Automated fruit counting and yield estimation systems are necessary for efficient orchard management. This study presents a computer vision system based on video multi-object-tracking for fruit load estimation in apple orchards and provides a comprehensive analysis of the system performance under diverse scanning conditions. The system integrates fruit detection, tracking, localization within orchard, and fruit load map generation. Experiments were carried out in an experimental apple orchard containing 420 apple trees. Data was collected with two different RGB-D sensors (Azure Kinect DK and ZED 2) at three different scanning distances (125 cm, 175 cm, and 225 cm) on two different dates prior to the harvest. Comparing the two evaluated sensors, Azure Kinect provided more consistent performance across different dates. Results also show that the longer scanning distance improves accuracy due to seeing the full tree view gives better fruit counts than close partial views. Between the two dates, best results were achieved near harvest due to fruit color at this stage, achieving a Mean Absolute Percentage Error (MAPE) of 6.91 % and a determination coefficient (R 2 ) of 0.733 (using ZED2 sensor at 225 cm distance). Finally, a test comparing scanning from one or both sides of the tree row showed that bilateral scanning improved fruit load estimation at the stretch level by incorporating information from both sides of the canopy. The results of this work demonstrate the effectiveness of the video fruit tracking systems as a useful tool for automating fruit load estimation.
Why it matches plant phenotyping methods動画ベースの果実検出・追跡手法を開発・評価し、リンゴ樹の果実負荷量を推定することが研究の中心であるため、植物フェノタイピング手法として含める。
abstractThis study presents a computer vision system based on video multi-object-tracking for fruit load estimation in apple orchards and provides a comprehensive analysis of the system performance under diverse scanning conditions.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsレタスのシュート段階を対象とした非破壊フェノタイピング手法と、強化セグメンテーションを含む画像解析モデルの開発が題名で明示されており、表現型取得・抽出が中心です。
titleMoeBi-ViT: Non-destructive shoot-stage phenotyping of lettuce in plant factory via dual-branch mixture-of-experts network and enhanced segmentation
Aboveground biomass density (AGBD) is a key indicator in agricultural systems, directly reflecting crop carbon storage potential and yield levels. The World Food Studies (WOFOST) model is widely used for crop growth simulation due to its process-based interpretability. However, its application is limited by complex calibration demands and struggles with spatial heterogeneity. To address these limitations, this paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model, which requires simple calibration. WOFOST is run in potential production mode, which assumes ideal conditions to reduce input data requirements. Before assimilation, phenology-related temperature sums are aligned with phenology and meteorological data to match local growth stages. Two quantities are then estimated and updated in the model through data assimilation by minimizing the difference between SAR-derived and simulated AGBD. These quantities are the proposed yield reduction factor, which represents the proportional decrease in potential CO 2 assimilation under stresses, and the initial total dry weight at sowing. Validation experiments were conducted using multi-year cotton datasets from two farms in Georgia, USA, differing in whether irrigation was applied. Compared to WOFOST simulations and evaluated against in situ AGBD measurements, the assimilation results improve agreement and reduce error (approximately 43% RMSE reduction at the rainfed site and 15% at the irrigated site). It also delivers spatial maps of biomass, together with model-derived yield and harvest-index diagnostics, and remains operational under frequent cloud cover where optical observations are sparse. This SAR-based assimilation strategy reduces calibration demands, providing a novel and practical pathway to extend WOFOST applications to diverse agricultural scenarios.
Why it matches plant phenotyping methodsSARデータを作物成長モデルに同化して綿の地上部バイオマスを推定する手法を開発し、複数年・複数圃場データで検証しているため、植物形質取得が中心である。
abstractthis paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model
Early crop establishment strongly influences plant performance and yield, making seedling emergence an important trait in crop phenotyping, breeding, and stress physiology studies. However, emergence monitoring is still commonly performed manually and typically records only the final emergence percentage, limiting the analysis to other dynamic observations. Automated image-based approaches are promising but remain challenging due to the small size of plant structures, heterogeneous soil backgrounds, and variability across imaging systems. Here, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction. The system integrates instance segmentation, object-detection–based data reduction, and temporal deep learning to estimate the emergence time of individual seedlings from RGB image sequences. The pipeline then automatically reconstructs emergence curves and extracts associated traits, including final emergence percentage, EC50, and emergence synchronicity. SPROUT was developed and evaluated using barley and wheat datasets acquired with different RGB cameras under controlled growth-chamber conditions. In the development and retraining settings, the best-performing TCN model achieved 90.0% per-well accuracy with a ± 2h tolerance, supporting accurate emergence curve reconstruction. In an independent inference-only dataset, the model still captured approximate emergence dynamics, although accuracy decreased to 59.3%, indicating that SPROUT is best used as a modular pipeline that can be retrained or fine-tuned for new crop, camera, or experimental domains. A cadmium-stress case study in two contrasting wheat genotypes showed that SPROUT-derived traits captured genotype-specific establishment strategies associated with growth and metabolic responses.
Why it matches plant phenotyping methodsRGB時系列画像から出芽動態を推定し、出芽率・EC50・同時性などの形質を抽出する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 SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction.
Reproduction assets foundThe paper's SPROUT emergence-prediction pipeline code is publicly available on GitHub with explicit availability language, and raw images plus morphology/metabolic data are deposited on Zenodo (10.5281/zenodo.18889863). The GitHub URL is in allowed_urls; the Zenodo DOI is not, so only the code asset is listed as an ad-Code · publiccan be found online at https://doi.org/10.1016/j.compag.2026.112184.Data availability
The raw images and raw data for the morphology and metabolic
profiling on the case study are available in ZENODO (10.5281/zen
odo.18889863), and the code for the machine learning pipeline and
emergence curve analysis are available on GitHub (https://github.com/kit-pef-czu-cz/sprout-emergence-prediction).References
Albarenque, S., Basso, B., Davidson, O., Maestrini, B., Melchiori, R., 2023. Plant
emergence and maize (Zea mays L.) yield across multiple farmers’ fields. Field Crops
Res. 302. https://doi.org/10.1016/j.fcr.2023.109090.Arsovski, A.A., Galstyan, A., Guseman, J.M., Nemhauser, J.L., 2012.
PhotomorpOpen asset ↗kit-pef-czu-cz/sprout-emergence-predictionpdf-raw-page:13 lines:78-112Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Fusarium head blight (FHB) is a major mycotoxigenic disease of wheat, causing yield and quality losses and deoxynivalenol contamination. Rapid, non-destructive tools are needed to detect FHB, monitor wheat physiological responses, and evaluate sustainable management strategies, including biological control agents. Although vegetation spectroscopy is widely used for high-throughput phenotyping, most spectral studies focus on binary disease detection, while the capacity of hyperspectral data to capture concurrent host–pathogen–biocontrol responses across leaf and canopy scales remains underexplored. Here, we tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085. Two cultivars, Bingo and Rebelde, with higher and lower FHB susceptibility, respectively, were treated with a chemical fungicide (Chem) or T. gamsii T6085 (Bioc) under FHB pressure. Leaf- and canopy-level spectra were acquired at 2, 5, and 14 days post-inoculation, alongside gas exchange, water status, and chlorophyll measurements. Permutational multivariate analysis of variance (PERMANOVA) tested whole-spectrum effects, partial least squares discriminant analysis (PLS-DA) explored class separability, and partial least squares regression (PLSR) estimated physiological traits. PERMANOVA detected genotype × inoculation × treatment interactions from 5 days post-inoculation at leaf and canopy levels. PLS-DA revealed treatment- and cultivar-dependent spectral fingerprints, but overall low-to-fair validation performance indicates that these class-specific patterns should be interpreted as exploratory and not as evidence of operational treatment discrimination. PLSR provided high accuracy for chlorophyll content and osmotic potential, moderate accuracy for CO 2 -assimilation traits, and poor accuracy for transpiration and leaf water potential. While the workflow is scalable as an experimental and analytical framework, its operational deployment will require broader validation across sites, seasons, cultivars, disease-pressure conditions, and sensing platforms.
Why it matches plant phenotyping methods小麦のFHB・生物防除応答を対象に、葉・群落ハイパースペクトル取得、分類、検証、形質推定を統合したフェノタイピング枠組みが中心である。
abstractwe tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods圃場トウモロコシの葉面積を点群から抽出するセグメンテーション・幾何学的補完手法の開発が題名の中心であり、植物形質の取得方法に該当する。
titleLeaf area extraction framework: Transformer-based segmentation and geometric-based completion approaches for accurate extraction of field maize leaf area from point clouds
Crop nutrition deficiency poses a major challenge to achieving optimal yield, particularly in smallholder farming systems where timely expert diagnosis is limited. Early detection is crucial to minimize losses and reduce unnecessary fertilizer or pesticide usage. While deep learning offers potential for automated visual diagnosis, most existing approaches operate as black boxes and lack interpretability, explainability, or actionable recommendations. In this work, we present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset. Our approach integrates a ResNet-50 backbone with a dual-head design: a classification head for deficiency prediction and a concept-prediction head that quantifies physiologically meaningful visual patterns such as yellowing, edge discoloration, spots, and vein greenness. These concept scores are combined with predefined domain rules to guide the learning of the neural component and to generate transparent, human-aligned explanations for each diagnosis. Building on the model outputs, we incorporate a Retrieval Augmented Generation (RAG)-based pipeline along with an agricultural knowledge base to generate targeted recommendations. This approach overcomes key shortcomings of pure neural models by incorporating domain knowledge in the form of differentiable fuzzy logic rules. The study demonstrates that the proposed framework improves both classification performance and interpretability compared to standard ResNet baselines. Grad-CAM analysis demonstrates that concept-guided attention aligns with symptom-specific regions, such as yellowed areas for Nitrogen deficiency or marginal discoloration for Potassium deficiency, providing visual validation of the reasoning process. Since EarlyNSD is limited in scale and visual diversity, the results are not directly comparable to large open-field datasets. Overall, our results establish a proof of concept for integrating neural detection with symbolic reasoning, enabling interpretable, actionable, and domain-informed nutrient management for practical applications.
Why it matches plant phenotyping methods葉画像から栄養欠乏状態と症状形質を推定する解釈可能な画像解析手法が研究の中心であり、植物表現型の取得・抽出に該当する。
abstractwe present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset.
Continuous monitoring of canopy temperature (Tc), a key indicator of crop water-heat stress and physiological dynamics, using unmanned aerial vehicle (UAV) imagery is inherently limited by temporal discontinuity and the limited physical realism of purely data-driven models. This study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products. The model leverages sparse UAV thermal measurements as supervisory signals while integrating them with continuous meteorological forcing and daily UAV-derived crop phenotypic features. Validated through a comprehensive season-long rice field experiment using walk-forward cross-validation, the proposed PINN framework demonstrated superior performance. It achieved R 2 values of 0.92 for reconstruction and 0.90 for forecasting, with RMSE of 0.71 °C and 0.82 °C, respectively. Ablation analysis further showed that crop phenotypic variables contributed more strongly than temporal descriptors, reducing predictive uncertainty by approximately 4.8–14.3 %, while the integration of SEB physical constraints and uncertainty modeling improved R 2 by 8.4–9.5 % and reduced Total STD by 28.4–37.7 %. The model successfully captures diurnal dynamics, spatial variability, and canopy thermal hysteresis while maintaining physical consistency through improved energy closure. This framework bridges sparse aerial observations with continuous physiological monitoring and highlights its potential to support precision irrigation, early stress detection, and high-throughput phenotyping in smart agriculture.
Why it matches plant phenotyping methodsUAV熱画像による疎な観測からイネ群落温度を連続再構成・予測するPINNを開発し、交差検証とアブレーション分析で性能評価しており、表現型取得・推定手法が研究の中心である。
abstractThis study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products.
AppleStrawberryGreenhouseLiDAR / point cloudRGB / grayscaleFruitSegmentationTracking
Accurate and consistent fruit monitoring over time is a key step towards automated agricultural production systems. However, this task is inherently difficult due to variations in fruit size, shape, occlusion, orientation, and the dynamic nature of orchards where fruits may appear or disappear between observations. In this article, we propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time. Our approach directly operates on dense colored point clouds, capturing fine-grained 3D spatial detail. We segment individual fruits using a learning-based instance segmentation method applied directly to the point cloud. For each segmented fruit, we extract a compact and discriminative descriptor using a 3D sparse convolutional neural network. To track fruits across different times, we introduce an attention-based matching network that associates fruits with their counterparts from previous sessions. Matching is performed using a probabilistic assignment scheme, selecting the most likely associations across time. We evaluate our approach on real-world datasets of strawberries and apples, demonstrating that it outperforms existing methods in both instance segmentation and temporal re-identification, enabling robust and precise fruit monitoring across complex and dynamic orchard environments. • We propose a new performant approach to autonomous fruit tracking in real greenhouses. • It segments fruits using learning-based instance segmentation and RGB 3D point clouds. • Segmented fruits are encoded by a 3D CNN and matched via attentive data association. • Experiments on real strawberry and apple datasets show our method outperforms others. • Our approach enables precise temporal fruit monitoring in real and complex scenarios.
Why it matches plant phenotyping methods果実を個体単位で3D点群からセグメンテーションし、時系列追跡する画像解析手法の開発・評価が研究の中心であり、植物器官の状態を抽出するため適格。
abstractwe propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time
Reproduction assets foundThe paper explicitly states that the authors' implementation of the fruit matching method (IRIS3D) is publicly available on GitHub, which is the computational analysis code for this paper's fruit segmentation and re-identification phenotyping pipeline.Code · publicThe implementation of our fruit matching method is publicly available at https://github.com/PRBonn/IRIS3D .Open asset ↗PRBonn/IRIS3Dlines:72-99Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods冬小麦の葉・植物体窒素量をリモートセンシングと植生指数クラスタリングで推定する手法開発が題名の中心であり、植物形質の取得・推定方法に該当する。
titleRemote sensing estimation for winter wheat plant nitrogen based on nitrogen distribution model building and leaf nitrogen quantitatively inversion using vegetation index clustering method
GrapevineNeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleFruitStem / branchPose / keypoint estimation2D/3D reconstructionSegmentation
• End-to-end pipeline from neural reconstruction to physical grape berry manipulation. • Efficient point clouds generation using NeRF and the metric scale derived directly from robot kinematics. • RANSAC sphere fitting achieves 92.1% berry detection precision without annotated training data. • Stem-aligned 6-DoF pose optimization improves end-to-end grip success by 17.2%. Table grape thinning requires selective removal of 20–40% of berries from dense clusters. In practice, workers decide which berries to remove by considering both the approximate berry count and local 3D spatial characteristics such as crowding and relative positioning. Automating this task is challenging because conventional 2D image-based approaches suffer from occlusion-related counting errors and lack explicit 3D spatial information necessary for reliable manipulation. We propose a robot-integrated vision pipeline that reconstructs grape bunch structure from posed multi-view RGB images. Neural Radiance Fields (NeRF) is used to learn a volumetric scene representation, from which a dense, low-noise point cloud is extracted via depth back-projection, and RANSAC-based geometric fitting models individual berries and stems, enabling berry-level segmentation and orientation estimation for manipulation planning. The perception pipeline uses an eye-in-hand RealSense D405 camera mounted on a Fanuc CRX-5iA collaborative robot. Camera poses are derived from the robot kinematic chain, allowing the reconstructed point cloud and detected berry centers to be expressed directly in the metric robot base frame without external scale recovery. In robot-mounted RealSense D405 experiments on 10 grape bunches, RANSAC sphere fitting achieved a counting MAE of 0.50 berries, RMSE of 0.71 berries, and mean center localization error of 2.71 mm. On a 52-bunch benchmark, RANSAC sphere fitting outperforms the learning-based SoftGroup++ method for 3D berry instance segmentation (92.1% vs 82.8% average precision) without requiring annotated training data. In 35 manipulation trials, the system achieved an 85.7% pre-grasp reachability rate and an 83.3% conditional target success rate demonstrating an end-to-end pipeline from neural reconstruction to manipulation-ready berry poses.
Why it matches plant phenotyping methodsブドウ房の3D再構成、ベリー分割・計数・位置推定を中核とするロボット統合型フェノタイピング手法であり、技術性能も定量評価している。
abstractWe propose a robot-integrated vision pipeline that reconstructs grape bunch structure from posed multi-view RGB images.
Accurate detection and orientation estimation of immature apples are crucial for effective thinning decisions in robotic apple thinning. Existing research either relies on computationally expensive RGB-D approaches with 3D geometric fitting to estimate orientation and size or requires multiple separate models for thinning decision, limiting their real-time performance on robotic platforms. To address these issues, a multi-task model, YOLOv9s-Multi, is proposed. First, this model integrates segmentation and keypoint heads to perform instance segmentation of immature apples and detect their calyx keypoint positions. Second, the detection head employs an efficient and lightweight Depthwise Convolution module (DWConv) to reduce model parameters while accurately capturing spatial features across channels. Finally, the orientation is derived from the segmentation centroid and calyx keypoint, enabling pixel-based fruit selection for thinning decision-making. This model is evaluated on a self-developed dataset that divides immature apples based on developmental stage: Flower-Retained Stage (FR-Stage) and Fruit-Visible Stage (FV-Stage). Results show that instance segmentation and keypoint AP@0.5 for FV-Stage are 89.3% and 86.4%, respectively, while for FR-Stage they are 71.6% and 79.8%. The model further achieves prediction accuracies of 92.80% (FV-Stage) and 72.59% (FR-Stage) within an acceptable error of 30 °. The pixel-based fruit selection method achieves 74.00% and 70.31% selection accuracy on the test and an additional measurement dataset, respectively. Compared with the baseline YOLOv9s-seg, the number of parameters is reduced by 11.4%. In contrast to 3D fitting methods, our approach provides lower computational complexity, faster inference speed, and higher accuracy. These results demonstrate that the proposed model can efficiently estimate the orientation of immature apples and perform fruit selection in close-range scenes and complex lighting environments, which are challenging for depth cameras to handle. The code and datasets are publicly available on GitHub: https://github.com/DIANSLEE/YOLOv9s-Multi.
Why it matches plant phenotyping methods未熟リンゴのセグメンテーション、萼点検出、重心との関係から果実の向きという器官形質を推定する画像解析手法が研究の中心であり、精度評価とデータセット検証も行っているため。
abstracta multi-task model, YOLOv9s-Multi, is proposed.
Identifying nutritional deficiencies at an early stage is crucial for maximizing yield production and ensuring healthy plants. Conventional methods generally rely on time-consuming analysis conducted by agronomic experts. To address this challenge, this study presents a data-driven approach for the early identification of nutritional deficiencies in hazelnut orchards. Different custom datasets, composed of images acquired in a real hazelnut orchard as well as in a controlled laboratory environment, are collected, and the performance of five state-of-the-art machine learning models in early detecting nutritional deficiencies is compared. In particular, ResNet, DenseNet, MobileNet, EfficientNet, and ConvNext models, along with a baseline based on support vector machines, are considered. Data augmentation techniques are introduced to synthetically increase the datasets, and their effectiveness is extensively evaluated. Additionally, a pipeline is designed to carry out the early identification of nutritional deficiencies onboard an agricultural robot. Experimental results on the early identification show that ConvNext achieves the highest performance: 81.79% accuracy and 0.8168 F1 score on a real-world dataset with four classes, and 75.54% accuracy with 0.7552 F1 score for the more challenging six-class scenario. Furthermore, the effectiveness of the integrated system is validated in preliminary laboratory experiments using a Turtlebot2 mobile base and a Franka Research 3 arm, equipped with RGB-D cameras. • Data-driven pipeline detects hazelnut nutrient deficiencies from leaf images. • Real and lab-acquired hazelnut leaf image datasets collected and publicly released. • ConvNext achieves 85.50% accuracy on 4-class hazelnut deficiencies in lab conditions. • 75.54% accuracy on 6-class real orchard dataset validates field robustness. • Two-stage pipeline with leaf detection and classification enables onboard robot monitoring.
Why it matches plant phenotyping methods葉画像から植物の栄養欠乏状態を推定する画像解析・機械学習パイプラインと、ロボット搭載システム、データセットを中心的に開発・評価しているため。
abstractthis study presents a data-driven approach for the early identification of nutritional deficiencies in hazelnut orchards
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsロボット統合型マルチセンサーと機械学習によるイネ葉面積指数（LAI）の推定精度向上が主題であり、植物形質の取得・推定手法が中心である。
titleImproving rice leaf area index monitoring accuracy via robot-integrated multi-sensors and meteorological data fusion with explainable machine learning
Timely acquisition of crop chlorophyll contents is essential for effective field management decisions and comprehensive crop nutritional monitoring. Unmanned Aerial Vehicle (UAV) has been extensively utilized for canopy chlorophyll content monitoring. However, prior studies predominantly concentrated on the horizontal variability of SPAD, with few researches dedicated to monitoring the vertical distribution of SPAD. This study aimed to develop a high-resolution vertical SPAD distribution model for winter wheat by integrating UAV-based Cross-Circle Oblique (CCO) photography with the Structure from Motion (SFM) and Multi-View Stereo (MVS) methods. The canopy was divided into upper, middle, and lower layers based on plant height. Nadir and CCO photography were used to capture images, with Nadir photography constructing the upper canopy SPAD model and CCO data used for the vertical distribution model (including upper, middle and lower layers). Shapley values were applied to evaluate feature importance in different machine learning models (GBR, gradient boosting regression; RF, random forest; SVM, support vector machine; RR, ridge regression). Finally, a vertical SPAD distribution model for winter wheat was created with a 0.1-meter gradient. The results demonstrated that the accuracy of SPAD vertical distribution inversion using single machine learning algorithms (KNN: k-nearest neighbor, RR: ridge regression) was lower than that achieved by ensemble learning methods (stacking, RF: random forest). Ensemble learning enhanced R 2 and RMSE by 0.13 and 1.23 for the training set, and by 0.09 and 0.56 for the test set, respectively. CCO photography exhibited high accuracy in capturing the SPAD spatiotemporal distribution of winter wheat, with R 2 values for the training sets across all three growth stages surpassing 0.8, and R 2 values for the test sets ranging from 0.6 to 0.81. The highest accuracy in SPAD vertical distribution inversion was achieved during the heading stage, with R 2 and RMSE values for the training and test sets of 0.89, 2.58, and 0.80, 3.34, respectively. Therefore, UAV-based CCO photography combined with the SFM-MVS algorithm demonstrates preliminary potential for vertical SPAD phenotyping and precision nutrient management; however, further validation across different growing seasons and wheat varieties is necessary to confirm its broad generalizability.
Why it matches plant phenotyping methodsUAV斜視画像とSfM-MVS、機械学習を統合し、コムギの垂直SPAD分布を推定する方法の開発・検証が研究の中心である。
abstractThis study aimed to develop a high-resolution vertical SPAD distribution model for winter wheat by integrating UAV-based Cross-Circle Oblique (CCO) photography with the Structure from Motion (SFM) and Multi-View Stereo (MVS) methods.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.
Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング融合と3D再構成を中心に扱うレビューであり、フェノタイピング手法の方法論的整理が主題。
abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を融合し、綿花の葉水分状態を3D推定・可視化する手法の開発と検証が研究の中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
Plant disease adversely impacts food production and quality. Alongside detecting the disease, estimating its severity is important in managing the disease. Artificial intelligence deep learning-based techniques for plant disease detection are emerging. Unlike most of these techniques, which focus on disease recognition, this study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation. We propose a novel approach that learns the disease symptoms, which are then used to segment disease lesions for severity estimation. To demonstrate the work, a dataset of barley images was used. We captured the images of barley plants inoculated with diseases on test-bed paddocks at various growth stages. The dataset was automatically annotated at a pixel level using a trained vision transformer to obtain the ground truth labels. The annotated dataset was applied to train salient object detection (SOD) methods. Two top-performing lightweight SOD models were used to segment the disease lesion areas. To evaluate the performance of the SODs, we have tested them on our dataset and several other datasets, including the Coffee dataset, which has expert pixel-level labels that were unseen during the training step. Several morphological and spectral disease symptoms, including those akin to the widely used ABCD rule for human skin-cancer detection, i.e., asymmetry (A), border irregularity (B), colour variance (C), and diameter (D), are learned. To the best of our knowledge, this is the first study to incorporate these ABCD features in plant disease detection. We further extract visual and texture features using the grey level co-occurrence matrix (GLCM) and fuse them with the ABCD features. For the coffee dataset, our method achieved 82 + % detection accuracy on the severity classification task. The results demonstrate the performance of the proposed method in detecting plant diseases and estimating their severity.
Why it matches plant phenotyping methods植物病斑の画素レベル分割と病害重症度推定という、植物状態を画像から定量化する手法が研究の中心であり、データセット構築・モデル評価も行っている。
abstractthis study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation
MaizeMultispectral / hyperspectralSeed / grainClassificationPhysiological trait estimationRoot system architecture
Seed vigor is a key indicator of seed quality, directly influencing plant growth and yield. This study proposes a novel deep learning framework for the qualitative and quantitative assessment of maize seed vigor. First, the Multi-scale residual gated recurrent unit network (MS-ResGRU-Net) was developed for maize spectral vigor detection, achieving an accuracy of 94.35 %. Second, a two-stage model optimization strategy was employed, transferring deep spectral features from MS-ResGRU-Net to the ensemble learning model, further improving the vigor detection accuracy to 95.48 %. The model facilitated quantitative analysis of vigor-related phenotypic traits and physiological indicator, achieving Pearson correlation coefficients of 0.8130 for root length, 0.8057 for root weight, and 0.7876 for physiological indicator between predicted and true values. Furthermore, Explainable artificial intelligence (XAI) was utilized to elucidate the relationships among model features, spectral features, and seed vigor traits, providing clearer insights into the model’s decision-making process. This study presents a non-destructive, efficient method for detecting maize seed vigor, offering a novel approach for assessing the vigor of other crop seeds.
Why it matches plant phenotyping methodsトウモロコシ種子の活力と根長・根重などの表現型を非破壊スペクトル測定と深層学習で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 proposes a novel deep learning framework for the qualitative and quantitative assessment of maize seed vigor.
Tomato, as a globally important economic crop, requires precise and timely disease management to secure yield and quality. Yet segmentation robustness is often limited by weak semantic understanding from single-modality images, narrow receptive fields of convolutional structures, and discontinuous boundary predictions. To address these issues, we propose the Multi-scale Linear Cross-modal Fusion Architecture for Tomato Leaf Disease Segmentation (MS-LCFNet). We construct a real-world field dataset covering five major tomato leaf diseases, annotated by experts with detailed textual descriptions to enable multimodal learning. MS-LCFNet strengthens semantic representation via cross-modal fusion, captures local and global context through an Adaptive Long-short Distance Perception module, and improves boundary continuity with a Physics-informed Smoothness-constrained Loss. Experiments show that MS-LCFNet achieves 87.13 % mIoU on our dataset and 90.78 % on PlantVillage, improving over previous state-of-the-art methods by + 4.62 % and + 4.48 %, respectively, and demonstrating superior accuracy and robustness in complex agricultural scenarios.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像からセグメンテーションする手法を開発し、独自データセットで性能評価しており、植物病害表現型の取得・抽出が中心である。
titleA multi-scale linear cross-modal fusion architecture for tomato leaf disease segmentation
LettuceGrowth chamberRGB / grayscaleLeafClassificationWater status / transpiration
Plant motion provides valuable indicators of physiological responses to water stress. In this study, we present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants subjected to varying irrigation regimes under controlled conditions. Four water availability treatments were imposed − Full Control (FC), Stress Control (SC), Mild Stress (SM), and Severe Stress (SS) − varying in timing, frequency, and intensity of irrigation protocols. Using dense optical flow on time-lapse RGB images, we extracted MK features that link leaf age to motion dynamics. These high-dimensional temporal features were compressed into descriptive and trend-based characteristics for classification. Multi-classification problem was divided into nine sub-tasks, for which feature selection and multiple machine-learning models were tested applying Leave-One-Sample-Out cross-validation. The best models were organised into four explainable hierarchical cascades. The presented system captures enough information to successfully distinguish among subtle differences in plants’ response to water availability dynamics (best architecture cascade obtained 0.93 out of fold balanced accuracy). The framework associating leaf age with MK features along with feature engineering allowed explainability – e.g., central rosette’s features were selected almost twice the expected frequency (19 out of 58) in tasks involving the stress-adapted control (SC), while features capturing linear trends in motion were generally selected over twice as often as simple descriptive statistics (44 vs. 19), proving essential for distinguishing most stress conditions. The MK approach proved effective for differentiating water stress levels, positioning it as a powerful tool for digital phenotyping and a solid foundation for developing advanced temporal-aware models.
Why it matches plant phenotyping methods画像時系列から光学フローで植物の運動形質を抽出し、水ストレス状態を分類する画像ベース表現型解析手法の開発・評価が研究の中心である。
abstractwe present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants
• Combines semantic segmentation with GSD-based spatial metrics for precise seedling evaluation. • Wheat Seedling Former model, with preprocessing, exceeds current methods in identifying seedling structures. • Utilizes UAV RGB and multispectral data for non-destructive, high-throughput phenotyping. • Facilitates rapid assessment of seedling vigor and canopy plasticity for large-scale screening of stress-resilient wheat cultivars. Traditional methods for estimating wheat seedling area, such as manual grid sampling or ground-based sensors, suffer from low precision, labour intensity, and limited scalability under complex field conditions. To address these challenges, this study introduces a pixel-to-area phenotyping framework that integrates Wheat Seedling Former semantic segmentation with Ground Sample Distance (GSD)-based spatial conversion to achieve high-throughput quantification of wheat seedling coverage and growth vigour. The framework employs a three-step preprocessing pipeline, linear regression-based colour calibration, super-green (ExG) segmentation, and modified anisotropic diffusion filtering, to enhance image quality and suppress noise. The Wheat Seedling Former network incorporates a spatial-channel dual attention module to mitigate background interference and a cross-layer feature pyramid architecture to capture fine-scale morphological traits (e.g., leaf edges, tiller distribution). By aligning RGB and multispectral imagery via geometric correction (holography transformation) and spectral correction (soil-reflection suppression), the framework quantifies six phenotypic indices: seedling coverage area, canopy compactness, NDVI, NDRE, chlorophyll index, and foliage projection coverage. Applied to 160 field plots, the model achieved a Pearson correlation coefficient of 0.942 with ground-truth measurements, demonstrating high accuracy. GSD-based spatial conversion reduced scaling errors to < 3 %, enabling precise area estimation (±0.5 m 2 ) even on uneven terrain. Phenotypic analysis stratified plots into three vigor classes: 35 high-performing (≥90 % canopy closure), 83 medium (60–90 %), and 42 low (<60 %), with high-performing genotypes showing 28 % higher drought tolerance. A software tool (Seedling Phenotype Extractor) automates image annotation, phenotypic calculations, and genotype ranking, reducing phenotyping time by 65 %. This pipeline bridges computational precision and field-scale breeding applications, offering a scalable tool for accelerating the discovery of stress-resilient wheat cultivars through rapid, non-destructive assessment of early-season canopy plasticity.
Why it matches plant phenotyping methodsUAV画像、意味分割、GSD変換を統合し、植物の被覆面積・樹冠構造・スペクトル指標などを高スループットで抽出する手法の開発と検証が中心である。
abstractTo address these challenges, this study introduces a pixel-to-area phenotyping framework that integrates Wheat Seedling Former semantic segmentation with Ground Sample Distance (GSD)-based spatial conversion to achieve high-throughput quantification of wheat seedling coverage and growth vigour.
As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following GitHub Repository .
Why it matches plant phenotyping methods小麦のハイパースペクトル画像と深層学習による植物形質・状態推定を扱う方法論レビューであり、データセット、手法、疾病検出、収量推定を中心に整理している。
abstractThis review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation.
The use of multiple camera technologies in a combined multimodal monitoring system for plant phenotyping offers promising benefits. Compared to configurations that rely on a single camera technology, cross-modal patterns can be recorded that allow a more comprehensive assessment of plant phenotypes. However, the effective utilization of cross-modal patterns depends on image registration to achieve pixel-precise alignment - a challenge often complicated by parallax and occlusion effects inherent in plant canopy imaging. In this study, we propose a novel multimodal 3D image registration method that addresses these challenges by integrating depth information from a time-of-flight camera into the registration process. By leveraging depth data, our method mitigates parallax effects, facilitating more accurate pixel alignment across camera modalities. Additionally, we introduce an automated mechanism to identify and differentiate various types of occlusions, thereby minimizing registration errors. To evaluate the efficacy of our approach, we conduct experiments on a diverse dataset comprising six distinct plant species with varying leaf geometries. Our results demonstrate the robustness of the proposed registration algorithm, showcasing its ability to achieve accurate alignment across different plant types and camera compositions. Compared to previous methods our approach is not reliant on detecting plant-specific image features, making it suitable for a wide range of applications in plant sciences. Moreover, the registration approach can scale to arbitrary numbers of cameras with varying resolutions and wavelengths. Overall, our study contributes to advancing the field of plant phenotyping by offering a robust and reliable solution for multimodal image registration.
Why it matches plant phenotyping methods植物フェノタイピング向けのマルチモーダル3D画像位置合わせ手法を開発し、複数植物種のデータセットで性能評価しているため、方法が研究の中心である。
abstractIn this study, we propose a novel multimodal 3D image registration method that addresses these challenges by integrating depth information from a time-of-flight camera into the registration process.
MaizeLiDAR / point cloudMorphology / geometry measurementSegmentation
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods植物群落の点群セグメンテーションと表現型形質抽出を主題とする計算手法であり、植物フェノタイピング手法が中心です。
titlePACANet: A Paired-Attention central axis aggregation network for plant population point cloud segmentation and phenotypic trait Extraction—A case study on maize
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Wheat is a staple crop that suffers significant yield reductions under drought conditions, especially during the critical reproductive stages. Traditional methods for assessing drought resistance in wheat are often destructive, labor-intensive, and fail to capture the multi-faceted nature of drought tolerance. Vegetation indices serve as effective non-destructive indicators of physiological and biochemical traits. However, the potential of high-throughput spectral indices for quantifying drought resistance traits in wheat have not yet been thoroughly investigated. In this study, we employed an unmanned aerial vehicle (UAV) platform combined with machine learning to assess 206 spectral indices across 52 wheat genotypes at various growth stages under both well-watered and drought conditions. We also evaluated 11 traditional traits to examine their correlations with UAV-based traits. Our study identified 127 spectral indices as drought-related traits and revealed significant correlations between traditional and UAV-based traits. We identified three novel drought-related traits-the Color Index of Vegetation (CIVE), Red-Green-Blue Index (RGBI), and Excess Green Minus Excess Red Index (ExG_ExR)-derived from RGB images and correlated with chlorophyll content, showing strong associations with kernel-related traits. Additionally, we developed an advanced prediction model for yield stability under drought conditions using 17 spectral indices selected through machine learning. A comprehensive evaluation value (D) based on these 17 indices enabled the identification of one highly drought-resistant genotype and 13 drought-resistant genotypes, further validated through field experiments. Our study not only confirms the effectiveness of UAV-based traits in indicating drought tolerance but also provides valuable germplasm for the genetic improvement of drought-resistant wheat.
Why it matches plant phenotyping methodsUAVによる高スループット画像・スペクトル形質取得と機械学習による耐乾性・収量安定性推定が研究の中心であり、圃場検証も実施しているため。
abstractwe employed an unmanned aerial vehicle (UAV) platform combined with machine learning to assess 206 spectral indices across 52 wheat genotypes
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAVフォトグラメトリとGANを用いた樹木の3D再構成手法が研究の中心であり、樹木の構造・形態という植物表現型の取得に該当する。
titleGeometry-based point cloud fusion of dual-layer UAV photogrammetry and a modified unsupervised generative adversarial network for 3D tree reconstruction in semi-arid forests
• RGB-D camera and Jetson platform enable precise, adaptive vineyard spraying. • Canopy volume estimation reduces plant protection product use by 57.4%. • Real-time system adjusts spray rates for efficient, sustainable vineyard management. • Instance segmentation and 3D meshing provide accurate canopy and trellis detection. • Jetson’s parallel computing accelerates processing for fast, reliable results. Sustainable vineyard management requires precise and efficient application of plant protection products to minimise environmental impact while ensuring plant health. This study presents a variable-rate spraying system that integrates an RGB-D camera with a GPU-equipped edge computing platform to enable accurate, real-time adjustment of spray flow rates in vineyards. A tensor-based representation of RGB-D data is employed to accelerate the entire processing pipeline. Based on this structure, a fast approximate meshing method is applied to rapidly generate 3D meshes from point clouds. To incorporate semantic information from RGB images, an instance segmentation model is used to detect grapevine canopies and trellis posts. The resulting canopy masks are used to isolate the canopy meshes, while the trellis posts serve as reference planes for canopy volume estimation via mesh projection. Based on the computed volume, pulse-width modulation signals are generated to dynamically control spray flow rates. Field experiments were conducted to evaluate the system’s effectiveness and real-time performance. The results demonstrated that the estimated canopy volume is a reliable indicator for regulating application rates. Compared to uniform-rate spraying, the proposed system reduced plant protection product consumption by 57.4% while ensuring adequate droplet coverage. Additionally, the system demonstrated satisfactory real-time performance even on entry-level hardware. Overall, the proposed variable-rate spraying system offers an accurate, real-time, and cost-effective solution for precision viticulture, highlighting its potential for commercial deployment in sustainable vineyard management.
Why it matches plant phenotyping methodsRGB-D画像、インスタンスセグメンテーション、3Dメッシュによりブドウ樹冠を抽出し、樹冠体積という植物形質を推定する手法とリアルタイム基盤が研究の中心であるため。
abstractThis study presents a variable-rate spraying system that integrates an RGB-D camera with a GPU-equipped edge computing platform to enable accurate, real-time adjustment of spray flow rates in vineyards.
This study introduces a robust framework for generating procedural 3D models of maize (Zea mays) plants from LiDAR point cloud data, offering a scalable alternative to traditional field-based phenotyping. Our framework leverages Non-Uniform Rational B-Spline (NURBS) surfaces to model the leaves of maize plants, combining Particle Swarm Optimization (PSO) for an initial approximation of the surface and a differentiable programming framework for precise refinement of the surface to fit the point cloud data. In the first optimization phase, PSO generates an approximate NURBS surface by optimizing its control points, aligning the surface with the LiDAR data, and providing a reliable starting point for refinement. The second phase uses NURBS-Diff, a differentiable programming framework, to enhance the accuracy of the initial fit by refining the surface geometry and capturing intricate leaf details. Our results demonstrate that, while PSO establishes a robust initial fit, the integration of differentiable NURBS significantly improves the overall quality and fidelity of the reconstructed surface. This hierarchical optimization strategy enables accurate 3D reconstruction of maize leaves across diverse genotypes, facilitating the subsequent extraction of complex traits like phyllotaxy. We demonstrate our approach on diverse genotypes of field-grown maize plants. All our codes are open-source to democratize these phenotyping approaches.
Why it matches plant phenotyping methodsLiDAR点群からトウモロコシ葉の3D形状を再構成し、複雑な形質抽出を可能にする手法開発が中心である。
abstractThis study introduces a robust framework for generating procedural 3D models of maize (Zea mays) plants from LiDAR point cloud data, offering a scalable alternative to traditional field-based phenotyping.
Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, which struggle with overlapping leaves and incomplete structural information in complex field environments. This study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape. UAV multi-view oblique images from 36 angles were used to perform 3D reconstruction, with the SAM module enhancing point cloud segmentation. The segmented point clouds were then converted into point cloud volumes, which were fitted to ground-measured biomass using linear regression. The results showed that 3DGS (7 k and 30 k iterations) provided high accuracy, with peak signal-to-noise ratios (PSNR) of 27.43 and 29.53 and training times of 7 and 49 min, respectively. This performance exceeded that of structure from motion (SfM) and mipmap Neural Radiance Fields (Mip-NeRF), demonstrating superior efficiency. The SAM module achieved high segmentation accuracy, with a mean intersection over union (mIoU) of 0.961 and an F1-score of 0.980. Additionally, a comparison of biomass extraction models found the point cloud volume model to be the most accurate, with an determination coefficient (R²) of 0.976, root mean square error (RMSE) of 2.92 g/plant, and mean absolute percentage error (MAPE) of 6.81 %, outperforming both the plot crop volume and individual crop volume models. This study highlights the potential of combining 3DGS with multi-view UAV imaging for improved biomass phenotyping.
Why it matches plant phenotyping methodsUAV多視点画像、3D再構成、SAMによる分割、体積からのバイオマス推定を統合・比較検証した、植物表現型取得法が研究の中心である。
abstractThis study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape.
• AMULET represents a groundbreaking advancement in plant phenotyping. • AMULET integrates plant detection, prediction, segmentation, and data analysis. • AMULET identify the latent space of the phenom by using machine learning models. • AMULET demonstrates unparalleled adaptability across species. Phenotypes, which define an organism’s behaviour and physical attributes, result from the complex interplay of genetics, development, and environment. Predicting future plant traits is mainly challenging due to these dynamic interactions. This work presents AMULET, a modular approach that combines imaging-based high-throughput phenotyping and machine learning to predict morphological and physiological plant traits hours to days before they are visible. Trained with over 30,000 Arabidopsis thaliana plants, AMULET streamlines the phenotyping process by integrating plant detection, prediction, segmentation, and data analysis, enhancing workflow efficiency and reducing time. AMULET achieved impressive performance metrics, including a dice loss of 0.0104 and an IoU score of 0.9948 for the test set, indicating high accuracy in the segmentation task or an R 2 score of 0.9289 for descriptor estimation. Moreover, Simpler yet Better Video Prediction (SimVP) appeared as the most effective model in predicting plant growth and health status. Using phenotyping images from studies focused on the Arabidopsis thaliana-Pseudomonas syringae pathosystem, AMULET analysed the latent phenom by identifying traits restrictive to human perception and essential to understanding plant response to concrete growth conditions. Techniques like TorchGrad and Gradient-weighted Class Activation Mapping helped to reveal these new hidden traits. AMULET also demonstrated its adaptability by accurately detecting and predicting phenotypes of in vitro potato plants after minimal fine-tuning with just 100 plants. This versatile approach streamlines phenotyping and holds significant promise for improving breeding programs and agricultural management by enabling pre-emptive interventions optimising plant health and productivity.
Why it matches plant phenotyping methodsAMULETは画像ベースのハイスループット植物表現型解析と機械学習による形態・生理形質の推定、予測、セグメンテーションを中核として開発・評価しており、方法論的貢献が明確です。
abstractThis work presents AMULET, a modular approach that combines imaging-based high-throughput phenotyping and machine learning to predict morphological and physiological plant traits hours to days before they are visible.
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
Globally, potatoes are the fourth most produced food crop, and in the United Kingdom alone, they generated approximately £705 million in 2022. However, to achieve the United Nations (UN) Sustainable Development Goals (SDG), potato farmers need to sustainably increase yields to address the growing demand for both food and land. Crop yield can be affected by various factors, including disease, pests, and nutrient deficiencies. To tackle these challenges and optimise yields, researchers have leveraged remote sensing platforms for high-throughput non-destructive phenotyping. Data collected from these platforms can be used to develop machine learning (ML) models aimed at addressing the aforementioned issues. To summarise recent developments in ML models applied to potato plant phenotyping, a systematic review of journal articles from the last seven years was conducted. This review underscored the advantages of Deep Learning (DL) approaches and the rising trend of Convolutional Neural Network (CNN)-based architectures, while also noting the limited availability of data for training these models. This review is intended to benefit researchers and farmers by providing an up-to-date review of ML models in potato plant phenotyping. • Remote sensing and ML models can optimise potato yield through non-destructive phenotyping. • Deep Learning (DL) and CNN-based approaches show promise in potato phenotyping. • Limited training data availability remains a challenge in ML model development for agriculture. • This review supports researchers and farmers with current insights on ML advancements in potato crop phenotyping.
Why it matches plant phenotyping methodsジャガイモ植物フェノタイピングにおけるリモートセンシングと機械学習手法を主題とする体系的レビューであり、フェノタイピング手法のレビューが中心です。
abstractTo summarise recent developments in ML models applied to potato plant phenotyping, a systematic review of journal articles from the last seven years was conducted.
Grapevine winter pruning is a labor-intensive and repetitive process that significantly influences grape yield and quality at harvest and produced wine. Due to its complexity and repetitive nature, the task demands skilled labor that needs to be trained, as in many other agricultural sectors. This paper encompasses an approach that targets using a robotic system to perform autonomous grapevine winter pruning using a vision system and artificial intelligence. In our previous work, we presented a 2D neural network that segmented images of grapevines into 5 different classes of plant organs during their dormant season. In this paper, we expand into the third dimension, introducing point clouds into our algorithm. The 3D approach creates instance-segmented point clouds using depth images and segmentation masks obtained with our 2D neural network. After the 3D reconstruction, the system extracts thickness measurement and uses agronomic knowledge to place pruning points for balanced pruning. The study not only delineates the integration of 2D and 3D methods but also scrutinizes their efficacy in pruning point identification. The real-world performance of the created system was evaluated and statistically analyzed on data collected during field trials in the winter pruning season 2022/2023, where the system was used in a potted vineyard to prune a set of test vines, where the positive success rate is 54.2%. Moreover, as one of the main contributions, the paper underscores a unique facet of adaptability, presenting a customizable framework that empowers end-users to fine-tune parameters according to the expected balanced pruning. This adaptability extends to variables such as the number of nodes to retain on pruned spurs and the preferred cane thickness, encapsulating the versatility of the 3D approach.
Why it matches plant phenotyping methods2D画像分割と3D点群再構成を統合し、ブドウ樹器官の厚さを測定して剪定点を生成・評価する手法が研究の中心であるため、植物表現型計測手法として収載する。
abstractThe 3D approach creates instance-segmented point clouds using depth images and segmentation masks obtained with our 2D neural network.
Yield and its components are the important traits for plant breeders to select the best genotypes in the breeding programs. However, traditional measurements of these traits across genotypes and environments are labor-intensive and time-consuming, as hundreds or even thousands of plots need to be estimated. A yield trial was carried out using seven sugarcane cultivars planted in a randomized complete block design with four replications for two ratoon crops to estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components in discriminating ability to assess sugarcane yield via a path coefficient analysis. UAS platforms mounted with sensors were flown over the trial. The result shows that UAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R 2 = 0.89, RMSE = 0.15 m). Likewise, an accurate millable stalk height (MSH) estimation, using UAS-derived PH as a predictor, was observed (R 2 = 0.54, RMSE = 0.15 m). Canopy height model (CHM)-derived canopy cover (CC) appeared to be a promising feature to indirectly select or to predict for stalk number (SN) (R 2 = 0.69, RMSE = 10,975 stalks ha −1 ). Based on a path coefficient analysis, UAS-based yield components performed equally to or slightly underperformed the traditional method. Traditionally, SN was the largest contributor to cane yield. Similarly, CC and CHM were the important components for UAS-based yield components. Additionally, the yield prediction model using UAS-derived canopy features with five cross validation schemes (CVs) revealed that model accuracy increased as association between predictor variables with a responding variable increased. The present study shows that random forest outperformed (higher r and lower RMSE) the linear regression models (stepwise, lasso, and ridge) in all CVs. The linear regressions were off when they were used to predict the performance of cultivars in untested crop/environments (CVs2 and CVs5), while a higher accuracy was observed when using random forest in those CVs. More importantly, the accuracy of all models reduced when they were tested in untested crop/environments (CVs2 and CVs5), indicating the challenge of using a prediction model applied to new environments.
Why it matches plant phenotyping methodsUASセンサーとHTPを用いて、植物形質（草丈、茎数、樹冠被覆、収量構成要素）を推定し、地上測定との検証および予測モデルの比較を行っており、表現型取得・抽出法が研究の中心である。
abstractto estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components
Wheat is one of the three primary staple crops globally, with the senescence of its leaves having a direct effect on yield. However, conventional senescence evaluation methods are mainly based on visual scoring, which are subjective, time-consuming, and hamper the investigation of mechanisms between senescence process and yield formation. High-throughput image-based plant phenotyping techniques offer a promising approach. However, extracting senescence-related semantic information from images presents challenges, including blurred edge segmentation, inadequate characterization of senescence features, and interference from complex field environments. Therefore, this study proposes a dual-branch image senescence segmentation model ( SenNet ), which integrates edge priors and local–global attention mechanisms, including local–global hierarchical attention mechanisms, gated convolution, and positional encoding modules. First, a wheat senescence dynamics image dataset (19530 images) was constructed, comprising 509 wheat varieties from a two-year and two-replicate field experiments. Then, the SenNet model achieved senescence image segmentation for various wheat varieties, enabling senescence dynamics analysis and high-yielding variety screening. The results showed that: 1) The mean Intersection over Union (mIoU) of the SenNet model was 95.41 %, which represented a 4.01 % improvement over the average mIoU of seven state-of-the-art models. 2) The contributions of the local–global hierarchical attention mechanism, gated convolution, and positional encoding module to the accuracy improvement of SenNet were 3.15 %, 1.62 %, and 1.03 %, respectively. 3) SenNet can be transferred across years and locations. The mIoU accuracy of the SenNet across locations is 96.01 %. Furthermore, the model trained in 2023 can be transferred to 2022 and 2024, achieving mIoU accuracies of 93.75 % and 93.27 %. 4) High-yielding varieties typically experience a later onset of senescence and faster senescence in later stages. Based on the senescence law, this study further constructed new dynamic traits of senescence (e.g., AreaUnderCurve ). Leveraging the random forest-based yield prediction (R 2 = 0.68) from the dynamic traits, high-yielding varieties were screened with an average precision, recall, F1 score, and accuracy of 81 %, 79 %, 80 %, and 87 %, respectively. This study provides an efficient method for monitoring senescence dynamics and predicting yield, offering new insights into the screening of high-yielding varieties.
Why it matches plant phenotyping methodsコムギ葉の老化状態を画像から分割・定量する手法を開発し、データセット構築、精度比較、年次・場所間移転性を検証しており、表現型取得法が研究の中心である。
abstractTherefore, this study proposes a dual-branch image senescence segmentation model ( SenNet )
• Hyperspectral imaging can detect Botrytis cinerea 1 day after inoculation. • Hyperspectral imaging detects it 1 day earlier than colour imaging. • Chemometric approaches allowed visualisation of disease progression. • Disease severity can be explained with R 2 = 0.84 using near-infrared spectroscopy. Botrytis cinerea is a fungal pathogen that can affect a wide range of plants, including roses. Resistance against Botrytis is quantitative, making breeding for resistance challenging. To enable proper genetic marker development, high-throughput and objective data on Botrytis sensitivity is essential. Rose petal discs of different cultivars were manually infected with Botrytis and were monitored with hyperspectral imaging using a fully automated spectral imaging setup. Predictive modelling analysis involved both detection of Botrytis and explaining the severity of infection by linking the spectral data to visual scoring by human eye. Furthermore, band selection analysis was performed to detect key spectral bands relevant for Botrytis detection and to facilitate development of lower cost multi spectral systems for detection of Botrytis infected areas in roses. The presented approach can help plant breeders to explore and adapt to new plant phenotyping technologies such as hyperspectral imaging for breeding against biotic and abiotic stresses.
Why it matches plant phenotyping methodsバラのBotrytis感染部位と感染重症度を、完全自動化ハイパースペクトル撮像および予測モデルで検出・推定する方法が研究の中心であり、植物表現型計測法として明確に該当する。
abstractRose petal discs of different cultivars were manually infected with Botrytis and were monitored with hyperspectral imaging using a fully automated spectral imaging setup.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
The 3D reconstruction of plants is challenging due to their complex shape causing many occlusions. Next-Best-View (NBV) methods address this by iteratively selecting new viewpoints to maximize information gain (IG). Deep-learning-based NBV (DL-NBV) methods demonstrate higher computational efficiency over classic voxel-based NBV approaches but current methods require extensive training using ground-truth plant models, making them impractical for real-world plants. These methods, moreover, rely on offline training with pre-collected data, limiting adaptability in changing agricultural environments. This paper proposes a self-supervised learning-based NBV method (SSL-NBV) that uses a deep neural network to predict the IG for candidate viewpoints. The method allows the robot to gather its own training data during task execution by comparing new 3D sensor data to the earlier gathered data and by employing weakly-supervised learning and experience replay for efficient online learning. Comprehensive evaluations were conducted in simulation and real-world environments using cross-validation. The results showed that SSL-NBV required fewer views for plant reconstruction than non-NBV methods. It achieved IG prediction in 0.0038s, making it over 800 times faster than a voxel-based NBV, and an online learning iteration in 0.099s. SSL-NBV reduced training annotations by over 90% compared to a baseline DL-NBV. Furthermore, SSL-NBV could adapt to novel scenarios through online fine-tuning. Also using real plants, the results showed that the proposed method can learn to effectively plan new viewpoints for 3D plant reconstruction. Most importantly, SSL-NBV automated the entire network training and uses continuous online learning, allowing it to operate in changing agricultural environments.
Why it matches plant phenotyping methods植物の3D再構成に必要な視点選択を自己教師あり学習で開発し、実植物を用いて評価しているため、植物形態フェノタイピングの取得手法が中心です。
abstractThis paper proposes a self-supervised learning-based NBV method (SSL-NBV) that uses a deep neural network to predict the IG for candidate viewpoints.
Growth monitoring of tomato plants in large greenhouse environments is critical for quality and efficient production. Stem diameter and elongation are key phenotypic traits for plant growth monitoring. Traditional methods, however, rely on manual operations, which are time-consuming and labor-intensive and do not apply to large-scale greenhouses. Currently, automated image-based methods exemplified by three-dimensional (3D) point cloud technology are among the preferred solutions. Nevertheless, the occlusion of plant structures during the information acquisition process is challenging for practical applications. To address this challenge, this study proposes a novel method for plant stem occlusion inpainting using Deep Reinforcement Learning (DRL). Unlike most existing 3D reconstruction approaches that require depth data from multiple viewpoints, our solution captures 3D point cloud data from a single direction. The DRL model is applied to inpaint the incomplete stem for accurate stem reconstruction and phenotypic measurements. Specifically, our approach consists of two parts, structural completion and stem diameter completion. First, we extract the point cloud of incomplete stems from the RGB-D camera data. Second, we obtain the spatial structure of the stems by inpainting the 3D stem centerline with the DRL model. Finally, we add shape features (stem diameters) by inpainting the two edge lines of the stem occlusion part with the DRL model. For stem inpainted 3D point cloud data, we conducted validation experiments by measuring several commonly used stem phenotypic traits in tomato plants, including stem diameter, stem length, and stem inclination. The experimental results show that the Mean Absolute Percentage Error (MAPE) of the occluded main stem diameter is 9.7%, stem length is 5.7%, and tilt angle is 1%. For the occluded branch stem, the MAPE of stem diameter is 23.1%, stem length is 7.9%, and tilt angle is 1.5%. The accuracy of these measurements for occluded stems is acceptable compared to that obtained from 3D point clouds of unoccluded stems. This highlights the significant potential of using DRL to effectively inpaint occluded 3D point cloud data of plants.
Why it matches plant phenotyping methods植物茎の遮蔽部分を3D点群と深層強化学習で補完し、茎径・茎長・傾斜角という表現型形質を推定・検証する手法が研究の中心である。
abstractthis study proposes a novel method for plant stem occlusion inpainting using Deep Reinforcement Learning (DRL).
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods冬コムギの植物体窒素濃度という植物形質を、近接ハイパースペクトルセンシングと機械学習で予測する手法が題名上の中心であり、気象情報との統合も含むため、植物フェノタイピング手法として採択。
titleImproving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning
Dwarf tomatoes, with high edible and ornamental value, require monitoring multiple growth parameters to balance yield and aesthetics. While deep learning has been widely applied in phenotype monitoring, most studies focus on individual growth parameters, overlooking intrinsic relationships. To simultaneously monitor multiple growth parameters across the entire growth stage and different cultivars, this study develops a multi-modal multi-task phenotype monitoring network for dwarf tomatoes (TomPhenoNet). The network model utilizes top-view RGB-D images to evaluate four key growth parameters: height, leaf area, fresh weight, and the number of red fruits. TomPhenoNet generates mask images, fruit detection features, and the number of detected fruits based on RGB images. By fusing RGB-D images, mask images, and fruit detection features, and introducing the cross-stitch network, the network predicts plant height, leaf area, and fresh weight. The predicted values are further used to generate the dynamic occlusion coefficient, adjusting the number of detected fruits to accurately predict the number of red fruits. Results reveal that TomPhenoNet achieves high prediction performances, with R 2 values of 0.828, 0.930, 0.945, and 0.881 for plant height, leaf area, fresh weight, and the number of red fruits, respectively. Ablation experiments show that the cross-stitch network and fruit detection features improve the prediction performances of growth parameters, with TomPhenoNet combining both modules performing best. Feature importance analysis indicates the network model captures plant growth characteristics and corrects the impact of leaf occlusion from the top view. This study promotes accurate tomato monitoring and provides data support for optimizing cultivation strategies.
Why it matches plant phenotyping methodsトマトの複数形質をRGB-D画像から推定するマルチモーダル・マルチタスク手法を開発し、性能評価とアブレーション実験を行っており、表現型取得・推定が研究の中心である。
abstractthis study develops a multi-modal multi-task phenotype monitoring network for dwarf tomatoes (TomPhenoNet).
Leaf shape is of great significance in plant phenotype research. Landmarks method is a widely used morphometric approach, which can comprehensively describe the morphological differences among leaves. However, the selection of landmarks is time-consuming and laborious. An automatic landmarking algorithm is proposed here. Based on conformal mapping, the leaf outline can be transformed into a monotonically increasing function curve, referred to as the ’fingerprint function’. The Dynamic Time Warping (DTW) algorithm was introduced to match landmarks between different leaves. Two leaf datasets were used to validate the algorithm separately in different species and developmental stages. Dataset1 is a public dataset which covers 26 different types of leaves. The average positional difference between automatic and manual landmarks for dataset1 was only 2.95%. Dataset2 consists of cotton leaves collected in the field at various growth stages, and the positional difference for this dataset was all below 5%. These results validate that our algorithm is applicable to a wide range of leaf types and capable of identifying and locating novel features that emerge during leaf growth. The automatic landmarking algorithm can simulate manual landmarking to a great extent. It provides a new approach for automated acquisition of plant leaf shape homology tailored to the research needs of botanists.
Why it matches plant phenotyping methods葉形態の自動ランドマーク取得アルゴリズムを開発し、複数の葉データセットで手動測定と比較検証しているため、植物表現型の取得手法が中心である。
abstractAn automatic landmarking algorithm is proposed here.
• First-person view drones capture high-resolution video, enabling accurate 3D photogrammetric reconstruction of trees. • Ray marching quantifies canopy transparency from point clouds, providing a precise alternative to visual estimates. • Drone-based estimates of tree height, DBH, and canopy spread strongly align with ground and lidar measurements across seasons. • Processing time increases with tree size and seasonality because of greater frame count and scene complexity. With many forests experiencing rapidly declining health, effective management requires increasingly accurate and precise tools to measure tree attributes across scales. Tree health, especially in deciduous species, is strongly correlated with crown condition, specifically crown transparency and dieback. Present-day assessment of these attributes is undertaken using ground-based visual approaches, which can be imprecise and subjective. Here we evaluate the feasibility of applying drone-based digital aerial photogrammetry (DAP) below, within, and above the tree canopy to estimate tree height, diameter at breast height, canopy transparency, and canopy spread. Video imagery was acquired across 18 deciduous trees under leaf-off and leaf-on conditions in Metro Vancouver, British Columbia, Canada, using small, lightweight first-person-view drones. Images were extracted and processed into coloured 3D point clouds using digital Structure-from-Motion Multiview-Stereo photogrammetry. Photogrammetry estimates were compared with field measurements and above-canopy drone-based aerial Light Detection and Ranging (lidar) estimates. The DAP estimates explained significant variance in the field observations and were strongly correlated with both ground-based measurements and lidar estimates, with correlations of height (DAP vs. ground: r = 0.93, RMSE = 1.54 m; DAP vs. lidar: r = 0.94), DBH (DAP vs. ground: r = 0.98, RMSE = 2.90 cm), transparency (DAP vs. ground: r = 0.66, RMSE = 12.61 %), and crown spread (DAP vs. ground: r = 0.88, RMSE = 3.35 m; DAP vs. lidar: r = 0.89). The reconstruction time for each tree using the drone footage was strongly correlated with tree size and seasonal condition, with minimal influence from crown form. This work suggests that first-person view drones can provide accurate information on individual tree attributes associated with tree health, offering a reliable alternative or complement to both ground-based methods and lidar for tree-level measurements in ongoing forest health assessment programs.
Why it matches plant phenotyping methodsドローン画像と3Dフォトグラメトリを用いて樹木の高さ、DBH、樹冠透明度、樹冠広がりを推定し、地上測定およびLiDARと比較検証しているため、植物形質取得手法が中心です。
abstractHere we evaluate the feasibility of applying drone-based digital aerial photogrammetry (DAP) below, within, and above the tree canopy to estimate tree height, diameter at breast height, canopy transparency, and canopy spread.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsハイパースペクトル画像とコントラスト学習を用いてダイズの収量および倒伏を推定・分類する方法が題名の中心であり、植物表現型の取得・抽出に該当する。
titleBridging the gap between hyperspectral imaging and crop breeding: soybean yield prediction and lodging classification with prototype contrastive learning
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsLIBSによる植物体内の重金属の移行・蓄積を定量マッピングし、解釈可能な深層学習で解析する手法が題名上の中心であり、植物の元素蓄積状態を測定するフェノタイピング手法に該当する。
titleQuantitative elemental mapping of heavy metals translocation and accumulation in hyperaccumulator plant using laser-induced breakdown spectroscopy with interpretable deep learning
• Method to assess high-resolution point cloud location/reconstruction errors in full 3D context. • The methodology provides values for both reconstruction and location error. • The methodology avoids the need for manual and usually less accurate measurements. • Minimized point pair picking error between assessed and reference GT point clouds. The aim of this paper is to address the lack of standard methodologies for the assessment of 3D point clouds. We present a methodology to realistically assess the accuracy of 3D point clouds, enabling the evaluation in a full 3D context rather than based on isolated points. Additionally, it introduces three significant innovations: a) it bridges the gap related to the unknown error of the reference ground-truth point cloud; b) it provides separate metrics for location error and reconstruction error; and c) it introduces a procedure to compute the location error that eliminates the bias in the selection of point-pair picking between the DGT points and their corresponding pairs in the point cloud being assessed. The geometry and structure of trees are related to the vegetative parameters and productivity in fruit orchards. In consequence, obtaining a precise and accurate geometric characterization of canopies is of interest for implementing site-specific management strategies that optimize input rates and minimize the costs and environmental risks of agricultural operations. Among the different sensing technologies, sensors based on the principle of light detection and ranging (LiDAR) have emerged as the primary choice for accurate geometric characterization of orchards. However, to make informed orchard management decisions based on LiDAR-derived geometric and structural data, it is essential to assess the accuracy of LiDAR-based scanning systems. Unfortunately, there is currently a lack of standard methodologies to evaluate the accuracy of LiDAR-based systems in agricultural environments. This research paper presents a novel methodology to assess the location error and the reconstruction error of 3D point clouds in full 3D context. The methodology involves comparing LiDAR-derived point clouds to an accurate high-resolution 3D digital ground truth (DGT) obtained using digital photogrammetric techniques. One of the main difficulties when using a reference point cloud to assess point cloud errors is the selection of the points to be compared so that they can be considered as corresponding point pairs. When developing the methodology, four procedures of point pair selection and distance calculation were compared. The best performing procedure was selected and proposed as a standard for accuracy assessment of 3D point clouds. The proposed procedure minimizes the error attributed to the selection of the corresponding point pairs between the assessed point cloud and the reference DGT point cloud. Subsequently, the proposed methodology was tested and validated by assessing the accuracy of 46 different point clouds. The conclusions regarding the accuracy, applicability, and practical utility of the proposed methodology are supported by the determination of reconstruction errors and location errors in 46 point clouds obtained with the 3 different MTLS systems operated with different settings. The proposed methodology will be very useful for scanning system manufacturers, researchers, advisors and eventually advanced farmers to quantify the errors committed when characterizing tree canopies. This is crucial to enable accurate management operations in the framework of Precision Agriculture based on canopy variability. Furthermore, the methodology is expected to facilitate the design of new applications requiring high accuracy to be implemented in the near future.
Why it matches plant phenotyping methods果樹キャノピーの3D形状・構造を対象に、LiDAR点群の位置誤差と再構成誤差を評価する手法を開発し、46点群で検証しており、植物形質取得の技術評価が中心である。
abstractWe present a methodology to realistically assess the accuracy of 3D point clouds, enabling the evaluation in a full 3D context rather than based on isolated points.
BarleyLaboratory / benchtopRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
Plant phenotyping plays a crucial role in agricultural research, especially in identifying resilient traits essential for global food security. Quantitative analysis of root growth has become increasingly vital in evaluating a plant’s resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images presents substantial challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions. In this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums. Our method involves several stages, beginning with preprocessing to identify the Region of Interest (ROI). Subsequent stages utilize deep neural network-based segmentation, skeleton construction, and graph generation to produce detailed representations of root systems stored in RSML format. Notably, our dataset exclusively comprises primary roots without secondary roots or bifurcations, allowing for a focused examination of primary root characteristics and environmental adaptability. Evaluation against established methods, RootNav 1.8 and 2.0, reveals significant improvements in root system reconstruction accuracy across various performance indicators. Although RootEx may exhibit slightly lower performance due to the absence of neural network-based tip detection, its advantages include minimal losses in missing root lengths and independence from dedicated training datasets. Our approach effectively mitigates detection errors, providing a reliable tool for precise barley root analysis in agricultural research. • RootEx: automated extraction of barley root systems from high-res images. • Improved precision in root analysis through deep learning-based segmentation. • Focus on primary roots, without the complexity of secondary root systems. • Significant accuracy improvement w.r.t. RootNav 1.8 and 2.0. • RootEx minimizes detection errors, ensuring reliabile root analysis.
Why it matches plant phenotyping methods根系画像から植物形質を抽出する自動手法を開発し、既存手法と精度比較しているため、植物フェノタイピング手法が中心である。
abstractwe introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
• UAV-derived canopy model quantified radiation availability of intercropped soybean. • Shadow fraction method was developed to calculate direct, diffuse radiation and RUE. • RUE of intercropped soybean was higher than that in monoculture. • Fraction of diffuse in intercropping was slightly lower than that in monoculture. • Other factors leading to the higher RUE of soybean in intercropping systems. Shading is an unavoidable phenomenon in strip intercropping systems for lower crops, which affects the amount and component of solar radiation, and thus the radiation use efficiency (RUE). The higher crop is usually treated as a homogeneous block instead of the actual canopy structure to calculate lower crop radiation availability (block-based method, BM), which underestimates the amount of light passing through gaps in the canopy. Here we proposed a new shadow fraction method (SFM) to separately quantify direct and diffuse radiation on lower crops. The SFM considered shadow fraction dynamic and view factor within a day, which was calculated based on UAV-derived canopy structural models. To test this method, UAV images and crop data were collected from a maize-soybean intercropping experiment with six planting configurations. For daily total radiation, as the width of the soybean strip decreased from 3.8 m to 1.6 m, the relative difference between BM and SFM increased from about 11.10% to 20.36%. Accordingly, the RUE of soybean calculated by the SFM was 0.2–0.3 g/MJ lower than the BM. Consistent with previous studies, the RUE of soybean in strip intercropping systems (1.36–1.61 g/MJ) calculated by the SFM was higher than that in monoculture (0.98 g/MJ). The higher RUE was usually attributed to the increasing fraction of diffuse in strip intercropping systems. However, SFM showed that the fraction of diffuse on intercropped soybean (ranged from 37.42% to 38.58%) was slightly lower than that in monoculture (39.48%), implying that other factors, such as light intensity and quality, may have an impact on soybean performance and warrant further investigation. The SFM was theoretically more accurate than BM as it considered the actual 3D canopy structure. This method can enhance the understanding of light distribution and use efficiency in intercropping systems, which can be integrated with crop growth models or functional structural plant models to optimize intercropping configurations for improved resource use efficiency.
Why it matches plant phenotyping methodsUAV由来の3Dキャノピー構造モデルを用いて、下層作物の光環境を推定する新しいshadow fraction法を開発・検証しており、植物キャノピー構造と放射利用効率の定量化が研究の中心である。
abstractHere we proposed a new shadow fraction method (SFM) to separately quantify direct and diffuse radiation on lower crops.
• We explore UAV-based multispectral imaging for phenotyping and disease detection in potatoes. • We auto-generate features with simple math and combine them with various vegetation indices. • This is the first study to integrate XGBoost, SHAP, and UMAP in potato phenotyping research. • We propose an analysis pipeline to enhance understanding of relevant agricultural traits. Fast and accurate identification of potato plant traits is essential for formulating effective cultivation strategies. The integration of spectral cameras on Unmanned Aerial Vehicles (UAVs) has demonstrated appealing potential, facilitating non-invasive investigations on a large scale by providing valuable features for construction of machine learning models. Nevertheless, interpreting these features, and those derived from them, remains a challenge, limiting confident utilization in real-world applications. In this study, the interpretability of machine learning models is addressed by employing SHAP (SHapley Additive exPlanations) and UMAP (Uniform Manifold Approximation and Projection) to better understand the modeling process. The XGBoost model was trained on a multispectral dataset of potato plants and evaluated on various tasks, i.e. variety classification, physiological measures estimation, and detection of early blight disease. To optimize its performance, nearly 100 vegetation indices and over 500 auto-generated features were utilized for training. The results indicate successful separation of plant varieties with up to 97.10% accuracy, estimation of physiological values with a maximum R 2 and rNRMSE of 0.57 and 0.129, respectively, and detection of early blight with an F1 score of 0.826. Furthermore, both UMAP and SHAP proved beneficial for comprehensive analysis. UMAP visual observations closely corresponded to computed metrics, enhancing confidence for variety differentiation. Concurrently, SHAP identified the most informative features – green, red edge, and NIR channels – for most tasks, aligning tightly with existing literature. This study highlights potential improvements in farming efficiency, crop yield, and sustainability, and promotes the development of interpretable machine learning models for remote sensing applications.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習・特徴量設計・解釈手法を統合した、ジャガイモ形質・生理値・病害の推定パイプラインが研究の中心であり、技術的評価も行っている。
abstractWe explore UAV-based multispectral imaging for phenotyping and disease detection in potatoes.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAVスペクトル画像と機械学習により茶樹の成長を非破壊モニタリングする手法が題名の中心であり、植物形質の取得・推定を主題としている。
titleNon-destructive monitoring of tea plant growth through UAV spectral imagery and meteorological data using machine learning and parameter optimization algorithms
High-density tree fruit production systems employ SNAP (Simple, Narrow, Accessible and Productive) canopy architectures, such as the UFO (Upright Fruiting Offshoots) system, that require intensive management practices. The growing adoption of these production systems in the USA, along with the decline of farm labor in the country, has sparked interest in automating manual orchard operations. Machine vision plays a key role in the development of robotic solutions because the success of these robots largely depends on the ability of the imaging systems (ISs) to quickly and accurately generate three-dimensional (3D) models of the surroundings. Tree models, for example, are essential to determine cutting points and guide cutting tools to the correct positions when pruning selectively. However, the ISs proposed in recent studies do not produce sufficiently comprehensive models, are expensive, and/or are impractical for commercial applications. In this study, a novel, time-efficient, and pose-versatile imaging system (Mobile IS) was developed and tested to overcome these issues. The Mobile IS utilized off-the-shelf cameras to capture an initial 3D point cloud model of a scene and then dynamically refined the model in real time by integrating additional point clouds from close range and different poses using simple photogrammetric techniques. To evaluate the performance of the Mobile IS, a wide range of UFO-trained tree offshoot diameters (OSDs), and side-branch lengths (SBLs) and spacings (SBSs)—parameters on which the pruning rules for UFOs are based—were measured on the models reconstructed by the Mobile IS and a fixed-pose imaging system (Fixed IS) and compared to ground truth. Mobile IS models exhibited higher accuracy compared to the Fixed IS models, as evidenced by the root mean square (RMS) errors of the Mobile IS measurements ( RMS EOSD of 4.9 mm, RMS ESBL of 8.0 mm, and RMS ESBS of 3.6 mm) and the Fixed IS measurements ( RMS EOSD of 5.9 mm, RMS ESBL of 18.1 mm, and RMS ESBS of 3.8 mm). The versatility of pose enabled the Mobile IS to overcome occlusions and areas with low-confidence depth values. The results suggest that the Mobile IS holds promise as an IS for various robotic applications, including automated pruning, thinning and harvesting, across different tree fruit crops and canopy architectures.
Why it matches plant phenotyping methods果樹の枝径・枝長・枝間隔という形態形質を3D画像から抽出する新規撮像システムを開発し、固定式システムおよび実測値と比較検証しており、画像ベース植物フェノタイピング手法が中心である。
abstractIn this study, a novel, time-efficient, and pose-versatile imaging system (Mobile IS) was developed and tested to overcome these issues.
In agricultural practices, plant phenotyping using object detection models is gaining attention, plant phenotyping is a technology that accurately measures the quality and condition of cultivated crops from images, contributing to the improvement of crop yield and quality, as well as reducing environmental impact. However, collecting the training data necessary to create generic and high-precision models is extremely challenging due difficulties associated with annotations and the diversity of domains. Such difficulties arise from the unique shapes and backgrounds of plants, as well as the significant changes in appearance due to environmental conditions and growth stages. Furthermore, it is difficult to transfer training data across different crops, and although machine learning models effective for specific environments, conditions, and crops have been developed, they cannot be widely applied in real-world conditions. Therefore, in this study, we propose a generative artificial intelligence data augmentation method (D4) and investigated its application towards a shoot detection task in a vineyard. D4 uses a pre-trained text-guided diffusion model based on a large number of original images culled from video data collected by unmanned ground vehicles or other means, and a small number of annotated datasets. The proposed method generates new annotated images with background information adapted to the target domain while retaining annotation information necessary for object detection. In addition, D4 overcomes the lack of training data in agriculture, including the difficulty of annotation and diversity of domains. We confirmed that this generative data augmentation method improved the mean average precision by up to 28.65% for the BBox detection task and the average precision by up to 13.73% for the keypoint detection task for vineyard shoot detection. D4 generative data augmentation is expected to simultaneously solve the cost and domain diversity issues of training data generation for agricultural applications and improve the generalization performance of detection models. • Proposed novel data augmentation method D4 using text-guided diffusion model. • Analyzed detection accuracy using D4 for BBox detection and keypoint detection. • D4 improved detection accuracy and demonstrated effectiveness of domain adaptation. • D4 uses automatic selection with DreamSim to maintain generated image quality. • D4 overcomes lack of training data in agriculture.
Why it matches plant phenotyping methodsブドウのシュートを画像から検出する植物フェノタイピング向けのデータ拡張・ドメイン適応手法を開発し、検出精度を検証しており、表現型取得手法が中心である。
abstractTherefore, in this study, we propose a generative artificial intelligence data augmentation method (D4) and investigated its application towards a shoot detection task in a vineyard.
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.
Ensuring plant health is a key factor to maximize crop yield. Despite that, the current field scouting and disease monitoring approaches often rely on visual evaluations and are, therefore, subjective and time demanding. New methods to assist in disease detection and severity assessment are required to allow better crop management and higher throughput in field phenotyping studies. With this objective, techniques involving the use of multi- and hyperspectral imagery for retrieval of plant traits and assessment of general crop health status are increasingly being proposed as alternatives to conventional disease monitoring approaches. Conversely, research focusing on specific pathogens are still lacking in many cases, in particular studies investigating multi-source sensing approaches, which have the potential to improve retrieval/classification accuracy. In this study, hyperspectral imagery and point clouds obtained with LiDAR or through Structure from Motion algorithm (SfM) applied to high resolution RGB images were evaluated as possible alternatives to detect Blackleg (caused by bacteria of the genera Pectobacterium and Dickeya ) in potato. It was demonstrated that all the different datasets have potential to discriminate healthy from diseased plants. The combination of Vegetation Indices (VIs) derived from hyperspectral images with structural features from LiDAR resulted in the best validation results (Balanced Accuracy – BA = 0.915). Small improvements were also achieved by combining VIs with SfM features (BA = 0.876) in comparison to VIs alone (BA = 0.846). Evaluation of feature importance for classification models derived from the different datasets indicated that after structural features derived from LiDAR or RGB imagery were added as predictor variables the relative importance of VIs for the predictions decreased, in particular for VIs related to LAI or other traits describing canopy properties. Finally, analysis of false negatives and positives indicated some limitations to the predictive potential of the different datasets, with diseased and healthy plants eventually presenting atypical structural and spectral characteristics in comparison to those expected for their classes. Therefore, multi-source sensing, including additional modalities (e.g., thermal or fluorescence), might be required to further improve detection of pathogens with complex symptoms, as those affecting roots, tubers and stems.
Why it matches plant phenotyping methodsUAVハイパースペクトル、LiDAR、SfMによる植物単位の病害状態判別を開発・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。
abstractNew methods to assist in disease detection and severity assessment are required to allow better crop management and higher throughput in field phenotyping studies.
Quantitative analysis for plant growth attributes has gained prominence in plant science and agriculture. Despite the availability of automated phenotyping systems as a solution to labor-intensive manual measurement techniques, these systems often require specialized knowledge and face challenges in scaling for high-throughput applications. This research introduces a scalable high-throughput plant phenotyping technique utilizing a Pan Tilt Zoom (PTZ) camera. The primary objective is to assess the application of a PTZ camera in a plant phenotyping system. By integrating open-source software and hardware technologies, the method captures images of cucumber plants in a controlled greenhouse environment. The operational procedure of the robot consists of a series of steps. It begins with the robot’s initial movement to capture infrared images, followed by an analysis to detect Aruco markers serving as location identifiers for capturing plant images. Subsequently, the PTZ camera is adjusted to capture specific plant traits from predefined viewpoints. The captured images with location IDs, preset viewpoints, and timestamps are then sent to a remote server. Validation of the system’s dependability includes manual measurements on fundamental operations and the evaluation of the effectiveness of zoomed images captured by the PTZ camera, tested through plant feature detection. Experimental results demonstrate promising outcomes, achieving a mean average precision (mAP) of 94%, 97.6%, 98.4%, 90.1%, and 97.6% for apical buds, male flowers, female flowers, tiny cucumbers, and mature cucumbers respectively when using the trained YOLOv8s on an augmented dataset tested on highly zoomed images validation set, outperforming less zoomed or less detailed validation sets. These findings underscore the efficacy of this innovative approach in capturing real-time plant images. Leveraging the PTZ camera’s zoom, pan, and tilt capabilities enables comprehensive visualization of plant traits and adaptability to evolving growth patterns, thereby improving the results of plant feature detection. The amassed imagery serves a dual purpose by acting as training data for AI models, highlighting their potential to facilitate future research endeavors demanding extensive and scalable plant information. • Introducing a high-throughput plant phenotyping method for capturing real-time imagery of plants. • Proposing PTZ camera’s imaging mechanisms for visualizing diverse and detailed plant features. • Improved results arise from applying plant feature detection to zoomed images taken with a PTZ camera.
Why it matches plant phenotyping methodsPTZカメラを用いた植物表現型取得システムを開発し、画像による器官・生育特徴検出で有効性を検証しており、方法が研究の中心である。
abstractThis research introduces a scalable high-throughput plant phenotyping technique utilizing a Pan Tilt Zoom (PTZ) camera.
Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimate the three-dimensional (3D) volume of individual potato tubers. A challenge, however, is that the 3D shape derived from RGB-D images is only partially completed, underestimating the actual volume. To address this issue, we developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images. CoRe++ is a deep learning network that consists of a convolutional encoder and a decoder. The encoder compresses RGB-D images into latent vectors that are used by the decoder to complete the 3D shape using the deep signed distance field network (DeepSDF). To evaluate our CoRe++ network, we collected partial and complete 3D point clouds of 339 potato tubers on an operational harvester in Japan. On the 1425 RGB-D images in the test set (representing 51 unique potato tubers), our network achieved a completion accuracy of 2.8 mm on average. For volumetric estimation, the root mean squared error (RMSE) was 22.6 ml, and this was better than the RMSE of the linear regression (31.1 ml) and the base model (36.9 ml). We found that the RMSE can be further reduced to 18.2 ml when performing the 3D shape completion in the center of the RGB-D image. With an average 3D shape completion time of 10 ms per tuber, we can conclude that CoRe++ is both fast and accurate enough to be implemented on an operational harvester for high-throughput potato yield estimation. CoRe++’s high-throughput and accurate processing allows it to be applied to other tuber, fruit and vegetable crops, thereby enabling versatile, accurate and real-time yield monitoring in precision agriculture. Our code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git . • CoRe++ is a high-throughput 3D shape completion network for RGB-D images. • CoRe++ uses a convolutional encoder and DeepSDF decoder to complete the 3D shape. • Testing on 1425 RGB-D images, CoRe++ achieved a completion accuracy of 2.8 mm. • The average 3D shape completion time was 10 ms per potato tuber.
Why it matches plant phenotyping methodsRGB-D画像からジャガイモ塊茎の3D形状と体積を推定する手法を開発・評価しており、植物形質取得が研究の中心である。
abstractwe developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images.
• UAV-based localization and mapping methods have been benchmarked in vineyards. • Five evaluation metrics were developed for agricultural scenarios. • Lighting variation impacts point cloud resolution. • Deep learning enhances SLAM for efficient plant phenotyping. UAVs equipped with various sensors offer a promising approach for enhancing orchard management efficiency. Up-close sensing enables precise crop localization and mapping, providing valuable a priori information for informed decision-making. Current research on localization and mapping methods can be broadly classified into SfM, traditional feature-based SLAM, and deep learning-integrated SLAM. While previous studies have evaluated these methods on public datasets, real-world agricultural environments, particularly vineyards, present unique challenges due to their complexity, dynamism, and unstructured nature. To bridge this gap, we conducted a comprehensive study in vineyards, collecting data under diverse conditions (flight modes, illumination conditions, and shooting angles) using a UAV equipped with high-resolution camera. To assess the performance of different methods, we proposed five evaluation metrics: efficiency, point cloud completeness, localization accuracy, parameter sensitivity, and plant-level spatial accuracy. We compared two SLAM approaches against SfM as a benchmark. Our findings reveal that deep learning-based SLAM outperforms SfM and feature-based SLAM in terms of position accuracy and point cloud resolution. Deep learning-based SLAM reduced average position error by 87% and increased point cloud resolution by 571%. However, feature-based SLAM demonstrated superior efficiency, making it a more suitable choice for real-time applications. These results offer valuable insights for selecting appropriate methods, considering illumination conditions, and optimizing parameters to balance accuracy and computational efficiency in orchard management activities.
Why it matches plant phenotyping methodsブドウ園でのUAV画像によるSfM・SLAM手法を比較検証し、植物レベルの空間精度や点群完全性などを評価しており、植物フェノタイピングの取得・解析基盤が中心である。
abstractDeep learning enhances SLAM for efficient plant phenotyping.
Phenotypic traits of stemwork are important indicators of plant growing status, contributing to multiple research domains including yield estimation, breeding engineering, and disease control. Traditional plant phenotyping with human work faces serious bottlenecks on labour intensity and time consumption. In recent years, the application of Quantitative Structural Modeling (QSM) together with three-dimensional (3D) sensor-based data acquisition techniques provides a feasible solution towards the automatic stemwork phenotyping. Nevertheless, existing QSM-based pipelines are sensitive towards the point cloud quality, and mostly focus on the phenotyping at plant or organ level. Information at internode level which are closely related to photosynthesis and light absorption was generally overlooked. To this end, a 3D automatic stemwork phenotyping pipeline is developed for tomato plants at both plant and internode level. Coloured point clouds are taken as the sensor input of the pipeline. A semantic segmentation based on PointNet++ was used to detect and localise the stemwork points. To improve the quality of the segmented stemwork point clouds, a density-based refining pipeline is proposed containing three main processes: non-replacement resampling, interference branch removal, and noise removal. A Tree Quantitative Structural Modeling (TreeQSM) algorithm was then applied to the stemwork point cloud to construct a digital reconstruction. The target phenotypic traits were finally calculated from the digital model by employing an internode association process. The proposed phenotyping pipeline was evaluated with a test dataset containing three tomato plant cultivars: Merlice, Brioso, and Gardener Delight. The related rooted mean squared errors of calculated internode length, internode diameters, leaf branching angle, leaf phyllotactic angle, and stem length range from 4.8 to 64.4%. Considering the time consuming manual phenotyping process, the proposed work provides a feasible solution towards the high throughput plant phenotyping, from which facilitates the related research on plant breeding and crop management. • Enhancing the quality of plant stemwork point clouds via non-replacement resampling, interference removal, and denoising process. • Enabling a TreeQSM-based plant stemwork reconstruction over point clouds with low resolution and signal-noise ratio. • Enabling an automatic phenotyping process for tomato plant stemwork at both plant and internode level.
Why it matches plant phenotyping methods3D点群、セマンティックセグメンテーション、TreeQSMを統合し、トマトの茎・節間形質を自動抽出するフェノタイピング手法の開発と評価が中心である。
abstracta 3D automatic stemwork phenotyping pipeline is developed for tomato plants at both plant and internode level
• The Glu/Gli in wheat grains using hyperspectral reflectance was estimated during early reproductive stages. • Integration of multiple VIs enhances accuracy in Glu/Gli estimation. • Mid grain-filling stage produces most precise Glu/Gli estimates. • RFR accurately estimated Glu/Gli combined with VIs, with an R 2 of 0.691, rRMSE of 0.096, RPD of 1.872 and RER of 6.028. Precise and timely estimation of the glutelin-to-gliadin ratio (Glu/Gli) in wheat grain is pivotal for crop monitoring, as it is a crucial quality indicator ensuring the production of high-quality wheat flour. Despite the recognized potential of hyperspectral technology in crop phenotype estimation, its application to estimate Glu/Gli in wheat grains faces challenges due to complex spectral-chemical relationships and the influence of growing seasons. This study addresses this gap by cultivating 11 wheat varieties and collecting high-dimensional hyperspectral data from field experiments during various growth stages of wheat (2018–2019 and 2019–2020). Utilizing vegetation indices (VIs) in conjunction with linear mixed-effects model (LMM) and random forest regression model (RFR), it constructs a robust Glu/Gli estimation model (with a Glu/Gli range of 1.063 to 2.218). Results reveal that a singular VI application suffers from data limitations, while the integration of multiple VIs significantly enhances estimation accuracy. The mid-grain filling period emerges as a critical stage for accurate Glu/Gli estimation, with TCARI (transformed chlorophyll absorption reflectance index) demonstrating notable significance and high correlation. In model performance, RFR (R 2 = 0.691, rRMSE = 0.096, RPD = 1.872, RER = 6.028) outperforms LMM (R 2 = 0.477, rRMSE = 0.131, RPD = 1.383, RER = 4.453), exhibiting superior accuracy in estimating grain Glu/Gli for diverse wheat varieties. This study introduces a rapid and accurate approach for early wheat grain Glu/Gli estimation, offering valuable insights for wheat value chain and precision farming.
Why it matches plant phenotyping methods小麦粒のGlu/Gli比という植物器官の品質形質を、ハイパースペクトル反射とVI・回帰モデルで推定する手法を構築し、複数品種・生育段階で精度評価しており、表現型取得・推定法が中心である。
abstractThe Glu/Gli in wheat grains using hyperspectral reflectance was estimated during early reproductive stages.
Monitoring tomato fruit growth in large greenhouse environments is crucial for estimating harvest volume and maximising labour efficiency. Mathematical modelling is commonly used to characterise crop and fruit growth and to understand crop responses to environmental influences. This study presents a logistic approach for estimating tomato fruit maturity based on fruit diameter. A set of logistic models were evaluated, and their precision and ability to forecast tomato fruit growth were analysed. Non-linear least square regression was used to fit each model to individual tomato fruits from a sample of over 600 fruits of two species (small and large fruit-bearing tomatoes) grown within one year. The focus was on fruit diameter and the necessary measurement precision for fruit maturity estimation. Low-dispersion variables were identified within our logistic model functions, and fruit species-specific model parameters were determined. Furthermore, we reduced the number of regression variables by identifying parameters of low variance. This allowed for high prediction precision ( > 80%) at the early fruit maturity stages ( < 30%). The fruit diameter data were collected manually using a caliper. Our results establish an upper limit for the measurement precision of automated fruit size estimation approaches utilising photogrammetry, with an optical range of 98% for 3 mm and 90% for 5 mm precision. • Long and short term Tomato fruit size prediction. • High precision fruit yield forecasting. • Optimised tomato fruit growth modelling and comparison between models. • Upper limit estimation for growth predictability.
Why it matches plant phenotyping methodsトマト果径から成熟・成長を推定するロジスティック予測モデルを開発・比較し、予測精度と自動果実サイズ推定への測定精度上限を評価しており、植物形質推定手法が中心です。
abstractThis study presents a logistic approach for estimating tomato fruit maturity based on fruit diameter.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species — peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatial–spectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.
Why it matches plant phenotyping methods地下部植物根のRGB・ハイパースペクトル画像データセットを構築し、根のセマンティックセグメンテーション手法を比較・評価しており、植物フェノタイピング手法が中心である。
abstractHere we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Maize (Zea mays) is one of the world’s most important crops, and its abundant and stable yield is crucial for ensuring global food security. Optimizing maize plant architecture can effectively enhance canopy structure, and ensure an ample supply of assimilates, thereby constituting a crucial strategy for achieving high yields in high-density planting systems. In this study, we used the phenotyping platform MVS-Pheno to synchronously collect multi-view image data of plant architecture, and the 3D phenotype analysis algorithm was developed to batch and automatically extract traits of spatial geometric structure. Using this phenotypic acquisition and analysis platform, 44 traits of maize plant architecture were defined and extracted, including 6 categories: basic phenotype, projection area related phenotype, leaf related phenotype, plant architecture dispersion related phenotype, volume related phenotype, and color related phenotype. Based on abundant phenotypic traits of plant architecture, we analyzed the phenotypic variations among a group of 495 inbred lines and further conducted GWAS to reveal the genetic components of the plant architecture. In summary, our work demonstrates valuable advances in high-throughput identification of qualitative traits for plant architecture, which could have major implications for improving high-density tolerant maize breeding and production.
Why it matches plant phenotyping methodsMVS-Phenoプラットフォームと3D画像解析アルゴリズムによる植物体構造形質の高スループット取得・抽出が研究の中心であるため。
abstractwe used the phenotyping platform MVS-Pheno to synchronously collect multi-view image data of plant architecture, and the 3D phenotype analysis algorithm was developed to batch and automatically extract traits of spatial geometric structure.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Rapid and non-destructive estimation of tea plant growth and nitrogen (N) nutrition status using hyperspectral remote sensing is crucial for precise management of tea gardens. This study aimed to mine and fuse sensitive hyperspectral features to achieve an accurate estimation of tea plant growth parameters (biomass and N accumulation) throughout the whole year. An ASD Handheld 2 sensor was used to collect canopy hyperspectral reflectance of tea plants across four periods (Period 1–4) within a year, with tea plant biomass and N accumulation indicators acquired synchronously. The measured spectral reflectance and its first derivative, and wavelet feature were extracted and used to establish quantitative relationships with tea plant growth parameters. Random forest and LASSO algorithms were employed to combine sensitive hyperspectral features and construct the biomass and N accumulation monitoring models. The results showed that wavelet features (R² = 0.35–0.58) had a stronger correlation with tea plant biomass and N accumulation parameters compared with the measured reflectance or first derivative spectral features. Similarly, the hyperspectral indices (R² = 0.51–0.69) derived from sensitive wavelet features performed an accurate estimation of tea plant growth parameters. Furthermore, the combination of sensitive hyperspectral indices derived from measured reflectance, first derivative, and wavelet feature using random forest (R² = 0.67–0.76) and LASSO (R² = 0.61–0.72) algorithms achieved the greatest accuracy for monitoring tea plant biomass and N accumulation compared with individual hyperspectral feature. Additionally, the above estimation models obtained higher accuracy in period 4 compared to periods 1–3. This study provides valuable remote sensing technical support for predicting biomass and N accumulation status of tea plant throughout the whole year.
Why it matches plant phenotyping methods茶植物のバイオマスと窒素蓄積を対象に、ハイパースペクトル特徴量の抽出・融合と推定モデル構築を中心的に扱う植物フェノタイピング手法研究である。
abstractThis study aimed to mine and fuse sensitive hyperspectral features to achieve an accurate estimation of tea plant growth parameters (biomass and N accumulation) throughout the whole year.
Field / plotLiDAR / point cloudRGB / grayscaleMultispectral / hyperspectralThermalWhole plant / canopy / plot / fieldObject detection
Current crop phenotyping mainly relies on manual measurements and visual inspection for data collection and crop assessment, which is labor-intensive, subjective, and inefficient. Hence, modern methods depend primarily on using sensors for phenotypic data collection to replace labor vision, developing algorithms for decision-making to replace human domain knowledge, and integrating autonomous phenotyping systems to improve efficiencies in the past decades. Despite the research progress in phenotyping, there is a lack of extensive review on this topic that will be useful to various stakeholders interested in this field. Therefore, this study was conducted to perform a comprehensive review of multiple methodologies and techniques used in high-throughput ground crop phenotyping systems. A Web of Science literature search was conducted with appropriate keywords for the recent past, and the research trends in this field were captured. The current review categorizes the progress of technology in terms of phenotyping platform, sensing, data processing, and system integration. Platforms have evolved from manual-based to autonomous. Manual-based platforms require workers for data collection, while autonomous platforms involve new technologies for navigation and data collection. Different sensing techniques are used for phenotyping data collection. This study mainly discusses the mainstream sensors, including RGB, multi/hyperspectral, thermal, stereo, and light detection and ranging, and concludes that multi-source sensors could provide more accurate phenotypic information. Algorithms are applied to collected data to extract useful phenotyping information at different scales (organ, individual plant, and community). Both machine learning (ML) and deep learning (DL) have been used for phenotyping information extraction, and the DL is gradually replacing ML due to its superior performance. A case study of integrated high-throughput proximal phenotyping robot was presented, showing how different sensors and navigation systems come together to achieve on-site and real-time measurements. Advancements in high-throughput proximal ground phenotyping systems through new information, communication, sensing, and autonomous technologies in agriculture are anticipated to be more integrated and efficient phenotyping. It is anticipated that autonomous robots would finally replace workers from laborious phenotyping work.
Why it matches plant phenotyping methods植物フェノタイピングの高スループット地上システムについて、プラットフォーム、センサー、データ処理、統合技術を体系的にレビューしており、方法論が中心である。
abstractTherefore, this study was conducted to perform a comprehensive review of multiple methodologies and techniques used in high-throughput ground crop phenotyping systems.
Phenotyping is a systematic process of quantifying and assessing a wide range of structural and physiological traits to understand the intricate interplay between an organism’s genetic makeup, its surrounding environment, and management practices, often referred to as genome-to-environment (GxE) interaction. In the context of plants, these traits can include aspects such as plant height, stem diameter, leaf size, angle, and shape, chlorophyll content, biomass, leaf area, etc. 3D plant phenotyping plays a crucial role in advancing our understanding of plant biology, improving crop breeding, and agricultural practices. 3D imaging has become a powerful phenotyping tool, offering in-depth insights into plant structures and traits. In contrast to 2D imaging, 3D imaging enables precise measurement of plant traits that cannot be sufficiently evaluated in two dimensions by overcoming challenges such as partial occlusion through the utilization of depth perception and multiple viewpoints. However, even with significant recent progress, various challenges persist, including the need for well-designed experimental setups for standardized data collection, the automation of processing pipelines, and the robust analysis techniques of 3D representations, which still impede the widespread adoption of 3D plant phenotyping. To propel the progress of 3D imaging-based phenotyping, an all-encompassing assessment of existing strategies is imperative, yet there is currently a lack of specialized reviews that scrutinize and emphasize distinct facets for future enhancement. To bridge this gap, we perform a systematic survey of 81 research studies that employ 3D imaging for various trait assessments of plants. Our review thoroughly investigates the stages of data acquisition, encompassing sensing technologies, representations, preprocessing approaches, analysis methodologies, and techniques for estimating phenotypic traits. We believe that this comprehensive review will serve as a valuable guide for researchers and professionals engaged in high throughput plant phenotyping, equipping them to formulate effective experimental setups and utilize appropriate processing and analysis methods, thereby fostering its continued advancement.
Why it matches plant phenotyping methods植物表現型解析における3D画像取得・前処理・解析・形質推定を体系的にレビューしており、方法論が中心です。
titleUnlocking plant secrets: A systematic review of 3D imaging in plant phenotyping techniques
Cercospora leaf spot (CLS) disease, triggered by the fungus Cercospora beticola, represents the most severe foliar disease affecting sugar beets globally. The significant vertical heterogeneity of the plant canopy makes traditional 2D spectral imaging insufficient to accurately determining the CLS disease ratio. Integrating 3D and spectral imaging from dual sensors to form a plant spectral point cloud faces challenges due to alignment issues and high costs. An approach combining multi-view spectral images with the Structure from Motion (SfM) algorithm was introduced to generate a detailed multispectral 3D point cloud of plant structure. This technique was employed to assess the CLS disease ratio and its spatial heterogeneity. Specifically, a discriminant-based model was developed to differentiate healthy and diseased leaves using various ratio-based or normalized vegetation indices at the leaf scale. This model was then utilized to extract 3D CLS point clouds from the multispectral point clouds reconstructed by the new method at both plant and plot levels. Three-dimensional spatial heterogeneity analysis explored the vertical and horizontal distribution patterns of CLS in sugar beets. The findings revealed that disease levels determined by the 3D CLS model surpassed those of expert visual assessments (75 % vs. 58.3 %). The estimated disease ratio closely matched the measured values (RMSE = 8.4 %). Additionally, plot-scale CLS distribution maps aligned well with RGB image distributions. The analysis indicated that CLS initially spread from lower leaves upwards and displayed a pattern moving from the periphery to the interior. The introduced method offers a cost-effective, convenient alternative for generating detailed multispectral 3D point clouds. This study emphasizes the potential of spectral point clouds in monitoring plant canopy health and physiological activities.
Why it matches plant phenotyping methodsマルチビュー分光画像とSfMを融合して植物の3Dスペクトル点群を生成し、テンサイ葉の病害比率を抽出・検証する手法が研究の中心であるため。
abstractAn approach combining multi-view spectral images with the Structure from Motion (SfM) algorithm was introduced to generate a detailed multispectral 3D point cloud of plant structure.
In this study, we introduce PlantSegNet, a novel neural network model for instance segmentation of nearby objects with similar geometric structures. Our work addresses the challenges of instance segmentation of plant point clouds, including the difficulty of annotating and labeling point clouds, the loss of local structural information in neural network components, and the generation of large numbers of incorrect small clusters due to poor choices of the loss function. One of the key contributions of our approach is a digital twin of sorghum, i.e., a procedural sorghum model, which was used to generate point clouds of sorghum fields. This allowed us to create a large-scale, annotated, synthetic dataset of sorghum plants that we used to train our PlantSegNet model. We demonstrated the effectiveness of our method in segmenting instances of sorghum leaves grown in outdoor field settings. To the best of our knowledge, this is the first study to address this specific instance segmentation problem for plants grown in such a setting. We compared our proposed method with other state-of-the-art methods for indoor settings, including SGPN and TreePartNet, on both synthetic and real data. Our results show that PlantSegNet outperforms these methods regarding accuracy, robustness, and efficiency.
Why it matches plant phenotyping methods植物葉の点群から器官インスタンスを抽出するニューラルネットワークを開発し、合成データセット作成、実データでの比較検証まで行っており、植物表現型取得手法が中心である。
abstractwe introduce PlantSegNet, a novel neural network model for instance segmentation of nearby objects with similar geometric structures.
Reproduction assets foundThe authors publicly release their PlantSegNet analysis code (PyTorch models and TreePartNet wrapper) together with their labeled synthetic and real sorghum point cloud datasets, and separately state the datasets (synthetic/real sorghum and Tree Dataset) are publicly available on their GitHub page.Dataset · publicobjects. To
that end, according to PlantSegNet input format requirements, we
developed a modified version of the TreePartNet paper’s dataset named
the Tree Dataset. The Tree Dataset includes 3,521, 440, and 440 point
clouds in the training, validation, and test sets. These datasets are now
publicly available on our GitHub page https://github.com/ariyanzri/PlantSegNet.3.2. Data augmentation
To enhance the diversity of our artificially generated dataset and
make it more resilient to the noises present in the real data, we intro-
duce a common noise to each point coordinate in all three dimensions
of the 3D space separately. The noise has a mean of zero and a standard
deviation of 0.01. FurtherOpen asset ↗ariyanzri/PlantSegNet.3.2pdf-raw-page:7 lines:1-145Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Rapid and precise 3D organ segmentation is crucial for the automatic extraction of phenotypic traits, forming a fundamental prerequisite for intelligent plant breeding. The advancement of deep learning technology has replaced labor-intensive manual measurements and traditional computer vision methods, which are sensitive to parameters, in phenotypic trait extraction. However, current larger network structures not only require extensive point cloud data but also consume substantial computational resources, rendering them unsuitable for agricultural tasks with limited plant samples. Therefore, this study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction. The adopted simple-but-effective network architecture and innovative modern operations, including a high-dimensional feature mapping strategy for preprocessing input points, a local feature extraction module based on inverted residual bottleneck block, and a cost-free attention block for spatial feature fusion, effectively implement multi-scale hierarchies and adaptively reduce computational overheads. Experimental results from cotton stem-leaf segmentation demonstrated that PEPNet not only presented approximately 2 × faster inference speed (9.59 ms) and throughput (146.32 plants per second) but also achieved competitive segmentation performance compared to other six state-of-the-art deep learning networks, namely PointNet++, DGCNN, CurveNet, Point Cloud Transformer, PointMLP, and SPoTr, achieving 95.99 %, 94.66 %, 95.32 %, and 91.31 % in Precision, Recall, F1-score, and mIoU, respectively. In transferability experiments with tomato and soybean plants, PEPNet achieved almost all the best metrics and significantly outperformed the second-best model (CurveNet). Furthermore, ablation study verified the optimal trade-off between efficiency and accuracy in this network. Any modifications to the modules could potentially disrupt the optimal trade-off. This work could contribute to reducing computational resources and annotation costs for applying segmentation methods in high-throughput phenotyping tasks.
Why it matches plant phenotyping methods植物器官の3D点群セグメンテーションと形質抽出を目的とする軽量深層学習ネットワークを開発し、複数作物で性能検証しており、フェノタイピング手法が中心です。
abstractthis study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Accurate and efficient acquisition of maize emergence rate and leaf emergence speed in the field is essential for detecting seed quality, evaluating crop field management plans, and yield assessment. This study constructs a system solution to obtain the maize seedling emergence rate and leaf emergence speed based on the field rail-based phenotyping platform and convolutional neural network. Firstly, we use the field rail-based phenotyping platform to collect a high-temporal sequence visible light images of maize plant during the seedling stage. In the first stage, an improved Faster R-CNN is used to detect maize seedlings in the plot images, and the plant ROI area is cropped as the input for the second stage network. In the second stage, the best performing ResNeSt network out of four backbone networks is chosen, using the Mask R-CNN model to segment the leaves of the input plant image, which is then used to calculate the number of leaves. We propose a quantification index for leaf emergence speed based on a weighted average combination of leaf numbers. Using the method described in this paper, we analyzed the plant images from 52 inbred lines plots of over seven consecutive days. The experimental results show that when the Intersection Over Union (IOU) is 0.50, the bbox_mAP of the maize seedling detection model is 0.969, with an accuracy rate of 99.53%. Compared with manual counting, the calculated R² is 0.997 and RMSE is 43.382. The segm_mAP of the plant leaf segmentation model is 0.942. The differences in emergence rate and leaf emergence speed across 52 inbred lines were compared, providing new phenotyping reference indices for further exploring the genotypic differences affecting seed emergence and leafing.
Why it matches plant phenotyping methods画像検出によりトウモロコシの出芽率と葉出現速度という植物形質を推定する手法であり、鉄道型フィールドフェノタイピング基盤上での取得・推定が中心です。
titleMaize emergence rate and leaf emergence speed estimation via image detection under field rail-based phenotyping platform
High throughput phenotyping of potatoes after canopy consolidation is crucial to crop breeding and management. A prior step is to segment their leafy potato stems, which is challenging after canopy consolidation because potato stems are dense and intertwined. Current methods for dense crop segmentation are manual. This study equipped unmanned aerial vehicles with a high-resolution RGB sensor in ultra-low flight as a high-throughput alternative. An end-to-end method was proposed to segment their leafy potato stems using YOLOv8x and five kinds of band combinations, i.e., RGB, RGB-DSM, RGB-CHM, RGB-DSM × 3, RGB-ExG. The YOLOv8x model with the RGB-DSM combination achieved superior performance with F1 score of 0.86 and Intersection over Union (IoU) of 0.83. Both F1 score and IoU improved by more than 16 %, when adding DSM or CHM to RGB images. Results demonstrated that height mutation at the edge of leafy potato stems played a crucial role in improving the segmentation of leafy potato stems. Millimeter-level ground sampling distance facilitates high throughput phenotyping of potatoes. The accuracy and efficiency of YOLOv8x has great potential for guiding the phenotypic automation of potatoes as well as other arable crops through remote sensing.
Why it matches plant phenotyping methodsジャガイモ茎の画像セグメンテーション手法をUAV・YOLOv8x・空間/スペクトル特徴で開発・評価しており、植物表現型取得が研究の中心です。
abstractThis study equipped unmanned aerial vehicles with a high-resolution RGB sensor in ultra-low flight as a high-throughput alternative.
The increasing demand for quality, year-round food production in limited space has led to the widespread adoption of protected cropping. Effectively monitoring and maintaining crops within these facilities requires substantial labour and expertise. Traditional manual monitoring is labour intensive and time consuming. Therefore, non-destructive image-based techniques, particularly those utilising 3D structural data, have gained attention. We developed a stereo vision-based system to estimate the height of vertically supported tall plants in protected facilities, given plant height serves as a vital measure of crop growth. Our system uses a mobile platform with a top-angle view of a stereo vision depth camera for data acquisition and machine learning in its core for data analysis. First, we collected weekly RGB and depth (RGBD) streams from plant gutters in three glasshouse compartments with different light treatments. We used part of the RGB data collected to train and validate a deep learning segmentation model to detect plant tops and bases. Detected tops and bases of an image were then mapped to the generated 3D scene using the depth image of the same frame. Thresholds and 3D clustering are used respectively to remove background and eliminate outliers in top and base detection mapped to 3D space. Finally, the height of each plant was calculated using the cluster centres of the tops and bases of the plants. Manually measured heights of ten selected plants per environment were used to validate the height estimations. Similar growing patterns were observed between imaged and manually measured plant heights, which showed strong correlations of 0.87, 0.96, and 0.79 R2 scores, respectively, under unfiltered ambient light, Smart Glass film, and shifted light. These promising results demonstrate the feasibility of our proposed method for a vertically supported capsicum crop in a commercial-scale protected crop facility.
Why it matches plant phenotyping methodsステレオビジョンと機械学習を用いて植物体高を推定する手法を開発し、手動測定で検証しており、植物表現型の取得が研究の中心である。
abstractWe developed a stereo vision-based system to estimate the height of vertically supported tall plants in protected facilities
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Effective identification of tomato plant traits is crucial for timely monitoring and evaluating their growth and harvest. However, conducting stress experiments on multiple tomato genotypes introduces challenges due to the nature of the data. One of these challenges arises from an imbalanced sample distribution, potentially leading to misclassification between classes and disruptions in model recognition. This paper addresses the effect of these challenges by considering the imbalanced classes of flowers, fruits, and nodes and proposing an improved detection approach through data balancing. A novel data-balancing approach is introduced in this study to overcome the issue of imbalanced data. The proposed solution involves the implementation of a YOLOv8 deep learning model, which effectively detects flowers, fruits, and nodes in tomato plants. This model significantly enhances the ability of the algorithm to detect objects of varying sizes within complex environments. To further bolster the recognition capability of the targeted classes, the proposed model integrates a Squeeze-and-Excitation (SE) block attention module into its head architecture. This module strengthens the model recognition ability by giving increased attention to the studied classes, thereby enhancing overall detection performance. The results demonstrate that the data balancing approach successfully improves the model performance in response to the data challenges. When applying the technique of pre-training the optimal weights obtained from balanced data on imbalanced data, the SE-block module showed significant improvements in outcomes.
Why it matches plant phenotyping methodsトマトの花・果実・節という器官形質を画像から検出するYOLOv8手法を開発・改良し、データ不均衡と複雑環境での性能を評価しており、フェノタイピング手法が中心である。
abstractThis paper addresses the effect of these challenges by considering the imbalanced classes of flowers, fruits, and nodes and proposing an improved detection approach through data balancing.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
This paper provides an overview of the recent developments in remote sensing technology and machine learning algorithms for estimating important biophysical and biochemical parameters for precision farming. The objectives are (i) to provide an overview of recent advances in remotely sensed retrieval of biophysical and biochemical parameters brought by the developments in sensor technologies and robust machine learning algorithms and (ii) to identify the sources of uncertainty in retrieving biophysical and biochemical parameters and implications for precision agriculture. The review revealed that developments in crop biophysical and biochemical parameters retrieval techniques were mainly driven by announcements and the availability of new sensors. Two ground-breaking events can be identified, i.e., the availability of Sentinel-2 and the SuperDove constellation. The two provide high temporal-high spatial resolution data relevant for site-specific management and super-spectral configuration, enabling retrieval of crop growth and health parameters. The free availability of Sentinel-2 triggered the testing of its spectral configurations and upscaling of retrieval approaches using simulated data from field spectrometers and airborne hyperspectral sensors. SuperDoves will likely reduce the cost of very high-resolution data while providing unprecedented capabilities for detailed, accurate and frequent characterisation of field variability. Studies showed that the red-edge bands and hybrid models coupling Radiative Transfer Model (RTM) and machine learning regression algorithms (MLRA) are promising for operational and accurate monitoring of stress-related crop parameters to aid time-sensitive agronomic decisions. However, such models were tested in Mediterranean climates and performed poorly in African semi-arid areas and China’s temperate continental semi-humid monsoon climates. Therefore, locally-calibrated RTM models incorporating crop-type maps and other spatio-temporal constraints may reduce uncertainties when adapted to data-scarce regions. Generally, permanent experimental sites and a lack of systematic calibration data on various crops are some limiting factors to using remote sensing technologies for PA in Sub-Saharan Africa. Other complexities arise from farm configurations, such as small field sizes and mixed cropping practices. Therefore, future studies should develop generic, scalable and transferable models, especially within under-studied areas.
Why it matches plant phenotyping methods作物の生物物理・生化学パラメータを推定するセンサー技術と機械学習の進展を概観するレビューであり、植物形質取得・推定手法が中心です。
titleOptical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture
Digital twins are a core industry 4.0 technology enabling the virtual replication of real-world objects, mimicking behaviours and states throughout their lifespan. While digital twins have shown significant benefits in industries such as manufacturing, transportation, and healthcare, their application in agriculture is still within its infancy. Their realisation also poses significant challenges, such as the creation of dynamic agricultural objects (e.g., plants). Existing literature on digital twins in agriculture identifies their limited ability to monitor physical objects without predictive capabilities and that there is a significant lack of 3D representations of plants with functional attributes. Yet, incorporating 3D representations of plants with underlying functionality in a digital twin can greatly improve growth, yield, and disease prediction accuracy. This enhancement enables various applications, such as assessing and developing pruning strategies, providing education to growers, guiding pruning robots, and optimizing spraying techniques. To that end, Functional Structural Plant Modelling presents a potential solution by representing the 3D architecture of plants and incorporating the functionality of different plant parts. By conducting a domain analysis of 3D plant phenotyping and FSPM, this study addresses the specific needs of digital twins in agriculture regarding FSPM. The investigation bridges the existing knowledge gap by identifying crucial concepts, including 3D plant modelling with underlying functionality and 3D plant phenotyping for digital twins. Specifically, a framework for 3D FSPM integration into agricultural digital twins is proposed. The framework not only acknowledges the associated requirements and challenges identified in existing literature but also lays foundation for the advancement of digital twins in the agricultural domain.
Why it matches plant phenotyping methods3D植物フェノタイピングと機能的構造植物モデリングをドメイン分析し、農業デジタルツインへの統合フレームワークを提案しており、方法論が中心です。
abstractBy conducting a domain analysis of 3D plant phenotyping and FSPM, this study addresses the specific needs of digital twins in agriculture regarding FSPM.
The scientific progress in artificial intelligence and robotics has enabled precision viticulture to pursue sustainability and improve the final yield. For instance, monitoring the canopy volume of each plant can allow the correct ripening of the bunches. In this context, this paper proposes a novel approach for the characterization of biomass volume using images acquired in a vineyard with the low-cost Azure Kinect RGB-D camera. Semantic image segmentation is implemented using three encoder–decoder deep architectures (U-Net, DeepLabV3+, and MANet) to produce accurate masks of the vine leaf structure. In a transfer learning approach, a public dataset acquired with the Intel RealSense D435 depth camera is used to train the segmentation networks. Then, a complete pipeline to estimate possible changes in biomass volume is presented. Experiments are run to analyze the biomass removed during the trimming process of grapevine plants. The best segmentation result is obtained by the U-Net architecture with ResNet50 backbone, showing an accuracy of 92.10%, although the training and test sets consist of images acquired by different cameras. However, the DeepLabV3+ network with ResNeXt50 backbone, which scores an accuracy of 90.25% on the test set, gives the best estimate of the removed biomass, requiring the shortest time for training. These outcomes prove the potential capability of this automatic approach for controlling leaf growth and ensuring sustainable viticulture practices.
Why it matches plant phenotyping methodsブドウ樹の葉構造を画像セグメンテーションし、バイオマス体積と剪定による変化を推定する手法・パイプラインが研究の中心であるため。
abstractthis paper proposes a novel approach for the characterization of biomass volume using images acquired in a vineyard with the low-cost Azure Kinect RGB-D camera.
High-throughput phenotyping of grapevine leafroll disease (GLD) at the canopy scale helps develop fast and effective management in viticulture. However, detecting GLD efficiently in a vineyard is challenging owing to the limited adaptation of prior art. Therefore, we propose a novel convolutional neural network called GLDCNet to improve GLD recognition using unmanned aerial vehicle–based imagery. The effectiveness of the GLDCNet is attributed to the four new network designs used and is validated through ablation experiments. The GLDCNet achieves a classification accuracy of 99.57% using the RGB dataset and obtains more efficient and accurate results than nine other state-of-the-art methods. Furthermore, we systematically evaluated the impacts of image spatial resolution and vegetation indexes on the classification performance of the model. Experimental results suggest that improving image spatial resolution is more cost-effective than enhancing multispectral information for improving GLD recognition. Our proposed method offers a rapid, scalable, and accurate diagnostic protocol for detecting GLD in vineyards.
Why it matches plant phenotyping methodsUAV画像からブドウ樹の葉巻病状態を推定するCNNを開発し、アブレーション実験、既存手法との比較、解像度・植生指数の評価で検証しており、植物病害表現型の取得手法が中心である。
abstractwe propose a novel convolutional neural network called GLDCNet to improve GLD recognition using unmanned aerial vehicle–based imagery.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像と自動ノイズ除去を用いてコムギ品種の干ばつ耐性を評価する手法が題名の中心であり、植物表現型の取得・推定を伴うため含める。
titleRapid evaluation of drought tolerance of winter wheat cultivars under water-deficit conditions using multi-criteria comprehensive evaluation based on UAV multispectral and thermal images and automatic noise removal
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.
Consistency of crop establishment is a measure of uniformity of crop attributes, such as plant stand count, crop emergence rate, and plant spacing across the field. Quantifying consistency during the early crop growth stage is important for establishment decisions to use targeted nutrients and to facilitate timely replanting in inconsistent crop regions. Crop consistency can be analysed using two key parameters: plant stand count and spacing statistics since they provide insight into plant density and its emergence percentage. However, manual assessment of them is time-consuming, prone to errors, and labour-intensive in large fields. An alternative method is proposed to automate estimating these parameters using field imagery under uncontrolled settings. We use the YOLOv5-based object detection model for plant counting, which attains a mean average precision of 0.956 to detect Canola plants. A Lightweight U-Net model is proposed to segment rows, followed by Guo–Hall thinning and Probabilistic Hough Transform to determine inter-row and inter-plant spacing. Our proposed row segmentation model achieves a mean Intersection over Union (mIoU) of 0.8444 with class-wise IoU of 0.9925 and 0.6963 for background and crop using fewer parameters. The new architecture uses only 14M parameters and achieves performance comparable to the state-of-the-art U-Net (32.5M) and SegNet (29M).
Why it matches plant phenotyping methods圃場画像から作物個体数、出芽率、株間・条間を自動推定する画像解析手法を開発・評価しており、植物形質の取得が研究の中心である。
abstractAn alternative method is proposed to automate estimating these parameters using field imagery under uncontrolled settings.
Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ‘Palmer amaranth growth stages – 8 (PAGS8)’ is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4.
Why it matches plant phenotyping methodsCNN物体検出により雑草の生育段階という植物状態を推定し、複数YOLOモデルの比較評価とベンチマークデータセット公開を中心的に扱うため、植物フェノタイピング手法研究として含める。
abstractThis paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures.
High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton ( Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R 2 value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R 2 value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense .
Why it matches plant phenotyping methods綿花の果実を画像から分割・列挙し、専門家および手作業カウントで検証する高スループット表現型解析システムの開発が中心である。
abstractThis study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 8 Sept 2026
The processing of multispectral images acquired with embedded cameras in unmanned aerial vehicles (drones) has brought new opportunities for precision agriculture. In this study a method for evaluating the number of corn plants (Zea mays L) in a crop area is presented. Plant density is one of the most important yield factors, yet its precise measurement after the emergence of plants is impractical in large and medium-scale production, since significant amount of labor is required. For validation, a dataset of spectral images was gathered from flights over an agricultural area, and digital image processing techniques were applied, taking into account the concept of intelligent processing. Therefore, pattern recognition and models to aid decision-making through machine learning were also used. After image acquisition, the processing of orthomosaics in the spectral channels, i.e., red (R), green (G), and blue (B), was performed, making it possible to register and organize all the images. Likewise, techniques for geometric transformation, brightness, and contrast adjustments were evaluated globally, whereas local adjustments were evaluated based on the use of adaptive equalization techniques, which were explored based in the choice of the HSV color space. For the post-processing step, segmentation based on the best observed color threshold technique, in conjunction with Gaussian filtering and morphological operations, were considered. To enable pattern recognition, techniques that use distance maps were evaluated, considering the use of Euclidean distance. Thus, the locations of canopy patterns in maize plants were studied using a template matching algorithm and Chamfer pattern mask. For feature extraction, chain code and circular pattern map techniques were considered. The analyses made it possible to establish vectors of features based on patterns related to the number of maize plants occurrences. Finally, three calibration steps were considered, one related to the plant height versus the canopy opening radius, other related to the number of maize plants for each position in the crop area versus the radii identified by the developed model, and the third related to the cross-correlation between the plant counting by human vision and the new method. In addition, the classification step was established using a set of classifiers based on support vector machine (SVM). Results have shown an accurate and timely counting methodology for maize plants, which can guide cultivation to ensure high yield. The results showed that as a new method it can effectively count the number of maize plants with an average accuracy rate equal to 88.47%. Besides, both selected SVM classifiers have presented accuracy higher than 84% and precision higher than 83%. Furthermore, the cross-correlation between the plant counting by human vision and the new method has presented a linear correlation coefficient equal to 0.98. Thus, the developed method proved to be adequate for counting the maize plants in the post-emergence stage.
Why it matches plant phenotyping methodsトウモロコシ個体数という植物形質を、UAVマルチスペクトル画像と画像処理・機械学習で抽出する手法を開発し、人的計数との相関や精度で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study a method for evaluating the number of corn plants (Zea mays L) in a crop area is presented.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
The inner leaves of crop canopies are obscured by outer branches and leaves, leading to information loss and observation difficulty of the occluded canopy structure using modern crop monitoring techniques. It has restricted the development of phenotypic analysis and precision agriculture. In this paper, we propose a neural network approach to reconstruct the occluded structure of crop canopies with an RGB-D sensor. Taking the cotton plant as the object of study, we propose a novel Cascade Leaf Segmentation and Completion Network (CLSCN) to reconstruct the occluded leaf images and propose a Fragmental Leaf Point–cloud Reconstruction Algorithm (FLPRA) to complete the missing point clouds. By combining the Instance Segmentation Network (ISN), Generative Adversarial Network (GAN) and Point-cloud Reconstruction Algorithm (PRA), the three-dimensional models of cotton plants with both completed internal and external structures of the canopy are smoothly reconstructed. Firstly, we collect a large number of leaf images and point clouds of cotton plants using an RGB-D sensor with the top view and construct a manually labeled cotton leaf dataset for training and evaluation. Secondly, a network named CLSCN is cascading constructed with an Instance Segmentation Network (ISN) and a Generative Adversarial Network (GAN), and the two parts of CLSCN are separately trained with our constructed dataset to output complete cotton leaves. Thirdly, with the fusion of the completed RGB images output by cascaded network segmentation and the point clouds captured by RGB-D sensor, the proposed FLPRA is used to filter, reconstruct, fuse and register the cotton canopy leaf point clouds, and to obtain the whole cotton canopy point-clouds with inner occluded structure recovery. Finally, the CLSCN and FLPRA are validated using the validation dataset of cotton leaf. The test results indicate that the front-end ISN of the proposed CLSCN can generate high-quality cotton leaf masks, with FID scores less than 35 and mIoU up to 84.65%. Additionally, the back-end GAN of CLSCN can complete the occluded leaves with an accuracy of over 94%. The reconstruction accuracy of the final three-dimensional model of the cotton canopy is as high as 82.70%. Therefore, the proposed neural network and algorithm effectively solve the problem of incomplete canopy point cloud caused by the occlusion of outer leaves and provide an effective way to recover the complete three-dimensional structure of crop canopy with internal occlusion. It is a meaningful theoretical and technical support to realize real-time crop status observation and precise field management in agriculture production.
Why it matches plant phenotyping methodsRGB-D画像、ニューラルネットワーク、点群再構成を用いて、遮蔽されたワタ個体群の3次元構造を復元する手法を開発し、データセットと精度検証も行っており、植物表現型取得が中心である。
abstractwe propose a neural network approach to reconstruct the occluded structure of crop canopies with an RGB-D sensor.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Nitrogen is an essential nutrient in crop growth cycle and directly affects the photosynthesis of crops. The leaf chlorophyll content (LCC) and leaf area index (LAI) are important for characterizing photosynthetic capacity of crops and are critical indicators for diagnosing nitrogen status of crops. Hyperspectral remote sensing technology provided a means to achieve crop LCC and LAI estimation. However, the redundancy of spectral data and canopy structure effect can cause poor robustness of the estimating models, further hindering the development and application of estimating models across different crop species. In this study, a method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops. First, for the spectral dataset generated by the PROSAIL model, a deep neural network was used to construct LCC and LAI estimation models (called DNNCA model). Secondly, for the hyperspectral data collected from field crops (soybean, canola and wheat), incremental learning using regularization (LwF algorithm) was used to update the DNNCA model parameters. Finally, the dilution curve model based on the LCC-LAI anisotropic growth relationship was developed to assess crop nitrogen status. The results showed that: (1) The constrained bi-objective optimizated DNNCA model can consider the interactive effect of LAI and LCC on spectral reflectance, and achieved reliable estimation on PROSAIL simulation data set (LAI:R² = 0.82, RMSE = 0.77 m²/m²; LCC:R² = 0.91, RMSE = 6.4 ug/cm²). (2) By incremental learning, DNNCA model has continuous learning capability and stable estimation on cross-crop (canola, soybean and wheat) field-measured data (LAI:R² = 0.64–0.82, RMSE = 0.58–1.02 m²/m²; LCC:R² = 0.56–0.82, RMSE = 3.9–10.5 ug/cm²). (3) The relationship between the NNILCC and the NNILNC was significant. The NNILCC derived from the anisotropy relationship between crop LAI and LCC was an effective tool for crop nitrogen status diagnosis. (4) The process of crop LAI, LCC and NNILCC reflected the effect of water and nitrogen supply on crop growth. The appropriate water-nitrogen treatments contributed to LAI increase and LCC accumulation. The study demonstrated that hyperspectral remote sensing technology combined with incremental learning is an effective method for cross-crop growth monitoring and nitrogen diagnosis. These results provide a reference and basis for filed water-nitrogen supply and management.
Why it matches plant phenotyping methodsハイパースペクトルデータからLAI・葉クロロフィル含量を推定し、増分学習によるモデル更新と作物間検証を行うことが研究の中心であるため、植物フェノタイピング手法に該当する。
abstracta method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops
The rapid and accurate estimation of maize above-ground biomass (AGB) is pivotal for precise agricultural management. The rapid evolution of unmanned aerial vehicles (UAVs) and sensor technology has introduced a novel method for obtaining AGB information. Nevertheless, individual sensors may lack comprehensive data, leading to reduced AGB estimation accuracy in certain scenarios. This study collected UAV multi-spectral (MS) and thermal infrared (TIR) data, alongside soil and plant analyzer development (SPAD) values, from maize across multiple growth stages (jointing, trumpet, and big trumpet) during 2022 and 2023. Diverse data fusion programs were devised to explore the potential of combining multi-source sensor data with SPAD values to estimate AGB. The efficacy of CatBoost was evaluated and benchmarked against Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms. For the entire growth, findings reveal that the fusion of multi-source sensor data (MS + TIR) can mitigate the data insufficiency in single-sensor estimations. The resulting R 2 values range from 0.608 to 0.817. Optimal estimation outcomes were achieved by the fusion of multi-source sensor data with SPAD values (MS + TIR + SPAD), yielding R 2 values ranging from 0.685 to 0.872. For a single growth stage, there are variations in the estimation accuracy across different growth stages. From the jointing stage to the big trumpet stage, the estimation accuracy consistently increases, with the highest accuracy observed during the big trumpet stage, with R 2 ranging from 0.721 to 0.901. Additionally, in alignment with the results for the entire growth stage, the fusion of multi-source sensor data with SPAD values still yields the highest estimation accuracy during different growth stages. In a comparison of different machine learning algorithms, for both the entire growth stage and single growth stages, SVR, RFR, and CatBoost achieved R 2 values ranging from 0.305 to 0.824, 0.368 to 0.881, and 0.451 to 0.901, respectively. Notably, the CatBoost algorithm exhibited heightened estimation accuracy. The fusion of multi-source sensor data with SPAD values combined with the CatBoost algorithm results in accurate and reliable maize AGB estimation accuracy. This high-throughput approach to crop phenotyping is characterized by speed and accuracy and serves as a valuable reference for rapidly acquiring AGB information in this geographical region.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外センサーとSPAD値を融合し、機械学習でトウモロコシの地上部バイオマスを推定する手法を比較・検証しており、植物表現型取得が研究の中心である。
abstractDiverse data fusion programs were devised to explore the potential of combining multi-source sensor data with SPAD values to estimate AGB.
Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.
Why it matches plant phenotyping methodsRGB-D画像と自動処理アルゴリズムを用いてリンゴのサイズ・重量を推定し、複数アルゴリズムとモデルの精度を評価しているため、植物表現型取得法が中心である。
titleAssessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples
We propose a novel probabilistic voxel carving algorithm to efficiently reconstruct 3D models of maize plants and extract leaf traits for phenotyping. Traditional voxel carving algorithm is restricted to a limited number of views and usually requires multiple coordinated cameras in the imaging setup. They are also not robust small movements of the object, which introduce noise into the data. These imperfections in data collection can lead to large regions of the object being carved away during the voxel carving process, leading to incomplete and disjoint objects. We have developed a novel probabilistic voxel carving algorithm to overcome these challenges. In this approach, instead of carving out or keeping a voxel in a binary manner, we associate a probability of a voxel corresponding to it being part of the plant. We then use a user-defined probability cutoff to obtain the final voxelized plant geometry. We optimize the data collection procedure by adopting a rotating base to hold the plant and then capturing videos of the rotating plants, thereby obtaining an arbitrary number of views by extracting the image frames. Additionally, we leverage GPU computing to implement our voxel carving and trait extraction pipeline for a large dataset with over 1000 maize plants with high voxel resolutions (such as 1024^3). Our results demonstrate that our algorithm is robust and can handle an arbitrary number of views, and can automatically extract plant traits such as the number of leaves and leaf angles. Our approach shows that 3D reconstructions of plants from multi-view images can accurately extract multiple phenotypic traits, enabling better plant breeding programs.
Why it matches plant phenotyping methods植物の3D画像再構成と葉形質抽出アルゴリズムを開発しており、植物フェノタイピング手法が研究の中心です。
abstractWe propose a novel probabilistic voxel carving algorithm to efficiently reconstruct 3D models of maize plants and extract leaf traits for phenotyping.
Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions. .
Why it matches plant phenotyping methodsLiDARとUAV写真測量を用いてブドウ樹冠の高さ・幅・体積を抽出し、両手法を定量比較・評価しており、植物形質取得法が研究の中心である。
abstracttwo systems were compared to characterise a 50 m long vineyard row section
Estimating above-ground biomass (AGB) and leaf area index (LAI) is crucial for determining crop developmental status and estimating grain yield, thus facilitating high-throughput phenotyping of maize (Zea Mays L.). Recently, unmanned aerial vehicle (UAV) have been widely used to monitor crop growth and estimate LAI and AGB. However, few studies have reported on the quantitative analysis of improved accuracy in biomass and leaf area index estimation using undistorted images obtained through inverse projection transformation algorithms. Additionally, there is limited research on predicting biomass and LAI during various growth stages by replacing traditional data augmentation methods with images captured from different azimuth and zenith angles. The results showed that when using undistorted image at single solar zenith angle of 0°, the accuracy of AGB and LAI estimation was improved by 20.3% and 7.3%, respectively, compared to the models estimated from orthoimages. The undistorted images with various azimuth and zenith angles were 9 ∼ 15 times larger than the orthomosaic image dataset, and could be used as a new training data that is similar to the original data but with variations. The AGB and LAI estimation model constructed using the data augmentation method achieved better performance with higher average R² (0.98). Model performance was increased by 40% and 16.7% for estimating AGB and by 19.3% and 11.3% for estimating LAI based on undistorted images, respectively, in comparison to models based on orthophotos and undistorted images due to the increased diversity and quantity of training data. Canopy structural including canopy coverage and plant height, characterized by 96.9%, was found to be the most reliable indication for estimating AGB and LAI, while other RGB sensor-derived feature combinations contributed only 3.1% to the estimation. Spectral features took the second place, and the textural features was the weakest. In summary, the data processing framework using data augmentation based on various azimuth and zenith angles' images can improve the performance of crop growth estimation and provide a mechanism for precision agriculture under field conditions.
Why it matches plant phenotyping methodsUAV画像の逆投影変換とデータ拡張による、トウモロコシのバイオマス・LAI推定手法の精度改善が研究の中心であり、植物形質推定の方法開発に該当する。
abstractfew studies have reported on the quantitative analysis of improved accuracy in biomass and leaf area index estimation using undistorted images obtained through inverse projection transformation algorithms.
Crop canopy parameters are critical for environmental remote sensing, describing crop phenotypes, and ensuring food security. Evaluating the effect of temperature on crop growth is crucial for estimating crop canopy parameters. However, existing crop growth and plant functional-structural models cannot simultaneously model temperature responses, perform accurate dynamic simulations, and provide multi-scale computer visualizations. This limitation has hindered the application of structural models of maize plants for use in 3D radiative transfer models, crop structure evaluations, and crop phenotype descriptions. We improve the leaf/organ-level thermal-driven crop growth model (MAIZSIM) and the plant functional-structural algorithm. To address these limitations, we propose the coupled maize model, a four-dimensional (4D) growth model based on growing degree days. This model can simulate and visualize the structural parameters of the maize canopy at the organ, plant, seasonal, and population levels. The model outputs three-dimensional (3D) predictions of the maize structure (file format.obj), enabling editing and 3D visualizations. We use maize datasets from multiple phenological periods to test the proposed model’s accuracy and stability in simulating the canopy parameters at multiple levels. The results show that the normalized root mean square errors (NRMSEs) between the simulated and measured maize leaf size, area, leaf node height, and vein curve derived from the coupled maize model are below 0.1, demonstrating the model's high accuracy.
Why it matches plant phenotyping methodsトウモロコシの器官・個体・群落レベルの構造形質をシミュレーション・可視化する4D成長モデルを開発し、実測値との精度検証も行っており、表現型取得・推定手法が研究の中心である。
abstractThis model can simulate and visualize the structural parameters of the maize canopy at the organ, plant, seasonal, and population levels.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsタイトルで植物の形態形質（幹径、枝角度、枝径）をステレオマシンビジョンと深層学習により推定する手法が明示されており、フェノタイピング手法が中心です。
titlePhenotyping of architecture traits of loblolly pine trees using stereo machine vision and deep learning: Stem diameter, branch angle, and branch diameter
The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping.
Why it matches plant phenotyping methodsUAV-DAPとMTLS-LiDARによる果樹キャノピー形状計測を比較・検証し、高スループット表現型解析への適用可能性を評価しているため、植物形質取得法が中心である。
abstractA comparison between both kinds of measurements was performed
Plant phenotyping is essential for understanding and managing plant growth and development. 3D point clouds provide a better understanding of plant 3D structures. Point cloud segmentation is the basis for studying the 3D structure of plants through 3D point clouds, and accurate point cloud segmentation is crucial for extracting relevant phenotypic parameters. In this study, cabbage was used as an example, and a plant point cloud segmentation method combining deep learning algorithms and clustering algorithms was proposed. Specifically, a cabbage point cloud dataset was constructed using a 3D scanning platform. The ASAP attention module was incorporated into the PointNet++ model, resulting in the improved ASAP-PointNet model. Superior semantic segmentation performance on the cabbage point cloud dataset was demonstrated by this model. The workflow of the DBSCAN algorithm was also optimized, which exhibited enhanced performance in organ-level plant point cloud segmentation experiments. Subsequently, five phenotypic features were extracted. The experimental results revealed that an accuracy of 0.95 and an intersection over union (IoU) of 0.86 for semantic segmentation were achieved by the ASAP-PointNet model. The correlation coefficients between the four phenotype parameters (plant height, leaf length, leaf width, and leaf area) and their corresponding measured values were 0.96, 0.91, 0.95, and 0.94, respectively. An automated data analysis, from plant 3D point clouds to phenotypic parameters, is enabled by the proposed method, which serves as a valuable reference for plant phenotype research.
Why it matches plant phenotyping methods3D点群の分割・解析手法を開発し、植物器官から複数の表現型形質を自動抽出・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。
abstracta plant point cloud segmentation method combining deep learning algorithms and clustering algorithms was proposed
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Efficiently analyzing the relationship between plant phenotypes, quality, and resistance remains challenging. In this study, deep learning models based on hyperspectral data and time-series phenotypes from the high-throughput plant phenotyping (HTPP) platform were proposed to predict quality attributes of lettuce under water stress, including SSC, pH value, nitrate (NO₃–), and calcium (Ca²⁺). First, deep learning models were developed using the Inception module and raw hyperspectral data to non-destructively predict the above quality attributes. In addition, partial least squares regression (PLSR) and support vector regression (SVR) were used to develop prediction models to evaluate performance of the Inception module. Second, the residual and attention modules were implemented to enhance performance of the Inception module. Third, time-series phenotypes were fed into four recurrent neural networks (RNNs), such as TimeDistributed (TD), long short-term memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN (BRNN) and combined with the optimal deep learning models based on hyperspectral data to enhance prediction precision. The optimal performance of the Inception-residual-attention-TD model was achieved with Rₚ² of 0.8900 and 0.9435 for SSC and NO₃–, respectively. The Inception-residual-TD model with Rₚ² of 0.9583 provided the most accurate pH value prediction. With Rₚ² of 0.8716, the Inception-attention-LSTM model provided the most accurate prediction of Ca²⁺. Meanwhile, the Inception-residual-TD model was used to detect water stress, producing an Accuracyₚ of 98.86%. The Inception-residual model based on pixel-wise hyperspectral data was used to visualize the spatial distribution of pH value, and the distribution map was used to detect early water stress. The results indicate that deep learning models can use hyperspectral data and time-series phenotypes to predict lettuce quality attributes and water stress in a non-destructive manner.
Why it matches plant phenotyping methodsレタスの品質形質と水ストレスを、ハイパースペクトルデータおよびHTPPの時系列表現型から非破壊推定する深層学習手法を開発・比較・評価しており、表現型取得・抽出が研究の中心である。
abstractdeep learning models based on hyperspectral data and time-series phenotypes from the high-throughput plant phenotyping (HTPP) platform were proposed to predict quality attributes of lettuce under water stress
Deep learning methods have become one of the fundamental blocks of high-throughput phenotyping using RGB imagery. In this study, we go beyond applying deep learning algorithms; we improve deep learning models using a multi-view fusion approach. The proposal dynamically merges information from two deep-learning models. We evaluate this approach to improve the estimation of total dry matter yield, leaf dry matter yield and total green matter yield of plots of Guineagrass, an important tropical forage species. The proposed approach, named Deep4Fusion fusion network, can be set to use two different deep learning models. The experimental results indicated that our approach improved the performance between 20% to 33% when compared with standard models reported in previous works, with a significant improvement (p-value < 0.05) for leaf dry matter and total dry matter yield. We believe that the flexibility of multi-view fusion in merging the predictions of several CNNs models through shared layers across the network has the potential to improve the results of many other single-view deep learning approaches.
Why it matches plant phenotyping methodsRGB画像から飼料作物の収量形質を推定する深層学習・マルチビュー融合手法の開発と性能比較が中心であり、植物フェノタイピング手法に該当する。
abstractwe improve deep learning models using a multi-view fusion approach
Field / plotRGB-D / ToFStem / branch2D/3D reconstructionSegmentationSkeletonization / topology
The shortage of agricultural labourers worldwide has left many groups unharvested and wasted, which motivates researchers worldwide to research fruit harvesting robots extensively. One of the major problems in fruit harvesting is to selectively avoid hard obstacles such as tree branches so that more optimal picking positions can be found and more fruits throughout the tree can be harvested. However, tree branches are often obscured in unstructured natural orchards and thus necessary branch reconstruction and recovery are required. The current branch reconstruction and recovery methods for harvesting robots focus on planar reconstruction with few occlusions while the existing 3D tree modelling methods are not optimised for harvesting purposes that require low computational cost and high localisation accuracy. This work presented a novel framework that reconstructs and recovers 3D obscured branches from planar images and depth maps captured by an RGB-D camera. The framework comprises three parts: branch segmentation using Unet++, branch reconstruction using Point2Skeleton and branch recovery using a novel obscured branch recovery (OBR) algorithm. Branch segmentation using Unet++ with InceptionV3 encoder shows the best overall result with IoU and F1-score of 0.6249 and 0.7692 respectively. OBR recovery algorithm achieves average reconstruction accuracy of 0.72. The mean error of the reconstructed total surface and obscured surface using OBR is 18.68 mm and 38.11 mm with a standard deviation of 14.3 mm and 32.64 mm. The result shows that this framework can effectively reconstruct spatial information of visible and obscured branches from a single view image which can potentially be utilised in harvesting robots.
Why it matches plant phenotyping methodsRGB-D画像から枝の分割・3D再構成・遮蔽部復元を行う技術が中心で、単なる収穫対象の位置検出を超えて植物器官の空間構造を定量化しているため、植物表現型計測法として含める。
abstractThis work presented a novel framework that reconstructs and recovers 3D obscured branches from planar images and depth maps captured by an RGB-D camera.
Efficiently estimating chlorophyll content is important in monitoring the photosynthesis capacity and growth status of maize canopy in precision agriculture management. Vegetation index (VI) easily obtained by proximal remote sensing has been used as a non-destructive and high-throughput way in crop monitoring, especially in chlorophyll estimation. However, the estimated results of the field chlorophyll content by VIs always face challenges from soil background inhibition and estimation stability under the dynamic changes of vegetation biomass. Thus, an unmanned aerial vehicle (UAV)-based chlorophyll content estimation was conducted by evaluating VI responses under different crop coverages. An analysis was conducted on 36 VIs under different crop coverage conditions to explore their response differences and robustness for chlorophyll estimation. This work focused on the three kinds of VIs named simple vegetation index, modified vegetation index, and functional vegetation index. In 2020, at the experimental station of Dryland Farming Institute of Hebei Academy of Agriculture and Forestry Sciences, UAV carrying multispectral sensor was used to collect visible and near-infrared images of the canopy at the jointing stage of maize under six fertilization levels to obtain VIs. After the UAV fled, ground calibration and sample collection were performed simultaneously, and chlorophyll content was measured. For data processing, correlation coefficient method (CCM) and maximal information coefficient (MIC) were first used to analyze the correlation response characteristics of VIs and chlorophyll content under three different coverage levels. The results showed that when the level of canopy coverage was increased, the linear correlation between VIs and chlorophyll content was substantially reduced. The MIC response indicating linear and non-linear combination relationship was more robust. In addition, the VIs obtained by UAV had a significant linear correlation with maize canopy chlorophyll under low (0.05–0.35) and medium (0.35–0.48) coverage, but an obvious non-linear correlation under high (0.48–0.75) coverage. Chlorophyll-sensitive parameters were then screened based on methods of CCM, MIC, and random frog method (RFM), respectively. Partial least squares regression (PLS) and random forest (RF) algorithms were used to establish the maize canopy chlorophyll content detection models. The findings showed that when Green minus red vegetation index (GMR), Red light normalized value (NRI), Normalized difference red edge (NDRE), Modified simple ratio with red edge (MSRREG), Enhanced Vegetation Index (EVI), Normalized red green difference vegetation index (NDIg), Normalized red blue difference vegetation index (NDIb), Soil-adjusted vegetation index (SAVI), Optimized soil-adjusted vegetation index with red edge (OSAVIREG), Soil-atmospherically resistant vegetation index (SARVI) were selected based on RFM as the optimal spectral variables, the chlorophyll content detection model constructed based on PLS had the least numbers of characteristic variables and the best model accuracy. The training set R² and RMSE were 0.753 and 2.089 mg/L, respectively, and the verification set R² and RMSE were 0.682 and 2.361 mg/L, respectively. Field chlorophyll content and detection error distribution maps were also drawn and combined with the distribution of fertilization management to provide support for the UAV monitoring of crop growth in the field and variable fertilization management decisions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数を用いたトウモロコシ冠層クロロフィル含量推定法を開発・評価しており、植物形質取得が研究の中心である。
titleUAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages
The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5 mm and R2 = 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE = 2.93 mm and R2 = 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.
Why it matches plant phenotyping methods果実の遮蔽に頑健な画像ベースのアモーダル分割と、リンゴ果径という植物形質の推定手法を開発・検証しており、方法が研究の中心である。
abstractThe predicted amodal masks were used to estimate the fruit diameters in pixels.
Reproduction assets foundThe paper's apple amodal segmentation dataset (RGB-D images, modal/amodal masks, calliper-measured diameters) and the authors' analysis code are both explicitly stated to be publicly available at the authors' GitHub repository GRAP-UdL-AT/Amodal_Fruit_Sizing.Dataset · publictain data from both maturity stages, of different fruit size and with
different fruit visibilities. The dataset split was performed randomly,
obtaining in each partition a similar distribution of diameters (Fig. 4.b)
and apples visibilities (Fig. 4.d) than in the original dataset. The dataset
has been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.The data used for the case study was acquired in an Elstar apple
orchard located in Randwijk (the Netherlands). Five different trees were
imaged at four different dates (Table 1), obtaining data at different
growth stages: BBCH75, BBCH77, BBCH78 and BBCH85 (Fig. 2b). To
have a complete representation of trees, images Open asset ↗GRAP-UdL-AT/Amodal_Fruit_Sizingpdf-raw-page:3 lines:1-74Code · publicft, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The code and the dataset used to evaluate the method have been
made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.Acknowledgements
This work was partly funded by the Departament de Recerca i Uni
versitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the
Spanish Ministry of Science, Innovation and Universities (grants
RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00
[PAgPROTECT project] and PID2020-117142GOpen asset ↗GRAP-UdL-AT/Amodal_Fruit_Sizingpdf-raw-page:12 lines:1-75Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods高密度植林における個体ごとの樹冠パラメータ抽出法を、地上近距離スキャンと写真測量で開発・適用しており、植物形態計測が中心である。
titleA novel solution for extracting individual tree crown parameters in high-density plantation considering inter-tree growth competition using terrestrial close-range scanning and photogrammetry technology
It is challenging to accurately and rapidly extract crops based on the ultra-high spatial resolution images of uncrewed aerial vehicle (UAV). Object-based image analysis (OBIA) was regarded as an effective technique for high-spatial-resolution image classification because of its ability to achieve high accuracy by integrating multi-dimensional features. In recent years, deep learning (DL) techniques, with their ability to automatically learn image features from a large number of images, have shown great potential for crop monitoring. However, a systematic comparison of these two mainstream methods for monitoring the crop phenotype has not been conducted. Therefore, this study compares the performance of two advanced methods, DL and OBIA, in individual cabbage plant detection tasks. The results show that the Mask R-CNN deep learning model outperforms the object-based image analysis-multilevel distance transform watershed segmentation (OBIA-MDTWS) method in crop extraction and counting, with an overall mean F1-Score, accuracy of 2.70, 4.15 percentage points higher, respectively. Moreover, the Mask R-CNN deep learning model has higher computing efficiency, which is 3.74 times higher than the OBIA-MDTWS model. In summary, this study shows that the Mask R-CNN deep learning model performs better in vegetable extraction and quantity estimation, providing technical support for subsequent field nursery management and fine planting.
Why it matches plant phenotyping methods個体キャベツの画像検出・抽出・計数手法を比較し、性能と計算効率を評価しており、植物フェノタイピング手法が研究の中心である。
abstractHowever, a systematic comparison of these two mainstream methods for monitoring the crop phenotype has not been conducted.
Traditionally, vegetable and fruit production has relied on empirical and ambiguous decisions made by human farmers. To overcome this uncertainty in agriculture, smart farm robots have been widely studied in recent years. However, measuring growth information with robots remains a challenge because of the similarity in the appearance of the target plant and those around it. In this study, we propose a smart farm robot that accurately measures the growth information of a target plant based on object detection, image fusion, and data augmentation with fused images. The proposed smart farm robot uses an end-to-end real-time deep learning-based object detector that shows state-of-the-art performances. To distinguish the target plant from other plants with a higher accuracy and improved robustness than those of existing methods, we exploited image fusion using both RGB and depth images. In particular, the data augmentation, based on the fused RGB, and depth information, contributes to the precise measurement of growth information from smart farms, regardless of the high density of vegetables and fruits in these farms. We propose and evaluate a real-time measurement system to obtain precise target-plant growth information in precision agriculture. The code and models are publicly available on Github: https://github.com/kistvision/Plant_growth_measurement.
Why it matches plant phenotyping methodsRGB・深度画像と物体検出を用いて対象植物の生育情報を取得するリアルタイム測定システム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。
abstractwe propose a smart farm robot that accurately measures the growth information of a target plant based on object detection, image fusion, and data augmentation with fused images.
Single-plant growth monitoring aids precision agricultural decision-making to reduce the costs related to pesticides, fertilizers, and labor. This study integrated visible/multi-spectral UAV imagery with two deep learning methods, object detection and semantic segmentation, to obtain a visualized map that could assist in precise field monitoring and management for broccoli cultivation. For plant detection, feature extraction was conducted using multiscale dilated convolution, which enabled the effective detection of broccoli in images taken under different photographic conditions and resolutions. Two crops of broccoli (cultivar: Broccoli No. 42) were planted in 2020 at Taichung Agricultural Research and Extension Station, in which the first crop was treated as the training data. The detection of individual broccoli plants was processed using a feature extraction architecture of the AlexNet-Like backend at the SSD frontend, where the input scale of the detector complies with the original SSD architecture. For the model test on the second crop, the recall and precision were 98.58% and 99.73%, respectively, after histogram matching based on the first crop images. Moreover, the proposed approach was applied to a real farming field to verify its robustness across different conditions, and achieved a recall of 61.13% using dilated convolution. This study also generated a visualized growth map on a single-plant basis, which allows operators to detect growth situations, such as uneven irrigation or fertilization and necrosis and apoptosis, to greatly enhance the viability of precision agriculture in the calculation of unit yield and intragroup differences for a regime. The proposed approach can be used to determine the optimal amount of fertilization and observe the size of broccoli heads to determine the optimal harvest time. Expectedly, the method may also be applied to the monitoring and management of other crops to improve the efficiency and reduce the labor demand for precision agriculture.
Why it matches plant phenotyping methodsUAV画像と深層学習によって個体検出・成長状態・ブロッコリー頭部サイズを推定する手法を開発し、精度検証と実圃場での頑健性評価を行っており、植物表現型取得が中心である。
abstractThis study integrated visible/multi-spectral UAV imagery with two deep learning methods, object detection and semantic segmentation, to obtain a visualized map that could assist in precise field monitoring and management for broccoli cultivation.
Plant phenotyping is the study of complex plant traits to evaluate its status depending on the life-cycle conditions. Often, these evaluations are carried out by human operators, and the accuracy could be biased by their experience and skill, especially when dealing with huge amounts of data produced by high-throughput phenotyping (HTP) platforms. With the rapid development of key enabling technologies, HTP is only made possible by the vast amounts of data made available by computer vision systems. In this scenario, artificial intelligence algorithms play a key role in the automation, standardization, and quantitative analysis of large data. This paper focuses on detecting tomato plants phenotyping traits using single-stage detectors (either stand-alone or ensemble) based on YOLOv5, aiming to effectively identify nodes, fruit, and flowers on a challenging dataset acquired during a stress experiment conducted on multiple tomato genotypes. Results demonstrate that the models achieve relatively high scores, considering the particular challenges of the input images in terms of object size, similarity between objects, and their color.
Why it matches plant phenotyping methodsYOLOv5ベースの画像解析手法を開発・評価し、トマトの節・果実・花という表現型形質を自動検出することが研究の中心である。
abstractThis paper focuses on detecting tomato plants phenotyping traits using single-stage detectors (either stand-alone or ensemble) based on YOLOv5
Wheat stripe rust is the main cause of yield loss in winter wheat. For nondestructive monitoring of wheat stripe rust by remote sensing, a high-precision stripe rust monitoring model can be constructed so that field management can be performed rationally and environmental damage from large doses of pesticides and chemicals can be avoided. Fractional order differential (FOD) equations can be used to flexibly control the differential step size, which enhances the spectral information and reduces the background noise. In this study, the canopy hyperspectral data under the influence of wheat stripe rust were evaluated by FOD, and the disease severity level (SL) of stripe rust in the field was analyzed by evaluating the polar difference, coefficient of variation, and correlation coefficient. Subsequently, the spectral features associated with stripe rust were screened using significance tests and Gaussian process regression sigma (GPR sigma) analysis methods. Then, models for the various wheat stripe rust severity levels were established by Gaussian process regression (GPR) with different data inputs. The results showed that the 0.8–1.4 differential order could effectively improve the correlation between spectral bands and disease severity, and the optimal correlation between the 1.2 order differential spectra and wheat stripe rust severity improved by 15% compared with the original reflectance spectra. The GPR sigma band analysis method screens only 8 bands at order 1.2, the R2 between the model-predicted SL and the measured SL is improved by 13% compared to the original reflectance spectrum, and the RMSE and MAE are reduced by 34% and 39%, respectively. Compared with the significance test method, GPR sigma band analysis selected fewer bands, constructed models with higher accuracy and was more suitable for the construction of models for estimating the severity of wheat stripe rust disease.
Why it matches plant phenotyping methods小麦の病害重症度という植物状態を、ハイパースペクトル計測と分数階微分・ガウス過程回帰で非破壊推定する手法の開発・評価が中心である。
abstractFor nondestructive monitoring of wheat stripe rust by remote sensing, a high-precision stripe rust monitoring model can be constructed
LiDAR / point cloudYield / biomass estimationBiomass / plant weight
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUASデジタルカメラとLiDARの情報融合により森林の植物バイオマスを推定する手法が題名上の中心であり、植物形質のセンサ計測・計算推定に該当する。
titleInformation fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR
Computer vision and machine learning have recently been applied to a number of sensing platforms, boosting their performance to a new level. These advances have shown the vast possibilities for enhancing remote plant health assessment and disease detection. Until now, however, the scanning time and spatial resolution of such automated tools have been limited, as well as the area of application. We developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture. For this purpose, we collected a dataset of over 360,000 images of healthy and infected apple trees to develop and test our system, which includes a Convolutional Neural Network (CNN) algorithm for leaves segmentation. The proposed solution automatically computed vegetation indices (VIs) accurate to a single pixel. Further, we developed a desktop application for data post-processing and visualization, which allows the user to rapidly assess the health status of a vast agricultural area and thoroughly examine each tree individually. The developed system was successfully tested under field conditions in a large apple orchard, confirming viability of a reliable, end-to-end solution based on a computer vision platform for remote assessment of plant health and identification of stressed plants with high precision and spatial resolution.
Why it matches plant phenotyping methodsリンゴ葉のセグメンテーション、マルチスペクトル画像、CNN、データセット、後処理アプリケーションを統合した植物健康状態評価プラットフォームの開発が中心であり、植物表現型の取得・抽出手法に該当する。
abstractWe developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture.
Hyperspectral imaging has become one of the most popular techniques for high-throughput plant phenotyping. Extracting and analyzing useful plant phenotypic traits from hyperspectral images represents a major bottleneck for plant science and breeding communities. This study aims to present a stand-alone easy-to-use software platform called HSI-PP to process and analyze hyperspectral images for high-throughput plant phenotyping. The HSI-PP software integrates pre-processing, feature extraction, and modeling functions. The application of HSI-PP is exemplified by investigating the response of different Arabidopsis thaliana genotypes to drought stress, and the impact of various imaging angles on predicting the canopy nitrogen content (CNC) of oilseed rape (Brassica napus L.). The results showed that HSI-PP can process 10 GB on an ordinary PC in time ranging from 30 to 73 min according to image size and the complexity of the pipeline. HSI-PP extracted multiple phenotyping traits (spectral, textural, and morphological) of Arabidopsis thaliana from a large image dataset (104 GB) within five hours. The fusion of these features achieved higher accuracy (94%) than only using spectral information (85%) as early as day 4 after drought stress treatment. For oilseed rape, about 384 GB image data was processed within eighteen hours, and it was found that the tilted imaging angle of 75° had the optimized PLSR fitting (0.83) to the ground truth. The results demonstrate that HSI-PP is a stand-alone, automated, and open-source hyperspectral image processing platform adapted to various applications in plant phenotyping without requiring professional programming skills to serve the plant research community.
Why it matches plant phenotyping methods植物フェノタイピング用のハイパースペクトル画像処理ソフトウェアを開発・提示し、特徴抽出、モデリング、処理性能、検証例を中心に扱っているため。
abstractThis study aims to present a stand-alone easy-to-use software platform called HSI-PP to process and analyze hyperspectral images for high-throughput plant phenotyping.
Sugar beet (Beta vulgaris L. ssp. vulgaris) accounts for roughly 20% of the global sugar production, with the remainder derived from sugar cane (Saccharum officinarum L.). To maximize sugar yield, high performing sugar beet varieties are needed, in combination with good agronomical practices. Delivering vigorous seeds to the market and meeting the highest quality standards is, therefore, essential. Seed vigor is highly determined by fruit morphology, with the main characteristics of interest being fruit and true seed size, pericarp morphology and fruit filling. Current methods for evaluating fruit morphology mostly rely on labor-intensive and destructive analyses. Here we present a high-throughput nondestructive method to quantitatively phenotype sugar beet fruit and true seeds using X-ray micro-CT imaging and deep learning. A 3D convolutional neural network was trained for the semantic segmentation of the pericarp, true seed and air in X-ray micro-CT scans. High average Dice scores of 0.996, 0.971 and 0.930 were found for the pericarp, true seed and air, respectively. Additionally, since farmers target single plants after emergence in the field, we present a method to identify whether sugar beet fruit are monogerm or contain more than one seed (bigerm). An excellent overall classification accuracy, false positive and false negative rate of respectively 98.6, 1.0and 1.8% were achieved. The presented methods have a high potential for integration into tools for breeding programs and the sample-wise monitoring and adjustment of production processes.
Why it matches plant phenotyping methodsX線マイクロCTと深層学習により、サトウダイコン果実・種子の形態を非破壊かつ高スループットに定量評価する手法を開発しており、表現型取得が研究の中心である。
abstractHere we present a high-throughput nondestructive method to quantitatively phenotype sugar beet fruit and true seeds using X-ray micro-CT imaging and deep learning.
Pasture production under integrated crop-livestock systems (ICLS) is a key element to support grazing management decisions. Therefore, further experimentation is required to produce reliable estimates of pasture productivity. An unmanned aerial vehicle (UAV) is a viable tool to obtain fast and accurate aboveground biomass (AGB) estimates in pastures under ICLS. We tested several datasets of variables composed of original spectral bands (RGB-NIR), vegetation indices, and gray-level cooccurrence matrix (GLCM) textures to estimate pasture AGB using the random forest (RF) algorithm and feature selection methods in a commercial ICLS farm in western São Paulo State, Brazil. Field measures of pasture AGB were carried out in three field campaigns over five months to capture the spatiotemporal variability of the pasture fields. Most tested models reached similar results on pasture AGB estimates (R²: 0.60 to 0.70), while the number of variables selected for the RF algorithm differed among models (from 6 to 160 variables). The three more accurate models used few variables, two of which used only texture measures, while the third also employed combined spectral bands and vegetation indices. The best model (R² = 0.70) used only 10 texture measures. Texture measures that represented images’ inner patterns, calculated from NIR, red-edge, and triangular greenness index data, were the most relevant variables for the main models. The texture contribution indicates important information to be considered to estimate pasture AGB when using UAV imagery. The results achieved by UAV estimates allow the design of multitemporal AGB pasture maps, which may be useful for building more reliable spatiotemporal data.
Why it matches plant phenotyping methodsUAV画像のスペクトル・テクスチャ特徴と機械学習により、牧草の地上部バイオマスという植物形質を推定する手法を検証しており、形質取得・推定法が研究の中心です。
abstractAn unmanned aerial vehicle (UAV) is a viable tool to obtain fast and accurate aboveground biomass (AGB) estimates in pastures under ICLS.
Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds, maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley, winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g. green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision.
Why it matches plant phenotyping methods街路画像とコンピュータビジョンで作物の生育段階を抽出する枠組みを開発・評価しており、植物状態の取得手法が研究の中心である。
abstractWe present a framework to collect and extract crop type and phenological information from street level imagery using computer vision.
Cost-share programs based on measures of participation rather than performance are available to farmers who plant cover crops. However, cover crops only provide significant ecological benefits like reduced nutrient loss when adequate biomass is established. The purpose of this study was to determine whether satellite imagery can effectively estimate cover crop biomass in fields with diverse species composition, and whether increased spatial resolution and satellite imaging frequency can increase biomass estimation accuracy. Aboveground biomass samples of 1 m² were collected for 86 sites within 26 agricultural fields containing unique cover crop species composition to assess biomass production. In-field sensors were used to measure normalized difference vegetation index (NDVI) and groundcover percentage. Three satellites (Landsat-8 [30 m resolution], Sentinel-2 [10 m resolution], and PlanetScope [3 m resolution]) were used to calculate eight vegetation indices (VIs) for comparison with cover crop biomass. Multiple linear regression, correlation coefficients, and root mean square error (RMSE) were used to perform hierarchical clustering to rank VIs calculated from each satellite for biomass estimation accuracy. Satellites predicted cover crop biomass at the field level very accurately (r² up to 0.79), demonstrating the potential of large-scale biomass estimation at relatively low cost compared to in-field sampling. All satellite-VI pairs estimated biomass more accurately than the in-field sensors. Performance of VIs varied by satellite, but each satellite had at least one VI that performed very well for both site-level and field-averaged data. When using PlanetScope or Landsat-8 imagery, the perpendicular vegetation index provided the most accurate cover crop biomass estimation on a per-site basis and ratio vegetation index performed best using Sentinel-2 imagery. PlanetScope was the only satellite to provide useable imagery for every site due to increased revisit period; however, its increased spatial resolution did not increase estimation accuracy overall compared to Landsat-8 or Sentinel-2.
Why it matches plant phenotyping methods衛星画像と植生指数を用いたカバークロップ地上部バイオマス推定を中心に、複数衛星・センサーの精度比較と検証を行っているため、植物形質の計測手法研究に該当する。
abstractThe purpose of this study was to determine whether satellite imagery can effectively estimate cover crop biomass in fields with diverse species composition, and whether increased spatial resolution and satellite imaging frequency can increase biomass estimation accuracy.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods植物3Dモデルを用いた自動フェノタイピングアルゴリズムの実装が題名の中心であり、水ストレスの早期検出という植物状態推定に応用しているため、方法開発・応用研究として含める。
titleImplementation of an algorithm for automated phenotyping through plant 3D-modeling: A practical application on the early detection of water stress
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Common beanField / plotMultispectral / hyperspectralLeafSegmentationLeaf traits
Detecting and identifying plants using image analysis is a key step for many applications in precision agriculture (from phenotyping to site specific weed management). Instance segmentation is usually carried on to detect entire plants. However, the shape of the detected objects changes between individuals and growth stages. A relevant approach to reduce these variations is to narrow the detection on the leaf. Nevertheless, segmenting leaves is a difficult task, when images contain mixes of plant species, and when individuals overlap, particularly in an uncontrolled outdoor environment. To leverage this issue, this study based on recent Convolutional Neural Network mechanisms, proposes a pixelwise instance segmentation to detect leaves in dense foliage environment. It combines “deep contour aware” (to separate the inner of big leaves from its edges), “Leaf Segmentation trough classification of edges” (to separate instances with a specific inner edges) and “Pyramid CNN for Dense Leaves” (to consider edges at different scales). But the segmentation output is also refined using a Watershed and a method to compute optimized vegetation indices (DeepIndices). The method is compared to others running the leaf segmentation challenge (provided by the International Network on Plant Phenotyping) and applied on an external dataset of Komatsuna plants. In addition, a new multispectral dataset of 300 images of bean plants is introduced (with dense foliage, individuals overlapping, mixes of species and natural lighting conditions). The ground truth (e.g. the leaves boundaries) is defined by labelled polygons and can be used to train and assess the performance of various algorithms dedicated to leaf detection or crop/weed classification. On the usual datasets, the performances of the proposed method are similar to those of the usual methods involved in the leaf segmentation challenges. On the new dataset, their results are strongly better than those of the usual RCNN method. Remaining errors are bad fusion between neighboring areas and over segmentation of multi-foliate leaves. Structural analysis methods could be studied in order to overcome these deficiencies.
Why it matches plant phenotyping methods葉のインスタンス分割による植物器官形状の抽出手法を開発・比較検証し、植物画像データセットも提供しているため、植物フェノタイピング手法が中心である。
abstractthis study based on recent Convolutional Neural Network mechanisms, proposes a pixelwise instance segmentation to detect leaves in dense foliage environment.
Breeding strategies for many crops require quantitative evaluations of many genotypes from within as large of a diverse breeding pool as possible. In selecting pathogen tolerant genotypes, accurate and fast phenotyping to investigate genetic responses to pathogen infection and reproduction is crucial. Analyzing leaf tissues with spectral tools along with ground truth data offers potentially large gains in screening efficiency. However, ground truth labels per plant may not capture the effects of asymptomatic leaves and heterogeneous canopy responses to stress. We explored a semi-supervised clustering-based technique in which spectral patterns unique to and common among leaves from nematode infected plants are distinguished from patterns with no relationship to infection; we utilize these spectral patterns in ranking genotype tolerances to infection as a secondary objective. Proximal hyperspectral leaf scans (360 nm–1700 nm) of three walnut rootstock genotypes (MS1 122, VX211, MS1 127) were used in an agglomerative clustering procedure based on spectral angle mapper (SAM) distances to choose a spectral endmember representing the root lesion nematode, Pratylenchus vulnus, stress symptom on leaf per genotype. The histogram of SAM distances between control samples and the endmember was calculated. Next, the histogram of SAM distances between infected samples and the endmember was calculated. The shift between these histograms was then found using the minimum difference of pair assignments (MDPA) measure. The MDPA measures were 4.88, 3.14, and 5.48 for MS1 122, VX211, and MS1 127, respectively. This meant a genotype ranking in the order VX211, MS1 122 and, MS1 127 from the least affected by nematode infection to most impacted, which agreed with classification by nematological examinations of the plants. Clustering leaves based on their spectral response has the potential to overcome the limitation of heterogeneous canopy responses to stress in high-throughput phenotyping and other applications.
Why it matches plant phenotyping methods葉の近接ハイパースペクトル測定と半教師ありクラスタリングを用いて、線虫感染ストレスの症状を抽出・分類し、遺伝子型耐性を順位付けする方法が研究の中心である。植物病害状態の表現型推定を技術的に検証している。
abstractWe explored a semi-supervised clustering-based technique in which spectral patterns unique to and common among leaves from nematode infected plants are distinguished from patterns with no relationship to infection
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Accurate detection of both immature and mature apples in orchard environments is essential for early crop load management. A near real-time method is proposed in this study for detecting green (early-stage), green–red-mixed (mid-stage; red varieties), or red apples (harvest-stage; red varieties). Both the number of fruits and fruit size were estimated for the entire tree with a single image captured by a low-cost smartphone using two different imaging methods (oblique and panorama modes). An attention mechanism module called the convolutional block attention module (CBAM) was added to the generic YOLOv4 detector to improve the detection accuracy by only focusing on the target canopies. Furthermore, an adaptive layer and larger-scale feature map were included in the modified network structure, enabling it to adapt to various characteristics of fruits and canopies during the entire growing season, such as different fruit colors and sizes, dense-foliage conditions, and severe occlusions. To verify the effectiveness of the proposed method, we compared our improved model, canopy-attention-YOLOv4 (or CA-YOLOv4), with other commonly adopted models available in the literature, such as the original YOLOv4, Faster R-CNN, and single-shot multibox detector (SSD). Two commonly planted apple varieties, “Envy” and “Scifresh”, were used in the study. The results showed that the proposed CA-YOLOv4 detector performed the best among all the algorithms, with up to ∼3% improvement in terms of fruit counting over the original YOLOv4. With the “Envy” variety, fruit detection accuracies were 86.2%, 87.5%, and 92.6% for the early-, mid-, and harvest stages, respectively, whereas the same were 71.0%, 83.6%, and 86.3% for the “Scifresh” variety, which has denser canopy foliage. Both imaging methods proposed in this study only needed one/single shot targeting the entire fruiting tree, which can be highly efficient for real-world applications in crop load management. Finally, CA-YOLOv4 estimated fruit sizes were compared to manual measurements, achieving up to R² values of 0.68 in fruit height and 0.66 in fruit width estimations.
Why it matches plant phenotyping methods果実の検出・計数・サイズ推定を行う画像ベースの植物表現型取得法を開発・比較検証しており、方法が研究の中心である。
abstractA near real-time method is proposed in this study for detecting green (early-stage), green–red-mixed (mid-stage; red varieties), or red apples (harvest-stage; red varieties).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
In modern agriculture, plant growth digital twin system helps breeders monitor plant growth, increase yield, and provide growth management advice. Research on the single view leaf 3D reconstruction in digital twin systems has achieved relative success. However, in traditional single-view reconstruction algorithms, the leaf reconstruction often contains the problems of low precision, achieving complexity, and slow speed, making it difficult for recovering three-dimensional information about leaves. Consequently, the reconstruction precision is significantly reduced, which further affects the accuracy of single-view leaf 3D reconstruction. In response to this problem, this study proposed a single-view leaf reconstruction approach in plant growth digital twin systems based on deep learning. The method in this paper mainly fuses the advantages of ResNet and differentiable rendering, and the model is used for further enhancing feature extraction capability and reconstruction precision. Finally, the experiment presented in this paper suggests that the method allows for the 3D reconstruction of plant leaves with different shapes using a single view. Moreover, the experiment results show that the F-Score, CD, EMD reached 76.192, 0.808, and 3.567. Compared with other models, the proposed model in this study has higher reconstruction accuracy, 3D evaluation indicators, and prediction results, providing important ideas and methods for recovering the leaves from a single view in a plant growth digital twin system.
Why it matches plant phenotyping methods単一画像から植物葉の3D形状を再構成する手法を開発・評価しており、葉の形態表現型取得が研究の中心である。
abstractFinally, the experiment presented in this paper suggests that the method allows for the 3D reconstruction of plant leaves with different shapes using a single view.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Plant segmentation is a crucial task in computer vision applications for identification/classification and quantification of plant phenotypic features. Robust segmentation of plants is challenged by a variety of factors such as unstructured background, variable illumination, biological variations, and weak plant-background contrast. Existing color indices that are empirically developed in specific applications may not adapt robustly to varying imaging conditions. This study proposes a new method for robust, automatic segmentation of plants from background in color (red-green-blue, RGB) images. This method consists of unconstrained optimization of a linear combination of RGB component images to enhance the contrast between plant and background regions, followed by automatic thresholding of the contrast-enhanced images (CEIs). The validity of this method was demonstrated using five plant image datasets acquired under different field or indoor conditions, with a total of 329 color images as well as ground-truth plant masks. The CEIs along with 10 common index images were evaluated in terms of image contrast and plant segmentation accuracy. The CEIs, based on the maximized foreground-background separability, achieved consistent, substantial improvements in image contrast over the index images, with an average segmentation accuracy of F1 = 95%, which is 4% better than the best accuracy obtained by the indices. The index images were found sensitive to imaging conditions and none of them performed robustly across the datasets. The proposed method is straightforward, easy to implement and can be potentially extended to nonlinear forms of color component combinations or other color spaces and generally useful in plant image analysis for precision agriculture and plant phenotyping.
Why it matches plant phenotyping methods植物画像から植物領域を抽出する新規セグメンテーション手法を開発し、複数データセットと正解マスクで精度検証しており、表現型取得の中心的方法である。
abstractThis study proposes a new method for robust, automatic segmentation of plants from background in color (red-green-blue, RGB) images.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Compared with the traditional visual detection method, hyperspectral imaging enables efficient and non-destructive plant monitoring. Besides, it has great potential in plant phenotyping in response to disease and insect infections. However, most previous studies on hyperspectral imaging have focused on detecting a single disease, which can rarely discriminate between multiple co-occurring diseases and insects. In this study, three tea plant stresses with similar symptoms, including the tea green leafhopper (Empoasca (Matsumurasca) onukii Matsuda), anthracnose (Gloeosporium theae-sinesis Miyake), and sunburn (disease-like stress), were evaluated. A multi-step approach was proposed based on hyperspectral imaging and continuous wavelet analysis (CWA) to discriminate the plant stresses. The process entailed: (1) Feature extraction for detection and discrimination of tea plant stresses based on CWA; (2) Detecting abnormal areas on tea leaves via the k-means clustering and support vector machine algorithms; (3) Construction of a model for identification and discrimination of the three tea plant stresses via the random forest algorithm. The results showed that CWA could effectively identify spectral features for distinguishing the three stresses. The overall accuracy (OA) of the proposed approach reached 90.26%-90.69%, with anthracnose having the highest OA (94.12%-94.28%), followed by tea green leafhopper (93.99%-94.20%), while sunburn damage was the least (82.50%-83.91%). Therefore, hyperspectral imaging is effective for plant phenotyping after diseases and insect infections.
Why it matches plant phenotyping methodsハイパースペクトル画像と波レット解析を中核に、茶葉の病害・虫害・日焼けによる植物状態を検出・識別する手法を開発し、精度を評価しているため。
abstractA multi-step approach was proposed based on hyperspectral imaging and continuous wavelet analysis (CWA) to discriminate the plant stresses.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Point cloud segmentation is essential for studying the 3D spatial characteristics of plants. Notably, the segmentation accuracy greatly impacts subsequent 3D plant phenotypes extraction and 3D plant reconstruction. Automated segmentation approaches for plant point clouds are a bottleneck in achieving big data processing of 3D plant phenotypes. Using maize as a representative crop, this study developed DeepSeg3DMaize, a technique for plant point cloud segmentation that integrates high-throughput data acquisition and deep learning. A high-throughput data acquisition platform for individual plants and an association mapping panel containing 515 inbred lines were used to construct the training dataset. Specifically, the MVS-Pheno platform was used to acquire high-throughput data, and Label3DMaize was used for point cloud data labeling. Based on the dataset, PointNet was introduced to implement stem-leaf and organ instance segmentation, and six phenotypes were extracted. According to the results, the mean precision and F1-Score of stem-leaf segmentation were 0.91 and 0.85, respectively. Meanwhile, the mean precision and F1-Score for organ instance segmentation were 0.94 and 0.93, respectively. The correlations of the six parameters (leaf length, leaf width, leaf inclination, leaf growth height, plant height, and stem height) extracted from the segmentation results with the measured values were 0.90, 0.82, 0.94, 0.95, 0.99, and 0.94, respectively. High-throughput data acquisition, automatic organ segmentation, and phenotypic data extraction form an automatic phenotypic data processing pipeline, which is practical for dealing with large amounts of initial data. Besides, it provides a systematic reference for the automated analysis of 3D phenotypic features at the individual plant level.
Why it matches plant phenotyping methodsトウモロコシの点群取得・深層学習による器官セグメンテーションと形質抽出パイプラインを開発・検証しており、植物フェノタイピング手法が中心である。
abstractthis study developed DeepSeg3DMaize, a technique for plant point cloud segmentation that integrates high-throughput data acquisition and deep learning.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Papaya trees (Carica papaya L.) can bear female, hermaphrodite, and male flowers. However, only the hermaphrodite type produces elongate fruit required and appreciated by the consumer market. Sex type determination is carried out based on the plant phenotype after planting the seedlings, increasing the production costs. In this regard, the objective of this study was to verify the feasibility of using near infrared spectroscopy (NIR) as a non-destructive method for sexing papaya trees. The NIR spectra were collected using seeds and leaves of the respective seedlings of the cultivars ‘T2′, ‘Formosa’, and ‘Calimosa’ (Formosa group), and ‘THB’ and ‘Ouro’ (Solo group). By using the seeds, it was possible to obtain a F-score value of 0.81 for the external validation set applying the principal component analysis and quadratic discriminant analysis (PCA-QDA). By using the leaves, the F-score values was slightly lower (0.79) applying PCA and linear discriminant analysis (PCA-LDA). It was possible to use NIR spectroscopy associated with multivariate techniques as a non-destructive method to determine the sex types in papaya trees using both seeds and leaves of the seedlings.
Why it matches plant phenotyping methodsNIR分光と多変量解析を用いて、パパイヤの植物状態（性型）を非破壊推定する方法の実現可能性と外部検証を扱っており、表現型取得法が中心である。
abstractthe objective of this study was to verify the feasibility of using near infrared spectroscopy (NIR) as a non-destructive method for sexing papaya trees
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
The study aims to estimate forage yield and quality parameters by fusing field spectroscopy data and crop height with regression-based mathematical models. Field experiments were carried out to obtain canopy spectral reflectance (CSR) of grass and clover mixtures. Additionally, grass height (Hgᵣₐₛₛ) and clover height (Hcₗₒᵥₑᵣ) were used as auxiliary explanatory variables with CSR to estimate forage yield and quality. Variable importance in projection (VIP) was utilized for sensitive wavelength selection. Two chemometric methods, namely partial least squares regression (PLSR) and support vector machine (SVM), were implemented to build models using full spectra and sensitive wavelengths for estimating dry matter yield (DMY), in vitro true digestibility (IVTD), neutral detergent fiber (NDF), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), crude protein yield (CPY), and botanical composition (BC). Of the total 235 samples, 157 samples were randomly selected for model calibration while the remaining 78 samples were used for model validation. Results showed that both PLSR and SVM could reasonably estimate forage yield and quality variables, although performances of PLSR were more stable in terms of R² and relative root mean square error (RRMSE) for both calibration and validation. Prediction performances of models using only full spectra data (PLSRₛₚₑc) and models also using crop height information (PLSRₛₚₑc₊H) as model inputs were compared in this study. PLSRₛₚₑc₊H presented higher R² and lower RRMSE than PLSRₛₚₑc models (e.g. R² improved from 0.83 to 0.90 for NDF and from 0.56 to 0.73 for IVTD, and RRMSE decreased from 8.14% to 6.58% for NDF and from 2.55% to 2.02% for IVTD). In addition, PLSR that used sensitive wavelengths and crop height (PLSRwₐᵥₑ₊H) as model inputs also had good performance, although slightly worse than PLSRₛₚₑc₊H. The results suggest that there is good potential to predict forage biomass and nutritive value by combining spectral and height variables with chemometric methods.
Why it matches plant phenotyping methodsスペクトル反射と作物高さを統合し、化学計量モデルで牧草の収量・品質・組成を推定する手法を構築・検証しており、植物形質の取得・推定が研究の中心である。
abstractThe study aims to estimate forage yield and quality parameters by fusing field spectroscopy data and crop height with regression-based mathematical models.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAS由来の樹冠サイズから個体の胸高直径という明示的な植物形質を推定するモデル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。
titleToward a comprehensive model for estimating diameter at breast height of Japanese cypress (Chamaecyparis obtusa) using crown size derived from unmanned aerial systems
In recent decades, a considerable number of sensors have been developed to obtain 3D point clouds that have great potential in optimizing management in agriculture through the application of precision agriculture techniques. In order to use the data provided by these sensors, it is essential to know their measurement error. In this paper, a methodology is presented for obtaining a 3D point cloud of a central axis training system defoliated fruit tree (Malus domestica Bork.) obtained from stereophotogrammetry techniques based on structure-from-motion (SfM) and multi-view stereo-photogrammetry (MVS). The point cloud was made from a set of 288 photographs of the scene including the ground truth tree which was used to generate the digital 3D model. The resulting point cloud was validated and proven to faithfully represent reality. The bias of the resulting model is −0.15 mm and 0.05 mm, for diameters and lengths, respectively. In addition, the presented methodology allows small changes in the ground truth actual tree to be detected as a consequence of the wood dehydration process. Having an actual and a digital ground-truth is the basis for validating other sensing systems for 3D vegetation characterization which can be used to obtain data to make more informed management decisions.
Why it matches plant phenotyping methods果樹の3D形状を取得するステレオフォトグラメトリ手法を開発・検証し、他の植生センシング手法の検証基盤として提示しており、植物形態計測が中心である。
abstracta methodology is presented for obtaining a 3D point cloud of a central axis training system defoliated fruit tree
Phenotyping is essential for the advancement of plant breeding and quantitative genetics. Image-based high throughput phenotyping (HTP) is a game changer to expedite a breeding pipeline by supporting large coverage and image analysis for morphological and spectral signature of the plant canopy. To meet the demand of a cost-effective and globally consistent HTP solution, a customized analytic tool, Image Mapping & Analytics for Phenotyping (IMAP), was developed to implement high throughput image processing and deliver plot-level metrics of plant phenotypes. IMAP is open source software written in Python and designed to provide data visualization and batch processes through algorithms for GIS interface, geometric and radiometric calibrations, geo-fencing, segmentation, and gridding. IMAP was applied and validated on aerial images collected by an unmanned aerial vehicle (UAV) and a manned aerial vehicle (MAV) on soybean field that was prepared for a drought resistance study under two different water treatments. The gridding algorithm extracted field boundary and delivered plot-level spectral analysis from aerial images in 1-4 s depending on the image size and the number of plots. The high correlation (R² = 0.90 in vegetation and 0.89 in leaf area) between UAV and MAV images indicated the consistent performance of IMAP software in calculating the plot-level metrics and proved successful deployment of a high throughput phenotyping pipeline through a series of image processing and gridding algorithm in a batch process.
Why it matches plant phenotyping methods植物表現型の画像処理・抽出を中核とするオープンソースソフトウェアを開発し、UAV/MAV画像で検証しているため含める。
abstracta customized analytic tool, Image Mapping & Analytics for Phenotyping (IMAP), was developed to implement high throughput image processing and deliver plot-level metrics of plant phenotypes.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Field / plotThermalWhole plant / canopy / plot / fieldSegmentationPlant / canopy temperature
Crop canopy temperature measurement is necessary for monitoring water stress indicators such as the Crop Water Stress Index (CWSI). Water stress indicators are very useful for irrigation strategies management in the precision agriculture context. For this purpose, one of the techniques used is thermography, which allows remote temperature measurement. However, the applicability of these techniques depends on being affordable, allowing continuous monitoring over multiple field measurement. In this article, the development of a sensor capable of automatically measuring the crop canopy temperature by means of a low-cost thermal camera and the implementation of artificial intelligence-based image segmentation models is presented. In addition, we provide results on almond trees comparing our system with a commercial thermal camera, in which an R-squared of 0.75 is obtained.
Why it matches plant phenotyping methods作物キャノピー温度という植物の生理状態指標を取得する低コスト熱画像センサーと画像セグメンテーション手法を開発し、商用カメラとの比較検証も行っており、フェノタイピング手法が中心である。
abstractthe development of a sensor capable of automatically measuring the crop canopy temperature by means of a low-cost thermal camera and the implementation of artificial intelligence-based image segmentation models is presented.
This paper presents an automated flower counting method based on Multiple Hypothesis Tracking (MHT) with a connected-flower plant model which is based on detections of flowers. Multiple viewpoints of each plant are taken into account as plants are considered in which flowers can occlude each other. To prevent double counting and to solve inconsistencies caused by false flower detections, a model is developed which describes the plant movement with respect to the camera. The uncertainty of the flower detections is considered in this model. To address variations in the velocity of the plant movement, the model realized in this work explicitly takes into account that motions of flowers are correlated since the flowers are connected to each other via the stem of the plant. This is in contrast to the traditional MHT approach where the movement of each object is typically modeled and estimated separately. In our approach, based on the set of detected flowers, the uncertainty of the plant movement is reduced. As a result, the movement of modeled but not always observed flowers is still properly tracked. To demonstrate the validity of the approach, the proposed counting method is tested on a dataset obtained in a real greenhouse containing multiple viewpoints of 71 Phalaenopsis plants and compared to existing methods. The methods considered include a single viewpoint approach, a heuristic state of the practice approach and an MHT approach with both an independent and connected object description. Within a margin of 1 flower, these methods respectively counted the number of flowers in 44%,58%,70% and 92% of the plants correctly. As a result, this work validates the superiority of the MHT approach with a connected-flower plant model.
Why it matches plant phenotyping methods花数という植物器官形質を画像検出から自動抽出する追跡・計数手法を開発し、実 greenhouse データで既存法と比較検証しており、フェノタイピング手法が中心である。
abstractThis paper presents an automated flower counting method based on Multiple Hypothesis Tracking (MHT) with a connected-flower plant model
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Spectroscopy is today and for two decades strongly used in many fields (pharmacy, agriculture, process, medicine…). This use in a very large number of applications is linked to the great spectral richness of the measurement and therefore to the large amount of accessible chemical information. For plant breeding, spectral reflectance in the visible and near-infrared range (VIS–NIR) embeds a lot of information about vegetation (pigments, structure, water, etc.). Discriminatory power between genotypes can be greatly improved by using high spectral resolution. NIR spectroscopy is still limited in the field for phenotyping compared to existing imaging solutions that are easier to implement.In this study, we will address the potential of high spectral resolution data by using NIR spectroscopy to describe phenotypic responses of maize genotypes to water stress. To that end, data acquired following an experimental design with water-deficient environment are processed using an analysis of variance method adapted to multivariate data called REP-ASCA. For each factor, this method gives its significance, the loadings describing the impacted spectral regions and the scores to classify observations. For a date with proven water stress, the treatment and genotype factors and the interaction term are significant with a p-value threshold at 0.05. Treatment term loadings highlight the spectral regions impacted by the change in irrigation while those of the genotype factor allows to group genotypes according to the yield potential regardless the irrigation. The interaction term loadings are used as a phenotyping trait related to water stress response. Based on this signature, tolerant genotypes are differentiated from sensitive genotypes according to a ranking based on final yield (R = 0.81). This spectral signature was then applied to another environment with a moderate water deficit. For most genotypes, we were able to recover the ranking previously established by the stressed environment (R = 0.60).
Why it matches plant phenotyping methodsNIR分光と多変量解析を用いて、トウモロコシの水ストレス応答を表現型形質として抽出・検証しており、表現型取得手法が研究の中心である。
titlePotential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Cereal plant density is a relevant agronomic trait in agriculture and high-throughput phenotyping of plant density is important for the decision-making process in precision farming and breeding. It influences the water as well as the fertilization requirements, the intraspecific competition, and the occurrence of weeds or pathogens. Recent studies have determined plant density using machine-learning approaches and feature extraction. This requires spatially very highly resolved images (0.02 cm) because the accuracy distinctly decreased when images had lower resolution. In this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV). We assumed that at an early seedling stage fractional cover is closely related to the number of plants. Spring barley and spring wheat experiments, each with three genotypes and four different sowing densities, were examined. The practicability and repeatability of the methodology were evaluated with an independent experiment consisting of 42 winter wheat genotypes. This experiment mainly differed for genotypes, sowing density and season.The empirical regression models that make us of multispectral images having a GSD of 0.69 cm were able to determine plant density with a high prediction accuracy for barley and wheat (R² > 0.91, mean absolute error (MAE) < 28 plants). In addition, prediction accuracy only slightly declines for multispectral image data having 1.4 cm GSD or RGB image data having 0.6 cm GSD (MAE < 35 plants m⁻²). BBCH stage 13 was identified as the ideal growth stage in which the plants were large enough to accurately determine fractional cover even from the lower resolution image data. Moreover, a developed empirical regression model was transferred to an independent experimental field verifying its robustness across different conditions. The prediction accuracy of UAV estimated plant density showed an R² value of 0.83 and an MAE of less than 21 plants m⁻². Furthermore, manual measurements of 11 randomly selected plots proved sufficient for a user-based training of the regression model (R² = 0.83, MAE < 23 plants m⁻²) adapted to the independent experimental field.The method and the use of UAV image data enable high-throughput phenotyping of cereal plant density with uncertainties of less than 10 %.The practicability, repeatability and robustness of the developed approach were demonstrated in this study.
Why it matches plant phenotyping methodsUAV画像から作物個体密度を推定する手法を開発し、精度・再現性・独立圃場での頑健性を検証しており、植物フェノタイピング手法が研究の中心である。
abstractIn this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV).
In-field fruit monitoring at different growth stages provides important information for farmers. Recent advances have focused on the detection and location of fruits, although the development of accurate fruit size estimation systems is still a challenge that requires further attention. This work proposes a novel methodology for automatic in-field apple size estimation which is based on four main steps: 1) fruit detection; 2) point cloud generation using structure-from-motion (SfM) and multi-view stereo (MVS); 3) fruit size estimation; and 4) fruit visibility estimation. Four techniques were evaluated in the fruit size estimation step. The first consisted of obtaining the fruit diameter by measuring the two most distant points of an apple detection (largest segment technique). The second and third techniques were based on fitting a sphere to apple points using least squares (LS) and M−estimator sample consensus (MSAC) algorithms, respectively. Finally, template matching (TM) was applied for fitting an apple 3D model to apple points. The best results were obtained with the LS, MSAC and TM techniques, which showed mean absolute errors of 4.5 mm, 3.7 mm and 4.2 mm, and coefficients of determination (R2) of 0.88, 0.91 and 0.88, respectively. Besides fruit size, the proposed method also estimated the visibility percentage of apples detected. This step showed an R2 of 0.92 with respect to the ground truth visibility. This allowed automatic identification and discrimination of the measurements of highly occluded apples. The main disadvantage of the method is the high processing time required (in this work 2760 s for 3D modelling of 6 trees), which limits its direct application in large agricultural areas. The code and the dataset have been made publicly available and a 3D visualization of results is accessible at http://www.grap.udl.cat/en/publications/apple_size_estimation_SfM.
Why it matches plant phenotyping methods果実の3D画像計測と点群解析により、リンゴ果径および遮蔽率を推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractThis work proposes a novel methodology for automatic in-field apple size estimation which is based on four main steps: 1) fruit detection; 2) point cloud generation using structure-from-motion (SfM) and multi-view stereo (MVS); 3) fruit size estimation; and 4) fruit visibility estimation.
During the past few years, milder autumn and winter seasons have caused severe problems to cauliflower harvest of Brittany region in France, mainly due to curd deformation. Consequently, cauliflower breeders are working on breeding new varieties that are more robust to climate change to stabilize the quality of cauliflower production. The aim of this study was to identify at which stage of the curd formation, significant difference can be detected between healthy and stressed cauliflower. A non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed. Plants exposed to vernalization stress were sampled at different times around primary meristem stage, then both MRI imaged and apex dissected. A work flow was developped to extract features from MRI images. A classification on phenotype was learned by LDA, QDA, PLSDA and CNN binary classification between two groups: healthy and stressed cauliflower. Promising F1 score and MCC up to 95% were achieved. Curd deformation is the main cause for cauliflower’s later physiological disorders when reaching maturity. Therefore, the cauliflowers with deformation could be removed at the earliest, e.g., screening for plant breeding. At the same time, the healthy cauliflowers are not destroyed and continue their life cycle.
Why it matches plant phenotyping methodsMRI画像からカリフラワーの健全・ストレス状態を分類する非侵襲的表現型解析ワークフローを開発し、複数の分類器で性能評価しており、表現型取得・抽出法が中心である。
abstractA non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed.
Unmanned aerial vehicles (UAVs) have emerged as a promising platform for determining the dynamic phenotypic traits of crops in the field in a rapid and cost-effective manner. Crop height is a common and important phenotypic trait, and its acquisition with high accuracy usually requires spatial auxiliary (SA) information, such as a digital terrain model in the early growing season, digital surface models later in the season, ground control points, and ground truth of crop height. The reasonable selection of SA information involves balancing the cost and accuracy of crop height acquisition, but this problem has not been systematically studied and it needs to be resolved urgently in the agricultural industry. In this study, we compared four rapeseed height estimation methods using UAV images collected at three growth stages based on the structure from motion algorithm, where one method had complete data and the other three had incomplete SA information. To reduce the crop height estimation errors with incomplete data, improved methods were developed to construct the missing SA information. The optimum results were obtained using complete SA information, where R² was 0.932 and the root mean square error (RMSE) was 0.026 m. For crop height acquisition using incomplete data, the R² values could be controlled above 0.445 and RMSE below 0.146 m. In this study, systematic strategies were developed for selecting appropriate methods to acquire crop height with reasonable accuracy while balancing the cost requirement for use in scientific research and agricultural production.
Why it matches plant phenotyping methodsUAV画像とSfMを用いた作物高という植物形質の取得法を比較・改良し、誤差と精度を評価しているため、フェノタイピング手法が研究の中心です。
abstractCrop height is a common and important phenotypic trait
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Close range spectra imaging of agricultural plants is widely performed to support digital plant phenotyping, a task where physicochemical changes in plants are monitored in a non-destructive way. A major step before analyzing the spectral images of plants is to distinguish the plant from the background. Usually, this is an easy task and can be performed using mathematical operations on the combinations of selected spectral bands, such as estimating the normalized difference vegetative index (NDVI). However, when the background of plants contains objects with similar spectral properties as plant then the segmentation based on the threshold of NDVI images can suffer. Another common approach is to train pixel classifiers on spectra extracted from selected locations in the spectral image, but such an approach does not take the spatial information about the plant structure into account. From a technical perspective, plant spectral imaging for digital phenotyping applications usually involves imaging several plants together for a comparative purpose, hence, the imaging scene is relatively big in terms of memory. To solve the challenge of plant segmentation and handling the memory challenge, this study proposes a novel approach, which combines chemometrics with advanced deep learning (DL) based semantic segmentation. The approach has four key steps. As a first step, the spectral image is pre-processed to reduce illumination effects present in the close-range spectral images of plants resulting from the interaction of light with complex plant geometry. Different chemometric pre-processing methods were explored to find possible improvements in the segmentation performance of the DL model. The second step was to perform a principal components analysis (PCA) to reduce the dimensionality of the images, thus drastically reducing their size so that they can be handled more easily using the available computer memory during the training of the DL model. As the third step, small random images (128 × 128) were subsampled from the tall and wide image matrices to generate the training and validation sets for training the DL models. In the last step, a U-net based deep semantic segmentation model was trained and validated on the sub-sampled spectral images. The results showed that the proposed approach allowed efficient handling and training of the DL segmentation model. The intersection over union (IoU) scores for the segmentation was 0.96 for the independent test set image. The segmentation based on variable sorting for normalization and standard normal variate pre-processed data achieved the highest IoU scores. A combination of chemometrics and DL led to an efficient segmentation of tall and wide spectral images which otherwise would have given out-of-memory errors. The developed method can facilitate digital phenotyping tasks where close-range spectral imaging is used to estimate the physicochemical properties of plants.
Why it matches plant phenotyping methods植物のスペクトル画像から背景を分離するセグメンテーション手法を開発・検証しており、デジタル表現型解析のための画像取得・抽出ワークフローが中心である。
abstractthis study proposes a novel approach, which combines chemometrics with advanced deep learning (DL) based semantic segmentation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Large-scale screening and assessment of lettuce resources are valuable to help discover significant traits and assist in genetic breeding. In this research, a greenhouse-based vegetable high-throughput phenotyping platform (VHPP) was established to evaluate the multidimensional characteristics of various lettuce varieties. The platform contains an imaging unit with four degrees of freedom (DOFs) to cruise on the crop overhead for image acquisition. The platform also has an automated global semantic phenotyping pipeline (GSPP) for locating pots in sequential images and matching each plant at different growth points. Multidimensional image-based traits were automatically extracted and classified into six categories, including geometry, structure, texture, color, color moment and color indices. We calculated and evaluated 63 static traits (ST) and 189 dynamic traits (DT) by principal component (PC), correlation and heritability analysis, and new PCs provided valuable perspective in describing the lettuce canopy. The results demonstrated the ability of a phenotyping system and pipeline to rapidly investigate and evaluate the growth status of thousands of vegetables. Besides, we identified lots of valuable traits that could be of positive significance in revealing the genetic basis of complex features and exploring the excellent attributes for use in the large-scale lettuce screening and assessment.
Why it matches plant phenotyping methodsレタスの画像取得プラットフォームと自動形質抽出パイプラインを開発・適用し、多数の形態・構造・色などの形質を評価しているため、植物フェノタイピング手法が研究の中心である。
abstracta greenhouse-based vegetable high-throughput phenotyping platform (VHPP) was established to evaluate the multidimensional characteristics of various lettuce varieties
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Estimation of biophysical parameters at various crop growth stages is vital for precision agricultural crop production. Spatial delineation of crops’ responses to various levels of nutrients helps optimise resources and reduce nutrient leaching. This paper explores the potential of 3D terrestrial laser scanning (TLS) for the estimation of plant height, crown area, and biomass of vegetable crops at various N levels. Experimental setup of growing three vegetable crops: tomato (Solanumlycopersicum L.), eggplant (Solanummelongena L.) and cabbage (Brassica oleracea L.) with three levels of N fertilization was laid out at the University of Agricultural Sciences, Bengaluru, India in 2017. LiDAR point clouds using a terrestrial laser scanner were collected at different growth stages. A methodology which included, among other processing steps, adaptive spatial filtering, canopy height modelling, watershed segmentation, and support vector regression has been adapted for the estimation of plant height, crown area, and biomass. Validation with ground measurements show high prediction accuracies for plant height (lowest coefficient of determination (R²), 0.96; highest symmetric mean absolute percentage error (SMAPE) of 3.18), and crown area (lowest R², 0.82; highest SMAPE, 8.82) for all the three crops across growth stages. The combined use of plant height and the crown area has enabled accurate and consistent estimation of biomass (lowest R², 0.92; highest SMAPE, 7.53) throughout the growing season. However, the mapping of a specific range of biomass to a specific N level is ambiguous due to wider variations in the crop growth due to rainfall, and wind interferences.
Why it matches plant phenotyping methodsTLSと点群処理・回帰を用いて植物形質（草丈、冠面積、バイオマス）を推定し、地上計測で精度検証しており、フェノタイピング手法が研究の中心である。
abstractThis paper explores the potential of 3D terrestrial laser scanning (TLS) for the estimation of plant height, crown area, and biomass of vegetable crops at various N levels.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
The use of unmanned aerial vehicles’ (UAV) remotely sensed data in crop evaluation is revolutionizing the field of plant phenotyping. This study was conducted to (1) develop protocol to predict maize streak virus (MSV) and grain yield using UAV-derived multispectral data; and (2) identify the suitable predictor variables and ideal phenological stages for MSV and grain yield prediction. Twenty-five maize varieties were evaluated under artificial MSV inoculation. Manual scoring and multispectral imaging measurements were performed at mid-vegetative, flowering and mid-grain filling stages. UAV-derived data were acquired in the multispectral bands of Green (0.53–0.57 μm), Red (0.64–0.68 μm), Red-edge (0.73–0.74 μm) and Near-Infrared (0.77–0.81 μm). Eight vegetation indices were determined: NDVI (normalized difference vegetation index), NDVIᵣₑd₋ₑdgₑ, GNDVI (green normalized difference vegetation index), SR (simple ratio), CIgᵣₑₑₙ (green chlorophyll index), CIᵣₑd₋ₑdgₑ (red-edge chlorophyll index), SAVI (soil-adjusted vegetation index) and OSAVI (optimized SAVI). Finally, predictions of MSV and grain yield were performed with 36 models using multiple regression, decision trees and linear regression. Frequently selected variables for MSV prediction were Green band at vegetative (61.5%), Red band at vegetative (68.4%) and flowering (80.4%), and GNDVI at mid-vegetative (88.7%). The best MSV predictors were GNDVI (r = 0.84; RMSE = 0.85), CIgᵣₑₑₙ (r = 0.83; RMSE = 0.86) and Red band (r = 0.77; RMSE = 0.99) measured at mid-vegetative stage. Six out of 36 models were selected as ideal for predicting maize grain yield: RF-REF-NIRF (r = 0.69; RMSE = 0.65); NDVIREG-GNDVIG (r = 0.74; RMSE = 0.56); RV-NIRV (r = 0.84; RMSE = 0.37); and the tree with the largest correlations are RV-NIRV-RF (r = 0.86; RMSE = 0.32); GNDVIV-OSAVIV (r = 0.84; RMSE = 0.36); GV-RV-NIRV (r = 0.84; RMSE = 0.35); the last two of which were at mid-vegetative stage. We conclude that UAV-based multispectral remote sensing is a reliable tool for phenotyping MSV disease and grain yield prediction, and mid-vegetative appear to be the most ideal phenological stage for MSV and grain yield prediction.
Why it matches plant phenotyping methodsUAVマルチスペクトル計測と予測モデルを開発・評価し、トウモロコシのMSV症状および収量という植物形質を推定することが研究の中心である。
abstractdevelop protocol to predict maize streak virus (MSV) and grain yield using UAV-derived multispectral data
Hyperspectral Imaging (HSI) has been widely adopted in field plant phenotyping activities. Current HSI solutions such as airborne remote sensing platforms and handheld spectrometers have been proven effective and have become popular in various phenotyping applications. However, the imaging quality of current airborne sensing systems still suffers from various noises due to the changing ambient lighting condition, long imaging distance, and comparatively low resolution. Handheld leaf spectrometers provide a higher quality of spectral data, but they only measure a small spot on the leaf, which cannot represent the whole leaf or canopy very well due to the great variation between different locations. In 2018, the Purdue Ag engineers developed a new handheld hyperspectral leaf imager, LeafSpec. For the first time, phenotyping researchers were able to collect high-resolution hyperspectral leaf images without the impacts of the changing ambient light and leaf slopes. However, the application of LeafSpec was still limited by its low throughput and intensive labor cost in the field measurements. The goal of this project was to develop a robotic system that could replace the human operator to perform in-field and leaf-level HSI using LeafSpec. The system consisted of a machine-operable version of the LeafSpec device, a machine vision system for target leaf detection, and a customized cartesian robotic manipulator with five Degrees of Freedom (DOF). In the 2019 field test, the designed system collected data from corn plants with two genotypes and three levels of nitrogen treatments with an average cycle time of 86 s. The nitrogen content predicted by the designed system had an R² value of 0.7307 against the ground truth. The prediction could also differentiate the different nitrogen treatments with P-values of 0.0193 and 0.0102. The performance was similar to human operators’. The developers, therefore, conclude that the robotic system has the potential of replacing human operators for LeafSpec hyperspectral corn leaf imaging in the field.
Why it matches plant phenotyping methodsトウモロコシ葉のハイパースペクトル表現型取得を自動化するロボットシステムを開発し、窒素含量推定性能を検証しており、表現型取得・抽出法が中心である。
abstractThe goal of this project was to develop a robotic system that could replace the human operator to perform in-field and leaf-level HSI using LeafSpec.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Alfalfa (Medicago sativa L.) is an important forage crop grown worldwide for animal feed, green manure, and as a land cover. However, very few approaches exist for timely field scale mapping of crop status, yield and quality attributes for management of inputs, harvest and storage resources, budgeting, crop insurance, etc. This study aims to apply high-resolution aerial multispectral and thermal infrared remote sensing (7 cm/pixel) to characterize above crop attributers. Imaged were two crop cutting cycles in 2018 season. Eight crop vigor index (VI) and a Crop Water Stress Index (CWSI) features were derived from collected imagery data. Modified Non-Linear Index (MNLI), Modified Simple Ratio (MSR) and CWSI reliably evaluated the spatial variations in crop vigor and stress traits (Coefficient of variation [CV] in the ranges of 24–69%). Yield was then predicted with indices as predictor variables through nine simple linear regression (LRs, variable: one image feature per model), seven multiple linear regression (MLRs, variables: one VI and CWSI per model), a stepwise linear regression (SLR), a partial least square regression (PLSR) and a least absolute shrinkage and selection operator (LASSO) models. The SLR, PLSR and LASSO initially used all image features for model training. Amongst simple models, MLR-4 (Variables: MNLI and CWSI) performed the best (Root mean square error [RMSE] = 0.45 kg, R² = 0.64) and LR-5 (Variable: MNLI) was the second-best model (RMSE = 0.51 kg, R² = 0.54). The complex SLR, PLSR and LASSO models predicted yield with similar accuracy as MLR-4 (RMSE in the ranges of 0.45–0.46 kg, R² in the ranges of 0.63–0.64). MNLI (canopy vigor) and CWSI (stress) were significant and sufficient for effective alfalfa crop status and yield prediction for their non-saturation and non-linearity features. Overall, high-resolution aerial remote sensing in the visible-NIR and thermal infrared domain showed potential for site-specific crop monitoring.
Why it matches plant phenotyping methods高解像度航空マルチスペクトル・熱赤外画像から作物活力、ストレス、収量を抽出・予測するセンシングおよび解析手法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractThis study aims to apply high-resolution aerial multispectral and thermal infrared remote sensing (7 cm/pixel) to characterize above crop attributers.
High-throughput phenotyping has been widely studied in plant science to monitor plant growth and analyze the influence of genotypes and environment on plant growth. To meet the demand of large-scale high-throughput phenotyping, unmanned aerial vehicles (UAVs) have been developed for near-ground remote sensing. UAVs based remote sensing has been used for high-throughput phenotyping of various traits of plants. This review focused on the applications of UAVs based remote sensing of different traits with different phenotyping sensors. In this review, the UAVs platforms and the phenotyping sensors were briefly introduced. The applications of UAVs to obtain and analyze plant phenotype traits were introduced and summarized by the traits in a more comprehensive way. A comparison of different phenotyping sensors was conducted. Furthermore, the challenges and future prospects of phenotype information acquisition and data analysis using UAVs as remote sensing platforms were also discussed. Since the current studies from various countries and researchers were fragmented to just explore the feasibility of UAVs based high-throughput phenotyping, this review aimed to provide the researchers and readers the current applications of UAVs for high-throughput phenotyping and how the studies were conducted, provide guidelines for future studies.
Why it matches plant phenotyping methodsUAVリモートセンシングによる植物形質取得を中心に、各種センサー、プラットフォーム、解析、比較、課題を包括的に扱う明確なフェノタイピング手法レビュー。
titleA comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Plant high-throughput phenotyping technology is taking more and more important roles in soybean breeding and genetic research thanks to the advance in sensing and data analytic technologies. However, commercial high-throughput phenotyping systems of general purpose are expensive and complicated for many research groups, and their data analytic methods are designed for specific research projects. The goal of this study was to develop and validate a customized image-based phenotyping system that was used to automatedly collect, process and analyze imagery data of soybean cultivars to evaluate their response to salt stress in controlled environments. The imaging system consisted of a consumer-grade digital camera and an automated platform was used to take sequential images of soybean plants of five cultivars under salt stress during the experimental period. An image processing and analytic pipeline was developed to automatically extract image features and evaluate their tolerance to salt stress. Results indicated that two image features, i.e. canopy area and ExV (the difference of excess green and excess red) were highly correlated with salinity tolerance trait of soybean. The image saturation and blue channel values were able to extract salt stress characteristics and identify different types of salt stress characteristics. In addition, the ratio of damaged leaf area to canopy area was extracted as a novel image feature to quantify the salinity tolerance grade. The overall results indicated that the automatic plant phenotyping system based on low-cost image sensors and automation platform was able to quantify plant stress due to salt stress and would be useful in soybean breeding programs.
Why it matches plant phenotyping methods大豆の塩ストレス応答を画像から自動抽出・定量する低コスト表現型解析システムの開発と検証が研究の中心である。
abstractThe goal of this study was to develop and validate a customized image-based phenotyping system
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Efficient selection of drought-tolerant crops requires identification and high-throughput phenotyping (HTP) of the complex functional (especially canopy-conductance) traits that elicit plant responses to continually fluctuating environmental conditions. However, phenotyping of such dynamic physiology-based traits has been immensely challenging especially due to the limited availability of adequate methods that can provide continuous measurements of plant-water relations. Therefore, gravimetric phenotyping of plants is being increasingly used to allow one-to-one monitoring of plant-water relations and generate continuous evapotranspiration (ET) profiles. The gravimetric sensors or load cells can provide ET estimates at very high frequencies, e.g. 15-min interval, as chosen by the user. There is however, no study on understanding the optimum frequency or the sampling time at which ET needs to be monitored, such that data-redundancy, noise and processing overhead could be reduced. Hence, this paper makes a novel attempt in identifying the optimum sampling time for phenotyping ET from load cells time series. The proposed procedure includes an ensemble Machine-Learning (ML) approach for optimizing the sampling time through time series forecasting of ET profiles and classification of genotypes using the forecasted ET values. High-frequency load cells data from the LeasyScan, HTP platform, ICRISAT were used to derive the ET profiles at frequencies or scales varying from 15-min to 180-min, followed by ET forecasting and classification at each frequency. For both forecasting and classification, an ensemble of three ML algorithms i.e. Support Vector Machines (SVM), Artificial Neural Network (ANN) and Random Forests (RF) were leveraged. Consequently, the performance metrics (of both the operations) obtained from the ensemble were used to compute the entropy-based optimum sampling time. The results reveal that 60-min interval HTP data could be credibly used for both, forecasting ET as well as correctly classifying the genotypes.
Why it matches plant phenotyping methods植物の蒸発散を連続測定する荷重センサー型HTPデータについて、最適なサンプリング時間を機械学習で決定する手法を開発・評価しており、フェノタイピング手法が中心です。
abstractthis paper makes a novel attempt in identifying the optimum sampling time for phenotyping ET from load cells time series.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Estimation of yield is a major challenge in the production of many agricultural crops, including sugarcane. Mapping the spatial variability of plant height (PH) and the stalk density is important for accurate sugarcane yield estimation, and this estimation can aid in the planning of upcoming labor- and cost-intensive actions like harvesting, milling, and forward selling decisions. The objective of this research is to assess the potential of a consumer-grade red-green-blue (RGB) camera mounted on an unmanned aerial vehicle (UAV) for sugarcane yield estimation with minimal field dataset. The study mapped the spatial variability of PH and stalk density at the grid level (4 m × 4 m) on a farm. The average PH was estimated at the grid level by masking the sugarcane area. An object-based image analysis (OBIA) approach was used to extract the sugarcane area by integrating the plant height model (PHM), extracted by subtracting the digital elevation model (DEM) from the crop surface model (CSM). Both CSM and DEM were generated from UAV images, where CSM was produced approximately one month before the harvest and the DEM after the sugarcane was harvested. The PHM improved the overall accuracy of classification from 61.98% to 87.45%. The UAV estimated PH showed a high correlation (r = 0.95) with ground observed PH, with an average overestimation of 0.10 m. An ordinary least square (OLS) linear regression model was developed to estimate millable stalk height (MSH) from PH, weight from estimated MSH, and stalk density from vegetation indices (VIs) at the grid-level. Excess green (ExG) derived from RGB showed R² of 0.754 with the stalk density. Likewise, R² of 0.798 and 0.775 were obtained between MSH and PH, and weight and MSH. Eventually, the yield was estimated by integrating the variability of PH and stalk density and weight information. The estimated yield from ExG (200.66 tons) was close to the actual harvest yield (192.1 tons). The very high-resolution RGB-based images from the UAV and OBIA approach demonstrate significant potential for mapping the spatial variability of PH and stalk density and for estimating sugarcane yield. This can aid growers and millers in decision making.
Why it matches plant phenotyping methodsUAV-RGB画像とOBIAを用いてサトウキビの草丈・茎密度を抽出し、地上測定との精度検証および収量推定を行う手法が研究の中心である。
abstractThe objective of this research is to assess the potential of a consumer-grade red-green-blue (RGB) camera mounted on an unmanned aerial vehicle (UAV) for sugarcane yield estimation with minimal field dataset.
Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high-end non-destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies for plant phenotyping is hyperspectral imaging (HSI). The main benefit of utilising HSI compared to other imaging techniques is the possibility to extract simultaneously structural and physiological information on plants. The use of HSI for analysis of parts of plants, e.g. plucked leaves, has already been demonstrated. However, there are several significant challenges associated with the use of HSI for extraction of information from a whole plant, and hence this is an active area of research. These challenges are related to data processing after image acquisition. The hyperspectral data acquired of a plant suffers from variations in illumination owing to light scattering, shadowing of plant parts, multiple scattering and a complex combination of scattering and shadowing. The extent of these effects depends on the type of plants and their complex geometry. A range of approaches has been introduced to deal with these effects, however, no concrete approach is yet ready. In this article, we provide a comprehensive review of recent studies of close-range HSI of whole plants. Several studies have used HSI for plant analysis but were limited to imaging of leaves, which is considerably more straightforward than imaging of the whole plant, and thus do not relate to digital phenotyping. In this article, we discuss and compare the approaches used to deal with the effects of variation in illumination, which are an issue for imaging of whole plants. Furthermore, future possibilities to deal with these effects are also highlighted.
Why it matches plant phenotyping methods植物全体のハイパースペクトル画像から構造・生理形質を抽出する手法と、照明変動補正を比較検討するレビューであり、フェノタイピング手法が中心です。
abstractIn this article, we provide a comprehensive review of recent studies of close-range HSI of whole plants.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
The current methods of phenotyping for breeding lines require a lot of time, labor and cost. In recent years, unmanned aerial system (UAS) has paved the way for the development of field high-throughput phenotyping for crops rapidly. Different sensors such as regular RGB camera (Red, Green and Blue), multispectral imaging camera (several wavebands), hyperspectral imaging camera (hundreds and even thousands of wavebands), thermal imaging sensor and light detection and ranging (LiDAR) sensor can be placed on unmanned aerial vehicle (UAV) to collect remote sensing data in field-scale trials. Based on this technique, the plant traits (e.g., yield, biomass, height, and leaf area index) can be estimated non-destructively, which is critical for high-throughput phenotyping in agriculture. Compared with ground vehicle-based sensors, UAS can increase throughput and frequency for phenotyping. It is low-cost and could provide high-resolution images compared with satellite-based technique. Based upon the phenotypic traits, those crops with high yield and strong stress resistance (e.g., disease resistance and salt resistance) can be selected, which could finally improve the production. This paper talked about the plant high-throughput phenotyping traits based on the sensors on the UAV. Also, the challenges and obstacles of UAV (e.g., flight safety, flight altitude, flight time, and sensor accuracy) were analyzed. In order to provide the updated information of the relationships between remote sensing information taken from UAV and plant phenotyping traits, we summarized the sensors, plants and traits reported in previous research articles. As a result, the review can be very useful for researchers to use appropriate UAV-based sensors to carry out plant phenotyping experiments, and for farmers to use this advanced technology in managing agricultural production.
Why it matches plant phenotyping methodsUAVセンサーによる植物形質推定と高スループット・フィールドフェノタイピングを中心に扱うレビューであり、方法論的役割が明確。
titleA review on plant high-throughput phenotyping traits using UAV-based sensors
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Stand counts is one of the most common ways farmers assess plant growth conditions and management practices throughout the season. The conventional method for early-season stand count is through manual inspection, which is time-consuming, laborious, and spatially limited in scope. In recent years, Unmanned Aerial Vehicles (UAV) based remote sensing has been widely used in agriculture to provide low-altitude, high spatial resolution imagery to assist decision making. In this project, we designed a system that uses geometric descriptor information with deep neural networks to determine early-season maize stands from relatively low spatial resolution (10 to 25 mm) aerial data, which covers a relatively large area (10 to 25 hectares). Instead of detecting individual crops in a row, we process the entire row at one time, which significantly reduces the requirements for the clarity of the crops. Besides, our new MaxArea Mask Scoring RCNN algorithm could segment crop-rows out in each patch image, regardless of the terrain conditions. The robustness of our scheme was tested on data collected at two different fields in different years. The accuracy of the estimated emergence rate reached up to 95.8%. Due to the high processing speed of the system, it has the potential for real-time applications in the future.
Why it matches plant phenotyping methodsUAV画像と深層学習によってトウモロコシの出芽率・スタンド数を推定する手法を開発し、異なる圃場・年で精度検証しており、植物形質の取得・抽出が研究の中心である。
abstractwe designed a system that uses geometric descriptor information with deep neural networks to determine early-season maize stands
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Efficient quantification of the sophisticated shading patterns inside the 3D vegetation canopies may improve our understanding of canopy functions and status, which is possible now more than ever, thanks to the high-throughput phenotyping (HTP) platforms. In order to evaluate the option of quantitative characterization of shading patterns, a simple image mining technique named “Green-gradient based canopy Segmentation Model (GSM)” was developed based on the relative variations in the level of RGB triplets under different illuminations. For this purpose, an archive of ground-based nadir images of heterogeneous wheat canopies (cultivar mixtures) was analyzed. The images were taken from experimental plots of a two-year field experiment conducted during 2014–15 and 2015–16 growing seasons in the semi-arid region of southern Iran. In GSM, the vegetation pixels were categorized into the maximum possible number of 255 groups based on their green levels. Subsequently, the mean red and the mean blue levels of each group were calculated and plotted against the green levels. It is evidenced that the yielded graph could be readily used for (i) identifying and characterizing canopies even as simple as one or two equation(s); (ii) classification of canopy pixels in accordance with the degree of exposure to sunlight; and (iii) accurate prediction of various quantitative properties of canopy including canopy coverage (CC), Normalized difference vegetation index (NDVI), canopy temperature, and also precise classification of experimental plots based on the qualitative characteristics such as subjection to water and cold stresses, date of imaging, and time of irrigation. The introduced model may provide a multipurpose HTP platform and open new windows to canopy studies.
Why it matches plant phenotyping methodsRGB画像から植生画素を分類し、キャノピー被覆率・NDVI・温度などの植物形質を推定する画像解析モデルを開発しており、フェノタイピング手法が研究の中心である。
abstracta simple image mining technique named “Green-gradient based canopy Segmentation Model (GSM)” was developed
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
The spectral reflectance signature of the plants contains rich information about their biophysical, physiological and chemical characteristics. Learning the patterns directly from the plant spectra is critical for predictive plant phenotyping applications. In this study, we developed an end-to-end deep learning model based on 1-D convolutional neural networks, called DeepRWC, to predict the relative water content (RWC) of plants directly from mean spectral reflectance. The proposed model incorporated a modified Inception module to learn multi-scale spectral features at different abstraction levels. To train the proposed network, maize plants grown under well-watered and drought-stressed treatments were imaged using push-broom style, top-view, visible near-infrared (VNIR) hyperspectral camera in the greenhouse environment. Results showed that our proposed model achieved good performance with an R² of 0.872 for RWC. The performance of the developed model was compared with two standard approaches, partial least squares regression (PLSR) and support vector machine regression (SVR) on two external test datasets. The quantitative analysis showed that the DeepRWC outperformed both linear (PLSR) and non-linear (SVR) approaches by achieving the lowest RMSE and better R² value on all test datasets included in the study. Our proposed DeepRWC eliminated the need for any preprocessing or dimensionality reduction, as in the case of other standard techniques (PLSR/SVR). These results confirmed the ability of DeepRWC to better predict the RWC of plants using spectral reflectance signature.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から相対含水量を推定する深層学習手法を開発し、既存手法および外部データセットで性能比較・検証しており、フェノタイピング手法が中心である。
abstractwe developed an end-to-end deep learning model based on 1-D convolutional neural networks, called DeepRWC, to predict the relative water content (RWC) of plants directly from mean spectral reflectance.
Plant phenotype plays an important role in genetics, botany, and agronomy, while the currently popular methods for phenotypic trait measurement have some limitations in aspects of cost, performance, and space-time coverage. With the rapid development of imaging technology, computing power, and algorithms, computer vision has thoroughly revolutionized the plant phenotyping and is now a major tool for phenotypic analysis. Based on the above reasons, researchers are devoted to developing image-based plant phenotyping methods as a complementary or even alternative to the manual measurement. However, the use of computer vision technology to analyze plant phenotypic traits can be affected by many factors such as research environment, imaging system, research object, feature extraction, model selection, and so on. Currently, there is no review paper to compare and analyze these methods thoroughly. Therefore, this review introduces the typical plant phenotyping methods based on computer vision in detail, with their principle, applicable range, results, and comparison. This paper extensively reviews 200+ papers of plant phenotyping in the light of its technical evolution, spanning over twenty years (from 2000 to 2020). A number of topics have been covered in this paper, including imaging technologies, plant datasets, and state-of-the-art phenotyping methods. In this review, we categorize the plant phenotyping into two main groups: plant organ phenotyping and whole-plant phenotyping. Furthermore, for each group, we analyze each research of these groups and discuss the limitations of the current approaches and future research directions.
Why it matches plant phenotyping methods植物フェノタイピングにおけるコンピュータビジョン手法、画像技術、データセット、解析手法を中心に体系的にレビューしており、方法論が研究の中心である。
titleA review of computer vision technologies for plant phenotyping
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Hyperspectral Imaging is one of the most popular technologies in plant phenotyping. Various kinds of hyperspectral imaging systems have been developed in the past years. However, there are always significant differences in sensors and imaging environmental conditions between different facilities, which makes it difficult to share image processing algorithms and plant features prediction models. Calibration transfer between the imaging systems is critically important. The white referencing calibration has been proved effective in removing the major lighting intensity differences, but significant spectral differences between systems still exist. In this study, we considered the calibration transfer as a spectral adjustment process and compared four different methods (DS, PDS, DPDS and SST) for adjusting the spectra. To perform the calibration transfer, maize plants were imaged using push-broom style, top-view VNIR hyperspectral camera in two greenhouse phenotyping facilities (inter-facility) and within the same facility (intra-facility) under different imaging conditions. The suggested spectral adjustment methods were tested using master partial least squares regression (PLSR) based calibration models developed for predicting the relative water content (RWC) and nitrogen content (N) of maize plants. Results showed that spectral space transformation (SST) decreased the RMSEᵥ from 9.450% and 10.636% to 3.280% and 2.424% for the predicted RWC of intra and inter-facility transfer, respectively, while the corresponding measures achieved by direct standardization (DS) were 3.412% and 3.105%, respectively. In case of N predictions, similar results were observed for DS and SST. The other two methods (PDS and DPDS) reduced the prediction error for RWC and N, but their performance was not on par with DS and SST. The results indicated that the proposed methods, especially DS and SST were able to alleviate the perturbations inherited in the spectra and thus can help to avoid the need for time-consuming, labor-intensive and costly full recalibration process that arose due to the intra or inter-facility variations.
Why it matches plant phenotyping methods植物フェノタイピング用ハイパースペクトル画像システム間のキャリブレーション転送手法を開発・比較検証しており、植物の水分・窒素形質予測に関する取得・解析ワークフローが中心である。
abstractCalibration transfer between the imaging systems is critically important.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Fast and accurate segmentation of winter wheat ears from canopy images can significantly promote the field phenotyping of ears by improving efficiency. In this study, a semantic segmentation based method, i.e., EarSegNet, was proposed to perform pixel-wise classification to segment wheat ears from canopy images captured in field conditions. The EarSegNet integrated the encoder-decoder structure and dilated convolution, aiming to further improve the segmentation accuracy and efficiency for the ears of winter wheat. The results showed that the proposed EarSegNet was able to achieve accurate segmentation of wheat ears from canopy images captured at the flowering stage (segmentation quality = 0.7743, F1 score = 87.25%, structural similarity index = 0.8773). In order to validate the proposed method, the performance of the proposed EarSegNet was compared to the widely used segmentation methods, i.e., SegNet, Two-stage method, and Panicle-SEG. Results showed that the proposed EarSegNet outperformed the compared methods, making a robust and efficient tool to segment ears of winter wheat from canopy images captured at the flowering stage. Generalization tests showed that the proposed EarSegNet achieved superior performances to the compared method, suggesting that the EarSegNet had great potentials for field applications. Obtained results showed that the depths of the encoder, i.e., VGG16, had no significant influences on the performance of EarSegNet, however, deepening the VGG16 would improve the performance of the EarSegNet on the evaluation metric of recall. The results showed that the EarSegNet was a promising tool for ears of winter wheat at flowering stage.
Why it matches plant phenotyping methodsコムギ穂の画像セグメンテーション手法を開発し、既存手法との比較および汎化性能を検証しており、植物フェノタイピング手法が研究の中心である。
abstracta semantic segmentation based method, i.e., EarSegNet, was proposed to perform pixel-wise classification to segment wheat ears from canopy images captured in field conditions.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
The analysis of the rhythm of leaf movement is a simple yet effective method to quantify the impacts of external (e.g. abiotic stress) and/or internal (e.g. gene mutations) perturbations on plant growth. We developed an automated monitoring system to quantify leaf movement using time-lapse imaging and a subsequent leaf-tracking algorithm. The leaf-tracking algorithm was based on dense optical flow algorithm to directly record temporal motion events. The algorithm measures motion directly, rather than detecting leaf or cotyledon tip in every image, so multiple leaves, including occluded leaves, can be measured simultaneously. To test the monitoring system, wild-type and drought-tolerant mutant genotypes of Arabidopsis (Arabidopsis thaliana) were subjected to a combinatorial two water and two nitrogen levels. High-frequency time-lapse images were acquired from top view for little over 6 consecutive days at a frequency of 4 min. Results showed that nitrogen and water treatments elicited differences in mean plant displacement in both genotypes. It also showed significant differences among the two different genotypes in the mean displacement when plants were under water or nitrogen stress. These results confirmed the new monitoring system’s ability to discern environmental and genotypic differences in plant response.
Why it matches plant phenotyping methods葉の動きを時系列画像と光学フローで自動定量するモニタリング手法を開発し、遺伝型・環境応答の識別能力を検証しており、植物表現型取得が研究の中心である。
abstractWe developed an automated monitoring system to quantify leaf movement using time-lapse imaging and a subsequent leaf-tracking algorithm.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Over the past ten years, plant phenotyping technologies that utilize sensing and data mining approaches to estimate crop traits in a high-throughput and objective manner, have been evaluated and applied in different crop improvement and breeding programs. Multiple platforms, from proximal to unmanned aerial systems based remote sensing, have been developed and applied to increase the throughput, efficiency, and objectivity during field phenotyping. In recent years, the development and availability of high-resolution satellite imagery from low-orbit satellites have offered yet another opportunity for phenotyping applications. This review demonstrates the applications of satellite imagery in agricultural production and crop phenotyping and suggests plant traits that can be evaluated using high-resolution satellite imagery. The review summarizes the merits (e.g. rapid/automated data capture from larger and multiple field sites) and challenges (e.g. cloud occlusion) of satellite-based phenotyping in crop breeding programs, and discusses future perspectives/opportunities of high-resolution satellite imagery as a phenotyping tool. High-resolution satellite imagery can serve as a phenotyping tool for the assessment of crop varieties, thus assisting plants breeders in the process of selecting high-yielding, stress (abiotic and biotic) tolerant variety that can contribute to addressing world food demand amidst climate change.
Why it matches plant phenotyping methods衛星画像を用いた作物形質推定を中心に、農業表現型計測への応用、利点・課題、評価可能な形質をレビューしており、表現型取得法が中心である。
abstractThis review demonstrates the applications of satellite imagery in agricultural production and crop phenotyping and suggests plant traits that can be evaluated using high-resolution satellite imagery.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput phenotyping technologies in controlled environments or field conditions have proven to be extremely useful in unravelling key quantitative traits of plants for breeding. Among many plant phenotyping methods, hyperspectral imaging (HSI) and three-dimensional (3D) sensing are the fastest growing and promising approaches for measuring multiple plant parameters. There are many types of HSI and 3D sensors available with each being designed for a specific purpose. Also, the same sensor could be set up and calibrated in different ways to measure different plant parameters on various platforms. This review aims to guide the use of HSI and 3D sensing technologies for plant phenotyping. It first introduces the preliminary knowledge of HSI and 3D sensing for plant phenotyping. In addition, it provides the detail of plant phenotyping using different HSI and 3D sensors on various platforms with different scales. Lastly, the problems and challenges of close-range HSI and 3D modelling of plants are discussed and potential solutions are suggested.
Why it matches plant phenotyping methods植物フェノタイピングにおけるHSIおよび3Dセンシング技術を中心に整理・評価する方法論レビューであり、測定対象、センサー、プラットフォーム、課題を体系的に扱っている。
abstractThis review aims to guide the use of HSI and 3D sensing technologies for plant phenotyping.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
One of the major goals in all wheat (Triticum aestivum L.) breeding programs is to develop disease-resistant varieties. Unmanned Aerial Vehicles (UAVs) equipped with remote sensors can provide spectral measurements that can be used to assess foliage disease severity. Measurement of disease severity trait during the genotype selection process has always been challenging as it takes a substantial amount of time, cost, and labor to phenotype many breeding lines. This study investigates the potential use of low-cost UAV, equipped with digital cameras as a field phenotyping tool for foliage disease severity in awheat breeding program. A field experiment was conducted in 2017 and 2018 at Castroville, Texas. The experiment site has favorable weather conditions for developing wheat leaf rust (Puccinia triticina f. sp.tritici). Red, Green, and Blue band (RGB) images were acquired by flying rotary-wing UAV. Images were then processed to develop orthomosaics and three vegetation indices were calculated. The obtained image dataset was further processed to generate plot-level data. Visual notes on field response and leaf rust severity were taken to calculate the coefficient of infection (CI). A significant variation in vegetation indices was found among the wheat genotypes in both years. Normalized Difference Index (NDI), Green Index (GI), and Green Leaf Index (GLI) were linearly related to CI with R² values ranging from 0.72 to 0.79 (p < 0.05) in 2017 and 0.63 to 0.68 (p < 0.05) in 2018. Ground-based Normalized Difference Vegetation Indices (NDVI) also showed a significant relationship with CI in both years (R² = 0.86, p < 0.05 in 2017 and R² = 0.83, p < 0.05 in 2018). The results showed that UAVs imaging and automated data extraction can facilitate the acquisition of high throughput phenotyping data for disease severity ratings.
Why it matches plant phenotyping methodsUAV画像と自動データ抽出を用いてコムギ葉さび病の病害重症度を圃場レベルで推定・検証することが中心であり、植物表現型取得手法として明確に該当する。
abstractThis study investigates the potential use of low-cost UAV, equipped with digital cameras as a field phenotyping tool for foliage disease severity in awheat breeding program.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping is the task of measuring plant attributes mainly for agricultural purposes. We term length phenotyping the task of measuring the length of a plant part of interest. The recent rise of low cost RGB-D sensors and accurate deep artificial neural networks provides new opportunities for length phenotyping. We present a general technique for length phenotyping based on three stages: object detection, point of interest identification, and a 3D measurement phase. We address object detection and interest point identification by training network models for each task, and develop a robust de-projection procedure for the 3D measurement stage. We apply our method to three real world tasks: measuring the height of a banana tree, the length and width of banana leaves in potted plants, and the length of cucumbers fruits in field conditions. The three tasks were solved using the same pipeline with minor adaptations, indicating the method’s general potential. The method is stagewise analyzed and shown to be preferable to alternative algorithms, obtaining error of less than 10% deviation in all tasks. For leaves’ length and width, the measurements are shown to be useful for further phenotyping of plant treatment and mutant classification.
Why it matches plant phenotyping methods植物部位の長さをRGB-Dセンサー、物体検出、関心点検出、3D計測で推定する汎用フェノタイピング手法の開発・評価が中心である。
abstractWe present a general technique for length phenotyping based on three stages: object detection, point of interest identification, and a 3D measurement phase.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
The supervised deep learning models rely on large labeled training samples, which is a common challenge affecting current plant phenotyping studies. One practical approach to alleviate the insufficient training samples is data augmentation. In this study, we investigated the data augmentation approach using improved cDCGAN (conditional deep convolutional generative adversarial network) for vigor rating of orchid seedlings, a significant but labor-intensive task in modern commercial greenhouse. Various modifications on the architecture of cDCGAN network were explored for generating high-quality fine-grained RGB plant images with designated class labels. ResNet deep learning classifier was employed for performance evaluation throughout the whole analysis. On the small training sets, which obtained obviously worse ResNet classification results than bigger sets, cDCGAN was employed to generate additional plant images. The synthesized images provided a significant boost in classification performance, up to a 0.23 increase in the testing F1 score after data augmentation, achieving comparable results with that obtained with larger training sets without data augmentation. Different size of real and augmented training sets for optimal classification was systematically evaluated. The advantage of the improved cDCGAN architecture with added bypass connections was also demonstrated. The proposed data augmentation approach might be extended to deal with the common challenge of insufficient data size in other plant science tasks.
Why it matches plant phenotyping methods植物の生育勢評価画像を対象とするデータ拡張・分類ワークフローの開発と系統的評価が中心であり、表現型取得・推定法に該当する。
titleData augmentation using improved cDCGAN for plant vigor rating
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
As far back as 1931, studies have shown that Chlorophyll a fluorescence (ChlF) is a useful tool in plant stress detection. Early and accurate detection of plant stress is invaluable in enabling appropriate and timely intervention. One of the major limitations of past work on ChIF-based plant stress identification is that, often, only a small number of commonly used inflection points, locally oriented within the ChlF transient curve, are utilized to calculate a single index value. These singular values offer limited insight into stressor level or stressor type. In this work, we present an unsupervised method for plant stress classification (identification) that utilizes global (versus local) time-varying ChlF signal data obtained via plant video imaging. The contributions of this work are multi-fold: (a) We classify via clustering these time-varying-intensity signals using unsupervised learning via Hidden Markov Models (HMMs). We show how: (b) the proposed globally based feature selection of a plant’s entire ChlF signal profile, via low-pass filtering, can, in some scenarios, improve classification accuracy of plant stress; (c) the rate-of-change-in-time of the plant’s ChlF intensity time-varying profile, as an additional global feature selection, can further improve the plant stress classification accuracy in some scenarios; (d) quantification can improve the proposed HMM classification method in certain scenarios; (e) HMMs allow more variability in categorizing data via clustering than other raw data distance based metrics. (f) We explore the ergodic and Bakis models for HMM state transition matrix initialization. (g) In addition we propose a new method for initialization of the HMM state transition matrix: state information based initial probability assignment (SIPA) and compare it to the often used heuristic initial probability assignment (HIPA) method. (h) We show how using the Bayesian Information Criterion (BIC) as a performance metric allows a good state number selection (using the quantified data with the proposed state transition matrix initialization methods). (i) We present a method to automatically determine the number of existing classes in the data set. Note this is in contrast to existing unsupervised learning methods, where the number of existing classes in the data set is, in general, guessed a priori, and often incorrectly. (j) In conclusion we show experimental results to illustrate the potential and value of the proposed methods to enable more specific and accurate classification of plant stressor levels in some scenarios, and (k) in some instances, stressor types.
Why it matches plant phenotyping methods植物の動画画像から取得したクロロフィル蛍光プロファイルを用い、HMMによるストレス状態分類と特徴抽出・クラス数決定手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractIn this work, we present an unsupervised method for plant stress classification (identification) that utilizes global (versus local) time-varying ChlF signal data obtained via plant video imaging.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Drought stress is one of the major limiting factors in soybean growth and productivity. Canopy leaf wilting (i.e. fast- and slow-wilting) is considered as an important visible symptom of soybeans under drought conditions. In soybean breeding programs, genotypes with the slow-wilting trait have been identified as drought-tolerant cultivars. Traditional method measures canopy leaf wilting traits using visual observations, which is subjective and time-consuming. Recent developments of field high-throughput phenotyping technology using Unmanned Aerial Vehicle (UAV)-based imagery have shown great potential in quantifying crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential use of UAV-based imagery in classifying soybean genotypes with fast- and slow-wilting traits. A UAV imaging system consisting of an RGB (Red-Green-Blue) camera, an infrared thermal camera, and a multispectral camera was used to collect imagery data of 116 soybean genotypes planted in a rain-fed breeding field at the reproductive stage. Visual-based canopy wilting scores were collected by breeders in the same day of imagery data collection. Seven image features were extracted, namely normalized difference vegetation index (NDVI), green-based NDVI (gNDVI), temperature, color hue, color saturation, canopy size and plant height for quantifying canopy wilting trait. Results show that all image features significantly (p-value < 0.01) correlated with soybean yield under drought. A Support Vector Machine model was developed to classify the two wilting traits using the images features and achieved an average classification accuracy of 0.8 with the highest one of 0.9. Slow-wilting genotypes had significantly (p-value < 0.01) higher NDVI, hue, saturation, canopy size, height, and lower temperature than fast-wilting genotypes. The significant broad-sense heritability (H²) indicates the dominating genetic factors in the variations of the image features. The study demonstrates the good potential use of UAV-based imagery technologies in the selection of soybeans genotypes with drought tolerance.
Why it matches plant phenotyping methodsUAV画像・複数センサーからキャノピー萎凋形質を抽出し、特徴量とSVM分類を開発・評価しており、植物表現型取得法が研究の中心である。
abstractThe goal of this study was to investigate the potential use of UAV-based imagery in classifying soybean genotypes with fast- and slow-wilting traits.
In the literature of hyperspectral remote sensing to assess nutrient status in crops, there is a general lack of studies conducted under greenhouse conditions. This may be attributed to technical issues associated with inconsistent lighting conditions during daytime data acquisitions due to shadows and spectral scattering inside greenhouse structures. In this proof-of-concept study, we developed a novel night-based hyperspectral remote sensing system with attached halogen lighting to study leaf reflectance of bok choy [Brassica rapa L. var Chinensis] and spinach [Spinacia oleracea L. ‘Correnta”] grown under high, medium and low fertilization regimes. The study objectives were to: 1) identify spectral regions in which average leaf reflectance values could be used accurately to characterize crop responses to overall fertilizer regimes, and 2) characterize consistency across crops of associations between crop leaf reflectance and levels of individual macronutrient elements. Our findings were: 1) leaf reflectance could be used to differentiate low versus medium/high fertilization regimes with 75% (bok choy) and 80% (spinach) accuracy, and 2) the following spectral regions: 700–709 nm, 780–787 nm and 817–821 nm were associated with N, K, Mg and Ca levels in bok choy and spinach. Based on comprehensive sensitivity analysis, we demonstrated that classification accuracy was highly similar when 50–80% of the crop reflectance data were used as training data, indicating robustness of the proposed linear discriminant classification models. We believe the proposed sensitivity analysis has broad relevance as a method to thoroughly examine the robustness of reflectance-based algorithms that are used to classify agricultural products.
Why it matches plant phenotyping methods夜間ハイパースペクトル撮像システムを開発し、葉反射率から施肥応答・栄養状態を推定する方法の精度と頑健性を評価しており、植物表現型取得法が中心である。
abstractwe developed a novel night-based hyperspectral remote sensing system with attached halogen lighting
Traditional sensing technologies in specialty crops production, for pest and disease detection and field phenotyping, rely on manual sampling and are time consuming and labor intensive. Since availability of personnel trained for field scouting is a major problem, small Unmanned Aerial Vehicles (UAVs) equipped with various sensors can simplify the surveying procedure, decrease data collection time, and reduce cost. To accurate and rapidly process, analyze and visualize data collected from UAVs and other platforms (e.g. small airplanes, satellites, ground platforms), a cloud and artificial intelligence (AI) based application (named Agroview) was developed. This interactive and user-friendly application can: (i) detect, count and geo-locate plants and plant gaps (locations with dead or no plants); (ii) measure plant height and canopy size (plant inventory); (iii) develop plant health (or stress) maps. In this study, the use of this Agroview application to evaluate phenotypic characteristics of citrus trees (as a case study) is presented. It was found, that this emerging technology detected citrus trees with mean absolute percentage error (MAPE) of 2.3% in a commercial citrus orchard with 175,977 trees (1,871 acres; 39 normal and high-density spacing blocks). Furthermore, it accurately estimated tree height with 4.5% and 12.93% MAPE for normal and high-density spacing respectively, and canopy size with MAPE of 12.9% and 34.6% for normal and high-density spacing respectively. It provides a consistent, more direct, cost-effective and rapid method for field survey and plant phenotyping.
Why it matches plant phenotyping methodsUAV画像を処理するクラウド型プラットフォームを開発し、樹木の検出、樹高、樹冠サイズ、健康状態を定量化・検証しており、植物表現型取得が研究の中心です。
abstracta cloud and artificial intelligence (AI) based application (named Agroview) was developed.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundreds of lines in multiple locations over several years. To facilitate the process of selecting advanced varieties, an automated framework was developed in this study. A hyperspectral camera was mounted on an unmanned aerial vehicle to collect aerial imagery with high spatial and spectral resolution in a fast, cost-effective manner. Aerial images were captured in two consecutive growing seasons from three experimental yield fields composed of hundreds experimental wheat lines. The grain of more than thousand wheat plots was harvested by a combine, weighed, and recorded as the ground truth data. To investigate the yield variation at sub-plot scale and leverage the high spatial resolution, plots were divided into sub-plots using image processing techniques integrated by domain knowledge. Subsequent to extracting features from each sub-plot, deep neural networks were trained for yield estimation. The coefficient of determination for predicting the yield was 0.79 and 0.41 with normalized root mean square error of 0.24 and 0.14 g at sub-plot and plot scale, respectively. The results revealed that the proposed framework, as a valuable decision support tool, can facilitate the process of high-throughput yield phenotyping by offering the possibility of remote visual inspection of the plots as well as optimizing plot size to investigate more lines in a dedicated field each year.
Why it matches plant phenotyping methods小麦の収量を航空ハイパースペクトル画像と深層学習で推定する高スループット表現型解析フレームワークを開発・評価しており、表現型取得・抽出手法が研究の中心である。
abstractan automated framework was developed in this study
The characteristics of the complex background in crop disease image, the small disease area, and the small contrast between the disease region and the background that easily causes confusion between them, seriously affect the recognition robustness and accuracy. To address these issues, we propose a Self-Attention Convolutional Neural Network (SACNN), which extracts effective features of crop disease spots to identify crop diseases. Our SACNN includes a basic network and a self-attention network: the basic network is for extracting the global features of the image, and the self-attention network is for obtaining the local features of the lesion area. Extensive experimental results show that the recognition accuracy of SACNN on AES-CD9214 and MK-D2 is 95.33% and 98.0%, respectively. The recognition accuracy of SACNN on MK-D2 has outperformed the state-of-the-art method by 2.9%, which implies that the CNN with self-attention can focus on the important areas of the image, and thus can improve the recognition accuracy. Adding different levels of noise to the AES-CD9214 test set shows the anti-interference ability and the strong robustness of SACNN. In addition, we discuss the influence of the location selection, channel size setting, network number and other aspects of the self-attention network on the recognition performance, in order to show the self-attention network working mechanism and provide inspiration for future research.
Why it matches plant phenotyping methods植物病斑を画像から抽出・認識する深層学習手法を開発し、複数データセットで精度と頑健性を評価しており、植物病害状態の表現型取得が中心である。
abstractwe propose a Self-Attention Convolutional Neural Network (SACNN), which extracts effective features of crop disease spots to identify crop diseases.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Maize is one of the three cereal crops in the world. The growth status of the maize is often observed via camera. Segmentation of the maize plant from digital images is the basis of the plant phenotype. However, the traditional image segmentation algorithms are not suitable for maize segmentation due to under-segmentation or over-segmentation problems when the maize images are affected by light condition or complex background. In order to obtain an accurate maize leaf segmentation result, an automatic maize leaf segmentation algorithm is proposed in this work. The algorithm first trained two traditional maize leaf segmentation models using color and spatial features of the pixels, and then applied an image repairing technology based on spatial structure analysis of the traditional maize segmentation results, including broken points detection and matching, as well as Bezier curve fitting of broken leaves. The evaluation experiment results show that the proposed algorithm is able to segment maize plant from images that have complex background or under different light conditions. The quantitative comparative experiment shows that the proposed algorithm performs better than the traditional image segmentation algorithms. It produced segmentation results that have a similar degree of 90.23% with the ground truth images, and have a PSNR of 24.23 dB, which are both higher than those of the traditional algorithms.
Why it matches plant phenotyping methodsトウモロコシ葉の画像分割アルゴリズムを開発し、複雑背景や照明条件下で定量評価しており、植物表現型取得の中心的方法である。
abstractSegmentation of the maize plant from digital images is the basis of the plant phenotype.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Several vegetation indices have been developed, with the normalized difference vegetation index (NDVI) been the most studied and commonly used. To generate an NDVI map, a relatively high-cost multispectral sensor is required; but currently, most UAVs are equipped with low-cost RGB cameras. For that reason, other indices that utilize RGB data have been developed to generate maps similar to NDVI and minimize the data acquisition cost, such as the triangular greenness index (TGI) and the visible atmospheric resistant index (VARI). However, several studies found that these indices cannot be recommended as reliable general-purpose crop health indicators. This study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms). Three experiments were conducted to create and validate the proposed index. First, the NDVI values generated from a multispectral camera were compared with the NDVI values generated by a hyperspectral camera. In the second experiment, the vNDVI formula was created using a genetic algorithm. The third experiment validates the proposed vNDVI, generated from two uncalibrated RGB cameras, in three different crops (citrus, grapes, and sugarcane). The proposed vNDVI proved to be highly accurate on estimating NDVI values by just using RGB cameras, with an overall mean percentage error of 6.89% and a mean average error of 0.052 in all three crops, providing a low-cost alternative for remote sensing and plant phenotyping.
Why it matches plant phenotyping methodsRGB画像から植物のNDVIを推定する新規可視指数を遺伝的アルゴリズムで開発し、複数作物・カメラで検証しており、植物表現型取得手法が中心である。
abstractThis study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 14 Sept 2026
The use of high-throughput phenotyping systems in crop research offers a powerful alternative to traditional methods for understanding plant behaviours. These systems provide a rapid, consistent, repeatable, non-destructive and objective sampling method to quantify complex and previously unobtainable traits at relatively fine resolutions. In this study, a field-based high-throughput phenotyping solution for wheat was developed using a sensor suite mounted on a self-propelled electric platform. A 2D LiDAR was used to scan wheat plots from overhead, while an odometry system was used as a local navigation system to determine the precise plot/plant/scan location. Accurate 3D models of the scanned wheat plots were reconstructed based on the recorded LiDAR and odometry data. Seven plots of different wheat cultivars were scanned to calculate the canopy height using LiDAR data, and these results were compared with manual ground truth measurements. Additionally, in each of these seven plots, the NDVI and PRI spectral indices were calculated using low-cost spectral reflectance sensors (SRSs) and an expensive visible/near-infrared (VIS/NIR) spectral analysis system used for reference purposes. The results of the validation showed good agreement between the LiDAR and manual wheat plant height measurements with an R² of 0.73 and RMSE = 2.63 cm for three days of campaign measurements. A statistically significant linear correlation was observed between the NDVI values obtained with the reference spectrometer and the low-cost SRS; the coefficients of determination were R² = 0.69 for day 1 and R² = 0.81 for day 2, suggesting a similar degree of accuracy among both sensing systems. The developed platform and the obtained wheat phenotyping results demonstrated the suitability of the system for acquiring reliable data under field conditions while maintaining a constant low speed and stability during field deployment. The adaptability of the platform to the structure of the crop and the repeatability of data collection throughout the growing season make the system suitable for integration into commercial breeding programmes.
Why it matches plant phenotyping methods小麦育種試験向けの自走式高スループット表現型計測プラットフォームを開発し、LiDARによる草冠高とスペクトルセンサーによるNDVI・PRIを基準測定と比較検証しており、表現型取得手法が研究の中心である。
abstracta field-based high-throughput phenotyping solution for wheat was developed using a sensor suite mounted on a self-propelled electric platform.
Root2D/3D reconstructionGrowth / development / phenologyWater status / transpiration
Imaging of the root zone of a plant is extremely important for studies of the root growth and development, root moisture and nutrient absorption, and water and nutrient transport in the root zone. Many noninvasive methods have been proposed for mapping, characterization or monitoring of the generally invisible and complex root zone. Benefiting significantly from its suitability for low-cost and portable device implementation, its applicability at various scales and its ability to characterize physiological functions, electrical imaging is a very promising method for use in root zone applications. Over the last twenty years, increasing numbers of electrical imaging studies have been performed on measurement of the roots or the root zone moisture. However, electrical imaging of the root zone poses many technical challenges, not only in terms of the measurements but also in the image reconstruction computations. This paper will systematically review the progress, advantages and limitations of the related research, along with the technical aspects, which include the measurement principles and the electrical properties of the root zone, modeling and inversion, inversion software, uncertainty, resolution, and parallel computing. Overall, we believe that electrical imaging will have a brighter future for root zone applications in the coming decade.
Why it matches plant phenotyping methods植物の根・根域の状態を電気イメージングで非侵襲的に測定・再構成する技術を、技術的課題や解析手法を含めて体系的にレビューしており、植物フェノタイピング手法が中心です。
abstractThis paper will systematically review the progress, advantages and limitations of the related research, along with the technical aspects, which include the measurement principles and the electrical properties of the root zone, modeling and inversion, inversion software, uncertainty, resolution, and parallel computing.