチャ特徴、独自指数、分類器を開発・評価しており、植物病害フェノタイピング手法が研究の中心です。
abstractdevelop wheat fusarium spectral indices (WFSI) and wheat fusarium texture indices (WFTI)
The dynamic growth of shoots and panicles determines the final agronomic traits and yield. However, it is difficult to quantify such dynamics manually for large populations. In this study, based on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits and tested the system using a recombinant inbred line population derived from a cross between Zhenshan 97 and Minghui 63. At the maturity stage, image recognition using a deep learning network (SegFormer) was applied to separate the panicle part of the image from the shoot. Eventually, with these obtained i-traits, the yield could be well predicted, and the R2 was 0.862. Quantitative trait loci (QTL) mapping was performed using an extra-high density single nucleotide polymorphism (SNP) bin map. A total of 3,586 time-specific QTLs were identified for the traits and parameters at various time points. Many of the QTLs were repeatedly detected at different time points. We identified the presence of cloned genes, such as TAC1, Ghd7.1, Ghd7, and Hd1, at QTL hotspots and evaluated the magnitude of their effects at different developmental stages. Additionally, this study identified numerous new QTL loci worthy of further investigation.
Why it matches plant phenotyping methods画像ベース形質を抽出する高スループット表現型解析プラットフォームと深層学習パイプラインを開発し、検証・応用しているため、表現型取得手法が研究の中心です。
abstractbased on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits
Corn canopy organs detection is critical in obtaining high-throughput phenotypic data. Accurate identification of each organ can provide a reliable data source for canopy phenotype determination, which has significant theoretical and practical value for corn variety breeding, cultivation management, and high-quality and high-yielding production. Due to the difficulty in quickly identifying corn canopy organs in the natural environment of the field, it is challenging to obtain high-throughput phenotypic data. Therefore, this paper proposed a method for corn canopy organs detection based on an improved network model (DBi-YOLOv8). Firstly, the Raspberry Pi 4B was used as the sensor control center to construct an embedded system for corn canopy image acquisition and collected 987 images of corn plants. Secondly, the improved deformable convolution and Bi-level routing attention were embedded into the backbone and neck structures of the YOLOv8 network. With training the improved network, a corn canopy detection model was obtained, which enabled the rapid detection of corn canopy organs. Finally, the LTNS algorithm and TBC algorithm were proposed for counting of the number of leaves, ears, and tassels. On the testing set data, the detection performance of the model was analyzed through different evaluation metrics. The results showed that the mAP and FPS of the detection model were 89.4% and 65.3, which increased by 12% and 0.6 compared to the original model. In addition, both algorithms have high reliability, with the coefficient of determination R² for counting crown leaves, ears, and tassel branches being 0.9336, 0.8149, and 0.917, respectively. This achievement proposed an accurate, non-destructive, and fast corn canopy organs detection model, providing reliable technical support for quantifying various traits of corn plants, field crop growth monitoring, and elite variety breeding.
Why it matches plant phenotyping methodsトウモロコシの器官画像検出・計数を通じた表現型データ取得手法の開発と性能評価が研究の中心であるため。
abstractCorn canopy organs detection is critical in obtaining high-throughput phenotypic data.
Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R 2 P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region.
Why it matches plant phenotyping methods携帯型近赤外分光計でトウモロコシの耳水分を測定し、全植物体水分（成熟度の表現型）を推定する手法を開発・検証しており、校正の機器間移植性も評価しているため、方法論が中心です。
abstractThe most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018-2019 and 2019-2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck.
Why it matches plant phenotyping methodsRGB画像とYOLOv4を用いて圃場の穂数を推定するフェノタイピング手法を開発・評価しており、形質取得と精度検証が研究の中心である。
abstractHere, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Grain count is an important trait in sorghum because it is highly correlated to the potential yield. By accurately phenotyping the number of grains per panicle, farmers and agronomists can better monitor crop development. Additionally, mapping the spatial variability of grain count can help identify areas of the field with higher or lower potential yields, allowing for targeted management strategies. This study introduces a method for predicting grain count for sorghum panicles by employing a deep learning-based regression framework for point clouds and Red Green Blue (RGB) images. The framework integrates global features derived from a point cloud model of the panicle and grain counts detected from a sequence of RGB images. The models were evaluated on a paired dataset of point cloud models and RGB images collected for 147 sorghum panicles, which included a variety of panicle structures and grain counts. The point cloud models were constructed via a proximal structure-from-motion-based photogrammetry workflow. The model uses PointNet as the backbone network for processing the point clouds and YoloV5 for detecting grains from RGB images. Following the grain detection step, a scaled dot product attention module is integrated into the network to process the grain counts obtained from the RGB image sequence. Finally, the global features for the point cloud model and the grain counts are combined to predict the total grain count for the panicle. Furthermore, the models are also evaluated on downscaled low-resolution point clouds to assess their potential to be adapted in the future for point cloud models for panicles acquired in the field. The models were able to predict grain counts for the high-resolution point cloud dataset with a mean absolute percent error of 6.5% and 6.8% for the low-resolution point cloud dataset. The results serve as a proof of concept to demonstrate the viability of using a multimodal approach based on point clouds and RGB images to estimate grain count per panicle. Additional enhancements to the model like the inclusion of a module to register the point cloud and the RGB images, and evaluating more point cloud backbone networks can help further strengthen the method.
Why it matches plant phenotyping methodsソルガム穂の粒数という植物形質を、点群とRGB画像の深層学習で非侵襲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 introduces a method for predicting grain count for sorghum panicles by employing a deep learning-based regression framework for point clouds and Red Green Blue (RGB) images.
Abstract It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding.
Why it matches plant phenotyping methods小麦の3D形態情報をMVS-Phenoと点群・深層学習で取得し、器官分割、形態パラメータ抽出、精度評価を行う手法研究であり、フェノタイピング手法が中心です。
abstractwe propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale.
Reproduction assets foundThe paper's Data Availability statement explicitly states that the data and code used in the article are publicly available on GitHub at the authors' repository, which matches an allowed URL. This qualifies as a paper-specific public asset covering the wheat 3D reconstruction/phenotyping analysis.Code · publicThe data and code used in this article are available on GitHub, at https://github.com/lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatOpen asset ↗lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatlines:280-436Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Feb 2024Computers and Electronics in Agriculture.
The current public acceptance rate towards medical cannabis feasibility has led to a worldwide increase in this plant species production. Nevertheless, the currently transforming legal framework does not prevent the originally unlawful knowledge around cannabis breeding, which lacks quality control regulations or standards for correct manufacturing processes, a fact that could subsequently lead to uncontrolled and even harmful crop products. In this line, the objective of this work was to develop a non-invasive methodology for cannabis chemotype classification in different cultivars during the plant cultivation process, in order to keep undoubtful production control over cannabis crops. Hence, hyperspectral imaging (HSI), coupled with various multivariate data analysis approaches, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), enabled the non-invasive in-situ analysis of the plants. Hence, two PLS-DA classification models were trained with the plant spectral data for three chemotypes, based on the cannabinoid content of the plant inflorescences, with the difference between both approaches being the regard of the stem part of the plant as a bias. Thus, obtained sensitivity and specificity values in the inflorescences were 0.845/0.845 for Chemotype I, 0.954/0.920 for Chemotype II, and 0.888/0.925 for Chemotype III. At last, a hierarchical PLS-DA, which considered the stem as a bias, presented an overall 94.7 % trueness in the external validation of 57 different plant individuals, divided as 92.3 % trueness for chemotype I, 100.0 % trueness for chemotype II and 88.9 % trueness for chemotype III. Based on these results, the proof of concept for comprehensive agricultural control of cannabis crops through a non-invasive analytical technique was demonstrated, a previously unproven fact. Therefore, this work could further pave the way for non-invasive technology development for horticultural quality control in medical cannabis productions, as this emerging industry will require strict control over the cannabis chemotypes, with the strong advantage of avoiding destructive and time-consuming analytical techniques such as chromatography.
Why it matches plant phenotyping methods植物の非侵襲的な化学型を推定するハイパースペクトル画像解析と機械学習手法を開発し、外部検証しており、植物状態の取得・抽出が研究の中心である。
abstractthe objective of this work was to develop a non-invasive methodology for cannabis chemotype classification
In this study, the first aim was to develop a rapid and non‐destructive method for analysing rice genotypes' tolerance to low temperatures (LT) during the seedling stage. Using a growth parameter and a physiological parameter, a discriminant formula was developed to differentiate between tolerant and sensitive genotypes based on their LT tolerance score. The study identified several benefits of the discriminant formula, including its low classification error rate, scalability, and ability to be used in controlled and reduced environments. Additionally, a second study was conducted, which found a strong correlation between the LT tolerance score during the seedling stage and plant yield at the ripening stage in plants grown under field LT during the vegetative stage. Panicle weight was the main mediator of the effect of the LT tolerance score on plant yield, but the number of panicles per plant also played a role. Overall, the results suggest that the LT tolerance score can serve as an indirect selection factor for plants for both LT tolerance and plant yield. This is especially relevant for rice‐growing regions with temperate climates and LT at the beginning of the cultivation season.
Why it matches plant phenotyping methodsイネの低温耐性を評価する迅速・非破壊的な方法と、耐性スコアを算出する判別式を開発しており、植物表現型の取得・分類が研究の中心である。
abstractthe first aim was to develop a rapid and non‐destructive method for analysing rice genotypes' tolerance to low temperatures (LT) during the seedling stage.
Rice (Oryza sativa L.) is the most important staple crop feeding more than half of the world’s population. Extensive effort currently undertaken to develop new and improve existing rice cultivars calls for high-throughput, ideally non-invasive methods for monitoring the phenology and performance of rice plants in the field. We report on the results of systematic application of canopy-level reflectance-derived absorption coefficients to the monitoring of rice stands with unmanned aerial vehicle multispectral sensors. The proposed approach was tested in the field on 39 rice varieties. It was capable of assessing rice phenology and physiology traits such as canopy absorption in different spectral regions, biomass productivity, panicle weight and, eventually, crop yield. Importantly, the proposed approach reflected the pigment transformation patterns accompanying the progression of rice phenological phases. Based on this information, our results showed it was possible to resolve with confidence the three key phases of rice phenology regardless of cultivar-specific variation in stand optical properties. The absorption coefficient in photosynthetically active radiation spectral range was significantly related to rice final yield. To the best of our knowledge, for the first time the absorption coefficients at blue and red bands were used to indicate panicle ripening, thus estimating panicle biomass accurately in the tested 39 varieties with the determination coefficient above 0.8. We argue that the proposed approach gives valuable insights into the rice phenology and physiology in the field, so it will become a useful complement to the traditional plant monitoring techniques welcomed by plant physiologists and practitioners, especially those involving in accelerated rice breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から吸収係数を算出し、イネの生育期・生理形質・穂重・収量を推定する方法を体系的に検証しており、表現型取得手法が研究の中心です。
abstracthigh-throughput, ideally non-invasive methods for monitoring the phenology and performance of rice plants in the field
Plants produce electrical signals which have traditionally been measured in voltage, but which have an unknown relation to plant health due to the unreliability of the voltage measurement. Using measurements of amperage, rather than voltage, we measured the electrical stress signatures produced by Douglas-fir (Pseudotsuga menziesii) trees in response to mechanical wounding. The electrical signature, which consists of a baseline signal, a spike initiated by wounding, and a subsequent signal recovery period, was measured along with controls for temperature, season, time-of-day, and soil type. We then compared the electrical stress signatures between healthy and unhealthy trees. We show that healthy trees have a characteristic electrical stress signature which is different from the electrical stress signature in unhealthy trees. Our results suggest that electrical stress signatures in amperage can be measured to reliably diagnose tree health providing a promising method for monitoring and early detection of changes in forest health.
Why it matches plant phenotyping methods植物の電気的ストレスシグネチャを電流測定で取得し、健全・不健全樹木の健康状態を診断する手法が研究の中心である。
abstractUsing measurements of amperage, rather than voltage, we measured the electrical stress signatures produced by Douglas-fir (Pseudotsuga menziesii) trees in response to mechanical wounding.
ABSTRACT Background Recent developments in hybridization chain reaction (HCR) have enabled robust simultaneous localization of multiple mRNA transcripts using fluorescence in situ hybridization (FISH). Once multiple split initiator oligonucleotide probes bind their target mRNA, HCR uses DNA base-pairing of fluorophore-labeled hairpin sets to self-assemble into large polymers, amplifying the fluorescence signal and reducing non-specific background. Few studies have applied HCR in plants, despite its demonstrated utility in whole mount animal tissues and cell culture. Our aim was to optimize this technique for sectioned plant tissues embedded with paraffin and methacrylate resins, and to test its utility in combination with immunolocalization and subsequent correlation with cell ultrastructure using scanning electron microscopy. Results Application of HCR to 10 µm paraffin sections of 17-day-old Setaria viridis (green millet) inflorescences using confocal microscopy revealed that the transcripts of the transcription factor KNOTTED 1 ( KN1 ) were localized to developing floret meristem and vascular tissue while SHATTERING 1 ( SH1 ) and MYB26 transcripts were co-localized to the breakpoint below the floral structures (the abscission zone). We also used methacrylate de-embedment with 1.5 µm and 0.5 µm sections of 3-day-old Arabidopsis thaliana seedlings to show tissue specific CHLOROPHYLL BINDING FACTOR a/b ( CAB1 ) mRNA highly expressed in photosynthetic tissues and ELONGATION FACTOR 1 ALPHA ( EF1 α ) highly expressed in meristematic tissues of the shoot apex. The housekeeping gene ACTIN7 ( ACT7 ) mRNA was more uniformly distributed with reduced signals using lattice structured-illumination microscopy. HCR using 1.5 µm methacrylate sections was followed by backscattered imaging and scanning electron microscopy thus demonstrating the feasibility of correlating fluorescent localization with ultrastructure. Conclusion HCR was successfully adapted for use with both paraffin and methacrylate de-embedment on diverse plant tissues in two model organisms, allowing for concurrent cellular and subcellular localization of multiple mRNAs, antibodies and other affinity probe classes. The mild hybridization conditions used in HCR made it highly amenable to observe immunofluorescence in the same section. De-embedded semi-thin methacrylate sections with HCR were compatible with correlative electron microscopy approaches. Our protocol provides numerous practical tips for successful HCR and affinity probe labeling in electron microscopy-compatible, sectioned plant material.
Why it matches plant phenotyping methods植物組織で複数mRNAを局在化するHCR法を最適化し、異なる切片材料・モデル植物・顕微鏡法で実証した方法開発研究である。
abstractOur aim was to optimize this technique for sectioned plant tissues embedded with paraffin and methacrylate resins, and to test its utility in combination with immunolocalization and subsequent correlation with cell ultrastructure using scanning electron microscopy.
The intelligent acquisition of phenotypic information on male tassels is critical for maize growth and yield assessment. In order to realize accurate detection and density assessment of maize male tassels in complex field environments, this study used a UAV to collect images of maize male tassels under different environmental factors in the experimental field and then constructed and formed the ESG-YOLO detection model based on the YOLOv7 model by using GELU as the activation function instead of the original SiLU and by adding a dual ECA attention mechanism and an SPD-Conv module. And then, through the model to identify and detect the male tassel, the model’s average accuracy reached a mean value (mAP) of 93.1%; compared with the YOLOv7 model, its average accuracy mean value (mAP) is 2.3 percentage points higher. Its low-resolution image and small object target detection is excellent, and it can be more intuitive and fast to obtain the maize male tassel density from automatic identification surveys. It provides an effective method for high-precision and high-efficiency identification of maize male tassel phenotypes in the field, and it has certain application value for maize growth potential, yield, and density assessment.
Why it matches plant phenotyping methodsUAV画像と改良YOLOモデルにより、トウモロコシ雄穂の検出・密度という植物形質を推定する手法の開発と性能評価が中心である。
abstractThe intelligent acquisition of phenotypic information on male tassels is critical for maize growth and yield assessment.
Introduction Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks. Methods Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images. Results For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.
Why it matches plant phenotyping methodsコムギ穂の画像から小穂の毛性という形態形質をCNNで抽出・分類する手法を開発し、セグメンテーションと分類性能を検証しているため、植物フェノタイピング手法が中心である。
abstractThis paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks.
Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances.
Why it matches plant phenotyping methodsUAVスペクトルデータと機械学習モデルによりイネの草丈・バイオマスを推定し、複数モデルの性能を評価しており、形質取得手法が研究の中心である。
abstractwe combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass
Maize ear rot poses a severe threat to maize yield and quality. Breeding and cultivating highly resistant maize varieties is a crucial approach for preventing and controlling maize ear rot. However, traditional methods of visually grading the severity of maize ear infection and resistance lack objectivity and repeatability. To meet the requirement of precise breeding and resistance assessment scenarios, a novel pipeline based on three-dimensional (3D) point clouds of maize ear was developed for ear rot precise evaluation. First, multi-view stereo (MVS) reconstruction was employed to obtain high-precision dense point clouds of maize ears. And the coordinate correction and circular sampling approaches were proposed to optimize the data structure of the input maize ear samples. Next, a specialized network called the ear rot segmentation network (ERSegNet) was proposed to detect the infected area of maize ears. This network incorporated an orientation-encoding (OE) module and point transformer (PT) attention, which effectively boosted the performance of PointNet++. The proposed ERSegNet achieved impressive results, including a mean intersection over union (mIoU) of 85.83%, a mean precision (mPrec) of 92.34%, a mean recall (mRec) of 92.23%, a mean F1-score of 92.28%, and an overall accuracy (OA) of 93.76%. This demonstrated the feasibility of using semantic segmentation algorithms to predict 3D point clouds of maize ears. Furthermore, a point cloud resampling method was suggested to enhance the spatial uniformity of maize ear point clouds and a point-level quantitative assessment approach based on the 3D point cloud data was provided for evaluating the severity of ear rot. The results showed an average evaluation error of 1.55% in the testing set, indicating the accuracy of the proposed method. This study provides a reliable and objective method for maize ear rot precise assessment, offering potential and valuable support for the identification of resistant varieties in breeding programs.
Why it matches plant phenotyping methodsトウモロコシ穂の感染領域と赤かび病重症度を、MVS由来3D点群とセマンティックセグメンテーションで定量化する評価手法を開発・検証しており、植物表現型取得が中心である。
abstracta novel pipeline based on three-dimensional (3D) point clouds of maize ear was developed for ear rot precise evaluation
Wheat Fusarium head blight (FHB) poses a significant threat to wheat quality and yield. However, accurately identifying the disease in wheat ears remains challenging due to limited data and weak hyperspectral signals. This study aimed to address these issues by obtaining effective wheat canopy hyperspectral data over three years of successive experiments. To reduce band redundancy and improve accuracy, nine different models were constructed, and the optimal algorithm was determined. Additionally, two types of new indices, were developed based on the spectral response mechanism of the wheat disease and published vegetation indices. Our study demonstrated that these newly constructed indices outperformed the published vegetation indices in terms of detection capability. By fusing the optimal algorithm with the new indices, a detection accuracy of 91.4% for the disease was achieved, surpassing the current level of wheat FHB detection. The high-accuracy model developed in this study not only provides methodological support for detecting wheat FHB but also serves as a reference for diagnosing diseases in other crops.
Why it matches plant phenotyping methods小麦の穂に生じた病害状態を対象に、現場ハイパースペクトルデータ、新規指標、CARS-Ridgeアルゴリズムを開発・比較し、検出精度を検証しているため、植物フェノタイピング手法が中心です。
abstractTo reduce band redundancy and improve accuracy, nine different models were constructed, and the optimal algorithm was determined.
Botrytis cinerea is one of the most destructive diseases for Vitis vinifera, and grape bunch morphology plays a crucial role in grey mould infection. However, the common visual evaluation technique for assessing bunch compactness suffers from a lack of sensitivity and objectivity. This study proposes a standardised digital twin shape analysis to evaluate the morphology of grape bunches. Seventeen Pinot Gris and six Pinot Noir clones were considered. The grey mould severity was evaluated in the field. Fully ripened bunches (138) were gathered and then photographed at different angulations. Digital twin reconstruction was carried out using the photogrammetry technique. Several measures and indices were extracted from each digital twin. Principal component analysis and multiple linear regression models were applied to identify the descriptors most related to grey mould symptoms. The results revealed that the most significant factors include the berries density, the estimated empty volume, and the bunch width. These results show that digital twins are a suitable tool for estimating grey mould infection risks. Two linear models, divided into 2D and 3D descriptor models, were proposed. The R-squared value and the root mean square error were compared between the models. For Pinot Gris, from the 2D to the 3D models, the R-squared value rose from 0.656 to 0.838, while the error decreased from 1.713 to 1.175. In Pinot Noir, the 2D model did not provide sufficient robustness, while the 3D model had an R-squared value of 0.936 and an error of 0.290.
Why it matches plant phenotyping methodsブドウ房形態をデジタルツインとフォトグラメトリで再構成し、形態記述子を抽出・検証して灰色かび感染リスクを推定する手法が研究の中心である。
abstractThis study proposes a standardised digital twin shape analysis to evaluate the morphology of grape bunches.
Fusarium head blight (FHB) is one of the most common and destructive infections in wheat, posing a serious threat to food security and human health. Real-time detection and severity assessment of wheat FHB are crucial for effective management and loss assessment. In this study, we proposed a novel and advanced YOLOv5s model for fast and accurate detection of wheat FHB. Firstly, we replaced the original backbone with MobileNetV3 and integrated spatial pyramid pooling-fast (SPPF). Subsequently, we replaced the C3 modules with the C3Ghost to reduce the parameters and complexity of this model without compromising detection performance. The proposed model was trained and evaluated on the wheat FHB dataset, achieving a high mean average precision (mAP) of 97.15 % using only 3.64 M parameters and 4.77 G floating-point operations (FLOPs). Compared with the original YOLOv5, these values have decreased by 49.72 % and 71.32 %, respectively. To assess the severity of FHB damage, the diseased spike rate was calculated and the coefficient of determination (R²) was 0.9802, achieving a satisfactory statistical effect. These results validate the effectiveness of the improved YOLOv5s model for real-time detection of wheat FHB spikes. This study contributes to the development of accurate and efficient plant disease detection systems and provides a valuable reference for accurate quantitative assessment of crop losses, thus ensuring food security.
Why it matches plant phenotyping methods小麦の赤かび病穂を画像から検出し、罹病穂率によって病害重症度を定量化する深層学習手法の開発・評価が中心であるため、植物病害フェノタイピングに該当する。
abstractwe proposed a novel and advanced YOLOv5s model for fast and accurate detection of wheat FHB
Timely and accurately predicting grain yield before harvest is of great importance for rice production management and grain trade. Numerous methods based on remote sensing (RS) technology have explored to estimate rice grain yield. However, in most cases, these methods are negatively affected by the mixed pixels of RS images due to the effect of background and panicles. To resolve such issues, the abundance information of leaves, soil and water background and rice panicles were extracted from the multispectral images of unmanned aerial vehicle (UAV) for the multiple endmember spectral mixture analysis (MESMA) method. Based on the analysis of contribution of vegetation index (VI) and abundance (ABD) to rice grain yield, a rice grain yield prediction model was developed by combining multi-stage time-series, ABD and VI. Results showed that MESMA can mitigate endmember variability in the estimation of rice yield, and achieve higher accuracy than conventional spectral mixture analysis (SMA). The NDRE and the sum of leaf and panicle abundance (ABDL₊P) can be well correlated to grain yield before heading stage (R² = 0.75) and at heading stage (R² = 0.72), respectively. In comparison to them, the multi-stage time-series model consisted of VI and ABD [∑(VI&ABD)] produces the highest correlation (R² = 0.80). It could also resolve the issues about the underestimation of yield using different datasets from various multi-spectral camera in comparison to the single-parameter models ∑VI and ∑ABD, and produce the good validation accuracy with R²=0.73, RRMSE=0.22 and R²=0.75, RRMSE=0.15, respectively. This study suggests that the combination of UAV multi-temporal VI and ABD data can achieve accurate prediction of rice grain yield. This method effectively utilizes the optical information of leaves and rice panicles and reduces background effects, which provides a new idea for accurate rice yield prediction.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から葉・穂・背景の存在量を抽出し、イネの収量を推定する手法を開発・検証しており、表現型取得・推定法が研究の中心である。
abstractthe abundance information of leaves, soil and water background and rice panicles were extracted from the multispectral images of unmanned aerial vehicle (UAV) for the multiple endmember spectral mixture analysis (MESMA) method.
In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high‐yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic‐by‐environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho‐physiological and yield‐related traits. The results indicated that a multi‐environment model outperformed a single‐environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral‐based estimated spike number was utilized as a secondary trait in a multi‐trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral‐genomic chain‐model workflow, which utilizes spectral‐based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.
Why it matches plant phenotyping methodsUAV搭載ハイパースペクトル反射から機械学習でコムギの穂数を推定する手法と、スペクトル表現型を用いた再利用可能な予測ワークフローが研究の中心である。
abstractwe integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data.
Agricultural robotics is an active research area due to global population growth and expectations of food and labor shortages. Robots can potentially help with tasks such as pruning, harvesting, phenotyping, and plant modeling. However, agricultural automation is hampered by the difficulty in creating high resolution 3D semantic maps in the field that would allow for safe manipulation and navigation. In this paper, we build toward solutions for this issue and showcase how the use of semantics and environmental priors can help in constructing accurate 3D maps for the target application of sorghum. Specifically, we 1) use sorghum seeds as semantic landmarks to build a visual Simultaneous Localization and Mapping (SLAM) system that enables us to map 78\\% of a sorghum range on average, compared to 38% with ORB-SLAM2; and 2) use seeds as semantic features to improve 3D reconstruction of a full sorghum panicle from images taken by a robotic in-hand camera.
Why it matches plant phenotyping methods植物の3D構造・器官形状を画像から再構成する手法開発が中心であり、単なるロボット位置推定に留まらず、ソルガム穂全体の3D再構成を扱っている。
abstractshowcase how the use of semantics and environmental priors can help in constructing accurate 3D maps for the target application of sorghum
Elevated temperatures during the flowing stage contribute to heat-induced spikelet sterility in rice, posing a major threat to production considering climate change projections. Developing effective strategies for stable rice production through breeding and crop management is critical; however, our understanding of regional, seasonal, and long-term trends in rice heat exposure remains limited. Previous studies on spikelet sterility revealed that panicle temperature, estimated using a micrometeorological model and common meteorological factors, serves as a reliable indicator of rice heat exposure. In this study, we employed this model to identify the differences between panicle and air temperatures (DPAT) and their causes over the past 45 years in Japan. A gridded daily meteorological dataset covering Japan was interpolated at an hourly time step and used as input data of the micrometeorology model for estimating panicle temperatures during flowering. Statistical analysis of the resulting data revealed an increasing trend in the frequency of rice panicle heat exposure over time across many locations in Japan. During heat-receptive periods, panicle temperature generally exceeded air temperature, indicating the inadequacy of relying solely on air temperature to gauge rice heat stress. DPAT values showed substantial inter-regional variations in both mean values (from -0.5 to 3.0) and seasonality. Through machine learning and statistical methods, the relationship between DPAT and meteorological factors was characterized, delineating the effects of the meteorological factors on regional and seasonal DPAT variations. Focusing on major high-risk regions, we show that mitigation strategies should be adapted to consider regional characteristics and avoid high DPAT conditions during rice heading periods.
Why it matches plant phenotyping methods水稲穂温という植物状態を微気象モデルで推定し、推定手法を用いた長期・地域比較と機械学習解析が研究の中心であるため、計算型フェノタイピングの応用として含める。
abstractpanicle temperature, estimated using a micrometeorological model and common meteorological factors, serves as a reliable indicator of rice heat exposure.
Botrytis cinerea is one of the most destructive diseases for Vitis vinifera, and grape bunch morphology plays a crucial role in grey mould infection. However, the common visual evaluation technique for assessing bunch compactness suffers from a lack of sensitivity and objectivity. This study proposes a standardised digital twin shape analysis to evaluate the morphology of grape bunches. Seventeen Pinot Gris and six Pinot Noir clones were considered. The grey mould severity was evaluated in the field. Fully ripened bunches (138) were gathered and then photographed at different angles. Digital twin reconstruction was carried out using the photogrammetry technique. Several measures and indices were extracted from each digital twin. Principal component analysis and multiple linear regression models were applied to identify the descriptors most related to grey mould symptoms. The results revealed that the most significant factors include the berries density, the estimated empty volume, and the bunch width. These results show that digital twins are a suitable tool for estimating grey mould infection risks. Two linear models, divided into 2D and 3D descriptor models, were proposed. The R-squared value and the root mean square error were compared between the models. For Pinot Gris, from the 2D to the 3D models, the R-squared value rose from 0.656 to 0.838, while the error decreased from 1.713 to 1.175. In Pinot Noir, the 2D model did not provide sufficient robustness, while the 3D model had an R-squared value of 0.936 and an error of 0.290.
Why it matches plant phenotyping methodsブドウ果房の形態をフォトグラメトリとデジタルツインで再構築し、形態形質を抽出して灰色かび症状との関連を検証する手法が研究の中心である。
abstractThis study proposes a standardised digital twin shape analysis to evaluate the morphology of grape bunches.
Published13 Dec 2023The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 3 · OpenAlex ↗
Abstract. The world relies heavily on wheat, corn, and rice for nutrition, with global challenges such as population growth and climate change threatening food security. To tackle this, plant breeding, supported by digital technologies, focuses on improving food quality and quantity. Currently, crop yield estimation uses indirect observations through hyperspectral data and spectral indices, such as NDVI, which suffer from low sensitivity in breeding scenarios. Terrestrial laser scanners (TLS) present an alternative, allowing observations of the quantity and morphology of wheat ears from point clouds, which are directly linked to grain yield. However, exploiting these observations under field conditions presents challenges, mainly due to reduced resolution and non-homogenous properties of point clouds. In response, we propose an approach for in-field wheat yield estimation using machine learning and stochastic features of TLS point clouds that are specifically handcrafted to be less sensitive to the abovementioned phenomena. This approach avoids the need for explicit 3D reconstruction of individual plants and plant organs. Our initial results show limited success in yield estimation when posed as a regression problem. However, when framed as a classification problem focusing on detecting top- and bottom-performing plant phenotypes, we achieved a promising accuracy of 84.4% and AUC of 0.93. While encouraging, these are only the first results under relaxed conditions and further work is needed to enhance practical applicability.
Why it matches plant phenotyping methodsTLS点群と機械学習を用いてコムギの収量および高・低収量表現型を推定する手法を開発しており、表現型取得・抽出が研究の中心である。
abstractwe propose an approach for in-field wheat yield estimation using machine learning and stochastic features of TLS point clouds
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
The objective was to estimate the correlation between VIs and grain yield and identify the optimal timing and VIs for precise corn grain yield estimation. Furthermore, the study aims to employ photographic quantification to measure corn ear traits and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in CRB with three rep-lications at three locations. Vegetation indices and green leaf area were estimated throughout the cycle using an unmanned aerial vehicle (UAV) and subsequently corre-lated with grain productivity. In addition, photographs were taken of the corn ear to esti-mate their length, width and total number of kernels and compare these values with manual measurements. The experiments consistently demonstrated significant experi-mental quality across sites, with accuracy ranging from 79.07% to 95.94%. UAV flights carried out at the beginning of the crop cycle revealed a positive correlation between grain productivity and the evaluated indices (NGRDI, VARI, GLI). Regarding the phenotyping of corn ears, the regression coefficients for width, length and TNG were 0.92, 0.88 and 0.62, respectively, indicating an association with manual measurements. However, stage V5 in the localities of Lavras and Ijaci and stage V8 in the locality of Nazareno showed a posi-tive correlation with productivity. The use of images for ear phenotyping is promising as a method for measuring corn components.
Why it matches plant phenotyping methodsUAV画像による植生指数・葉面積推定と、画像によるトウモロコシ穂の形質推定を手動測定と比較検証しており、フェノタイピング手法が中心的です。
abstractFurthermore, the study aims to employ photographic quantification to measure corn ear traits and establish their correlation with corn grain yield.
High throughput plant phenomics enables precise quantification of structural information for the complex crop canopy. Leaf to panicle ratio (LPR) in terms of light interception is a physiological trait we formerly developed to clarify the light distribution pattern within the canopy of japonica rice. Here, using the methodology of deep learning neural network (Transformer Feature Pyramid Network), we proposed a general method for LPR calculation for both japonica and indica rice, and tested it in the study on variation of canopy structure across nitrogen (N) fertilization modes. Field experiments over three years (2020–2022) with three nitrogen levels and two basal to topdressing ratios were conducted for two cultivars of each japonica and indica rice. Results showed contrasting dynamic variation of LPR between the two species, ascending for indica rice but descending for japonica rice along with the grain-filling progression. Indica rice had larger temporal variation in LPR than the japonica. N topdressing significantly increased the LPR of indica rice cultivars at same N level, whereas that of japonica was dependent on N level and genotype. Morphological measurement revealed that the differential response of LPR to N was associated with the height difference between the flag leaf and panicle, panicle curvature, leaf area index and panicle area index. Correlation analysis revealed that the relation between LPR and grain yield was significantly positive for indica rice but negative for japonica rice. Our findings suggest that LPR can effectively reflect the characteristics of canopy structure as affected by cultivars and fertilization modes, thus being a valuable physiological indicator for crop science.
Why it matches plant phenotyping methods深層学習を用いてイネ群落の葉・穂比（LPR）を算出する一般的方法を提案・検証しており、植物形態・生理形質の取得が研究の中心である。
abstractusing the methodology of deep learning neural network (Transformer Feature Pyramid Network), we proposed a general method for LPR calculation for both japonica and indica rice
Background The metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm. Results The results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m 2 , rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m 2 , rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m 2 , rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field. Conclusions The algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.
Why it matches plant phenotyping methodsTLSと点群アルゴリズムを用いたコムギ穂数の非破壊推定法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractIn this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.
Why it matches plant phenotyping methodsRGB・マルチスペクトル画像から小麦の生物物理形質を推定するCNN/PLSr比較と、転移学習・疑似ラベリングを含む訓練パイプラインが研究の中心であり、植物フェノタイピング手法に該当する。
abstractIn this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined.
In wheat (Triticum aestivum L.), the grain size varies according to position within the spike. Exposure to drought and high temperature stress during grain development in wheat reduces grain size, and this reduction also varies across the length of the spike. We developed the phenomics approach involving image-based tools to assess the intra-spike variation in grain size. The grains were arranged corresponding to the spikelet position and the camera of smart phone was used to acquire 333 images. The open-source software ImageJ was used to analyze features of each grain and the image-derived parameters were used to calculate intra-spike variation as standard deviation (ISVAD). The effect of genotype and environment were highly significant on the ISVAD of grain area. Sunstar and Raj 4079 contrasted in the ISVAD of grain area under late sown environment, and RNA sequencing of the spike was done at 25 days after anthesis. The genes for carbohydrate transport and stress response were upregulated in Sunstar as compared to Raj 4079, suggesting that these play a role in intra-spike assimilate distribution. The phenomics method developed may be useful for grain phenotyping and identifying germplasm with low intra-spike variation in grain size for their further validation as parental material in breeding.
Why it matches plant phenotyping methods画像取得とImageJ解析による穀粒サイズおよび穂内変異の表現型評価手法を開発し、遺伝子型・環境比較に適用しており、表現型取得・抽出法が中心である。
abstractWe developed the phenomics approach involving image-based tools to assess the intra-spike variation in grain size.
Background Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of seed productivity in grasses and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits. These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. Results Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass (Lolium perenne L.) spikes. SpykProps is able to rapidly and accurately identify spikes (RMSE 2 = 0.96), and number of spikelets (R 2 = 0.61). It also quantifies color and shape from hundreds of interacting descriptors that are accurate predictors of architectural and agronomic traits such as seed yield potential (R 2 = 0.94), rachis weight (R 2 = 0.83), and seed shattering (R 2 = 0.85). Conclusions SpykProps is an open-source platform to characterize inflorescence architecture in a wide range of grasses. This imaging tool generates conventional and latent traits that can be used to better characterize developmental and agronomic traits associated with inflorescence architecture, and has applications in fields that include breeding, physiology, evolution, and development biology.
Why it matches plant phenotyping methodsイネ科花序の形態を画像から定量化するPythonベースの表現型解析パイプラインを開発・検証しており、植物表現型の取得・抽出が研究の中心である。
abstractHere, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences
Reproduction assets foundThe paper's SpykProps Python pipeline is openly available on GitHub, and the original/processed spike images, data files, and analysis code are deposited in the University of Minnesota DRUM repository. Both are paper-specific, public, and actionable.Code · publicSpykProps is an open-source program that can be accessed from https://github.com/joanmanbar/SpykProps along with detailed instructions to analyze single spikes using a Python integrated development environment, or to automate it on a set of images using Bash and the SpykBatch.py function.Open asset ↗joanmanbar/SpykPropslines:69-75Dataset · publicAll the original and processed images, along with the data files and code to analyze them, can be accessed through the Data Repository for University of Minnesota (DRUM) at https://hdl.handle.net/11299/256105 .Open asset ↗Data Repository for University of Minnesota (DRUM) · 11299/256105lines:134-257Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Nov 2023Computers and Electronics in Agriculture.
Accurately and quickly detecting maize tassels is crucial for successful maize breeding and seed production. However, the complex planting environment in the field, including unsynchronized growth stages, cluttered backgrounds, severe occlusions, and varying tassel sizes and shapes, presents a challenge for detection methods. Current methods either use mainstream object detectors off-the-shelf or employ shallow adaptations on the modular level, lacking a systematic design that balances both speed and accuracy. Furthermore, most studies only report evaluation results on sliced image patches instead of original high-resolution images, which can offer limited guidance for large-scale crop management. In this study, we propose a novel one-stage anchor-free maize tassel detector (MT-Det), which is based on a single level feature and designed to be simple yet effective. Extensive comparisons with both one-level counterparts and feature pyramid detectors demonstrate that MT-Det outperforms them in both detection accuracy and inference speed. To address the issue of significant accuracy drop when directly inferring on high-resolution images, we introduce a bundle of slicing-aided hyper inference technologies to our proposed MT-Det, which results in a 13% and 38% improvement of mean average precision (mAP) on proximal and unmanned aerial vehicle (UAV) high-resolution images, respectively. We believe that MT-Det provides a promising high-throughput solution for accurate and efficient detection and counting of maize tassels in real-world field conditions.
Why it matches plant phenotyping methodsトウモロコシ雄穂の検出・計数という植物器官形質の取得を目的に、画像検出器と高解像度画像向け推論技術を開発・比較評価しており、表現型取得手法が中心である。
abstractwe propose a novel one-stage anchor-free maize tassel detector (MT-Det)
Plant response to environmental stresses varies with time and is not uniformly manifested across the entire plant or specific organs. However, in most cases the phenotypic responses are measured at a single time point and lack spatial resolution. In this study, we aimed to develop and test a non-destructive approach to capture the dynamic plant stress responses over a time course and with spatial resolution. We used the rice panicle as the organ with known spatial heterogeneity and heat stress as the environmental perturbation. We used a series of 2D RGB images to reconstruction the rice panicle at high resolution. This analysis was applied to the rice diversity panel and enabled us to identify multiple loci regulating heat stress response by combining the 3D-reconstruction derived-approach digital traits with genome-wide association analysis. We further validated this approach with gene edits to confirm the role of the identified targets genes in heat stress response. In summary, our results present a high spatiotemporal resolution approach to identify digitals traits and underlying genetic variation that is unlikely to have emerged from conventional image-based phenotyping.
Why it matches plant phenotyping methodsイネ穂の2D RGB画像から高解像度3D再構成を行い、時空間的なストレス応答をデジタル形質として抽出する手法の開発・検証が中心である。
abstractwe aimed to develop and test a non-destructive approach to capture the dynamic plant stress responses over a time course and with spatial resolution
Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.
Why it matches plant phenotyping methods小麦穂の画像検出を目的とするWheatNetを開発し、異なる生育段階・ドローン画像で精度評価しており、植物表現型取得手法が中心である。
abstractThis paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage.
BACKGROUND: Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS: In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS: This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の形態・構造形質を定量化する高スループット表現型計測法を開発し、手動測定で検証しているため。
abstractwe developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet).
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the authors' repository, which is an allowed URL.Code · publicAll code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_barley_spike_detection .Open asset ↗zerosky010/CT_barley_spike_detectionlines:160-268Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Fusarium head blight (FHB) is an economically important disease in wheat which can cause yield losses >50%. Breeding for host resistance is the most effective control method, however time, labor, and human subjectivity limit phenotyping efforts. A novel, high-throughput phenotyping rover was used to collect in-field RGB images of inoculated wheat spikes at multiple time points in 2021 and 2022. A deep neural network pipeline was developed to classify wheat spikes, segment healthy and diseased tissue, and quantify FHB severity as the region of intersection between spike and disease masks. To validate the pipeline, model inferences on a plot and spike scale were compared to five raters who performed disease scoring in the field and on images. The precision and throughput of the phenotyping rover and FHB quantification pipeline exceeded conventional rating methods. The plot aggregate disease scores based on pipeline outputs correlated strongly with plot-level disease scores by raters in the field and imagery. When comparing disease annotations on spike images, pipeline to human disease correlations were equivalent to correlations between raters, however location tended to influence disease assessment. The pipeline has strong generalizability and performed well on images taken across environments, with different camera orientations, and throughout disease progression. These results demonstrate a breakthrough in FHB phenotyping and facilitate precise and efficient disease quantification on spikes and plot aggregates across time and imaging conditions that are unachievable using conventional methods.
Why it matches plant phenotyping methodsRGB画像、深層学習、ローバーを用いてコムギ穂のFHB重症度を定量化し、従来評定者との技術検証も行う、中心的な植物フェノタイピング手法研究。
abstractA novel, high-throughput phenotyping rover was used to collect in-field RGB images of inoculated wheat spikes at multiple time points in 2021 and 2022.
Abstract Improvements in trait phenotyping are needed to increase the quantity and quality of data available for genetic improvement of crops. In this study, we used moderate throughput image analysis and machine learning as a pipeline for phenotyping a key wheat spike characteristic: spikelet number per spike. A population of 594 soft red winter wheat inbred lines was evaluated in the field for 2 years and images of wheat spikes were taken and used to train deep‐learning algorithms to predict spikelet number. A total of 12,717 images were used to train, test, and validate a basic regression convolutional neural network (CNN), a visual geometry group application regression model, VGG16, the ResNet152V2 model, and the EfficientNetV2L model. The EfficientNetV2L model was the most accurate, having the lowest mean absolute error, second lowest root mean square error, and highest coefficient of determination (mean absolute error [MAE] = 0.60, root mean square error [RMSE] = 0.79, and R 2 = 0.90). The ResNet152V2 model was slightly less accurate with a slightly better fit (MAE = 0.61,m RMSE = 0.78, and R 2 = 0.87), followed by the basic CNN (MAE = 0.75, RMSE = 1.00, and R 2 = 0.74) and finally by the VGG16 (MAE = 1.51, RMSE = 1.29, and R 2 = 0.076). With an average error of just above one half of a spikelet, utilizing image analysis and machine learning counting methods could be used for multiple breeding applications, including direct selection of spikelet number, to provide data to identify quantitative trait loci, or for training whole genome selection models.
Why it matches plant phenotyping methods画像解析と機械学習を用いてコムギ穂の小穂数を推定するフェノタイピング手法の開発・検証が中心である。
abstractwe used moderate throughput image analysis and machine learning as a pipeline for phenotyping a key wheat spike characteristic: spikelet number per spike.
Rice ( Oryza sativa ) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM 2 ) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM 2 trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM 2 trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM 2 trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM 2 trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.
Why it matches plant phenotyping methodsイネ穂数という植物形質をドローン画像から定量化するAIプラットフォーム、データセット、検出モデルを開発・検証しており、表現型取得手法が研究の中心である。
abstractwe present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery.
Reproduction assets foundThe paper's Data Availability statement provides a public GitHub releases page containing the authors' source code and the paper-specific DRPD dataset (5,372 annotated rice panicle subimages), plus a public cloud platform URL for panicle detection. These directly reproduce the paper's phenotyping measurements and are,Code · publicRelease page and source code can be found via https://github.com/changcaiyang/Panicle-AI/releases/; the DRPD dataset: 5,372 RGB subimages with annotate 259,498 panicles collected from 229 rice varieties can also be downloaded for the GitHub repository.Open asset ↗https://github.com/changcaiyang/Panicle-AI/releases/lines:230-241Dataset · publicthe DRPD dataset: 5,372 RGB subimages with annotate 259,498 panicles collected from 229 rice varieties can also be downloaded for the GitHub repositoryOpen asset ↗DRPDlines:230-241Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Fusarium head blight (FHB) is an economically important disease in wheat which can cause yield losses >50%. Breeding for host resistance is the most effective control method, however time, labor, and human subjectivity limit phenotyping efforts. A novel, high-throughput phenotyping rover was used to collect in-field RGB images of inoculated wheat spikes at multiple time points in 2021 and 2022. A deep neural network pipeline was developed to classify wheat spikes, segment healthy and diseased tissue, and quantify FHB severity as the region of intersection between spike and disease masks. To validate the pipeline, model inferences on a plot and spike scale were compared to five raters who performed disease scoring in the field and on images. The precision and throughput of the phenotyping rover and FHB quantification pipeline exceeded conventional rating methods. The plot aggregate disease scores based on pipeline outputs correlated strongly with plot-level disease scores by raters in the field and imagery. When comparing disease annotations on spike images, pipeline to human disease correlations were equivalent to correlations between raters, however location tended to influence disease assessment. The pipeline has strong generalizability and performed well on images taken across environments, with different camera orientations, and throughout disease progression. These results demonstrate a breakthrough in FHB phenotyping and facilitate precise and efficient disease quantification on spikes and plot aggregates across time and imaging conditions that are unachievable using conventional methods.
Why it matches plant phenotyping methodsRGB画像と深層学習を用いてコムギ赤かび病の症状・重症度を定量化する手法を開発し、従来評価者との比較で検証しているため、植物表現型取得が研究の中心である。
abstractA novel, high-throughput phenotyping rover was used to collect in-field RGB images of inoculated wheat spikes at multiple time points in 2021 and 2022.
Fusarium spp. are important pathogens on cereals, capable of causing considerable yield losses and significantly reducing the quality of harvested grains due to contamination with mycotoxins. The European Union intends to reduce the use of chemical-synthetic plant protection products (csPPP) by up to 50% by the year 2030. To realize this endeavor without significant economic losses for farmers, it is crucial to have both precise early detection of pathogens and effective alternatives for csPPP. To investigate both the early detection of Fusarium head blight (FHB) and the efficacy of selected biological control agents (BCAs), a pot experiment with spring wheat (cv. 'Servus') was conducted under semi-field conditions. Spikes were sprayed with different BCAs prior to inoculation with a mixture of F. graminearum and F. culmorum conidia. While early detection of FHB was investigated by hyperspectral imaging (HSI), the efficiency of the fungal ( Trichoderma sp. T10, T. harzianum T16, T. asperellum T23 and Clonostachys rosea CRP1104) and bacterial ( Bacillus subtilis HG77 and Pseudomonas fluorescens G308) BCAs was assessed by visual monitoring. Evaluation of the hyperspectral images using linear discriminant analysis (LDA) resulted in a pathogen detection nine days post inoculation (dpi) with the pathogen, and thus four days before the first symptoms could be visually detected. Furthermore, support vector machines (SVM) and a combination of LDA and distance classifier (DC) were also able to detect FHB symptoms earlier than manual rating. Scoring the spikes at 13 and 17 dpi with the pathogen showed no significant differences in the FHB incidence among the treatments. Nevertheless, there is a trend suggesting that all BCAs exhibit a diminishing effect against FHB, with fungal isolates demonstrating greater efficacy compared to bacterial ones.
Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習によるコムギ赤かび病の早期検出が中心的な方法的貢献であり、植物病害状態の推定を技術的に評価している。
abstractWhile early detection of FHB was investigated by hyperspectral imaging (HSI)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Abstract Background Detection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications. Results In our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R 2 value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61). Conclusions Extensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.
Why it matches plant phenotyping methods小麦穂の検出・計数という植物器官形質の画像ベース推定手法を開発し、データセット上で精度・速度・汎化性能を評価しているため、方法が研究の中心である。
abstractwe have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection.
Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient. In this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild. Specifically, the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates, capturing rich contextual information at different scales more effectively. In addition, a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds. Finally, a new up-sampling module, UP-Block, is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process. Extensive experiments on 2 publicly available tassel-counting datasets, maize tassels counting and maize tassels counting from unmanned aerial vehicle, demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods. The model is publicly available at https://github.com/ShiratsuyuShigure/MLAENet-pytorch/tree/main.
Why it matches plant phenotyping methodsトウモロコシ雄穂の画像ベース計数という植物形質推定手法を開発し、公開データセットで精度・速度を検証しているため、方法が研究の中心である。
abstractIn this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild.
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · Crossref · checked 7 Sept 2026
Abstract Fusarium head blight (FHB) in wheat is an economically important disease, which can cause yield losses exceeding 50% and the causal pathogen that infects spikes produces harmful mycotoxins. Breeding for host resistance remains the most effective disease control method; but time, labor, and human subjectivity during disease scoring limits selection advancements. In this study we describe an innovative, high-throughput phenotyping rover for capturing in-field RGB images and a deep neural network pipeline for wheat spike detection and FHB disease quantification. The image analysis pipeline successfully detects wheat spikes from images under variable field conditions, segments spikes and diseased tissue in the spikes, and quantifies disease severity as the region of intersection between spike and disease masks. Model inferences on an individual spike and plot basis were compared to human visual disease scoring in the field and on imagery for model evaluation. The precision and throughput of the model surpassed traditional field rating methods. The accuracy of FHB severity assessments of the model was equivalent to human disease annotations of images, however individual spike disease assessment was influenced by field location. The model was able to quantify FHB in images taken with different camera orientations in an unseen year, which demonstrates strong generalizability. This innovative pipeline represents a breakthrough in FHB phenotyping, offering precise and efficient assessment of FHB on both individual spikes and plot aggregates. The model is robust to different conditions and the potential to standardize disease evaluation methods across the community make it a valuable tool for studying and managing this economically significant fungal disease.
Why it matches plant phenotyping methodsRGB画像と深層学習によりコムギ穂のFHB病徴・重症度を抽出し、人手評価との比較検証も行う、植物フェノタイピング手法の開発・評価が中心です。
abstractwe describe an innovative, high-throughput phenotyping rover for capturing in-field RGB images and a deep neural network pipeline for wheat spike detection and FHB disease quantification
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R 2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.
Why it matches plant phenotyping methodsイネ穂上粒の形態形質を画像・深層学習で抽出する手法を開発し、手動測定との比較で検証しており、表現型取得が研究の中心である。
abstractThis study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology
Reproduction assets foundThe paper's data availability statement explicitly points to a public GitHub repository (BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection) hosting the study's datasets, which per the statement contain the paper's rice panicle images and phenotyping resources. No separate trained-model checkpoint or analysis URLDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection .Open asset ↗BME-PhenoTeam/Method-for-on-panicle-rice-grain-detectionlines:521-553Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses.
Why it matches plant phenotyping methodsドローンリモートセンシングと画像解析により、個々のブロッコリー頭部サイズを自動・非破壊推定するパイプラインを開発・検証しており、植物形質取得が中心的です。
abstractwe developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis.
Reproduction assets foundThe authors' full phenotyping/analysis pipeline source code is publicly available on GitHub (UAVbroccoli). Original drone image data (224 GB for 2020, 72 GB for 2021) exist but are only available upon request via Google Drive. Generic tools (YOLOv5, BiSeNet, labelme, EasyIDP, scikit-image) are third-party libraries, soCode · publicurvey powered by ML/DL for sustainable agricultural development, there are some limitations to its use. First, our system is neither fully automated nor app-based; therefore, farmers without computer science backgrounds cannot use this system directly in their own fields. However, because the source code is open to the public ( https://github.com/UTokyo-FieldPhenomics-Lab/UAVbroccoli ), local agricultural institutes and agricultural companies are able to modify and use the system according to their target. This study is definitely not a one-stop solution, but is a pioneer in real agriculture applications. Second, unlike traditional manual methods with limited throughput, the proposed method Open asset ↗UTokyo-FieldPhenomics-Lab/UAVbroccolilines:291-292Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Advances in technology have increased the adoption of high‐throughput phenotyping (HTP) methodologies, potentially replacing laborious and time‐consuming measurements and data recording. One promising HTP tool for fine‐featured and small‐sized characteristics is the three‐dimensional (3‐D) scanning and imaging system, but the utility of present two‐dimensional (2‐D) technology has not been fully explored for this purpose. The objective of this work was to reduce assessment and field evaluation time by developing 2‐D photogrammetric and 3‐D point cloud imaging methods for HTP of spike characteristics in perennial ryegrass (Lolium perenne L.), with special attention to traits that might be associated with seed retention. These HTP imaging systems were compared with direct data capture by hand on spikes of 21 diverse global accessions of perennial ryegrass. The Fiji (ImageJ) open‐source imaging software was used for photogrammetric analysis of spike structure, including spike length, spikelet number, internode length, and 2‐D curvature of the spike. An original approach to nondestructive HTP for crop characterization was developed using the commercially available Artec Space Spider. This 3‐D optical sensor was used to generate dense 3‐D point clouds to measure spike length, spikelet number, internode length, spikelet length, spikelet angle, and 3‐D curvature of the spike. Both methods were found to accurately characterize the subject; the 3‐D method was slower than 2‐D but was more precise (p ≤ 0.01) than 2‐D image analysis with a linear measurement deviation of only 0.17%. Fiji was effectively used for post‐processing image analysis, and the Space Spider can be used directly in the field to support HTP data collection. This non‐destructive field measurement system facilitates HTP in perennial ryegrass spikes and likely in other applications.
Why it matches plant phenotyping methodsイネ科植物の穂形質を対象に、2D画像処理および3D点群イメージング手法を開発し、手測定と比較検証しているため、表現型取得法が研究の中心です。
abstractThe objective of this work was to reduce assessment and field evaluation time by developing 2‐D photogrammetric and 3‐D point cloud imaging methods for HTP of spike characteristics in perennial ryegrass (Lolium perenne L.)
Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model's hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants.
Why it matches plant phenotyping methods小麦穂の圃場画像から穂をセグメンテーション・計数する画像解析手法と注釈付きデータセットを開発・評価しており、植物形質取得が研究の中心である。
abstractThis study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' codebase and the curated SPIKE-segm wheat spike segmentation dataset in a public Figshare project, which qualifies as a paper-specific public asset. The Roboflow URL is only a cited generic tool and does not qualify.Dataset · publicThe codebase developed and the datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/projects/WheatSpikeNet_An_Improved_Wheat_Spike_Segmentation_Model_for_Accurate_Counting_from_Field_Imaging/163225 .Open asset ↗figshare · 163225lines:914-939Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background: Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of fitness and seed productivity in grasses, and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits. These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. Results Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass ( Lolium perenne L.) spikes. SpykProps is able to rapidly and accurately identify spikes (RMSE < 1), estimate their length (R 2 = 0.96), and number of spikelets (R 2 = 0.61). It also quantifies color and shape from hundreds of interacting descriptors that are accurate predictors of architectural and agronomic traits such as seed yield potential (R 2 = 0.94), rachis weight (R 2 = 0.83), and seed shattering (R 2 = 0.85). Conclusions SpykProps is an open-source platform to characterize inflorescence architecture in a wide range of grasses. This imaging tool generates conventional and latent traits that can be used to better characterize developmental and agronomic traits associated with inflorescence architecture, and has applications in fields that include breeding, physiology, evolution, and development biology.
Why it matches plant phenotyping methodsイネ科花序の形態形質を画像から抽出するPythonベースの画像解析システムを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractHere, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicSpykProps is an open-source program that can be accessed from https://github.com/joanmanbar/SpykProps along with detailed instructions to analyze single spikes using a Python integrated development environment, or to automate it on a set of images using Bash and the SpykBatch.py function.Open asset ↗joanmanbar/SpykPropslines:59-64