UAV-based multi-view reconstruction is an important approach for high-precision, non-destructive 3D crop phenotyping. However, in greenhouse environments, UAV image acquisition is often restricted to sparse viewpoints because of UAV-induced airflow disturbances and the structural complexity of the greenhouse, which severely hinders accurate 3D phenotyping. To address this challenge, this study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments, integrating a vision-triggered flight planning strategy with an improved sparse-view 3DGS pipeline, termed SparseBerry-3DGS, for multi-view image acquisition and 3D phenotyping of greenhouse strawberries in GNSS-denied environments. Specifically, 3D Gaussian Splatting (3DGS) is improved by incorporating flow-guided initialization, depth supervision, and an adaptive pruning strategy, which effectively alleviate geometric collapse and floating artifacts under sparse-view conditions. Furthermore, sequential semantic masks generated by SAM2 are utilized to guide the segmentation of strawberry point clouds, thereby reducing background interference and segmentation errors. Experimental results show that the vision-triggered flight strategy enables stable capture of 16 surrounding images for each target fruit. Under sparse-view conditions, SparseBerry-3DGS improves reconstruction stability, with the average peak signal-to-noise ratio (PSNR) reaching 18.25 dB, corresponding to an 18% improvement. The SAM2-based segmentation module achieves high accuracy, with the mean intersection over union (mIoU) above 0.95. Geometric evaluation based on strawberry longitudinal diameter yielded an of 0.88, supporting the accuracy of fruit-scale geometric reconstruction. For weight estimation, five-fold cross-validation yielded an of 0.90 and an RMSE of 3.62 g, showing better predictive performance than models based on 2D projected area and standard 3DGS point clouds. This study provides a new approach for high-throughput, non-invasive digital crop phenotyping in greenhouse horticulture.
Why it matches plant phenotyping methods温室イチゴの3D形状再構成と重量推定を目的に、制約視点UAV撮影、SparseBerry-3DGS再構成、点群セグメンテーションを統合した表現型取得手法を開発・検証しており、方法が研究の中心である。
abstractthis study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments
Camera-based visual sensing provides a non-destructive and scalable approach for monitoring strawberry diseases and pests in greenhouse environments. However, greenhouse images acquired under practical cultivation conditions often contain early-stage tiny lesions, complex leaf backgrounds, uneven target scales, illumination variations, and partial occlusions, making accurate and efficient visual detection challenging. To address these issues, this study proposes YOLOv8n-DSLW (YOLOv8n enhanced by Dense reuse, Shuffle attention, LSKA-LAMP lightweight modeling, and Wise-IoU optimization), an AI-enabled vision-sensing detection model based on YOLOv8n for tiny strawberry disease and pest detection. Specifically, Shrink Residual Dense Block (ShrinkRDB) dense connection blocks and the C2f with Shuffle Attention (C2fSA) module are introduced to preserve weak lesion textures and suppress background interference in greenhouse visual data. A high-resolution P2 detection layer combined with Wise-IoU (WioU) dynamic regression loss is further incorporated to enhance tiny-target perception and localization. In addition, the Spatial Pyramid Pooling-Fast with Large Separable Kernel Attention (SPPF-LSKA) module strengthens contextual modeling under occlusion and clutter, while Layer-Adaptive Magnitude-based Pruning (LAMP) is adopted to mitigate model redundancy and improve the accuracy-efficiency balance. Experiments on a self-collected greenhouse strawberry disease and pest dataset show that YOLOv8n-DSLW achieves a mean Average Precision at 0.5 IoU threshold (mAP@0.5) of 94.3% and a mAP@0.5:0.95 of 77.5%, outperforming the YOLOv8n baseline. The final model has a parameter count of 4.386 M and a computational cost of 27.6 GFLOPs, achieving a frame rate of 45 FPS on the test workstation. It shows application potential for real-time visual monitoring in greenhouses under controlled data acquisition conditions. The results demonstrate that the proposed method improves tiny lesion detection under dense targets, complex backgrounds, and leaf occlusions, providing an AI-enabled vision-sensing framework for automated strawberry health monitoring in greenhouses. Nevertheless, due to limitations associated with imaging equipment, dataset representativeness, and the inherent constraints of the algorithm, further optimization and validation are required to support large-scale field deployment.
Why it matches plant phenotyping methodsイチゴ葉の病斑を画像から検出・局在化する新規YOLOモデルを開発し、データセット上で性能評価しており、植物病害状態の画像ベース表現型取得が中心である。
abstractthis study proposes YOLOv8n-DSLW (YOLOv8n enhanced by Dense reuse, Shuffle attention, LSKA-LAMP lightweight modeling, and Wise-IoU optimization), an AI-enabled vision-sensing detection model based on YOLOv8n for tiny strawberry disease and pest detection.
Reproduction assets foundThe paper's self-collected greenhouse strawberry disease/pest image dataset (with COCO annotations and train/test splits) is explicitly stated as publicly deposited on GitHub at the allowed URL. No author analysis code or trained model checkpoint is mentioned as publicly available.Dataset · publicThe dataset used in this study, including the training and independent test subsets, has been uploaded to a GitHub repository for dataset verification and is available at: https://github.com/dataset-review-2026/strawberry-dataset (accessed on 26 July 2026).Open asset ↗dataset-review-2026/strawberry-datasetlines:111-131Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Strawberry production in hydroponic systems requires precise and spatially explicit information on fruit load, maturity, and size to support harvest planning and quality control. This study presents an integrated computer vision and GNSS RTK framework that detects individual strawberries, estimates their maturity and physical dimensions, assigns fruits to operational size–maturity categories, and generates high-resolution yield maps in an open-field hydroponic system. RGB-D images are processed with YOLOv8-seg and a BoT-SORT-based tracking module to obtain instance-level masks, from which a color-based ripeness index and 3D point clouds are derived. After DBSCAN-based filtering, fruit length and width are computed in metric space and combined with ripeness percentage to assign each strawberry to one of nine operational size–maturity categories using predefined threshold rules. The system was evaluated in a commercial hydroponic crop in Arcabuco, Boyacá, Colombia, achieving accurate segmentation, with median IoU values up to 0.83 for bounding boxes and 0.71 for masks, and mean absolute errors of 2.51 mm in length and 1.85 mm in width with respect to Vernier caliper measurements. Yield maps aggregated in 1 m2 cells and by crop row revealed marked spatial variability in fruit density, maturity state, and size–maturity composition, allowing the identification of zones with a high concentration of fruits ready for harvest versus areas dominated by immature fruits. The proposed framework provides a practical and low-cost decision support tool for hydroponic strawberry management and may be adapted to other high-value horticultural crops after recalibration under different crop architectures, cultivars, and environmental conditions.
Why it matches plant phenotyping methodsRGB-D画像と追跡・3D解析により、イチゴ果実の成熟度、寸法、果実密度および収穫状態を個体レベルで推定し、精度検証も行う中心的なフェノタイピング手法研究である。
abstractThis study presents an integrated computer vision and GNSS RTK framework that detects individual strawberries, estimates their maturity and physical dimensions, assigns fruits to operational size–maturity categories, and generates high-resolution yield maps in an open-field hydroponic system.
Florida and California produce 98% of U.S. strawberries, with Florida growers' profitability depending on high yields early in the season (November-January), when prices are the highest in the U.S. market. This study aims to model the cumulative Marketable Yield and Delta Yield curves (difference in cumulative Marketable Yield between consecutive harvest time points) of strawberry genotypes to facilitate selection for greater early season productivity. The dataset comprised thirteen seasons (2013-14 to 2025-26) of advanced selection trial data. Marketable Yield trajectories were modeled using Legendre polynomial smoothing, with optimal degree selection balancing flexibility and noise reduction. The resulting coefficients served as surrogate phenotypes for genomic prediction. Forward prediction cross-validation was implemented for five seasons (2021-22, 2022-23, 2023-24, 2024-25, and 2025-26), with each season predicted using the information from all preceding seasons. For cumulative yield, across all five seasons, reconstructed curves from the Legendre models presented a clear temporal trend, with predictive ability increasing from low early-season values to peaks around Trait Dates (weeks) 7-8. In the 2021-22 and 2022-23 seasons, Legendre models showed higher predictive ability than single time-point predictions but were comparable to single-time point predictions for the other seasons. Legendre polynomial models utilizing Delta Yield achieved moderate predictive ability across five validation seasons, with consistent advantages over single time point models particularly in earlier seasons, indicating that genetic control extends beyond total yield to the trajectory of yield accumulation. Overall, Legendre modeling effectively captured the temporal dynamics of yield development while describing the trajectory with only a few parameters.
Why it matches plant phenotyping methodsイチゴの収量軌跡をLegendre多項式でモデル化し、少数の係数を代理表現型としてゲノム予測に利用・検証しており、収量表現型の計算的抽出が研究の中心である。
abstractMarketable Yield trajectories were modeled using Legendre polynomial smoothing, with optimal degree selection balancing flexibility and noise reduction.
Abstract Zero-shot visual anomaly detection in complex textured domains remains a fundamental challenge for building adaptive, self-organizing cyber-physical systems. Conventional deep learning approaches often rely on closed-set assumptions, require prohibitive pixel-level annotation costs, and suffer severe performance degradation under cross-domain shifts---limiting their deployability in real-world agricultural CPS where novel disease types and unseen crop species continuously emerge. To address these issues, we present TopoLeaf, a training-free and annotation-free anomaly detection framework. By leveraging the robust semantic representations of foundation models (specifically DINOv2), our method introduces two complementary scoring mechanisms: a geometric anomaly score based on local KNN distance in a stability-selected feature subspace, and a topological anomaly score derived from local persistent homology. The topological score effectively captures subtle structural deviations and micro-texture mutations that geometric distances often miss. Extensive experiments on cross-species plant disease benchmarks (3,100+ images across 40 source--target pairs) demonstrate that TopoLeaf achieves highly competitive and structurally robust zero-shot performance, providing a robust perception layer for closed-loop agricultural cyber-physical systems that must maintain diagnostic stability under previously unseen perturbations. Under well-aligned domains, our geometric score achieves near-perfect detection (e.g., 0.994 AUROC on Strawberry). The method exhibits informative failure modes on structurally isolated domains such as Corn (0.169 AUROC), revealing fundamental structural properties of the foundation model's feature manifold. Furthermore, the topological score demonstrates structural complementarity, achieving 0.542 AUROC on the challenging Corn-to-Apple pair where geometric scoring degenerates to 0.221. Module ablation studies confirm that stability-based dimensionality selection consistently improves cross-domain generalization.
Why it matches plant phenotyping methods植物病害の視覚的異常（植物の病徴・状態）を推定する新規画像解析手法を開発し、複数種の病害ベンチマーク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。
abstractwe present TopoLeaf, a training-free and annotation-free anomaly detection framework.
Reproduction assets foundThe paper publicly releases its complete TopoLeaf source code (implementation, baselines, evaluation scripts) under the MIT License on GitHub, and all experimental image data derives from the publicly available PlantVillage dataset, which is the leaf-image input used for the paper's anomaly-detection phenotyping and isCode · public427 7.4 Consent to Publish
428 Not applicable.
429 7.5 Data Availability
430 All experimental data used in this study is derived from the publicly available
431 PlantVillage dataset [17], which can be accessed at https://github.com/spMohanty/
432 PlantVillage-Dataset.
433 7.6 Code Availability
434 The complete source code, including implementation of TopoLeaf, baseline com-
435 parisons, and evaluation scripts, is publicly available at https://github.com/
436 Shutong-Hou/TopoLeaf under the MIT License.
437 7.7 Funding
438 This research received no specificOpen asset ↗Shutong-Hou/TopoLeafpdf-layout-page:26 lines:1-44Plant phenotyping relevance match · UnverifiedOpenAlex · checked 11 Sept 2026
Real-time strawberry volume estimation is a pivotal technology for automated harvesting and precision grading. However, conventional contact methods are prone to damaging fruits, while existing vision-based approaches struggle to balance high accuracy with low computational overhead. To address these challenges, this study proposes a two-stage real-time volume estimation framework coupling a red-green-blue-depth (RGB-D) sensor with an “Instance segmentation–Principal-axis slicing” framework. First, to precisely extract target contours in complex backgrounds, we designed Deformable Feature Aware-YOLO (DFA-YOLO) based on the YOLO11-seg architecture. This model enhances the geometric perception of irregular fruit edges and effectively overcomes the challenges of background noise and multi-scale variations, providing high-precision masks for subsequent spatial mapping. Subsequently, a principal-axis-slicing algorithm extracts the mask’s centroid and principal axis, perpendicularly slicing the mask into infinitesimal micro-slices. By computing and accumulating the pixel-space volume of these slices, the system converts them into precise 3D physical volumes based on RGB-D depth mapping. The entire system was deployed on an NVIDIA Jetson Orin edge computing platform and validated in a greenhouse. Experimental results demonstrate that the estimated volume highly agrees with the true volume, achieving a coefficient of determination (R2) of 0.945 and a mean absolute percentage error (MAPE) of 9.0%. Under typical operating conditions (1–5 targets per field of view), the system maintains an overall frame rate of 8–15 FPS, requiring only 55 ms for single-fruit estimation. This method exhibits favorable stability and lightweight efficiency under the tested greenhouse conditions, offering a reliable solution for real-time non-destructive crop phenotypic monitoring in computationally constrained agricultural environments.
Why it matches plant phenotyping methodsイチゴ果実の体積という植物器官形質を、RGB-D画像、インスタンスセグメンテーション、主軸スライシングで推定する方法を開発し、精度とリアルタイム性能を検証しているため、フェノタイピング手法が中心です。
abstractthis study proposes a two-stage real-time volume estimation framework coupling a red-green-blue-depth (RGB-D) sensor with an “Instance segmentation–Principal-axis slicing” framework.
Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support.
Why it matches plant phenotyping methods温室イチゴのフェノタイピング、マルチモーダルセンシング、AIによる作物状態解釈を中心に整理する方法論レビューであり、植物状態の取得・推定手法が主題。
abstractThis review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation.
StrawberryField / plotFlowerFruitObject detectionGrowth / development / phenology
To address the challenges of recognizing small strawberry targets and achieving accurate phenological perception in complex field environments, this paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net. The backbone is a Residual Efficient Layer Aggregation Network (R-ELAN) enhanced with a Multi-Scale Convolutional Attention (MSCA) mechanism, which emphasizes subtle color and texture variations to differentiate key phenological phases. For feature fusion, hypergraph convolution (from HyperC2Net) and a Mixed Aggregation Network (MANet) are incorporated, modeling the clustered morphology of strawberries and strengthening the representation of sparse small fruits. The detection head incorporates a lightweight Conv2Former module to capture long-range dependencies and spatial contextual information across growth stages, thereby enhancing the model's capacity to represent continuous phenological changes. A Shape-Normalized Wasserstein Distance (Shape-NWD) loss is introduced to stabilize optimization against minor pixel deviations. Experimental results demonstrated that HCMS-Net achieved a mean average precision (mAP) of 94.9% and an F1-score of 90.0%. Specifically, the average precision (AP) values for the flowering, young fruit, green fruit, veraison, and mature fruit stages reached 99.3%, 88.3%, 90.9%, 97.0%, and 98.2%, respectively. Heatmaps confirmed HCMS-Net's precise attention focus across all five phenological stages, effectively suppressing irrelevant backgrounds. Compared to ten mainstream detectors, HCMS-Net surpassed alternatives such as RT-DETR and the YOLOv5n to v13n by 3.4-8.0 percentage points in mAP. It even surpassed YOLOv12s by 2.7 percentage points, while containing only 32.86% of its parameters. The model offers high accuracy and efficiency for phenological period detection, supporting selective harvesting and intelligent agricultural management.
Why it matches plant phenotyping methodsイチゴの生育フェノフェーズを画像から認識する新規検出モデルを開発し、複数手法との性能比較・検証を行っているため、植物フェノタイピング手法が中心である。
abstractthis paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net
Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.
Why it matches plant phenotyping methods3D点群による非破壊フェノタイピングと連続重量計測を組み合わせ、植物形態・バイオマス・水利用形質を定量化し、測定精度も検証しているため、手法が研究の中心である。
abstractUsing 3D point-cloud phenotyping, this study compared the canopy structure and WUE
Abstract Accurate detection of strawberry fruit, reliable ripeness estimation, and precise localization of the picking point are essential for automated harvesting and yield prediction in smart farming. However, real-world environments introduce significant challenges, including occlusion, illumination variability, and high visual similarity between ripeness stages. Deep learning (DL)-based object detection methods have become the dominant approach to address these issues. This paper presents a systematic review of 60 peer-reviewed studies published between 2023 and 2026, focusing on detecting strawberries and their ripeness using the YOLO family (v5–v11) of DL algorithms. The studies are analyzed with respect to dataset characteristics, preprocessing and augmentation strategies, model architectures, and evaluation protocols. Our results show a clear dominance of YOLOv8, used in 28 (46.7%) of the 60 reviewed works, due to its real-time capability and architectural flexibility. Despite its short history, YOLOv11 has been adopted in 13 studies (21.7%) owing to its balanced precision and computational efficiency. Hybrid CNN–ViT models that integrate Transformer modules or networks into YOLO are gaining attention (8 studies, 13.3%) and show improved performance in complex scenarios, but they still incur higher computational cost. However, we identify critical methodological issues that affect the validity of reported results. In particular, the improper application of data augmentation prior to dataset splitting — a practice observed in precisely one-third of the reviewed studies — poses a significant risk of data leakage and can result in overly optimistic performance estimates. Additional challenges include inconsistent evaluation metrics, limited dataset diversity, and a lack of standardized benchmarks. This review provides a structured overview of current approaches, a critical assessment of existing research practices, and actionable guidance for developing robust, deployment-ready DL solutions for precision agriculture.
Why it matches plant phenotyping methodsイチゴ果実の検出・成熟度推定を対象とする画像ベース手法の系統的レビューであり、データセット、モデル、評価法、データリークやベンチマーク不足を批判的に検討しているため、フェノタイピング手法レビューとして中心的である。
abstractThis paper presents a systematic review of 60 peer-reviewed studies published between 2023 and 2026, focusing on detecting strawberries and their ripeness using the YOLO family (v5–v11) of DL algorithms.
The integration of artificial intelligence (AI), machine learning (ML), and precision agriculture has created new opportunities for efficient and sustainable crop monitoring. These technologies enable large-scale analysis of agricultural data to assess plant health, optimize resource usage, and support data-driven decision-making. This work presents a machine-learning-based framework for assessing strawberry plant health using RGB imagery collected from unmanned aerial vehicles (UAVs). Unlike traditional object detection approaches, this study adopts a hierarchical classification strategy using convolutional neural networks, including different ResNet and EfficientNet architectures. Individual plant regions are extracted as blobs through a preprocessing pipeline, and these image tiles are used to train stage-wise binary classifiers that progressively distinguish plant health categories. To enhance reliability, model predictions are validated using field-collected ground-truth data including chlorophyll measurements and visual plant health ratings, as well as real-time deployment scenarios, where predictions are made from live UAV video feeds. Geospatial alignment associates image-based predictions with real-world measurements, enabling comprehensive evaluation of model performance. Experimental results showed that ResNet18 achieved 87.75% accuracy with an F1 score of 0.8524 for healthy plant classification and 93.75% accuracy with an F1 score of 0.6115 for unhealthy plant classification. EfficientNet-B0 demonstrated superior performance for moderately healthy and moderately unhealthy categories, achieving accuracies of 66.83% and 75.65%, with F1 scores of 0.6350 and 0.6070, respectively, highlighting the effectiveness of the hierarchical classification framework. This framework demonstrates the practical potential of RGB-based plant health monitoring integrated with geospatial alignment and field-validated measurements, offering a scalable, efficient solution for precision agriculture applications.
Why it matches plant phenotyping methodsRGB画像と深層学習を用いてイチゴ個体の健康状態を推定する分類フレームワークを開発し、地上測定・目視評価・実運用映像で検証しており、植物表現型取得が中心的です。
abstractThis work presents a machine-learning-based framework for assessing strawberry plant health using RGB imagery collected from unmanned aerial vehicles (UAVs).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.
Why it matches plant phenotyping methods植物葉のデンプン状態を非破壊推定・空間再構成するSWIR画像計測とケモメトリック解析ワークフローを開発・検証しており、フェノタイピング手法が中心です。
abstractHere, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves.
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:72-99Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Manual quantification of specialty crop traits, such as flowers and fruits, is often labor-intensive, time-consuming, and inconsistent, limiting scalability and precision. We present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images. PhenoSnap bridges the gap between advanced computer vision technologies and practical agricultural applications by eliminating the need for programming expertise. This ready-to-use solution can enable growers, breeders, and Extension faculty to accelerate field work and enhance decision-making related to strawberry and tomato yield estimation for breeding selections and strawberry runner management. Written by Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, and Jessica Chitwood-Brown, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2026.
Why it matches plant phenotyping methods画像から花・果実などの植物形質を自動抽出するAIウェブアプリケーションの開発・提供が中心であり、植物フェノタイピング手法およびソフトウェアとして適格。
abstractWe present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images.
Reproduction assets foundThe article describes PhenoSnap, a publicly accessible AI web application for specialty crop trait extraction, and cites a publicly released Dryad imagery dataset (Zhou et al. 2025b) that is a subset of the training data for the Strawberry Runner model. Both are paper-specific, public, and actionable. No author code orDataset · publicDataset preparation
and the training process are detailed in Zhou et al. (2025a),
and a subset of the dataset has been publicly released on
Dryad (Zhou et al. 2025b).Open asset ↗Dryadpdf-raw-page:5 lines:1-55Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Precise, non-destructive detection of fruit maturity is a cornerstone of modern precision agriculture, directly impacting harvest scheduling and post-harvest quality control. In the case of strawberries (Fragaria × ananassa), in-field automated assessment is persistently hampered by the fruit’s diminutive size, subtle physiological colour transitions, and frequent occlusion by foliage. To overcome these limitations, we developed SMLO-YOLO, a specialised lightweight vision system designed to reliably detect different maturity stages on edge devices under complex orchard conditions. The proposed architecture incorporates a Cross-Scale Aggregation Neck (HDP-Neck) driven by entropy-guided dynamic sampling, which effectively concentrates computational resources on fruit regions while filtering background noise. Additionally, we introduce a Shape-aware Intersection-over-Union (ShapeIoU) loss and a Boundary- and Class-aware Knowledge Distillation (BCKD) strategy to specifically address the challenge of detecting overlapping clusters and low-maturity fruits. Validation on custom datasets collected from commercial orchards in Sichuan and Shanxi demonstrated that the final SMLO-YOLO model, after BCKDloss-based knowledge distillation, achieved an mAP50 of 92.4% at an inference speed of 256.41 FPS, with 6.49 M parameters and 15.0 GFLOPs. These metrics indicate that the system successfully balances high-throughput detection with the non-harvestable low-maturity fruits of agricultural robotics, offering a robust tool for objective, real-time maturity monitoring.
Why it matches plant phenotyping methodsイチゴ果実の成熟段階という植物器官の状態を画像から推定する軽量YOLO手法を開発し、実圃場データで性能検証しており、フェノタイピング手法が研究の中心です。
abstractwe developed SMLO-YOLO, a specialised lightweight vision system designed to reliably detect different maturity stages on edge devices under complex orchard conditions.
Robust in-field sensing technologies are essential for advancing precision agriculture and autonomous field robotics toward analysing internal quality attributes of fruits and vegetables. This study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions. A compact NIR interaction instrument (750–1020 nm), designed for robotic operation, was built and tested in a polytunnel environment under varying day- and night-time conditions. The instrument was calibrated using a partial least squares regression (PLSR) model built on laboratory data collected in 2025 from 200 strawberries of a single variety. It was tested on 100 strawberries of two varieties that were measured in 2024, while still attached to the plant. During night-time operation, TSS was predicted with a standard error of prediction ( SEP ) of 0.73 % TSS and a bias of 0.65 % TSS. Under challenging daytime conditions with strong and fluctuating ambient light, measurements were more affected by additional shot noise from the ambient light, resulting in SEP s up to 1.35 % TSS and biases up to 1.45 % TSS, both of which are acceptable for most applications. The measurement time was 12 s. Robust performance was achieved by implementing rapid and continuous ambient light sampling and correction, combined with outlier rejection of spectra of insufficient quality. These findings confirm the feasibility of in-field, on-the-plant NIR spectroscopy for assessing internal fruit quality and provide practical design guidelines to support further in-field implementations of NIR spectroscopy.
Why it matches plant phenotyping methodsイチゴ果実の糖度という植物器官形質を、ロボット搭載可能なNIRセンサーで非接触測定する手法を開発・検証しており、環境光補正や性能評価も中心的に扱っている。
abstractThis study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized 'Yotsuboshi' strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model's classifications of 'NormalN' and 'LowN' statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions.
Why it matches plant phenotyping methods植物の窒素状態をRGB画像から推定する深層学習法、標準化撮像用デバイス、アノテーション基準、データセットを開発し、NDVIおよび圃場条件で検証しており、表現型取得・推定手法が中心である。
abstractthis study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images.
Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized 'Yotsuboshi' strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model's classifications of 'NormalN' and 'LowN' statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions.
Why it matches plant phenotyping methodsイチゴ葉の窒素状態という植物状態を、RGB画像・深層学習・低コスト撮影筒で非破壊推定する手法を開発し、データセット作成と複数条件で検証しており、表現型取得・抽出法が研究の中心である。
abstractthis study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images.
The article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies. The YOLO11x and YOLOx-seg models, pre-trained by transfer learning, are adapted to recognize and classify plants (plant class), leaves (leaf class), and a reference marker (ref_obj class) of a known size. Segmentation of strawberry leaves using the YOLO11x-seg model makes it possible to analyze the morphometric parameters of individual leaf plates (area, perimeter, roundness, aspect ratio). A set of RGB images (2000 pieces) obtained using a GoPro HERO11 camera under controlled laboratory conditions was formed and annotated, followed by augmentation to increase the model's resistance to variations in shooting conditions. The developed algorithm converts the coordinates of the bounding boxes and segmentation masks of recognized objects into metric units using calibration coefficients calculated from a marker of known size (100×100 mm). The software implemented using PyQt5, TensorFlow, Keras, and OpenCV libraries provides not only visualization of results but also data storage in a local SQLite database with the ability to export to JSON and Excel formats. Validation of the model showed high accuracy in detecting plant bounding boxes (mAP50 = 0.906) and leaf segmentation (mAP50 -mask = 0.625). The average processing speed was 20.3 ms/frame for detection and 34.5 ms/frame for segmentation. The measurement error was less than 3.5 % for the overall parameters of the plant and 5.2 % for the morphometric parameters of the leaves, confirming the effectiveness of the method for assessing the height, width and area of plants, as well as the analysis of the leaf apparatus. The research results show the promise of an approach for automating plant phenotyping in real time.
Why it matches plant phenotyping methods植物の成長・葉形態を画像から自動抽出するアルゴリズム、ソフトウェア、データセットを開発し、精度・処理速度・測定誤差を検証しているため、植物フェノタイピング手法が中心である。
abstractThe article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies.
With the rapid development of precision agriculture and smart farming management, accurate crop disease detection has become a critical tool for optimizing agricultural resource allocation, controlling operational costs, and supporting scientific plant protection strategies. However, real-world field environments are often characterized by strong background interference, multiple concurrent diseases, and fine-grained lesion differences, posing significant challenges to existing detection methods in practical agricultural Internet of Things (IoT) applications. In this paper, we propose Freq-spatial Context Dynamic Network(FCDNet), an efficient and cost-effective detection model tailored for multi-category strawberry disease recognition in complex field management scenarios. The proposed model integrates a Freq-Spatial Feature Module (FSFM), a Context Guide Fusion Module (CGFM), and a Task Align Dynamic Detection Head (TADDH), enabling enhanced expression of high-frequency micro-lesions, adaptive filtering of field background noise, and spatial alignment of classification and regression tasks, while maintaining a lightweight architecture suitable for low-cost agricultural edge devices. Extensive experiments conducted on the newly constructed Strawberry Disease Dataset-7(S7DD) demonstrate that FCDNet consistently outperforms existing mainstream methods, achieving an F1-score of 91.0% and an mAP@0.5 of 94.6%. The model's architectural robustness and capacity for generalization are further substantiated by evaluations across diverse agricultural datasets using PlantDoc and ALDOD. Ultimately, FCDNet became a practical and cost-effective tool for real-time detection of strawberry diseases, directly supporting more accurate yield forecasting and risk management in smart agriculture systems.
Why it matches plant phenotyping methodsイチゴ葉の病斑・病害状態を画像から認識するモデルを開発し、複数データセットで性能評価しているため、植物病害フェノタイピング手法が中心である。
abstractwe propose Freq-spatial Context Dynamic Network(FCDNet), an efficient and cost-effective detection model tailored for multi-category strawberry disease recognition
Deep learning has accelerated progress in plant disease recognition, providing strong technical support for early diagnosis and precision management. However, models often lack robustness and generalization when confronted with novel crops absent from the training set, leading to a marked performance drop in cross-unseen-crop scenarios. Cross-crop generalization for plant disease recognition requires models to identify known disease categories in crop domains never observed during training. A central challenge is that disease symptoms are strongly coupled with crop-specific appearance cues, which severely degrades generalization. Here, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition. The proposed method employs a dual-branch visual encoder to separately capture disease semantic representations and crop-domain representations, and it leverages a frozen CLIP text encoder to use disease and crop prompts for text-guided semantic anchoring. A semantic-anchor-only contrastive disentanglement strategy is further formulated under a hybrid label space, where crop-branch features are incorporated as stop-gradient hard negatives to suppress semantic–domain information leakage and strengthen the intra-class aggregation of the same disease across crops. Residual domain-discriminative cues are mitigated via domain-adversarial learning. During inference, only the disease branch is retained for classification, improving generalization while reducing deployment overhead. Experiments demonstrate that under the PlantVillage cross-crop setting, the method achieves 98.04% and 74.29% Top-1 accuracy on seen and unseen crop domains, respectively. Moreover, it attains 81.99% on a real-world field dataset of strawberry powdery mildew and 76.31% on a low-illumination degradation set, validating robustness under realistic imaging distribution shifts.
Why it matches plant phenotyping methods植物病害症状を画像から認識する新規深層学習手法を中心に提案し、未知作物・実環境データで性能検証しているため、植物フェノタイピング手法として含める。
abstractHere, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition.
• The investigation of the phenotypic variation of F. vesca has provided significant insights into the factors influencing fruit traits, including shape, size, and overall quality. • Digital phenotyping has been proven to be more effective in distinguishing F. vesca fruit traits that are difficult to phenotype in the selection process. • The integration of digital phenotyping and genomic analysis have proven to be a powerful strategy for improving the selection of quality traits in F. vesca . • Genome-based breeding and predictive models facilitate the development of climate- resistant F. vesca cultivars that meet consumer preferences. The quality of Fragaria vesca berry fruits is an important factor in their marketability, and therefore, it has become a major target of breeding programs. However, berry traits are difficult to dissect due to the complex interaction of genetic and environmental factors. In this study, we evaluated phenotypic variation in commercially relevant traits, including shape, size, pH and total soluble solids (SSC) in an open-pollinated F. vesca population grown in a region characterized by high temperature fluctuations. The observed variability underscores the intricate interplay between genetic background and environmental factors on fruit morphology and quality traits. Digital imaging phenotyping proved to be a robust and objective approach for capturing morphological traits difficult to phenotype, providing quantitative data necessary for effective selection. Moreover, the ddRAD sequencing facilitated the identification of significant genetic diversity in a F. vesca population, generating approximately 4000 SNP polymorphic markers used to investigate the population structure and the potential of genomic models for selecting desirable traits, such as fruit shape and size. Several genomic selection models were tested to predict breeding values for fruit morphological traits. Prediction accuracy was substantially improved through training set optimization strategies, particularly those based on CDmean criteria. The integration of digital phenotyping, high-throughput genotyping and genomic predictive modelling has proven to be a powerful strategy for improving the selection of desirable traits in F. vesca . Overall, the findings from this study provide a foundation for further genetic improvement efforts, which will ultimately enhance the quality and marketability of strawberry cultivars.
Why it matches plant phenotyping methodsイチゴ果実の形状・サイズを対象に、デジタル画像による表現型取得を明示的に評価しており、育種への応用だけでなく形態形質の定量的取得が研究の中心的要素である。
abstractDigital imaging phenotyping proved to be a robust and objective approach for capturing morphological traits difficult to phenotype, providing quantitative data necessary for effective selection.
Timely identification of canopy decline in commercial strawberry production is challenging because visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling. The framework was applied across three commercial strawberry fields in Oxnard, California using nine UAV surveys collected from December 2022 to June 2023, yielding 159,220 plant-level monitoring units. NDRE- and Redness Index-based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported survival-based modeling of canopy decline progression. Across withheld test plants from all survey dates, overall concordance indices ranged from 0.88 to 0.95 across fields, indicating strong ability to rank plants by time-to-decline risk under heterogeneous field conditions. Spatial risk maps revealed localized high-risk clusters that expanded over time in fields with greater canopy deterioration, while fields with minimal visible decline exhibited diffuse but stable risk distributions. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy decline risk. These results demonstrate that framing repeated UAV observations as a time-to-event process enables fine-scale spatiotemporal modeling of canopy decline dynamics and supports risk stratification for targeted field monitoring in commercial strawberry systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物体レベルのキャノピー衰退・枯死を抽出し、時系列解析と生存モデルで評価するフレームワークが研究の中心であり、性能検証も行っている。
abstractWe developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling.
• Modular RGB–D pipeline for real-time multi-attribute grading of strawberries. • ResNet-101 multi-head classifier predicted maturity, disease, and deformity jointly. • CycleGAN restored strawberry shape and texture from partially occluded views. • QH and MADM strategies converted multi-attribute predictions to grading decisions. • Field tests validated end-to-end perception-to-placement pipeline in farms. Strawberry grading was critical for meeting market standards, yet manual post-harvest sorting remained labor-intensive, damage-prone, and inconsistent, while most vision-based methods targeted single attributes and lacked integrated in-field operation. We presented a real-time RGB–D grading framework for tabletop-cultivated strawberries in greenhouse environments. The system combined instance segmentation (YOLOv11–Seg), occlusion-aware completion via CycleGAN, and a three-branch multi-task network based on ResNet-101 to simultaneously infer maturity, disease status (including asymptomatic fruit), and deformity. Pose normalization and depth alignment supported calibrated volume-to-mass regression for non-destructive weight estimation. To improve robustness to rare morphology, 500 deformed-fruit samples were synthesized with a Gemini-based generative model to mitigate class imbalance. Built upon this perception stack, we implemented two grading strategies: a deterministic Quality Hierarchy (QH) for real-time robotic routing and a weighted multi-attribute decision-making (MADM) scheme for flexible batch evaluation. The multi-attribute classifier achieved 93.65% overall accuracy (disease 94.09%, maturity 94.07%, shape 94.42%) with an inference latency of 33.65 ms (29.7 FPS). Depth-assisted mass estimation yielded average errors of 8.11% for complete fruits and 10.47% under occlusion after completion; in field deployment on marketable fruits routed to size grading, it achieved MAE/RMSE of 1.85/2.20 g with R 2 = 0.9384 (MAPE 8.48%) and a three-bin size-grade accuracy of 90.91% (100/110). In single-target harvesting mode on an RTX 4060 GPU (640x480, 30 fps), end-to-end latency was 87.56 ms per harvested target in complete mode and 197.56 ms when completion was invoked (CycleGAN 110.00 ms/instance). These results demonstrated a practical, non-destructive, occlusion-aware, and deployable multi-attribute grading solution for intelligent strawberry harvesting in real greenhouse scenarios.
Why it matches plant phenotyping methodsRGB-D画像解析でイチゴの成熟度、病徴、形状、重量を推定し、性能検証とロボット実装まで行う中心的なフェノタイピング手法研究。
abstractModular RGB–D pipeline for real-time multi-attribute grading of strawberries.
The objective of this study is to create a highly accurate and interpretable deep learning (DL) model for the multi-class classification of fruit using convolutional and transformer architectures. The classification performance can be enhanced by making sure that the used technique is explainable and interpretable. This research data was obtained from Kaggle which contains images of banana, grape, lemon, mango, and strawberry fruit classes. The total data was divided into 70:15:15 for training, validating and testing. To ensure consistent size and quality, all images were pre-processed before use. This study considered four pretrained models namely RegNetY-B3-GE, DarkNet53-SCSE, BEiT, and PVTv2 for performance assessment. We proposed a lightweight hybrid (convolution plus attention-based) CoAT-AgriLite model for fruit disease classification which extracts local lesion features and global context. Transferring training and data augmentation technique was utilized during training for better performance. To ensure interpretability of model decisions, Gradient-weighted Class Activation Mapping (Grad-CAM) which captures the discriminative regions from the input images for model predictions. Among all evaluated models, the proposed model achieved the highest classification accuracy of 99.37% on the testing dataset. Comparative results demonstrated that the proposed model outperformed other pretrained models in terms of precision, recall, and F1-score, confirming its robustness and effectiveness in real-world agricultural classification tasks. The experimental findings validate that the proposed model not only achieves superior classification accuracy but also provides interpretability through Grad-CAM visualizations. This hybrid framework offers a promising solution for intelligent and transparent fruit classification systems, with potential applications in precision agriculture and automated sorting systems.
Why it matches plant phenotyping methods果実病害を画像から分類する深層学習フレームワークを開発・比較し、病斑特徴の抽出とGrad-CAMによる説明性を評価しており、植物の病害状態の画像計測が中心である。
titleA hybrid convolution and attention-based framework with visual explanation for fruit disease identification.
Accurate and efficient detection of the pollination status of strawberry flowers is essential for intelligent pollination robots, as it directly affects the determination of optimal pollination timing and improves fruit set rates. However, the small size of strawberry anthers, their visual similarity, varied opening states, and complex field environments make pollination status detection highly formidable. To overcome these constraints, this paper presents a streamlined and resource-efficient detection approach (ELSF-DETR), built upon the Real-Time DEtection Transformer (RT-DETR) and specially refined for detecting densely packed and visually similar small objects in agricultural scenes. A lightweight LS-ResNet backbone is constructed to better capture small and densely clustered anther structures in strawberry flowers while reducing model complexity for improved deployment efficiency. In addition, the integration of a P2 detection head with full-kernel convolution enhance the network’s capacity to focus on delicate anther contours and cracking characteristics. Furthermore, the Hierarchical Attention Fusion Block (HAFB) is employed to balance local detail extraction with global context understanding, reducing misjudgments caused by misleading fine-grained features. Lastly, by employing the Wise-IoU (WIoU) loss mechanism, the model achieves improved sensitivity to minor positional discrepancies in visually similar anther objects. Experiments conducted on a self-built strawberry flower dataset demonstrate that ELSF-DETR achieves superior performance, it achieves 88.2 % accuracy, 85.8 % recall, 87.1 % mAP@50, and F1 score of 86.98 %. Relative to the baseline architecture, mAP@50 and F1 improved by 7.1 % and 4.33 %, respectively, while the model parameters and GFLOPs were reduced by 6.86 MB and 13.7 G, meeting the requirements of high precision and low complexity. This work provides practical support for intelligent pollination systems in precision agriculture.
Why it matches plant phenotyping methodsイチゴ花の受粉状態という植物状態を画像から推定する検出モデルを開発・評価しており、植物フェノタイピング手法が中心である。
abstractthis paper presents a streamlined and resource-efficient detection approach (ELSF-DETR)
Accurate yield forecasting is crucial in optimizing resource management and decision-making processes in agriculture, particularly in crops such as strawberries, which require precise predictions due to their rapid and continuous ripening cycles. This study introduces PheMuT, a novel phenology-informed, multi-modal time-series model that integrates visual and meteorological data streams to enhance strawberry yield forecasting. The proposed method employs advanced computer vision techniques, including two YOLOv11 detectors, an optimized ByteTrack tracker, Segment Anything (SAM), and Depth Anything v2 (DAv2), for precise fruit detection, canopy, and volume estimation. Concurrently, high-frequency weather data are processed using a self-supervised autoregressive Temporal Convolutional Network (TCN), resulting in concise and informative weather embeddings. These visual and weather features are fused within an LSTM-based model to produce weekly yield forecasts. PheMuT was validated using two strawberry cultivars at a Florida research facility over two consecutive seasons. Results indicated that PheMuT improved forecasting accuracy, reducing mean absolute error (MAE) by 10.7%, root mean squared error (RMSE) by 12.5%, and mean absolute percentage error (MAPE) by 18.6% compared to baseline manual methods. Additionally, the model exhibited a notable improvement of 17.2% in the coefficient of determination (R²). PheMuT offers an efficient, automated framework for yield forecasting. Code and data are available athttps://github.com/Sycamorers/PheMuT. The full datasets used in this study are available from the authors upon request.
Why it matches plant phenotyping methods果実検出、キャノピー・体積推定などの画像ベース表現型取得と時系列モデルを統合した収量予測手法を開発・検証しており、表現型取得ワークフローが中心的である。
abstractThis study introduces PheMuT, a novel phenology-informed, multi-modal time-series model that integrates visual and meteorological data streams to enhance strawberry yield forecasting.
• XGBoost classified healthy and spider mite–infested leaves of cucumber and strawberry • Classification accuracy remained above 70% even with a reduced set of wavelengths • A combined model detected spider mite infestations across two crop species effectively The two-spotted spider mite is a globally significant pest affecting over 150 crop species, including cucumbers and strawberries. Its feeding activity leads to chlorophyll degradation and physiological changes in leaf tissue, which alter spectral reflectance properties and enable image-based detection. In this study, hyperspectral imaging (HSI) under controlled conditions was used to classify healthy and spider mite-infested leaves of cucumber and strawberry plants, including asymptomatic infested leaves. Spectral data were analyzed and classified with three supervised machine learning algorithms built on extreme gradient boosting (XGBoost) models. The study had three objectives: (1) to assess the ability of XGBoost to classify multiple infestation states, (2) to evaluate model performance with a reduced set of effective wavelengths, and (3) to determine whether infestation across both crops can be classified using a single, merged model. Using all wavelengths, results showed that classification accuracy was 93% for cucumber leaves, 84% for strawberry leaves, and 87% when combined. With five most effective wavelengths, classification accuracy reached 70% for cucumber leaves, 65% for strawberry leaves, and 65% for cucumber and strawberry leaves combined. The most effective wavelengths were consistently selected from the red-edge and near-infrared (NIR) spectral regions, which highlights their importance for early detection. To the best of our knowledge, this is the first known study to successfully apply a combined machine learning model for early spider mite detection across two different crop species using hyperspectral data under controlled conditions. The results show the potential of machine learning for multi-crop pest detection and could lay the groundwork for practical, sensor-based tools in precision agriculture.
Why it matches plant phenotyping methodsハイパースペクトル画像とXGBoostにより、植物葉のダニ感染状態を直接推定し、波長削減と作物間モデル性能を評価しているため、病害・害虫状態のフェノタイピング手法が中心である。
titleMulti-crop early detection of spider mite damage using hyperspectral data and XGBoost
Accurate and non-destructive assessment of fruit maturity is critical for sustainable agricultural practices. This study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification. A dataset of 443 strawberries spanning eight maturity stages was analyzed using six metaheuristic algorithms—Binary Grey Wolf Optimizer, Binary Particle Swarm Optimizer, Bee Colony Optimizer, Genetic Algorithm, Ant Colony Optimizer, and Gravitational Search Optimizer—integrated with four classifiers: Naïve Bayes, Decision Tree, Linear Discriminant Analysis, and Support Vector Machine. A new fitness function was designed to optimize classifier performance, and results were validated through Self-Organizing Map Neural Networks, cross-validation, and statistical significance testing. The Genetic Algorithm–Linear Discriminant Analysis combination achieved the highest and most stable accuracy (94.6–99%), outperforming existing image-based, deep learning, and conventional spectroscopic approaches while retaining interpretability. These findings demonstrate that metaheuristic-driven MIR analysis provides a robust, explainable, and efficient method for precise strawberry maturity assessment, offering significant potential for advancing eco-friendly and intelligent agricultural practices.
Why it matches plant phenotyping methodsイチゴ果実の成熟度という植物器官の状態を、MIR分光と特徴選択・分類器で非破壊推定する方法を開発し、交差検証や統計検定で性能評価しており、フェノタイピング手法が中心である。
abstractThis study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification.
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available at the authors' GitHub release URL. The spectral dataset itself is not public and is available only from the corresponding author on request.Code · publicCode availability
The code is available publicly on:
https://github.com/RabihAssaf89/RabihAssaf-codes/releases/tag/v1.0.Open asset ↗RabihAssaf89/RabihAssaf-codes · v1.0html-lines:822-851Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Abstract Crop diseases remain a significant threat to agricultural productivity and fruit quality, particularly for high-value crops such as strawberries and grapes. Early and reliable detection of these diseases under real-world conditions is essential but remains challenging due to variations in environment, illumination, and imaging perspectives. Leveraging recent advances in deep learning and computer vision, this study presents a robust framework for the automated detection and classification of strawberry and grape diseases using convolutional neural network (CNN) models, i.e., VGG16, ResNet101v2, InceptionV3, and DenseNet121. Unlike many existing works that rely solely on controlled or publicly available datasets, we constructed two specialized datasets by combining field-captured images under diverse environmental conditions with online sources, thereby enhancing robustness and ecological validity. The strawberry dataset includes six disease classes, while the grape dataset encompasses seven classes, covering economically significant pathologies such as anthracnose, black rot, gray mold, powdery mildew, sour rot, and leaf scorch. Extensive experiments were conducted using state-of-the-art CNN architectures, including VGG16, ResNet101v2, InceptionV3, and DenseNet121. On strawberries, DenseNet121 and InceptionV3 achieved accuracies of 94% (training) and 95% (testing), respectively, while VGG16 delivered superior performance on grapes, achieving 95% (training) and 92% (testing). Beyond technical accuracy, the proposed models were explicitly designed for applicability in actual field conditions, ensuring that the system can be directly adapted for use by farmers and plant pathologists as a practical decision-support tool. The findings provide a foundation for scalable, automated, and field-ready disease detection systems, contributing to more sustainable, data-driven crop management practices.
Why it matches plant phenotyping methods植物画像からイチゴ・ブドウ病害を自動検出・分類する深層学習手法を開発し、実圃場画像を含むデータセットで性能評価しているため、植物病害表現型の取得・推定が中心である。
abstractthis study presents a robust framework for the automated detection and classification of strawberry and grape diseases using convolutional neural network (CNN) models
Early detection of canopy decline in strawberry production is essential for timely management, yet visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a UAV-based monitoring framework that integrates multispectral imagery, plant-level canopy metrics, clustering, and Random Survival Forest (RSF) modeling. This framework was used to predict the onset and spatial progression of soilborne pathogen-associated canopy decline in three commercial strawberry fields in Oxnard, California. Nine UAV surveys collected from December 2022 to June 2023 were processed into 159,220 plant-level monitoring units. NDRE- and Redness Index–based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported a time-to-event modeling approach. RSF models achieved consistently high concordance during periods of active decline, with strongest performance in the field exhibiting the greatest disease pressure. Spatial risk maps revealed early hotspots that expanded into contiguous high-risk zones by June, while fields with minimal visible symptoms showed diffuse but consistent risk patterns. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy risk, consistent with the possibility that lower application rates may be sufficient in portions of fields with historically low disease pressure. These results demonstrate that UAV multispectral time series combined with survival modeling can track fine-scale spatiotemporal canopy decline and provide an early-warning framework to support spatially targeted disease monitoring and management in commercial strawberry systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物レベルのキャノピー指標と枯死率を抽出し、病害に伴うキャノピー衰退を時系列・空間的に推定する方法が研究の中心である。
abstractWe developed a UAV-based monitoring framework that integrates multispectral imagery, plant-level canopy metrics, clustering, and Random Survival Forest (RSF) modeling.
Precision agriculture requires accurate fruit outlining to support automated harvesting and yield assessment. Manual pixel annotation limits scalability and slows deployment in farm environments. An annotation light approach is presented for ripe strawberry detection and instance segmentation across greenhouse and field imagery. The primary claim states reliable masks emerge from coupling few sample trained detectors with prompt driven foundation segmentation. A fast object locator trained with limited images provides region proposals, while a large pretrained segmenter generates masks without pixel supervision. Evaluation uses two datasets with controlled and natural conditions and reports precision recall, intersection over union, and Dice statistics. Results show high detection accuracy under sparse supervision and stable segmentation scores above 0.92 across datasets. These findings advance annotation efficient phenotyping by demonstrating scalability with minimal labeling effort. Applications include real time monitoring, ripeness assessment, and robotic harvesting support. Future work targets multiclass maturity analysis, improved occlusion handling, and multimodal sensing integration.
Why it matches plant phenotyping methodsイチゴ果実の検出・インスタンスセグメンテーションによる輪郭・成熟度推定手法を開発し、複数データセットで性能検証しており、表現型取得が研究の中心である。
abstractAn annotation light approach is presented for ripe strawberry detection and instance segmentation across greenhouse and field imagery.
Accurate real-time quantification of strawberry ripeness is critical for advancing selective strawberry harvesting robots. However, existing methods often overlook inconsistencies caused by color distortion under varying light intensities. This study aims to verify and quantitatively analyze the influence of illumination on strawberry ripeness, and further presents a novel light-resilient vision-based ripeness regression method that overcomes these limitations through three key innovations. First, the research established the first fine-grained ripeness metric under controlled lighting conditions and introduced the comprehensive LightStrawberry dataset, featuring multi-illumination strawberry images. Second, we propose SRR-Net, an innovative end-to-end strawberry ripeness regression network built upon YOLOv8/YOLOv11. The network incorporates a dedicated ripeness regression branch that operates in parallel with the detection and segmentation heads, enabling simultaneous and efficient estimation of strawberry maturity. To further mitigate lighting-induced color distortion, RetinexNet was integrated to decompose, adjust, and reconstruct images by normalizing illumination and reflectance. Experiments demonstrated that SRR-Net achieved 0.918 mAP@50 for segmentation and operated at 210.3 FPS based on YOLOv11, while SRR-Net with RetinexNet attained 0.898 mAP@50 and 43.39 FPS. Though slightly lower in precision than other methods, both significantly improved ripeness accuracy, with mean absolute errors (MAE) of 0.040 and 0.037, representing 68.75 % and 71.09 % improvements over conventional Mask R-CNN approaches. Field experiments further demonstrated that SRR-Net and SRR-Net with RetinexNet achieved superior performance in ripeness regression. However, their detection and segmentation performance showed limited adaptability to real orchard conditions due to the characteristics of the LightStrawberry dataset. Overall, both models outperformed the standard YOLOv8/v11 baselines but were slightly inferior to Mask R-CNN. This work provides a robust solution for strawberry-harvesting robotics, enabling reliable ripeness assessment in challenging field environments.
Why it matches plant phenotyping methods画像からイチゴの成熟度を定量推定する回帰手法、照明補正、データセットを開発・評価しており、植物形質取得が中心である。
abstractpresents a novel light-resilient vision-based ripeness regression method
Image segmentation is a fundamental component of vision-based agricultural robotics, enabling accurate fruit localization, disease detection, and automated harvesting. However, real-world strawberry fields present significant challenges due to irregular fruit morphology, dense foliage occlusions, variable ripeness, and strong illumination variability. Moreover, segmentation models trained on a single dataset often fail to generalize across domains, limiting their practical deployment. This paper presents a comprehensive benchmark of classical computer vision methods, convolutional neural networks, instance-based models, and transformer-based architectures across three heterogeneous public strawberry datasets: Db1 (instance segmentation), Db2 (lesion segmentation), and Db3 (semantic segmentation). A unified preprocessing and evaluation framework is adopted to ensure fair comparison using standard metrics, including Intersection-over-Union (IoU), Dice coefficient, Precision, and Recall. Extensive in-domain experiments demonstrate that deep learning models significantly outperform classical approaches, with U-Net and SegFormer achieving IoU values above 0.95 on Db1 and up to 0.83 on Db3. Cross-domain zero-shot evaluations reveal a substantial generalization gap, with U-Net suffering IoU drops of up to 100\%, while SegFormer consistently exhibits improved robustness and reduced cross-domain degradation across most transfer scenarios. To our knowledge, these results establish the first systematic multi-dataset benchmark for strawberry segmentation under domain shift, highlighting the importance of transformer-based architectures for robust agricultural perception and providing practical insights for real-world robotic deployment.
Why it matches plant phenotyping methodsイチゴの病斑・果実を画像から分割する手法を複数データセットで比較・ベンチマークし、ドメインシフト下の性能を評価しているため、植物の病害状態・器官形態の取得が中心である。
abstractThis paper presents a comprehensive benchmark of classical computer vision methods, convolutional neural networks, instance-based models, and transformer-based architectures across three heterogeneous public strawberry datasets
Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
Why it matches plant phenotyping methods植物の3D再構成、背景除去、クラスタリングを統合し、草丈やキャノピー幅を自動推定するフェノタイピング手法の開発が中心である。
abstractwe propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions.
This study addresses the challenges of early recognition of fruit and vegetable diseases and pests in facility horticultural greenhouses and the difficulty of real-time deployment on edge devices, and proposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection. The method is built upon a lightweight Mobile-Transformer backbone and integrates a cross-scale lightweight attention mechanism, a small-object enhancement branch, and an alternative block distillation strategy, thereby effectively improving robustness and stability under complex illumination, high-humidity environments, and small-scale target scenarios. Systematic experimental evaluations were conducted on a greenhouse pest and disease dataset covering crops such as tomato, cucumber, strawberry, and pepper. The results demonstrate significant advantages in detection performance, with mAP@50 reaching 0.872, mAP@50:95 reaching 0.561, classification accuracy reaching 0.894, precision reaching 0.886, recall reaching 0.879, and F1-score reaching 0.882, substantially outperforming mainstream lightweight models such as YOLOv8n, YOLOv11n, MobileNetV3, and Tiny-DETR. In terms of small-object recognition capability, the model achieved an mAP-small of 0.536 and a recall-small of 0.589, markedly enhancing detection stability for micro pests such as whiteflies and thrips as well as early-stage disease lesions. In addition, real-time inference performance exceeding 20 FPS was achieved on edge platforms such as Jetson Nano, demonstrating favorable deployment adaptability.
Why it matches plant phenotyping methods植物の病変・病害状態を画像から検出する軽量モデルの開発と性能評価が中心であり、単なる生物学的実験の routine 測定ではない。害虫検出も含むが、早期病変検出という植物状態の推定を技術的に評価しているため含める。
abstractproposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection.
Early warning of overgrowth in strawberry seedlings is essential to balance vegetative and reproductive growth. However, existing monitoring methods face major challenges, including subtle visual symptoms and limited abnormal samples. To address this, we propose MM-CAPNet, a multimodal fusion framework for early detection of seedling overgrowth. We first developed a representative sample collection of strawberry seedlings through a systematic induction experiment, integrating historical environmental time-series data with contemporaneous plant images. The MM-CAPNet architecture uses a dual-stream design to process these inputs, with a Transformer encoder for environmental sequences and a MobileNetV2 encoder for images. A critical component of the proposed framework lies in the image-guided Cross-Attention mechanism, which uniquely treats the current phenotype as an active query to adaptively retrieve and aggregate the most diagnostically relevant segments of past environmental data. Experiments show MM-CAPNet outperforms baselines, reaching 87.6% accuracy and 0.901 AUC, with strong discriminative ability for early overgrowth categories. Ablation studies confirm its interpretability by linking visual phenotypes to key environmental drivers. This work provides growers with a proof-of-concept framework to regulate fertilization, irrigation, and light management during the nursery stage, thereby reducing the risk of excessive vegetative growth. The proposed framework supports precision cultivation strategies that enhance resource efficiency and crop resilience.
Why it matches plant phenotyping methods画像と環境時系列を統合し、イチゴ苗の過繁茂という植物状態を早期推定する新規マルチモーダル手法を開発・評価しており、表現型取得・判定が研究の中心である。
abstractwe propose MM-CAPNet, a multimodal fusion framework for early detection of seedling overgrowth.
Moisture plays a critical role in crop growth and development, making accurate, efficient, and non-destructive detection and monitoring of crop water stress essential for advancing crop science research and optimizing production management. Traditional non-destructive methods for monitoring water stress primarily rely on color imaging or partial 2D spectral analysis. However, these methods are limited to two-dimensional features and fail to capture the spatial variability of water stress within the three-dimensional canopy structure of crops. To address this limitation, this study integrates RGB-D cameras and thermal infrared cameras and introduces a method for calculating the 3D spatial distribution characteristics of crop water stress using RGB-D-T fusion analysis. This approach enables high-precision detection and analysis of water stress in strawberry plants. An RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments. Using the YOLOv8-seg deep learning model, semantic segmentation of the crop canopy and the wet reference surface was performed. The segmentation results were fused with 3D point cloud data to generate a 3D dataset incorporating temperature, color, and semantic information. Subsequently, the three-dimensional distribution characteristics and dynamic changes in the canopy water stress index (CWSI) of strawberry plants were analyzed under varying moisture conditions. The results demonstrated that under low moisture gradients (15%–30%), the CWSI value increased significantly and exhibited a concentrated distribution, indicating severe water stress. Conversely, under high moisture gradients (75%–90%), the CWSI value approached zero, reflecting sufficient water supply and complete stress alleviation. Additionally, the study highlighted the variation in the temperature difference between strawberry leaves and the surrounding air, confirming the sensitivity of strawberries to water stress across different reproductive stages. The response to water deficit was most pronounced during the growth phase. By fusing multi-source data, this study achieves 3D visualization and precise quantification of water stress in strawberries, providing innovative insights and technical support for precision irrigation and crop phenotyping research.
Why it matches plant phenotyping methodsRGB-D・熱赤外センサーの融合、3D点群化、深層学習セグメンテーションにより、イチゴの水ストレスを3D定量化する取得・解析手法が研究の中心である。
abstractAn RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection. This perspective paper synthesizes current advances, identifies major barriers, and proposes future directions to realize the transformative potential of AI‐enabled plant phenomics. We first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing. We then present three case studies focusing on specialty crops (blueberry [ Vaccinium corymbosum L.] mechanical harvestability traits, strawberry [ Fragaria × ananassa (Duchesne ex Weston)] production, and citrus [ Citrus L.] disease) to illustrate practical applications of AI‐driven phenomics. Moreover, we highlight future perspectives and opportunities for further research and innovation. These include large foundation models, real‐time inference on edge devices, explainable AI, generative AI and digital twins, AI‐enhanced multi‐omics, agentic AI, and knowledge‐guided and data‐driven hybrid approaches. Finally, we discuss key challenges and limitations of applying AI to plant phenomics, including data curation, model generalization and bias, and ethical considerations related to equitable access to AI tools.
Why it matches plant phenotyping methods植物フェノミクスにおけるAIセンシング・形質抽出を主題とする展望論文であり、方法論のレビューとして中心的に扱っている。
abstractWe first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing.
Traditional segmentation methods are slow and rely on manual annotations, which are labor-intensive. To address these limitations, we propose YOLO-SAM AgriScan, a unified framework that combines the fast object detection capabilities of YOLOv11 with the zero-shot segmentation power of the Segment Anything Model 2 (SAM2). Our approach adopts a hybrid paradigm for on-plant ripe strawberry segmentation, wherein YOLOv11 is fine-tuned using a few-shot learning strategy with minimal annotated samples, and SAM2 performs mask generation without additional supervision. This architecture eliminates the bottleneck of pixel-wise manual annotation and enables the scalable and efficient segmentation of strawberries in both controlled and natural farm environments. Experimental evaluations on two datasets, a custom-collected dataset and a publicly available benchmark, demonstrate strong detection and segmentation performance in both full-data and data-constrained scenarios. The proposed framework achieved a mean Dice score of 0.95 and an IoU of 0.93 on our collected dataset and maintained competitive performance on public data (Dice: 0.95, IoU: 0.92), demonstrating its robustness, generalizability, and practical relevance in real-world agricultural settings. Our results highlight the potential of combining few-shot detection and zero-shot segmentation to accelerate the development of annotation-light, intelligent phenotyping systems.
Why it matches plant phenotyping methodsイチゴ果実の検出・セグメンテーションを行う画像ベース手法を開発し、複数データセットで性能評価している。単なる収穫対象の位置特定ではなく、果実マスク抽出を行うため、植物表現型取得手法が中心である。
abstractOur approach adopts a hybrid paradigm for on-plant ripe strawberry segmentation
Reproduction assets foundThe paper uses a public Roboflow Universe strawberry segmentation dataset (D2) directly for its detection/segmentation experiments, which qualifies as a paper-specific public asset. The authors' custom greenhouse dataset (D1) is only available upon request, and no authors' analysis code is deposited (Ultralytics is a CDataset · public3007-022-00866-2.
22. Song Y., Wang T., Cai P., Mondal S.K., Sahoo J.P. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. ACM Comput. Surv. 2023;55:1–40. doi: 10.1145/3582688.
23. Objectdetection Strawberry Seg Dataset. 2024. [(accessed on 10 September 2025)]. Available online: https://universe.roboflow.com/objectdetection-mnlwg/strawberry_seg-zkh1y .
24. Sekachev B., Manovich N., Zhiltsov M., Zhavoronkov A., Kalinin D., Hoff B., TOsmanov, Kruchinin D., Zankevich A., DmitriySidnev, et al. opencv/cvat: V1.1.0. 2020. [(accessed on 13 April 2025)]. Available online: https://zenodo.org/records/4009388 .
25. Ronneberger O., Fischer P., Brox T. U-Open asset ↗strawberry_seg-zkh1ylines:316-338Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Dexfruit is a robotic manipulation framework that enables gentle, autonomous handling of fragile fruit and precise evaluation of damage. Soft fruits have long faced an issue of produce loss in both the harvesting and post-harvesting processes due to their extreme fragility and susceptibility to bruising, making them one of the hardest produce type to manipulate with automation. In this work, we demonstrate by using optical tactile sensing, autonomous manipulation of fruit with minimal damage can be achieved. We show that our tactile informed diffusion policies outperform baselines in both reduced bruising and pickand- place success rate across three fruits: strawberries, tomatoes, and blackberries. In addition, we introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS). Existing metrics for measuring damage lack quantitative rigor or require expensive equipment. With FruitSplat, we distill a 2D fruit mask as well as a 2D bruise segmentation mask into the 3DGS representation from just a web-cam video. Furthermore, this representation is modular and general, compatible with any relevant 2D model. Overall, we demonstrate a 92% grasping policy success rate, up to a 15% reduction in visual bruising, and up to a 31% improvement in grasp success rate on challenging fruit compared to our baselines across our three tested fruits. We rigorously evaluate this result with over 630 trials. Please checkout our website, which contains our code and datasets athttps://dex-fruit.github.io/.
Why it matches plant phenotyping methodsFruitSplatは、果実の損傷・打撲を3D表現として定量化する画像ベースの植物状態計測手法であり、開発と厳密な評価が研究の中心です。
abstractwe introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS).
Background Developing an effective machine vision system is crucial to successfully deploying robotic inspection in open field conditions and controlled environments like greenhouses. Robotic arms with vision-based deep learning models offer an efficient, real-time, non-invasive crop monitoring solution. In agricultural settings, they enable consistent, automated inspection under varying conditions, reduce labor dependency, and support early disease detection, enhancing productivity and sustainability in precision farming. Although considerable progress has been made in computer vision-based approaches, significant challenges persist in developing models that reliably perform under the diverse and variable conditions encountered in real-world agricultural settings. Method Within the domain of precision agriculture, we introduce an advanced robotic system for the detection of plant diseases, utilizing an innovative model based on deep learning principles. This system introduces an algorithm for real-time analysis, called as Strawberry Leaf Disease Inspection (SLDI). The algorithm integrates the use of Receptive Guided Channel Attention (RGCA) alongside a Deep Context Aggregator (DCA), designed to significantly improve the characterization and representation of feature sets, thereby enhancing the overall accuracy and efficiency of disease identification. To optimize the system performance and preserve real-time performance, a Multi-Scale Feature Fusion Module (MSFF) is proposed that facilitates a comprehensive multi-level representation, enabling the model to capture disease symptoms promptly. The SLDI algorithm is deployed on a robotic platform equipped with an RGB camera, enabling real-time, in-field inspection of strawberry crops. Results The proposed system is trained on two publicly available datasets, PlantDoc and PlantVillage. It attains a precision of 91.10% and a recall of 88.50%, while maintaining a real-time processing speed of 76.50 frames per second (fps). Experimental field inspection of strawberry studies demonstrates that the proposed model significantly outperforms existing approaches in accuracy and efficiency.
Why it matches plant phenotyping methodsイチゴ葉の病徴をRGB画像と深層学習でリアルタイム検出するアルゴリズムおよびロボットプラットフォームを開発・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 introduce an advanced robotic system for the detection of plant diseases, utilizing an innovative model based on deep learning principles.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe publicly avail- able datasets can be accessed at [ www.plantvillage.org ] and [ https://github.com/pratikkayal/PlantDoc-Dataset ].Open asset ↗pratikkayal/PlantDoc-Datasetlines:271-381Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Freeze damage presents a critical threat to agricultural productivity, resulting in substantial economic losses, especially in sensitive crops such as strawberries. Traditional methods for assessing freeze damage, including manual inspection, are time-consuming, subjective, and labor-intensive. In this study, a deep learning (DL) and computer vision-based approach was proposed to automate freeze damage classification in strawberry plants using RGB images. The performance of four convolutional neural network (CNN) architectures was evaluated: DenseNet-121, Inception V3, ResNet-50, and Xception. Two training methods are compared: transfer learning (TL) using pretrained ImageNet weights and training models from scratch. The models are assessed based on classification accuracy, precision, recall, F1-score, and inference time. The results indicate that models trained from scratch outperform TL models, achieving up to 97% accuracy with ResNet-50, whereas TL models attained a maximum accuracy of 84%. The ResNet-50 model also achieved the fastest inference time (3.0 s) while DenseNet-121 was the smallest (26. 86 MB). Furthermore, the models were most effective at identifying severely damaged plants but struggled to differentiate mild damage from minimal or no damage. The findings suggest that scratch-trained models deliver more accurate solutions for freeze damage classification in strawberry plants. Additionally, DenseNet-121 was the best choice for memory-limited applications, while ResNet-50 excelled in speed-sensitive tasks. This study underscores the potential of deep learning and computer vision to automate freeze damage assessment in strawberry plants, providing a more accurate, rapid, and nondestructive alternative to traditional methods.
Why it matches plant phenotyping methodsRGB画像と深層学習によりイチゴ植物の凍害状態を分類する手法が研究の中心であり、植物状態の非破壊的評価を技術的に比較・検証しているため。
abstracta deep learning (DL) and computer vision-based approach was proposed to automate freeze damage classification in strawberry plants using RGB images.
During the 3D reconstruction of strawberry plants, methods based on 3D Gaussian Splatting (3DGS) face significant challenges due to motion-induced image blur. Such blurring substantially reduces the feature matching accuracy in Structure from Motion (SfM) algorithms and compromises the reliability of camera pose estimation, thereby degrading the quality of subsequent 3DGS reconstruction. This ultimately manifests as geometric distortion and loss of texture details in the reconstructed models. The issue is particularly severe on the surface of strawberry fruits: under blurred image conditions, point cloud registration fails, resulting in the loss of high-frequency details in the high-density achene regions, which blurs seed contours and degrades reconstruction accuracy. To address this technical bottleneck, this study proposes an optimized reconstruction scheme integrated with 3DGS. By incorporating the Canny edge detection algorithm to filter h i gh-quality i n put i m ages, t h e a c curacy of the reconstructed model is significantly improved. The optimized approach achieves remarkable results on the strawberry plant dataset: the average Peak Signal-To-Noise Ratio (PSNR) of the 3DGS model reaches 35.99, representing a 15.2% improvement over the baseline 3DGS. The morphology of high-density achenes on the fruit surface is clearly distinguishable, supporting the accurate monitoring of phenotypic parameters in strawberry plants.
Why it matches plant phenotyping methodsイチゴ植物の3D再構成精度を向上させる画像処理・3DGS手法を開発し、果実表面形態などの表現型パラメータ監視に直接利用するため、方法開発が中心である。
abstractTo address this technical bottleneck, this study proposes an optimized reconstruction scheme integrated with 3DGS.
Introduction The traditional strawberry picking operation has long relied on manual work. With the aging trend of the population becoming more and more obvious, the application of intelligent picking technology has become an irreversible trend. However, existing recognition methods still face bottlenecks such as suboptimal recognition accuracy and low computational efficiency. To address these issues, this study constructs a lightweight detection model, LBS-YOLO, based on an improved YOLOv11n architecture, significantly the model's accuracy and interference robustness while greatly compressing the parameter quantity. Methods The LBS-YOLO model is built upon YOLOv11n as the baseline network. In order to enhance the ability of backbone network feature representation, the model designs a lightweight LAWDS module. This design combines channel attention with spatial reconstruction operation to optimize the information retention efficiency in the down-sampling process, thus effectively enhancing the multi-scale feature representation ability and gradient flow propagation performance. Then in the feature fusion stage, the model introduces a Bidirectional Feature Pyramid Network (BiFPN), which not only enables cross-scale feature fusion but also achieves adaptive weighting through a learnable weight allocation mechanism. At last, adopts the C3k2_Star module to replace the conventional C3K2 for improved feature representation. Results On the used strawberry dataset, the LBS-YOLO model reached 88.6% mAP@0.5 and 75.8% mAP@0.5:0.95, which were 2.2 and 1.3 percentage points higher than YOLOv11n, respectively. The LBS-YOLO model improves the recall rate from 83.2% of YOLOv11n to 86.4%, and the F1-score from 81.2% to 82.9%. Its computational complexity is 6.6 GFLOPs and its reasoning speed is 260.7 FPS. Even better, LBS-YOLO only needs 3.4MB of storage space and 1.6 million parameters, which are 34.6% and 38% less than YOLOv11n respectively. Discussion The experiment demonstrates that, the LBS-YOLO model can significantly reduce the number of parameters and effectively improve the detection accuracy and operation efficiency. It successfully alleviated the problems of false detection and missed detection, thereby providing reliable technical support for strawberry growth monitoring, maturity identification and automatic picking.
Why it matches plant phenotyping methodsイチゴの成熟度という植物状態を画像から推定する軽量検出モデルを開発・評価しており、フェノタイピング手法が中心である。
abstractthis study constructs a lightweight detection model, LBS-YOLO, based on an improved YOLOv11n architecture
Reproduction assets foundThe paper uses a public strawberry image dataset from Baidu AI Studio (Paddle) as its phenotyping input, with an explicit public URL provided in the article text. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe dataset used in this study is a publicly available dataset from Baidu Paddle. Detailed dataset information can be found at: https://aistudio.baidu.com/aistudio/datasetdetail/147119 . A total of 3,000 strawberry images are included here.Open asset ↗Baidu Paddle · 147119lines:317-334Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Phenomic selection is a breeding approach that incorporates phenomic data into statistical models to predict new genotypes. There is still much to learn about the efficacy of phenomic selection compared to genomic selection and best practices for its application in different crops. We utilized multispectral imaging of 1122 strawberry (Fragaria × ananassa Duchesne) clones across four consecutive seasons to compare genomic selection and phenomic selection within and across seasons. Phenomic selection within seasons was more predictive than genomic selection for fruit yield but was less predictive than genomic selection for fruit quality traits. Phenomic models incorporating vegetation indices (VI) were 16% more effective than models with independent spectral bands. Models combining both phenomic and genomic data were most effective for across-season prediction of yield-related traits, with average predictive abilities of 56% for fruit size and 57% for yield. Models with single timepoints were 91% as predictive as models with weekly data across the season, but this was largely influenced by the specific timepoint of data capture. Lastly, we show that the predictive ability of phenomic selection increased significantly with the number of clonal replicates in the training set. Overall, these results suggest that phenomic selection is highly effective in strawberry breeding but is dependent on the trait, timepoint of data capture, and level of clonal replication.
Why it matches plant phenotyping methodsイチゴのマルチスペクトル画像から得たフェノミックデータを用い、ゲノム選抜との予測性能を比較・検証しており、植物形質推定法の技術評価が中心である。
abstractWe utilized multispectral imaging of 1122 strawberry (Fragaria × ananassa Duchesne) clones across four consecutive seasons to compare genomic selection and phenomic selection within and across seasons.
Introduction Real-time diagnosis of strawberry diseases plays a key role in sustaining yield and improving field management. However, achieving reliable recognition remains challenging. Lesions often display irregular shapes and appear at different scales, which complicates detection. Field images also contain cluttered backgrounds, while many diseases look visually alike, making differentiation more difficult. In addition, collecting data under real conditions is not easy, resulting in small datasets on which deep learning models tend to overfit and fail to generalize. Methods To address these issues, this study introduces ENet-CAEM, a redesigned EfficientNetB0 framework equipped with modules tailored for disease recognition. The Channel Context Module helps the network capture key lesion features while suppressing background noise. The Multi-Scale Efficient Channel Attention module applies multiple one-dimensional filters of varying sizes in parallel, enabling the model to highlight critical patterns, tell apart similar diseases, and adapt to lesions of different scales. A lightweight version of Atrous Spatial Pyramid Pooling is further integrated, allowing the network to perceive features at multiple spatial ranges. To balance local detail with global context, a mixed pooling strategy is adopted, enhancing robustness when lesion shapes change. Finally, Learnable DropPath and label smoothing are applied as regularization strategies, reducing overfitting and improving generalization on limited data. Results Experiments show that ENet-CAEM achieves 85.84% accuracy on a self-built dataset, outperforming the baseline by 4.29%. On a public strawberry dataset, the model reaches 97.39%, surpassing existing approaches. Discussion The proposed ENet-CAEM model shows superior accuracy and robustness over existing methods, providing an effective solution for strawberry disease recognition in practical field environments.
Why it matches plant phenotyping methodsイチゴ葉の病斑・病害状態を画像から認識する深層学習手法を開発し、複数データセットで性能評価しているため、植物病害表現型の取得・推定が中心である。
abstractthis study introduces ENet-CAEM, a redesigned EfficientNetB0 framework equipped with modules tailored for disease recognition.
ABSTRACT Background Methods: to predict the heritable component of phenotypes from genetic markers, collectively known as genomic prediction, have been widely applied in plant and animal breeding and in human genetics. Currently, genomic prediction is limited to numeric phenotypes. In some cases, though, plant and animal phenotypes are better understood through images and text rather than numbers. The current best practice for incorporating images and text in genomic prediction is to first extract scalar numeric phenotypes from images and text, and then to perform genomic prediction on the numeric phenotypes. While this approach is effective for some traits, it involves discarding most of the information in the image or text, including potentially useful information. Additionally, numeric phenotypes derived from images and text may not be as interpretable as the images and text themselves. Approach We present a novel approach for predicting images and text from SNP markers, which we refer to as end-to-end genomic prediction, and validate this approach using genotypes, text, and image phenotypes derived from a strawberry ( Fragaria × ananassa ) diversity panel. Our approach combines nonlinear latent space encoding with linear genomic prediction to generate accurate breeding values for a high-dimensional phenotype, for example, images or text. Results For both genome-to-image and genome-to-text prediction, we found that predicting images and text and then extracting numeric traits from them was in some cases as accurate as directly predicting extracted numeric phenotypes, demonstrating for the first time that genome-to-image prediction accuracy can be comparable to conventional genomic prediction accuracy. Conclusions Based on our proof-of-concept using the same core end-to-end method in both images and text, we believe end-to-end genomic prediction could be of use in a wide range of visual and multidimensional phenotypes in plants and animals, although further work to improve the accuracy of the embedding and genomic prediction steps is needed.
Why it matches plant phenotyping methods植物画像を対象に、ゲノムマーカーから画像を直接予測し、画像から数値形質を抽出する新規計算手法を提示・検証しており、表現型取得・推定法が研究の中心である。
abstractWe present a novel approach for predicting images and text from SNP markers, which we refer to as end-to-end genomic prediction, and validate this approach using genotypes, text, and image phenotypes derived from a strawberry ( Fragaria × ananassa ) diversity panel.
Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
Why it matches plant phenotyping methodsイチゴ植物の3D再構成、背景除去、形質推定を一体化した新規フェノタイピング手法の開発・評価が中心であり、植物高と樹冠幅を自動推定して精度・効率を比較している。
abstractwe propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions.
Strawberry production in Indonesia has high economic value but is often hindered by plant diseases that reduce yield quality and quantity. Manual disease identification requires time, cost, and expertise, making it inefficient for farmers. This study proposes a web-based strawberry disease detection system by applying a Convolutional Neural Network (CNN) model using the EfficientNet-B0 architecture. The dataset consists of leaf, fruit, and flower images of strawberries in both healthy and infected conditions. The research followed the CRISP-DM framework, including business understanding, data preparation, modeling, evaluation, and deployment. The model was trained using transfer learning and fine-tuning techniques, with evaluation conducted through a confusion matrix and K-Fold Cross Validation. Experimental results indicate that the EfficientNet-B0 model achieved an overall accuracy of approximately 95.2% and demonstrated stable performance in classifying various strawberry plant diseases. The model achieved perfect accuracy (100%) in several classes such as Healthy Leaf, Leaf Spot, and Healthy Flower, while maintaining high accuracy in other classes like Fruit (95.2%) and Anthracnose Fruit Rot (94.7%), confirming its effectiveness in capturing essential visual features for accurate disease classification. The deployment of the model into a website using the Streamlit framework enables users to upload strawberry images and obtain automatic, fast, and accurate disease detection results. This system is expected to provide a practical solution to help farmers improve productivity and minimize losses caused by plant diseases.
Why it matches plant phenotyping methodsイチゴの葉・果実・花の画像から健全／感染状態や病害を推定するCNN手法を開発・評価し、Webシステムとして実装しており、植物状態の画像ベース表現型推定が中心である。
abstractThis study proposes a web-based strawberry disease detection system by applying a Convolutional Neural Network (CNN) model using the EfficientNet-B0 architecture.
Background To address the challenge of real-time plant monitoring in greenhouse environments, this industry-driven research focuses on developing an autonomous quadrotor UAV system specifically designed for monitoring strawberry plants. Traditional methods for greenhouse monitoring are labor-intensive and lack scalability, particularly in precision agriculture applications. Method This research introduces a mature strawberry detection model specifically designed for greenhouse environments. The proposed YOLOv9-GLEAN approach enables the identification of small mature strawberries through an onboard camera mounted on the quadrotor. Additionally, a hybrid trajectory tracking controller for the quadrotor is developed and tested in both simulated and real-world conditions. The UAV navigates through the greenhouse using predetermined waypoints, operating as a semi-autonomous system for navigation while maintaining full autonomy in mature strawberry detection tasks. The system incorporates an integrated onboard vision platform that utilizes an innovative YOLOv9-GLEAN-based algorithm to perform real-time and offline detection and counting of mature strawberries. Results The YOLOv9-GLEAN model achieves high detection accuracy, as confirmed by evaluation metrics such as precision, recall, and F1-score. The proposed hybrid (PID+LQR) controller demonstrates superior tracking performance compared to other conventional controllers. The integrated control and perception system proves effective in both simulated and real-world greenhouse environments. Discussion The research validates the efficacy of deep learning models, with YOLOv9-GLEAN showing exceptional performance in enabling rapid, precise, and automated detection of ripe strawberries through quadrotor deployment in greenhouse environments. Such agricultural monitoring technologies represent a substantial advancement beyond conventional manual inspection approaches, empowering farmers and greenhouse operators to execute well-informed, time-sensitive management decisions that minimize crop losses and optimize production yields. This investigation underscores the revolutionary impact that deep learning technologies can have within greenhouse agriculture.
Why it matches plant phenotyping methodsイチゴ果実の成熟状態を画像から検出・計数する深層学習モデルとUAV搭載視覚プラットフォームの開発・評価が研究の中心であり、植物器官の状態を直接推定している。
abstractThis research introduces a mature strawberry detection model specifically designed for greenhouse environments.
Hyperspectral imaging systems that operate in the visible-near infrared (VIS-NIR) and short-wave infrared (SWIR) spectral regions are increasingly recognized as practical and effective tools for enhancing crop management. However, hyperspectral systems can have some limitations when focusing on specific spectral ranges, particularly for spatial and spectral resolution. Image fusion techniques combining information from different sensors to enhance hyperspectral data can significantly improve spatial and spectral resolution. Fusion data of image and spectral data from the two HSI cameras (VIS-NIRandSWIR)provide complementary information on plant physiology, biochemistry, and morphology before visible plant stress symptoms. This study presents advancements in hyperspectral image fusion achieved by using two line-scan sensors, one for VIS-NIR (397–1003 nm) and the other for SWIR (894–2504 nm), to detect asymptomatic drought stress in strawberry plants. The images from both hyperspectral imaging systems were aligned based on feature and intensity, combined with various geometric transformations for fusion. The resulting fused hyperspectral cube contained 403 bands covering a broad spectrum from 397 to 2500 nm. Given the vulnerability of strawberry plants to drought, which can significantly affect growth and yield, this study aimed to explore the potential of hyperspectral image fusion for high-throughput detection of drought-stressed strawberry plants. The fused images improved the performance of the PLS-DA detection model, increasing classification accuracy by up to 10 %, achieving 99 % accuracy in the prediction set, and reducing error rates compared to independently generated models.
Why it matches plant phenotyping methodsVIS-NIRとSWIRのハイパースペクトル画像融合を開発・評価し、イチゴの無症候性乾燥ストレスという植物状態を高スループットに検出する手法が研究の中心であるため。
abstractThis study presents advancements in hyperspectral image fusion achieved by using two line-scan sensors, one for VIS-NIR (397–1003 nm) and the other for SWIR (894–2504 nm), to detect asymptomatic drought stress in strawberry plants.
Yield forecasting is crucial for growers, enabling efficient resource management and informed decision-making. Such decisions impact storage, product processing, and logistics, leading to increased productivity and cost savings. However, this heavily relies on accurate yield forecasts. This work addresses such a need by presenting the development and testing of a reliable method for yield forecasting. The proposed methodology combines high-resolution object detection with a multi-variate input forecasting model that accurately computes the yield for incoming harvests. The forecasting approach incorporates a physically-constrained model based on a Long Short-Term Memory (LSTM) network. This model dynamically applies weights to the time-series data composed of counts for the phenological stages: flower, green, small white, large white, pink, and red (ripe fruit). These counts are obtained from detections made by a YOLOv10s, achieving an mAP@50 of 0.74 for all classes. As a result, the forecasting model's capacity to interpret input data is enhanced, translating it into a valid ripe count forecast. To validate the proposed approach, the forecasting model was trained and evaluated using (a) untreated count sequences and (b) weighted count sequences. The results indicate that phenologically-weighted input sequences outperform untreated sequences, with the following evaluation metrics: R² = 0.74, Root Mean Square Error (RMSE) = 12.67, Mean Absolute Error (MAE) = 10.95, and Mean Absolute Percentage Error (MAPE) = 39.4, improving 15%, 19.26%, 17.13%, and 11.3%, respectively.
Why it matches plant phenotyping methods画像ベースのYOLO検出でイチゴの生育段階・果実数を抽出し、収量予測へ利用する方法を開発・検証しており、植物表現型の取得と解析が中心的な貢献である。
abstractThis work addresses such a need by presenting the development and testing of a reliable method for yield forecasting.
Strawberry (Fragaria x ananassa) cultivation plays an important role in local economies, especially in agrotourism regions like Bandung, Indonesia. Traditional disease identification methods, which rely on manual visual inspection by experts, are time-consuming, inconsistent, and infeasible for large-scale applications due to their subjective nature. To address these challenges, this study investigates the use of machine learning and computer vision models to automate and improve the accuracy of strawberry disease classification. We conducted a comparative analysis of nine machine learning models: DenseNet121, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50V2, VGG16, VGG19, YOLOv8n, and YOLOv11n. The dataset used in this study consists of 877 labeled images representing healthy and infected strawberry plants, collected from online sources (Kaggle, Roboflow) and field images. The data was preprocessed and split into training (70%), validation (20%), and testing (10%) subsets. Among all models, YOLOv8n and YOLOv11n achieved the highest classification accuracy of 94%, while also demonstrating fast inference speeds and relatively small model sizes. These results highlight their potential suitability for real-time disease detection in agricultural settings. The outcomes of this study aim to support the development of accessible, automated tools for strawberry disease diagnosis, particularly benefiting local farmers in agrotourism-based regions like Bandung.
Why it matches plant phenotyping methodsイチゴ植物の病害状態を画像から分類・推定する深層学習手法が研究の中心であり、植物病害表現型の取得・自動化に該当する。
abstractthis study investigates the use of machine learning and computer vision models to automate and improve the accuracy of strawberry disease classification.
StrawberryField / plotLiDAR / point cloudFlowerObject detectionPose / keypoint estimation
The small scale of urban farms and the commercial availability of low-cost robots (such as the FarmBot) that automate simple tending tasks enable an accessible platform for plant phenotyping. We have used a FarmBot with a custom camera end-effector to estimate strawberry plant flower pose (for robotic pollination) from acquired 3D point cloud models. We describe a novel algorithm that translates individual occupancy grids along orthogonal axes of a point cloud to obtain 2D images corresponding to the six viewpoints. For each image, 2D object detection models for flowers are used to identify 2D bounding boxes which can be converted into the 3D space to extract flower point clouds. Pose estimation is performed by fitting three shapes (superellipsoids, paraboloids and planes) to the flower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. All code will be made available at https://github.com/harshmuriki/flowerPose.git.
Why it matches plant phenotyping methodsカスタムカメラ付きロボットによる3D花姿勢推定アルゴリズムとプラットフォームを開発・評価しており、花の姿勢という植物形質の取得が中心である。
abstractenable an accessible platform for plant phenotyping
Reproduction assets foundThe paper's flower pose estimation pipeline (translating occupancy grid, 2D/3D conversion, shape fitting) has an explicit authors' code deposit statement with a public GitHub URL, phrased as future availability ('will be made available'), so actionability is likely but not fully confirmed. No public dataset of the FarmCode · publiclower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. All code will be made available at https://github.com/harshmuriki/flowerPose.git .
I Introduction
Urban farms [ 1 ] provide healthy food to local communities and can serve as platforms for education and sustainability. Unlike their rural counterparts, urban farms are usually small in scale and commercially available robotic systems such as the FarmBot [ 2 ] have been developed to help automate basic cuOpen asset ↗harshmuriki/flowerPoselines:1-53Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
Traditional methods of early diagnosis of diseases, such as pure culture method, microscopic, mycological, polymerase chain reaction, enzyme immunoassay are invasive and require highly qualified personnel, expensive equipment and are not suitable for their effective use in practice. Since plant health is a fundamental indicator in assessing the phenotype of a crop plant, modern non-invasive methods for early diagnosis and determination of plant phenotype are considered. The purpose of the research is to select a rational method for early diagnosis of plant diseases and determination of their phenotype directly in the field of cultivated crops. Advantages and disadvantages of the vision method based on the analysis of the changes in color parameters of RGB images of plant leaves; fluorescence analysis, in which the efficiency of photosynthesis is estimated; multispectral and hyperspectral imaging methods carried out by determining the limited or continuous spectrum reflected from the surface of plant leaves; thermal imaging method in which the distribution of infrared radiation emitted by the plant is recorded. The analysis of the methods showed that the determination of thermal energy dissipation is a promising potential indicator of health and the presence of disease. In addition, when exposed to most environmental factors, the thermal properties of plant organs, such as leaf, stem, root, and reproductive organs, change. The reason for the limited use of thermometry in the early diagnosis of plant diseases is explained: false rejection by researchers of the fact that it is a highly organized complex of terrestrial and underground organisms. The requirements for devices for obtaining and processing thermal images are formulated and justified. An experimental setup based on the TE-Q1 thermal imaging camera, capable of working with Android devices, has been developed. Its operation has been tested on garden strawberry samples.
Why it matches plant phenotyping methods植物病害の表現型を非侵襲的に取得する画像・熱画像手法を比較検討し、熱画像取得装置を開発・検証しており、フェノタイピング手法が中心である。
abstractmodern non-invasive methods for early diagnosis and determination of plant phenotype are considered
Yield mapping in agricultural crops remains a significant challenge, particularly in uncontrolled environments. This study evaluates four instance segmentation algorithms: YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l, along with a low-cost GNSS RTK system to detect and count strawberries in a hydroponic environment. A depth camera is used to remove background information from nonrelevant furrows, improving fruit detection accuracy. The low-cost RTK receiver, configured in Base Rover mode, provides centimeter-level precision and enables the generation of detailed yield maps that can be seamlessly integrated into commercial systems to increase growers' yield and profit. Data were collected in the municipality of Arcabuco, Boyacá (Colombia), resulting in 8848 images processed after augmentation. Among the models evaluated, YOLOv8l achieved the highest performance with a maximum F1-Score of 0.9295 and a mAP50 of 0.9689 during validation. Furthermore, in the fruit counting process - evaluated against manual counts - the same model achieved a R 2 of 0.9997 and a mean relative error (MRE) of 1.5511%. In general, this work presents a systematic methodology for the extraction and visualization of information in fruit crops using computer vision and deep learning, showcasing a robust yield mapping system. The approach integrates pre-processing and post-processing steps, as well as 2D–3D image acquisition, georeferencing, and processing technologies, offering thus a novel solution for accurate and efficient hydroponic strawberry yield mapping. • Evaluation of instance segmentation to detect and count hydroponic strawberries. • 3D camera integration to remove background noise and improve fruit detection. • Use of GNSS RTK with centimeter accuracy for fruit localization in yield maps. • Real-world yield mapping system showing exact strawberry count and location. • Pre/postprocessing and georeferencing for a yield mapping approach in strawberries.
Why it matches plant phenotyping methodsイチゴ果実の検出・計数という植物器官の収量形質を、RGB-D画像、深層学習、GNSS RTKで取得・抽出・地図化する方法が研究の中心である。
abstractThis study evaluates four instance segmentation algorithms: YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l, along with a low-cost GNSS RTK system to detect and count strawberries in a hydroponic environment.
StrawberryTomatoNeRF / 3D Gaussian SplattingFruit2D/3D reconstructionSegmentation
DexFruit is a robotic manipulation framework that enables gentle, autonomous handling of fragile fruit and precise evaluation of damage. Many fruits are fragile and prone to bruising, thus requiring humans to manually harvest them with care. In this work, we demonstrate by using optical tactile sensing, autonomous manipulation of fruit with minimal damage can be achieved. We show that our tactile informed diffusion policies outperform baselines in both reduced bruising and pick-and-place success rate across three fruits: strawberries, tomatoes, and blackberries. In addition, we introduce FruitSplat, a novel technique to represent and quantify visual damage in high-resolution 3D representation via 3D Gaussian Splatting (3DGS). Existing metrics for measuring damage lack quantitative rigor or require expensive equipment. With FruitSplat, we distill a 2D strawberry mask as well as a 2D bruise segmentation mask into the 3DGS representation. Furthermore, this representation is modular and general, compatible with any relevant 2D model. Overall, we demonstrate a 92% grasping policy success rate, up to a 20% reduction in visual bruising, and up to an 31% improvement in grasp success rate on challenging fruit compared to our baselines across our three tested fruits. We rigorously evaluate this result with over 630 trials. Please checkout our website at https://dex-fruit.github.io .
Why it matches plant phenotyping methodsFruitSplatは果実の外観損傷・打撲を3D表現として定量化する画像ベースの植物表現型手法であり、手法開発と大規模な技術評価が中心である。
abstractwe introduce FruitSplat, a novel technique to represent and quantify visual damage in high-resolution 3D representation via 3D Gaussian Splatting (3DGS).
This study presents an end-to-end workflow Segment Any Strawberry Plant (SASP) for estimating strawberry canopy volume from multi-view images. The approach utilizes several recent advances in computer vision and 3D reconstruction. First, a Planar-based Gaussian Splatting Reconstruction (PGSR) method is employed to generate high-fidelity 3D point clouds of strawberry plants, offering improved geometric consistency compared to standard 3D Gaussian Splatting. Next, the Segment Any 3D Gaussians (SAGA) framework is adapted with fully automated prompts derived from YOLO (You Only Look Once) detection and color-based prompt selection which can eliminate the need for manual user input in the segmentation process. The resulting point clouds of plant canopies are calculated via concave hull to estimate their volumes. A reference box of known volume is included in the scene as a calibration object, mapping computed volumes from the virtual 3D space into real-world measurements. Experimental evaluations show that the proposed method achieves high segmentation quality and offers volume estimates across multiple plant shapes. This end-to-end pipeline addresses both the labor-intensive nature of manual canopy measurements and the computational complexity of large-scale 3D reconstructions, offering a potential for high-throughput phenotyping and yield prediction in future strawberry cultivation studies.
Why it matches plant phenotyping methodsイチゴ植物の3D画像から樹冠体積を推定するエンドツーエンド手法を開発・評価しており、植物形態形質の取得が研究の中心である。
abstractThis study presents an end-to-end workflow Segment Any Strawberry Plant (SASP) for estimating strawberry canopy volume from multi-view images.
Plants biosynthesize a wide range of antioxidants capable of attenuating ROS-induced oxidative damage. There exist several in vitro methods to analyze antioxidants and total antioxidant capacity from different tissues and of various plant species. We have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods for determination of the level of the key antioxidants phenolics, anthocyanins and flavonoids in combination with the determination of total antioxidant capacity using ferric reducing antioxidant power (FRAP) and trolox equivalent antioxidant capacity (TEAC). The method was optimized and verified with samples from different strawberry species and cultivars with known differences in the parameters measured. This method proved to be suitable for analyses of eight model and crop plants, and distinct antioxidant signatures were determined for the different tissues and organs analyzed, including leaf, root, fruit, spike, and tuber samples. The method was robust and was shown in two case studies to be a resource-efficient and fast experimental platform also to assess biotic and abiotic stress responses, notably including fungal infection and the impact of a progressive drought regime. Since method was adapted for a semi-high throughput 96-well assay format it is well-suited for integration of cell physiological phenotyping into a holistic phenomics approach for germplasm assessment and plant breeding screening. This analytical platform uses microplate spectrophotometer which proved to be suitable to determine the antioxidant contents and total antioxidant capacity signatures of various plant species and tissues with similar findings as reported in literature.
Why it matches plant phenotyping methods植物組織の抗酸化物質と抗酸化能を測定する、最適化・検証済みの半ハイスループット分析法および表現型解析プラットフォームが研究の中心である。
abstractWe have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods
StrawberryStereoFlowerFruitObject detectionGrowth / development / phenology
Introduction To enhance the quality and yield of strawberries, it is essential to effectively supervise the entire growing process. Currently, the monitoring of strawberry growth primarily relies on manual identification and positioning methods. This approach presents several challenges, including low efficiency, high labor intensity, time consumption, elevated costs, and a lack of standardized monitoring protocols. On the basis of this, there was an urgent need in the market to automate the whole process of target recognition and localization in strawberry growing. Methods Aiming at the above problems, we innovatively constructed a model for target recognition and localization of strawberries based on the YOLOv8s benchmark model, named the WCS-YOLOv8s model. In this paper, the whole growth process of the strawberry was divided into four stages, namely, the bud, flower, fruit under-ripening, and fruit ripening stages, and a total of 1,957 images of these four stages were captured with a binocular depth camera. Using the constructed WCS-YOLOv8s model to process the images, the target recognition and localization of the whole growth process of the strawberry were accomplished. This model proposes a data enhancement strategy based on the Warmup learning rate to stabilize the initial training process. The self- developed SE-MSDWA module is integrated into the backbone network to improve the model's feature extraction capability while suppressing redundant information, thereby achieving efficient feature extraction. Additionally, the neck network is enhanced by incorporating the CGFM module, which employs a multi-head self-attention mechanism to fuse diverse feature information and improve the network's feature fusion performance. Results and discussion The model's Precision (P), Recall (R), HYPERLINK "mailto:mAP@0.5" mAP@0.5, and mAP@0.5:0.95 of detection were 83.4%, 86.7%, 87.53%, and 60.48%, respectively, and the detection speed was 45.9 FPS(21.8 ms/per image, which significantly improved on the detection accuracy and generalization ability of with the YOLOv8s benchmark model. This model can meet the demand for online real-time target identification and localization of strawberries and provide a new detection method for the automated monitoring and management of the whole growth process of strawberries.
Why it matches plant phenotyping methodsイチゴの生育段階（芽、花、未熟果、成熟果）を画像から認識・定位するYOLOモデルを開発し、検出性能も評価しており、植物状態の取得手法が研究の中心である。
abstractwe innovatively constructed a model for target recognition and localization of strawberries based on the YOLOv8s benchmark model, named the WCS-YOLOv8s model.
StrawberryField / plotFruitClassificationGrowth / development / phenology
Abstract Accurate strawberry ripeness detection plays a vital role in quality assurance and market competitiveness enhancement within agricultural production. This study proposes an enhanced YOLOv8n-ESG model for efficient in-field strawberry maturity classification. The methodology categorizes strawberry growth stages into two phases (unripe vs. ripe), addressing quality deterioration risks from improper harvesting timing. Our technical improvements to the baseline YOLOv8n architecture include: 1) backbone network convolution layer optimization, 2) C2f module refinement, 3) attention mechanism integration, 4) loss function modification, and 5) data augmentation implementation. Experimental results demonstrate the model achieves 89.8% precision, 90.5% recall, and 94.8% mAP50 in complex scenarios, effectively mitigating misdiagnosis and missed detection issues. The proposed system enables growers to optimize harvesting schedules through precise ripeness identification, contributing to intelligent agricultural technology development. These advancements in visual recognition systems offer practical solutions for improving postharvest quality control and strengthening market position in perishable fruit supply chains.
Why it matches plant phenotyping methodsイチゴ果実の成熟段階という植物器官の状態を画像認識で推定する手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis study proposes an enhanced YOLOv8n-ESG model for efficient in-field strawberry maturity classification.
Strawberries have high consumer demand due to their palatability and nutritional benefits. Commercial strawberry production in plant factories with artificial lighting (PFALs) is gaining popularity as a viable strategy for improving economic viability through high-quality fruit production. Accurate information of the optimal harvest date is crucial for optimizing harvesting decisions. While numerous studies utilize deep learning to assess strawberry ripeness, they typically only categorize generalized ripeness levels instead of predicting specific harvest dates, leaving a gap with the practical needs of growers. In this study, we proposed a two-stage multi-feature fusion model for strawberry harvest date prediction and integrated it with a web application to facilitate practical production management in PFALs. The model consists of a fruit segmentation network and a ripeness prediction network. A time-series image dataset of single fruits was constructed to continuously track the ripening process of strawberries, and a five-stage division of strawberry ripeness stages depending on optimal harvest dates was defined. A U-Net based segmentation network with post-processing was developed to automatically extract only the target fruits, which showed a reliable performance with a mIoU of 0.977. A multi-feature fusion network called Triple-Branch Attention Fusion (TBAF) was built to predict the ripeness categories with information on optimal harvest dates. The results showed that the TBAF model with fusion of color, attention-enhanced, and low-level shape features exhibited the highest performance compared to baseline models, with an overall accuracy of 0.859 and an F1 score of 0.859. In addition, a user-friendly web application was developed with the deployment of deep learning models and inspection video processing workflow to support strawberry harvesting in PFALs. Overall, this study demonstrated a prototype approach utilizing deep learning to provide essential information for grower’s decision making in practical strawberry production.
Why it matches plant phenotyping methodsイチゴ果実の画像から成熟段階・最適収穫日を推定するセグメンテーションおよび深層学習ワークフローを開発しており、植物状態の取得・推定手法が中心である。
abstractA U-Net based segmentation network with post-processing was developed to automatically extract only the target fruits
In order to successfully deploy robotic harvesting in open field conditions, the development of an effective machine vision system becomes crucial. In this research, we proposed a novel two-step deep learning model consisting of a modified YOLOv8s and a YOLOv5s-cls to accomplish strawberry detection and pickability classification (whether a mature fruit is pickable by a robot). Firstly, the YOLOv8s was enhanced by incorporating C3x modules and an additional head network structure, specifically tailored for accurate strawberry detection. To further improve training performance, the α-IOU (intersection over union) technique was integrated. Subsequently, the YOLOv5s-cls was utilized to determine suitability of the detected mature strawberries. Through evaluations, Model D (+C3x+head+αIoU), which was a model based on modifying YOLOv8 using the new modules and techniques mentioned above, was found to perform the best among the tested models achieving the highest AP scores of 84.2% in Stage I (immature), 77.8% in Stage II (nearly mature), and 87.8% in Stage III (mature), along with the highest mAP of 83.2%. Overall, this modified model achieved a 2.5% improvement in mAP compared to the same achieved by original YOLOv8s model. Despite a slightly slower inference speed of 8.4 ms per image, Model D maintains real-time capabilities, making it an optimal choice for strawberry detection. Additionally, YOLOv5s-cls was identified as the preferred model for classifying mature strawberries into pickable and unpickable groups, offering a good inference speed of 2.8 ms per image and comparable accuracy with other compared models including YOLOv8s-cls, ResNet 18, EfficientNet-b0, and EfficientNet-b1. Finally, the combined two-step model developed in this study was evaluated in 10 different field scenarios from a completely different strawberry field that was not used in model training and initial testing. In this validation test the machine vision system achieved an AP of 89.0%, 82.0%, and 90.0% in detecting strawberries from Stage I, II, and III while the classification accuracy was 100.0% in unpickable group and 95.0% in pickable group. The results showed that the developed two-step machine vision system has a potential to improve the overall robotic harvesting system for strawberries grown in open-field conditions.
Why it matches plant phenotyping methodsイチゴ果実の成熟段階と収穫可能性という植物器官の状態を推定する機械視覚法を開発し、別圃場で検証しており、単なる収穫対象の位置検出を超えた中心的なフェノタイピング手法である。
abstractwe proposed a novel two-step deep learning model consisting of a modified YOLOv8s and a YOLOv5s-cls to accomplish strawberry detection and pickability classification (whether a mature fruit is pickable by a robot).
StrawberryField / plotFruitObject detectionGrowth / development / phenology
Strawberry farming requires efficient and adaptable solutions for real-time monitoring to tackle challenges like rapid ripening, perishability, and bad fruit recognition in field applications. However, existing methods often lack the robustness and lightweight design necessary for resource-constrained environments. To address these limitations, we propose MFD-YOLO, a feature-enhanced, distilled neural architecture based on YOLOv7-tiny, for accurate detection of strawberry growth states. First, we developed the MobileNet-MCA (M-MCA) backbone, which enhances feature extraction while significantly reducing redundant computations. Additionally, Partial Convolution (PConv) is incorporated into the E-ELAN module in the neck, improving feature fusion efficiency while reducing parameters. We also proposed the FocusDownNet (FDN) adaptive downsampling method to better capture and fuse multi-scale features. The DepthLiteBlock is designed to replace the CBL module in the prediction layer, further reducing computational complexity. Finally, an adaptive weighted knowledge distillation (AWKD) strategy is employed to balance performance and efficiency. Experimental results demonstrate that MFD-YOLO achieves a mAP@.5 of 97.5%, precision of 96.5%, recall of 93.8%, and an F1 score of 95.0%, operating at 128 FPS with a model size of only 3.58 MB. The proposed model outperforms state-of-the-art models and is successfully deployed on both desktop and Android devices, enabling real-time, efficient detection in resource-constrained environments.
Why it matches plant phenotyping methodsイチゴの生育状態を画像から検出する軽量モデルを開発し、精度・速度・実装性を評価しており、植物状態の取得手法が研究の中心である。
abstractwe propose MFD-YOLO, a feature-enhanced, distilled neural architecture based on YOLOv7-tiny, for accurate detection of strawberry growth states.
Abstract Background To address the challenge of real-time plant monitoring in greenhouse environments, this industry-driven research focuses on developing an autonomous quadrotor UAV system specifically designed for monitoring strawberry plants. Traditional methods for greenhouse monitoring are labor-intensive and lack scalability, particularly in precision agriculture applications. Method The study begins by proposing the mature strawberry detection model for greenhouse environment. The YOLOv9 with GLEAN advantage is proposed to detect small mature strawberries via on board camera on the quadrotor. Also the hybrid trajectory tracking controller for quadrotor is proposed and validated in both simulation and real time environment. The UAV follows predefined way points for navigation in the greenhouse environment. An onboard vision system is integrated, employing a novel YOLOv9-GLEAN-based algorithm for online and offline mature strawberry detection and counting. Results The YOLOv9-GLEAN model achieves high detection accuracy, as confirmed by evaluation metrics such as precision, recall, and F1-score. The proposed hybrid (PID+LQR) controller demonstrates superior tracking performance compared to other conventional controllers. The integrated control and perception system proves effective in both simulated and real-world greenhouse environments. Discussion The research validates the efficacy of deep learning models, with YOLOv9-GLEAN showing exceptional performance in enabling rapid, precise, and automated detection of ripe strawberries through quadrotor deployment in greenhouse environments. Such agricultural monitoring technologies represent a substantial advancement beyond conventional manual inspection approaches, empowering farmers and greenhouse operators to execute well-informed, time-sensitive management decisions that minimize crop losses and optimize production yields. This investigation underscores the revolutionary impact that deep learning technologies can have within greenhouse agriculture.
Why it matches plant phenotyping methods温室イチゴの成熟果実を画像認識で検出・計数する手法とUAV搭載システムが研究の中心であり、果実の成熟状態・数量という植物器官の形質を抽出しているため含める。
abstractThe YOLOv9 with GLEAN advantage is proposed to detect small mature strawberries via on board camera on the quadrotor.
Why it matches plant phenotyping methodsイチゴうどんこ病の重症度を圃場キャノピー反射率から推定する複数手法を評価・検証し、高スループット表現型解析への適用性を検討しており、表現型取得手法が研究の中心である。
abstractIn this study, we evaluated multiple methods for canopy reflectance as a phenotyping approach that can be applied across many research contexts.
At market, strawberries are usually picked based on their external attributes, basically surface color. However, internal parameters are also important for the final quality of the fruit and, therefore, can strongly influence acceptance by consumers. The development of analytical methods that allow the evaluation of the strawberry internal quality in a rapid, cost-effective and environmentally friendly way will improve competitiveness in the fruit industry and profitability at markets. In this study, a feasibility assessment on the use of near infrared hyperspectral imaging for the control of the internal quality of strawberries from Huelva (an important strawberry exporting region) has been carried out. For it, modified partial least square regressions have been performed for the screening of vitamin C and total soluble solids. The standard errors of prediction in external validation (4.71 mg/100 g and 1.28 °Brix respectively) demonstrate the potential of the analyzed technique for the proposed objective.
Why it matches plant phenotyping methodsイチゴ果実の内部品質（ビタミンCと可溶性固形分）を近赤外ハイパースペクトル画像から推定する手法を開発・外部検証しており、表現型取得・推定法が研究の中心である。
abstracta feasibility assessment on the use of near infrared hyperspectral imaging for the control of the internal quality of strawberries
Precisely identifying missing virus-free strawberry mother plants in nutrient pots post-transplantation is crucial for optimizing seedling management and maximizing yields in glass greenhouses. Thus, we present an automated method for detecting and counting missing seedlings based on SSP-MambaNet. Challenges in this process include the variable growth morphology of seedlings and complex environmental conditions in the greenhouse. Our approach starts with SPDFFA (Spatial-to-Depth Feature Fusion Attention) to enhance feature representation while retaining critical information, ensuring the preservation of key details. Additionally, the multi-scale CVSSB(Complex Visual State Space) and CVSSB-E(Expanded CVSSB) modules combine multi-scale and multi-directional spatial features, augmenting the model's capacity to recognize inter-image dependencies. Secondly, the MPDIoU is a novel loss function to tackle the optimization challenge of bounding boxes with similar shapes but different sizes, which enhances the accuracy of localizing strawberry seedlings and nutrient pots. Finally, Distance Intersection over Union is utilized for establishing a belongingness relationship between strawberry seedlings and pots, accurately identifying missing seedlings and counting the corresponding pots. Experimental results demonstrate that SSP-MambaNet achieves 94.9 %in average precision, 92.8 % in recall rate,88.1 % in precision, and 90.4 % F1 score for strawberry seedlings and pots. It outperforms the YOLOv7 by 4.7 % in average precision, and 2.6 % in recall rate while reducing 66.7 f/s in FPS. Furthermore, the proposed method shows 94.29 % accuracy in detecting missing seedlings and 97.14 % accuracy in counting nutrient pots with missing seedlings. These results showcase its effectiveness in improving overall seedling quality and providing timely replanting guidance in glass greenhouses.
Why it matches plant phenotyping methods温室内のイチゴ苗の欠損状態を画像から検出・計数する自動手法を開発し、精度比較・検証しており、植物状態の取得方法が研究の中心です。
abstractwe present an automated method for detecting and counting missing seedlings based on SSP-MambaNet.
Strawberries, as an indeterminate crop, produce fruit multiple times per season, making fruit monitoring and wave-specific yield prediction essential for optimizing harvest planning. This study developed an AI-driven approach to predict next week’s yield using real-time plant image data collected by a machine vision system and environmental data. YOLOv8n was employed to count flowers, immature fruit, and mature fruit per plant, with manual counts used to evaluate the system’s accuracy. The YOLOv8n-based data, combined with weather features, were used to train several AI models for yield prediction. These models included traditional time series machine learning approaches, such as Multiple Linear Regression (MLR) with time lag features, Vector Autoregression (VAR), Gradient Boosting Machines (GBM), Random Forest, and deep learning time-series models, including Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN). Recursive Feature Elimination (RFE) was employed to identify the most relevant features. The performance of these models was evaluated across three strawberry varieties: Sensation, Brilliance, and Medallion. Results showed that MLR outperformed other models for Sensation and Brilliance, with R² values of 0.633 and 0.908, respectively. For Medallion, GBM achieved the best performance with an R² score of 0.848. LSTM, which outperformed TCN, achieved R² scores of 0.522 (Sensation), 0.839 (Brilliance), and 0.740 (Medallion). This AI-driven system automates yield forecasting, reducing labor costs and enabling more efficient harvest planning. The study highlights the potential of combining machine vision and predictive analytics for precise, scalable yield prediction, offering valuable insights for proactive farm management and supply chain optimization.
Why it matches plant phenotyping methods機械視覚で植物ごとの花・未熟果・成熟果を計数し、手動計数で精度評価する手法が収量予測の中心であるため、植物表現型計測・検証を含む。
abstractYOLOv8n was employed to count flowers, immature fruit, and mature fruit per plant, with manual counts used to evaluate the system’s accuracy.
Capturing crop physiological information by phenotyping is a key trend in smart agriculture. However, current studies underutilize spatial structural information in phenotypic imaging. To evaluate the feasibility of crop cold stress monitoring based on phenotypic spatial variability, we conducted controlled experiments on ‘Toyonoka’ strawberry plants under four dynamic cooling gradients and three stress durations and analyzed the dependence of their photosynthetic physiology and phenotypic traits on temperature-time interactions. The results revealed that NPQ/1D-Parallel/TENT, Y(NO)/2D-Region/INEM, and qP/1D-Parallel/TENT presented the highest mutual information, with the maximum net photosynthetic rate (Pₘₐₓ), relative electrolyte conductivity (REC), and total chlorophyll content (Chlₐ ₊ b), respectively. The difference between the Photosynthetic Physiological Potential Index (PPPI) and relative negative accumulated temperature (RNAT)/650 effectively was used to calculate the cold damage risk (CDRI). An XGBoost-based model integrating the PPPI and RNAT outperformed AdaBoost and RandomForest, achieving an R² of 0.98, an RMSE of 0.337, a classification accuracy of 92.13 %, and a Kappa coefficient of 0.904. qP/1D-Parallel/TENT contributed the most to the model. This study provides a scientific basis for phenotypic information mining and agro-meteorological disaster monitoring.
Why it matches plant phenotyping methods植物の生理状態・低温障害リスクを、表現型画像由来の空間変動特徴と機械学習で推定し、複数モデルの性能比較・検証を行っており、表現型取得・解析手法が中心である。
abstractCapturing crop physiological information by phenotyping is a key trend in smart agriculture.
High-throughput digital phenotyping (DP) has been widely explored in plant breeding to assess large numbers of genotypes with minimal manual labor and reduced cost and time. DP platforms using high-resolution images captured by drones and tractor-based platforms have recently allowed the University of Florida strawberry (Fragaria × ananassa) breeding program to assess vegetative biomass at scale. Biomass has not previously been explored in a strawberry breeding context due to the labor required and the need to destroy the plant. This study aims to understand the genetic basis of predicted vegetative biomass and biomass-related traits and to chart a path for the combined use of DP and genomics in strawberry breeding. Aboveground dry vegetative biomass was estimated by adapting a previously published model using ground-truth data on a subset of breeding germplasm. High-resolution images were collected on clonally replicated trials at different time points during the fruiting season. There was moderate to high heritability (h 2 = 0.26-0.56) for predicted vegetative biomass, and genetic correlations between vegetative biomass and marketable yield were mostly positive (r G = -0.13-0.47). Fruit yield traits scaled on a vegetative biomass basis also had moderate to high heritability (h 2 = 0.25-0.64). This suggests that vegetative biomass can be decreased or increased through selection, and that marketable fruit yield can be improved without simultaneously increasing plant size. No consistent marker-trait associations were discovered via genome-wide association studies. On the other hand, predictive abilities from genomic selection ranged from 0.15 to 0.46 across traits and years, suggesting that genomic prediction will be an effective breeding tool for vegetative biomass in strawberry.
Why it matches plant phenotyping methodsデジタル画像からイチゴの地上部乾物バイオマスを推定する手法を適応し、育種集団で大規模に適用しており、表現型取得・推定ワークフローが研究の主要部分である。
abstractHigh-throughput digital phenotyping (DP) has been widely explored in plant breeding to assess large numbers of genotypes with minimal manual labor and reduced cost and time.
Phyllosticta fragaricola -induced angular leaf spot causes substantial economic losses in global strawberry production, necessitating advanced severity assessment methods. This study proposed a dual-phase grading framework integrating deep learning and computer vision. The enhanced You Only Look Once version 11 (YOLOv11) architecture incorporated a Content-Aware ReAssembly of FEatures (CARAFE) module for improved feature upsampling and a squeeze-and-excitation (SE) attention mechanism for channel-wise feature recalibration, resulting in the YOLOv11-CARAFE-SE for the severity assessment of strawberry angular leaf spot. Furthermore, an OpenCV-based threshold segmentation algorithm based on H-channel thresholds in the HSV color space achieved accurate lesion segmentation. A disease severity grading standard for strawberry angular leaf spot was established based on the ratio of lesion area to leaf area. In addition, specialized software for the assessment of disease severity was developed based on the improved YOLOv11-CARAFE-SE model and OpenCV-based algorithms. Experimental results show that compared with the baseline YOLOv11, the performance is significantly improved: the box mAP@0.5 is increased by 1.4% to 93.2%, the mask mAP@0.5 is increased by 0.9% to 93.0%, the inference time is shortened by 0.4 ms to 0.9 ms, and the computational load is reduced by 1.94% to 10.1 GFLOPS. In addition, this two-stage grading framework achieves an average accuracy of 94.2% in detecting selected strawberry horn leaf spot disease samples, providing real-time field diagnostics and a high-throughput phenotypic analysis for resistance breeding programs. This work demonstrates the feasibility of rapidly estimating the severity of strawberry horn leaf spot, which will establish a robust technical framework for strawberry disease management under field conditions.
Why it matches plant phenotyping methodsイチゴ葉の病斑面積比に基づく病害重症度を、改良YOLOv11とOpenCV画像処理で自動推定する手法を開発・評価し、専用ソフトウェアも開発しているため、植物フェノタイピング手法が中心である。
abstractThis study proposed a dual-phase grading framework integrating deep learning and computer vision.
AvocadoStrawberryFruitClassificationObject detectionGrowth / development / phenology
This paper presents a novel and innovative 14,630 fruit images dataset, consisting of 1333 original images and the remaining augmented images for strawberry and avocado fruits. The dataset records the growth of strawberries and avocados in four different stages: unripe, partially ripe, ripe, and rotten. Though the fruit ripening process is commonly known, a lack of systematic datasets to show the fruit changing from an unripe state to a rotting state was prevalent for the two fruits in question. Over two months, the dataset was collected through rigorous tracking to effectively provide a measure of each of the fruits' conditions. The fruits were obtained from Mahabaleshwar farms in Maharashtra, India, as well as from local markets in Maharashtra and Pune. The fruits were monitored continuously from the time of harvesting, and all observed changes were carefully recorded. The uniqueness of this dataset is that it covers both strawberries and avocados, which have different patterns of ripening and are highly commercially valuable. The images were annotated using the online annotation tool - makesense.ai, with a total of 1499 bounding boxes for each fruit. By encompassing these two diverse fruit types, the dataset provides a valuable resource for researchers, agriculturalists, and food scientists to investigate and compare the ripening behaviours of different fruit species.
Why it matches plant phenotyping methodsイチゴとアボカドの果実画像を用いて、未熟から腐敗までの可視的な成熟・状態を体系的に記録し、注釈付きデータセットとして提供しているため、植物器官の状態を対象とする画像ベースのフェノタイピングデータセットに該当する。
abstractThis paper presents a novel and innovative 14,630 fruit images dataset, consisting of 1333 original images and the remaining augmented images for strawberry and avocado fruits.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the authors' own fruit image dataset (14,630 strawberry/avocado images with YOLO bounding-box annotations across ripening stages), which directly constitutes the paper's phenotyping measurements. No analysis code or trained模型s是Dataset · publicset up using a white background for enabling consistent and uniform image acquisition..
Data source location
Dataset was collected from (i) Mahabaleshwar, Maharashtra, India; and (ii) Pune, Maharashtra, India.
Data accessibility
Repository name: mendeley.com
Data identification number: 10.17632/zysvgmxcyz.1
Direct URL to data: https://data.mendeley.com/datasets/zysvgmxcyz/1
Related research article
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Value of the Data
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This dataset is a useful resource for machine learning solutions in fruit maturity detection and can contribute to the design of automated sorting and classification systems by ripeness stages.
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Food processing companies and agricultural scientists may utilize this data to Open asset ↗10.17632/zysvgmxcyz.1lines:1-51Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Capturing crop physiological information by phenotyping is a key trend in smart agriculture. However, current studies underutilize spatial structural information in phenotypic imaging. To evaluate the feasibility of crop cold stress monitoring based on phenotypic spatial variability, we conducted controlled experiments on 'Toyonoka' strawberry plants under four dynamic cooling gradients and three stress durations and analyzed the dependence of their photosynthetic physiology and phenotypic traits on temperature-time interactions. The results revealed that NPQ/1D-Parallel/TENT, Y(NO)/2D-Region/INEM, and qP/1D-Parallel/TENT presented the highest mutual information, with the maximum net photosynthetic rate (P max ), relative electrolyte conductivity (REC), and total chlorophyll content (Chl a + b ), respectively. The difference between the Photosynthetic Physiological Potential Index (PPPI) and relative negative accumulated temperature (RNAT)/650 effectively was used to calculate the cold damage risk (CDRI). An XGBoost-based model integrating the PPPI and RNAT outperformed AdaBoost and RandomForest, achieving an R 2 of 0.98, an RMSE of 0.337, a classification accuracy of 92.13 %, and a Kappa coefficient of 0.904. qP/1D-Parallel/TENT contributed the most to the model. This study provides a scientific basis for phenotypic information mining and agro-meteorological disaster monitoring.
Why it matches plant phenotyping methodsイチゴの低温ストレスを対象に、表現型画像の空間変動特徴を抽出・融合し、光合成生理や冷害リスクを推定する手法を開発・評価しており、表現型取得・解析が研究の中心である。
abstractcurrent studies underutilize spatial structural information in phenotypic imaging
To address the low estimation accuracy of deep learning-based crop yield image recognition methods under untrained shooting distances, this study proposes a shooting distance adaptive crop yield estimation method by fusing RGB and depth image information through multi-modal data fusion. Taking strawberry fruit fresh weight as an example, RGB and depth image data of 348 strawberries were collected at nine heights ranging from 70 to 115 cm. First, based on RGB images and shooting height information, a single-modal crop yield estimation model was developed by training a convolutional neural network (CNN) after cropping strawberry fruit images using the relative area conversion method. Second, the height information was expanded into a data matrix matching the RGB image dimensions, and multi-modal fusion models were investigated through input-layer and output-layer fusion strategies. Finally, two additional approaches were explored: direct fusion of RGB and depth images, and extraction of average shooting height from depth images for estimation. The models were tested at two untrained heights (80 cm and 100 cm). Results showed that when using only RGB images and height information, the relative area conversion method achieved the highest accuracy, with R2 values of 0.9212 and 0.9304, normalized root mean square error (NRMSE) of 0.0866 and 0.0814, and mean absolute percentage error (MAPE) of 0.0696 and 0.0660 at the two untrained heights. By further incorporating depth data, the highest accuracy was achieved through input-layer fusion of RGB images with extracted average height from depth images, improving R2 to 0.9475 and 0.9384, reducing NRMSE to 0.0707 and 0.0766, and lowering MAPE to 0.0591 and 0.0610. Validation using a developed shooting distance adaptive crop yield estimation platform at two random heights yielded MAPE values of 0.0813 and 0.0593. This model enables adaptive crop yield estimation across varying shooting distances, significantly enhancing accuracy under untrained conditions.
Why it matches plant phenotyping methodsRGB・深度画像とCNNを用いてイチゴ果実重量（収量）を推定する撮影距離適応型の方法とプラットフォームを開発・検証しており、表現型取得・推定が研究の中心である。
abstractthis study proposes a shooting distance adaptive crop yield estimation method by fusing RGB and depth image information through multi-modal data fusion.
The aim of this study was to train a Vision Transformer (ViT) model for semantic segmentation to differentiate between ripe and unripe strawberries using synthetic data to avoid challenges with conventional data collection methods. The solution used Blender to generate synthetic strawberry images along with their corresponding masks for precise segmentation. Subsequently, the synthetic images were used to train and evaluate the SwinUNet as a segmentation method, and Deep Domain Confusion was utilized for domain adaptation. The trained model was then tested on real images from the Strawberry Digital Images dataset. The performance on the real data achieved a Dice Similarity Coefficient of 94.8% for ripe strawberries and 94% for unripe strawberries, highlighting its effectiveness for applications such as fruit ripeness detection. Additionally, the results show that increasing the volume and diversity of the training data can significantly enhance the segmentation accuracy of each class. This approach demonstrates how synthetic datasets can be employed as a cost-effective and efficient solution for overcoming data scarcity in agricultural applications.
Why it matches plant phenotyping methods合成画像、セグメンテーション、ドメイン適応を用いてイチゴの成熟状態を推定する画像解析手法を開発・実データで評価しており、植物器官の状態取得が中心である。
abstracttrain a Vision Transformer (ViT) model for semantic segmentation to differentiate between ripe and unripe strawberries using synthetic data
Reproduction assets foundThe authors state that all data (synthetic strawberry images and masks) and all Python analysis code are publicly available on the Open Science Framework at https://osf.io/5kzcb/, making both the paper-specific phenotype/segmentation dataset and the authors' code directly actionable.Code · publicAll code for this study was written in Python and has been made publicly available on the Open Science Framework (OSF) [ 44 ] and based on [ 45 ].Open asset ↗Open Science Frameworklines:177-199Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Agriculture is vital for human sustenance and economic stability, with increasing global food demand necessitating innovative practices. Traditional farming methods have caused significant environmental damage, highlighting the need for sustainable practices like nutrition management. This paper addresses the emerging integration of artificial intelligence (AI) in agriculture, focusing on the specific challenge of growth stage classification of strawberry plants for optimized nutrition management. While AI has been successfully applied in various agricultural domains, such as plant stress detection and growth monitoring, the precise classification of strawberry growth stages remains underexplored. Accurate growth stage identification is vital for timely nutrient application, directly impacting yield and fruit quality. Our research identifies common gaps in existing literature, including limited or inaccessible datasets, outdated methodologies, and insufficient benchmarking. To overcome these shortcomings, we introduce a robust greenhouse-based dataset covering seven distinct strawberry growth stages, captured under diverse conditions. We then benchmark multiple state-of-the-art models on this dataset, finding that EfficientNetB7 achieves a testing accuracy of 0.837-demonstrating the promise of AI-driven approaches for precise and sustainable nutrient management in horticulture.
Why it matches plant phenotyping methodsイチゴの生育段階という植物状態を画像データセットで分類し、複数モデルをベンチマーク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収載する。
abstractwe introduce a robust greenhouse-based dataset covering seven distinct strawberry growth stages, captured under diverse conditions.
Plants biosynthesize a wide range of antioxidants capable of attenuating ROS-induced oxidative damage. There exist several in vitro methods to analyze antioxidants and total antioxidant capacity from different tissues and of various plant species. We have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods for determination of the level of the key antioxidants phenolics, anthocyanins and flavonoids in combination with the determination of total antioxidant capacity using ferric reducing antioxidant power (FRAP) and trolox equivalent antioxidant capacity (TEAC). The method was optimized and verified with samples from different strawberry species and cultivars with known differences in the parameters measured. This method proved to be suitable for analyses of eight model and crop plants, and distinct antioxidant signatures were determined for the different tissues and organs analyzed, including leaf, root, fruit, spike, and tuber samples. The method was robust and was shown in two case studies to be a resource-efficient and fast experimental platform also to assess biotic and abiotic stress responses, notably including fungal infection and the impact of a progressive drought regime. Since method was adapted for a semi-high throughput 96-well assay format it is well-suited for integration of cell physiological phenotyping into a holistic phenomics approach for germplasm assessment and plant breeding screening. This analytical platform uses microplate spectrophotometer which proved to be suitable to determine the antioxidant contents and total antioxidant capacity signatures of various plant species and tissues with similar findings as reported in literature.
Why it matches plant phenotyping methods植物組織の抗酸化物質と抗酸化能を測定する抽出・96ウェルアッセイを開発、最適化・検証し、ストレス応答や育種スクリーニング向けの生理的フェノタイピング基盤として提示しているため。
abstractWe have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods
Rapid detection of plant infections is crucial for minimising crop loss and optimising management strategies, particularly in the context of climate change. While traditional diagnostic methods provide precise measurements of phytohormones such as salicylic acid (SA), a key regulator of plant defence responses, their reliance on bulky equipment and lengthy analysis times limits field applicability. This study presents a microfluidic-based aptamer assay for SA detection, enabling rapid and sensitive fluorescence-based readout from plant samples. A tailored sample pre-treatment protocol was developed and validated with real strawberry samples using HPLC measurements. The assay demonstrated a detection limit ranging from 10 -9 to 10 -6 mg/mL, within the relevant range for early infection diagnosis. The integration of the microfluidic platform with the optimised pre-treatment protocol offers a portable, cost-effective solution for on-site phytohormone analysis, providing a valuable tool for early infection detection and improved crop management.
Why it matches plant phenotyping methods植物試料中のサリチル酸を対象とするマイクロ流体・アプタマー測定法を開発し、実試料とHPLCで検証しており、感染に関連する植物の生理状態の取得が中心である。
abstractThis study presents a microfluidic-based aptamer assay for SA detection, enabling rapid and sensitive fluorescence-based readout from plant samples.
Abstract Botrytis cinerea is a filamentous fungus that infects over 200 species of crops causing grey mold disease with devastating losses to agriculture worldwide. The heavy reliance on synthetic fungicides in the strawberry industry has led to the emergence of fungicide resistance in B. cinerea . Therefore, understanding the fundamental biology of B. cinerea is the first step in the search for novel antifungals. Although B. cinerea is one of the most serious pathogens of strawberry ( Fragaria x ananassa ), few protocols have been specifically developed to study this pathosystem. Consequently, early development of pathogen penetration in strawberry is poorly understood. Here we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection. These assays allow comparison of treatment effect on the same fruit, and facilitate the screening of fungicides or biocontrol agents. Through real-time PCR, chlorophyll fluorescence analysis, scanning electron and confocal microscopy, we quantified the lesion and fungal biomass of B. cinerea in the early stages of infection in fruit and petals, and demonstrated that B. cinerea penetrates through stomata of strawberry achenes, revealing a previously unrecognized infection route in this host. These data provide a deeper understanding of the B. cinerea -strawberry interaction and will serve as a foundation for future studies seeking novel antifungal treatments against B. cinerea .
Why it matches plant phenotyping methodsイチゴの病徴（病斑）と感染状態を定量するアッセイを開発し、顕微鏡・蛍光解析などによる表現型取得が研究の中心である。
abstractHere we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection.
StrawberryGreenhouseLiDAR / point cloudLeafStem / branchSegmentationLeaf traits
With effective protective covering and microclimate control, greenhouse crops offer significant advantages, such as high yield and quality, remaining unaffected by seasonal variations and meeting the demand for diverse agricultural products. Leaf area is a critical growth parameter influencing the indoor microclimate and the transport of nutrients within plants. This study introduces a strawberry 3D point cloud instance segmentation method based on SGC to address the challenge of stem and leaf instance segmentation in calculating plant leaf area using 3D point cloud data. High-quality point cloud data were obtained using a 3D scanner, and feature enhancement was achieved through the Leaf Vein and Boundary Preserving Sampling (LVBPS) method. The SGC network achieved an average precision of 90.41% (AP25) and 89.47% (AP50) for instance segmentation, with the precision of leaf segmentation reaching 93.63% (AP25) and 92.80% (AP50). These findings provide valuable technical support and references for greenhouse cultivation and smart agriculture applications. The source code and dataset can be accessed at https://github.com/suyangsuluo/SGC.
Why it matches plant phenotyping methodsイチゴの3D点群から茎・葉をインスタンス分割し、葉面積算出に用いる画像解析手法を開発・評価しており、植物表現型取得が中心である。
abstractThis study introduces a strawberry 3D point cloud instance segmentation method based on SGC to address the challenge of stem and leaf instance segmentation in calculating plant leaf area using 3D point cloud data.
Precisely identifying missing virus-free strawberry mother plants in nutrient pots post-transplantation is crucial for optimizing seedling management and maximizing yields in glass greenhouses. Thus, we present an automated method for detecting and counting missing seedlings based on SSP-MambaNet. Challenges in this process include the variable growth morphology of seedlings and complex environmental conditions in the greenhouse. Our approach starts with SPDFFA (Spatial-to-Depth Feature Fusion Attention) to enhance feature representation while retaining critical information, ensuring the preservation of key details. Additionally, the multi-scale CVSSB(Complex Visual State Space) and CVSSB-E(Expanded CVSSB) modules combine multi-scale and multi-directional spatial features, augmenting the model's capacity to recognize inter-image dependencies. Secondly, the MPDIoU is a novel loss function to tackle the optimization challenge of bounding boxes with similar shapes but different sizes, which enhances the accuracy of localizing strawberry seedlings and nutrient pots. Finally, Distance Intersection over Union is utilized for establishing a belongingness relationship between strawberry seedlings and pots, accurately identifying missing seedlings and counting the corresponding pots. Experimental results demonstrate that SSP-MambaNet achieves 94.9 %in average precision, 92.8 % in recall rate,88.1 % in precision, and 90.4 % F1 score for strawberry seedlings and pots. It outperforms the YOLOv7 by 4.7 % in average precision, and 2.6 % in recall rate while reducing 66.7 f/s in FPS. Furthermore, the proposed method shows 94.29 % accuracy in detecting missing seedlings and 97.14 % accuracy in counting nutrient pots with missing seedlings. These results showcase its effectiveness in improving overall seedling quality and providing timely replanting guidance in glass greenhouses.
Why it matches plant phenotyping methods幼苗の欠損状態を画像から検出・計数する自動化手法の開発が研究の中心であり、植物の状態を直接推定しているため。
abstractwe present an automated method for detecting and counting missing seedlings based on SSP-MambaNet.
Reproduction assets foundThe paper's Data Availability statement explicitly provides the authors' source code and the strawberry seedling/nutrient pot image dataset via a public GitHub repository, matching an allowed URL.Code · publicThis study's source code and datasets can be accessed at https://github.com/STRABf5/SSPMambaNet.git.Open asset ↗STRABf5/SSPMambaNet · STRABf5/SSPMambaNethtml-lines:448-494Dataset · publicThis study's source code and datasets can be accessed at https://github.com/STRABf5/SSPMambaNet.git.Open asset ↗STRABf5/SSPMambaNet · STRABf5/SSPMambaNethtml-lines:538-610Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 6 Sept 2026
Strawberry grading by picking robots can eliminate the manual classification, reducing labor costs and minimizing the damage to the fruit. Strawberry size or weight is a key factor in grading, with accurate weight estimation being crucial for proper classification. In this paper, we collected 1521 sets of strawberry RGB-D images using a depth camera and manually measured the weight and size of the strawberries to construct a training dataset for the strawberry weight regression model. To address the issue of incomplete depth images caused by environmental interference with depth cameras, this study proposes a multimodal point cloud completion method specifically designed for symmetrical objects, leveraging RGB images to guide the completion of depth images in the same scene. The method follows a process of locating strawberry pixel regions, calculating centroid coordinates, determining the symmetry axis via PCA, and completing the depth image. Based on this approach, a multimodal fusion regression model for strawberry weight estimation, named MMF-Net, is developed. The model uses the completed point cloud and RGB image as inputs, and extracts features from the RGB image and point cloud by EfficientNet and PointNet, respectively. These features are then integrated at the feature level through gradient blending, realizing the combination of the strengths of both modalities. Using the Percent Correct Weight (PCW) metric as the evaluation standard, this study compares the performance of four traditional machine learning methods, Support Vector Regression (SVR), Multilayer Perceptron (MLP), Linear Regression, and Random Forest Regression, with four point cloud-based deep learning models, PointNet, PointNet++, PointMLP, and Point Cloud Transformer, as well as an image-based deep learning model, EfficientNet and ResNet, on single-modal datasets. The results indicate that among traditional machine learning methods, the SVR model achieved the best performance with an accuracy of 77.7% (PCW@0.2). Among deep learning methods, the image-based EfficientNet model obtained the highest accuracy, reaching 85% (PCW@0.2), while the PointNet + + model demonstrated the best performance among point cloud-based models, with an accuracy of 54.3% (PCW@0.2). The proposed multimodal fusion model, MMF-Net, achieved an accuracy of 87.66% (PCW@0.2), significantly outperforming both traditional machine learning methods and single-modal deep learning models in terms of precision.
Why it matches plant phenotyping methodsイチゴのRGB-D画像から重量・サイズを推定する画像／点群解析手法を開発し、複数モデルと比較評価しており、植物形質取得が中心である。
abstractthis study proposes a multimodal point cloud completion method specifically designed for symmetrical objects
Leaf spot, leaf scorch and phomopsis leaf blight are the most common fungal diseases of strawberry in Western Siberia, which significantly reduce its yield and quality. Accurate, fast and non-invasive diagnosis of these diseases is important for strawberry production. This article explores the ability of hyperspectral imaging to detect and differentiate symptoms caused to strawberry leaves by pathogenic fungi Ramularia tulasnei Sacc., Marssonina potentillae Desm. and Dendrophoma obscurans Anders. The reflection spectrum of leaves was acquired with a Photonfocus MV1-D2048x1088-HS05-96-G2-10 hyperspectral camera under laboratory conditions using the line scanning method. Five machine learning methods were considered to differentiate between healthy and diseased leaf areas: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), and Random Forest (RF). In order to reduce the high dimensionality of the extracted spectral data and to increase the speed of their processing, several subsets of optimal wavelengths were selected. The following dimensionality reduction methods were explored: ROC curve analysis method, derivative analysis method, PLS-DA method, and ReliefF method. In addition, 16 vegetation indices were used as features. The support vector machine method demonstrated the highest classification accuracy of 89.9 % on the full range spectral data. When using vegetation indices and optimal wavelengths, the overall classification accuracy of all methods decreased slightly compared to the classification on the full range spectral data. The results of the study confirm the potential of using hyperspectral imaging methods in combination with machine learning for differentiating fungal diseases of strawberries.
Why it matches plant phenotyping methodsイチゴ葉の病徴をハイパースペクトル画像と機械学習で非侵襲的に識別し、波長選択や分類精度も評価しており、植物病害状態の取得・推定手法が中心である。
abstractThis article explores the ability of hyperspectral imaging to detect and differentiate symptoms caused to strawberry leaves by pathogenic fungi
The growth of strawberries is influenced by environmental diversity and spatial dispersion, which present significant challenges for accurate identification and real-time image processing in complex environments. This paper addresses these challenges by proposing an advanced recognition model based on YOLOv8, tailored for strawberry identification. In this study, we enhanced the YOLOv8 architecture by replacing the traditional backbone with an EfficientNetV2 feature extraction network and using ODConv instead of the standard convolution. The loss function was modified with a dynamic nonmonotonic focusing mechanism, and WiseIoU was introduced to replace the traditional CIoU. The experimental results showed that the proposed model outperformed the original YOLOv8 regarding mAP50, precision, and recall, with improvements of 16.91%, 14.92%, and 8.4%, respectively. Additionally, the model's lightness increased by 15.67%. The proposed model demonstrated superior accuracy in identifying strawberries of different ripeness levels. The improvements in the proposed model indicate its effectiveness in strawberry recognition tasks, providing more accurate results in varying environmental conditions. The lightweight nature of the model makes it suitable for deployment on picking robots, enhancing its practical applicability for real-time processing in agricultural settings.
Why it matches plant phenotyping methodsYOLOv8画像認識モデルを改良し、イチゴ果実の成熟度という観察可能な植物器官の状態を推定する手法を開発・評価しており、単なる収穫対象の位置検出を超えている。
abstractThis paper addresses these challenges by proposing an advanced recognition model based on YOLOv8, tailored for strawberry identification.
StrawberryGreenhouseFruitObject detectionGrowth / development / phenology
Introduction In the context of intelligent strawberry cultivation, achieving multi-stage detection and yield estimation for strawberry fruits throughout their full growth cycle is essential for advancing intelligent management of greenhouse strawberries. Addressing the high rates of missed and false detections in existing object detection algorithms under complex backgrounds and dense multi-target scenarios, this paper proposes an improved multi-stage detection algorithm RLK-YOLOv8 for greenhouse strawberries. The proposed algorithm, an enhancement of YOLOv8, leverages the benefits of large kernel convolutions alongside a multi-stage detection approach. Method RLK-YOLOv8 incorporates several improvements based on the original YOLOv8 model. Firstly, it utilizes the large kernel convolution network RepLKNet as the backbone to enhance the extraction of features from targets and complex backgrounds. Secondly, RepNCSPELAN4 is introduced as the neck network to achieve bidirectional multi-scale feature fusion, thereby improving detection capability in dense target scenarios. DynamicHead is also employed to dynamically adjust the weight distribution in target detection, further enhancing the model's accuracy in recognizing strawberries at different growth stages. Finally, PolyLoss is adopted as the loss function, which effectively improve the localization accuracy of bounding boxes and accelerating model convergence. Results The experimental results indicate that RLK-YOLOv8 achieved a mAP of 95.4% in the strawberry full growth cycle detection task, with a precision and F1-score of 95.4% and 0.903, respectively. Compared to the baseline YOLOv8, the proposed algorithm demonstrates a 3.3% improvement in detection accuracy under complex backgrounds and dense multi-target scenarios. Discussion The RLK-YOLOv8 exhibits outstanding performance in strawberry multi-stage detection and yield estimation tasks, validating the effectiveness of integrating large kernel convolutions and multi-scale feature fusion strategies. The proposed algorithm has demonstrated significant improvements in detection performance across various environments and scenarios.
Why it matches plant phenotyping methodsイチゴ果実の生育段階検出と収量推定を目的に、改良YOLOv8アルゴリズムを開発・評価しており、画像から植物器官の状態・数量を推定する方法が中心である。
titlemulti-stage detection of strawberry fruits throughout the full growth cycle in greenhouses
Growing consumer demand for high-quality strawberries has highlighted the need for accurate, efficient, and non-destructive methods to assess key postharvest quality traits, such as weight, size uniformity, and quantity. This study proposes a multi-objective learning algorithm that leverages RGB-D multimodal information to estimate these quality metrics. The algorithm develops a fusion expert network architecture that maximizes the use of multimodal features while preserving the distinct details of each modality. Additionally, a novel Heritable Loss function is implemented to reduce redundancy and enhance model performance. Experimental results show that the coefficient of determination (R²) values for weight, size uniformity and number are 0.94, 0.90 and 0.95 respectively. Ablation studies demonstrate the advantage of the architecture in multimodal, multi-task prediction accuracy. Compared to single-modality models, non-fusion branch networks, and attention-enhanced fusion models, our approach achieves enhanced performance across multi-task learning scenarios, providing more precise data for trait assessment and precision strawberry applications.
Why it matches plant phenotyping methodsRGB-D画像を用いてイチゴ果実の重量・サイズ均一性・個数を推定する融合ネットワークの開発と性能評価が研究の中心であり、植物器官の形質取得手法に該当する。
abstractThis study proposes a multi-objective learning algorithm that leverages RGB-D multimodal information to estimate these quality metrics.
Online detection of internal quality of strawberries presents challenges particularly concerning fruit damage, detection accuracy, and processing efficiency. This study explores the feasibility of using Vis/NIRS for online detection of SSC in strawberries during hanging transportation. After analyzing SSC distribution in strawberries, an optical sensing system was developed, and optimal configurations were identified using PLSR models. When employing a horizontal optical beam through the strawberry center, the PLSR model combined with SNV preprocessing and CARS feature selection achieved the best conventional chemometric results (RPD of 4.793). Additionally, three 1D-CNN approaches were investigated, with the 1D-CNN-LSTM method exhibiting superior performance (R p 2 of 0.963, RMSEP of 0.209°Brix, RPD of 5.332). These findings demonstrate the excellent capability of our developed system, enhanced by deep learning methods, for online detection of SSC in strawberries. This work may open new avenues for the online assessment of internal quality in small and delicate fruits.
Why it matches plant phenotyping methodsイチゴの可溶性固形分という植物器官の品質形質を対象に、オンラインVis/NIR光学センシングシステムを開発し、PLSRおよび1D-CNNで推定性能を評価しているため、フェノタイピング手法が中心です。
Early automation in identifying plant diseases is crucial for the precise protection of crops. Plant diseases pose substantial risks to agriculture-dependent nations, often leading to notable crop losses and financial challenges, particularly in developing countries. Symptoms such as chlorosis, structural deformities, and wilting, characterize these diseases. However, early identification can be challenging due to symptoms similarity. Researchers using artificial intelligence (AI) for plant disease classification, challenges like data imbalance, symptom variability, real-time performance, and costly annotation hinder accuracy and adoption. This work introduced a novel approach using the You Only Look Once (YOLO) deep learning model, chosen for its exceptional accuracy and speed. The study focuses on analyzing YOLO models, specifically YOLOv3 and YOLOv4, to identify fruit plant diseases. This work examines healthy peach and strawberry leaves, as well as peach leaves affected by bacterial spots and strawberry leaves with scorch disease. These models underwent thorough training using data from the publicly accessible Plant Village dataset. The simulation results were highly promising, numerically YOLOv3 model achieved 97% accuracy and a Mean Average Precision (mAP) of 92%, within a total detection time of 105 s. In comparison, the YOLOv4 model outperformed, with a 98% accuracy and an impressive mean average precision of 98%, all while completing the detection process in just 29 s. YOLOv4 demonstrated lower complexity, significantly faster, and more precise performance, especially in detecting multiple items. Serving as an efficient real-time detector, it holds the potential to transform plant disease diagnosis and mitigation strategies, ultimately leading to increased agricultural productivity and enhanced financial outcomes for developing nations.
Why it matches plant phenotyping methods植物葉の病徴・病害状態を画像から識別するYOLO手法の開発と性能比較が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis work introduced a novel approach using the You Only Look Once (YOLO) deep learning model
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available Plant Village dataset on Kaggle (4,222 peach/strawberry leaf images across four classes), used to train and evaluate YOLOv3/YOLOv4 disease-detection models. No author analysis code, trained model checkpoints, or paper-specific supplements are publicly statedDataset · publicThis study utilizes data from the publicly available Plant Village dataset 41 , accessible on Kaggle.Open asset ↗Kagglelines:139-155Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2025Computers and Electronics in Agriculture.
Yield prediction is an essential part of farm management and has been investigated with various kinds of data and technologies in the last decades. With the advent of deep learning technology, recent studies are focusing on crop growth analysis with image processing. Instead of measuring crops in a destructive way, image analysis enables crop measurement without manipulation of the crop itself. Counting crops using tracker algorithms such as DeepSORT is one of the famous approaches for yield prediction and analysis. However, to enable crop growth monitoring and analysis, it needs consideration of temporal analysis along with spatial analysis. It should be able to compare the previous status of the target crop to the current status to analyze the growth, for example, from bud to flower to strawberry. This paper proposes a novel method for monitoring crop growth with crop clustering. Instead of counting the crops from the images, the proposed methods recognized a crop cluster from the image and measured how it changed during its lifespan. Further, the proposed method is implemented in an edge device for a greenhouse that is able to collect and measure. The proposed method has been validated on a strawberry greenhouse for around a year, which shows MoTA score from 0.57 to 0.86, with respect to the dataset.
Why it matches plant phenotyping methods画像から作物クラスターの成長変化を追跡・測定する手法を開発し、エッジデバイスに実装してイチゴ温室で約1年間検証しており、植物表現型の取得が中心です。
abstractThis paper proposes a novel method for monitoring crop growth with crop clustering.
Using chemical laboratory procedures to estimate the fruit quality parameters (biochemical parameters) of mango "Succarri" and strawberry "Florida" as indicators of ripening degrees in a large area presents challenges such as low throughput, labor intensity, time consumption, and the need for multiple samples. So, using spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits is important to find the best time to harvest, make food ripen faster, and the processing of food easier. This has significant economic and ecological advantages. The objective of this study was to evaluate the biochemical parameters of mango and strawberry fruits at various ripening stages. This was done by utilizing a combination of established and newly developed spectral reflectance indices (SRIs) in conjunction with machine learning (ML) models, including artificial neural networks (ANN), random forests (RF), and decision trees (DT). For mango fruit, the parameters estimated were chlorophyll content, total soluble solids (TSS), and firmness, whereas for strawberry fruit, the parameters were L*, b*, TSS, and firmness. These results revealed significant differences in SRI values across various ripening stages, indicating variances in the fruit's biochemical parameters. The newly developed SRIs showed superior efficacy in evaluating these parameters. The integration of SRIs with diverse ML models proved to be a successful strategy for precisely estimating biochemical parameters. For mango's biochemical parameter prediction, the ANN models demonstrated R2 values ranging from 0.92 to 1.00 and from 0.93 to 0.98 for training and testing, respectively. On the other hand, the RF models exhibited R2 values ranging from 0.98 to 1.00 and from 0.93 to 0.99 during training and testing, respectively. The DT models showed high performance, with R2 values ranging from 0.95 to 1.00 and from 0.88 to 0.99 for the training and testing phases. For strawberry's biochemical parameter prediction, the ANN models achieved R2 values between 0.75 and 0.91 and between 0.58 and 0.91 during training and testing phases, respectively. On the other hand, RF models showed R2 values between 0.85 and 0.91 during training and between 0.74 and 0.86 during testing. The DT models demonstrated excellent results, with R2 values ranging from 0.75 to 0.91 for the training set and 0.74 to 0.81 for the testing set. It can be concluded that combining SRIs with ML models, such as ANN, RF, and DT, can accurately predict the biochemical properties of mango and strawberry fruits.
Why it matches plant phenotyping methods果実の成熟関連形質を対象に、分光反射センシング、独自スペクトル指数、機械学習モデルによる非破壊推定法を開発・評価しており、表現型取得・抽出が中心である。
abstractusing spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits
Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers. Firstly, a multi-branch architecture-based fast convolutional sampling module, designated as Light C2f, is proposed to replace the C2f module in the backbone of YOLOv8, in order to enhance the network's capacity to perceive multi-scale features. Secondly, a state space model-based lightweight neck with a global sensitivity field, designated as VMambaNeck, is proposed to replace the original neck of YOLOv8. After the training and testing of the improved algorithm on a self-constructed strawberry flower dataset, a series of experiments is conducted to evaluate the performance of the model, including ablation experiments, multi-dataset comparative experiments, and comparative experiments against state-of-the-art algorithms. The results show that the VM-YOLO network exhibits superior performance in object detection tasks across diverse datasets compared to the baseline. Furthermore, the results also demonstrate that VM-YOLO has better performances in the mAP, inference speed, and the number of parameters compared to the YOLOv6, Faster R-CNN, FCOS, and RetinaNet.
Why it matches plant phenotyping methodsイチゴ花の検出を目的とする画像解析モデルを開発し、データセット上でアブレーション、比較、性能評価を行っており、植物器官の取得・抽出法が研究の中心である。
abstractwe put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers.
Reproduction assets foundThe authors' self-constructed strawberry flower dataset (3388 labeled images used for all VM-YOLO experiments) is explicitly made publicly available via a Google Drive link in the Data Availability statement. The other listed datasets (Global Wheat Head 2020, CropAndWeed, Strawberry Disease) are cited prior public setsDataset · publicThe datasets can be found at the following: The strawberry flower dataset (Accessed on 2 February 2024): https://drive.google.com/drive/folders/1aT6ur3cLPp0xD0urIH6ex_mrFYkIAtm8 ; The Global Wheat Head Detection Dataset 2020 (Accessed on 10 February 2024): http://www.global-wheat.com/gwhd.html ; The CropAndWeed dataset (Accessed on 15 February 2024): https://github.com/cropandweed/cropandweed-dataset ; and The Strawberry Disease dataset (Accessed on 20 February 2024): www.kaggle.com/usmanafzaal/strawbeOpen asset ↗lines:145-326Plant phenotyping relevance match · UnverifiedCrossref · checked 6 Sept 2026
Early and accurate detection of plant diseases is crucial for making informed decisions to increase the yield and quality of crops through the decision of appropriate treatments. This study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm. In particular, we introduced a transformer module, a fusion of the SPP and C3TR modules, to synthesize features in various sizes and handle uneven input image sizes. The proposed model combined with transformer-based long-term dependency modeling and convolution-based visual feature extraction to improve object detection performance. To optimize a model to a lightweight version, we integrated the proposed transformer model with the Ghost module. Such an integration acted as regular convolutional layers that subsequently substituted for the original layers to cut computational costs. Furthermore, we adopted the SIoU loss function, a modified version of CIoU, applied to the YOLOv8s model, demonstrating a substantial improvement in accuracy. We implemented quantization to the YOLOv8 model using ONNX Runtime to enhance to facilitate real-time disease detection on strawberries. Through an experiment with our dataset, the proposed model demonstrated mAP@.5 characteristics of 80.30%, marking an 8% improvement compared to the original YOLOv8 model. In addition, the parameters and complexity were reduced to approximately one-third of the initial model. These findings demonstrate notable improvements in accuracy and complexity reduction, making it suitable for detecting strawberry diseases in diverse conditions.
Why it matches plant phenotyping methodsイチゴの病害を画像から検出する軽量Transformer/YOLOベース手法を開発・評価しており、植物の病害状態の画像計測が中心である。
abstractThis study introduces an automated system for early disease detection in plants that enhanced a lightweight model based on the robust machine learning algorithm.
The compact, high-throughput phenotyping platform, characterized by its portability and small size, is well-suited for crop phenotyping across diverse environments. However, integrating multi-source sensors to achieve synchronized data acquisition and analysis poses significant challenges due to constraints in load capacity and available space. To address these issues, we developed a robotic platform specifically designed for phenotyping greenhouse strawberries. This system integrates an RGB-D camera, a multispectral camera, a thermal camera, and a LiDAR sensor, enabling the unified analysis of data from these sources. The platform accurately extracted key phenotypic parameters, including canopy width (R² = 0.9864, RMSE = 0.0185 m) and average temperature (R2 = 0.8056, RMSE = 0.1732 °C), with errors maintained below 5%. Furthermore, it effectively distinguished between different strawberry varieties, achieving an Adjusted Rand Index of 0.94, underscoring the value of detailed phenotyping in variety differentiation. Compared to conventional UGV-LiDAR systems, the proposed platform is more cost-effective, efficient, and scalable, with enhanced data consistency, making it a promising solution for agricultural applications.
Why it matches plant phenotyping methods複数センサーを統合したロボット植物フェノタイピング基盤を開発し、キャノピー幅と平均温度の抽出精度を検証しているため、フェノタイピング手法が中心である。
abstractwe developed a robotic platform specifically designed for phenotyping greenhouse strawberries.
The development of strawberries is often impacted by inorganic and genetic terms, leading to significant risks to both quality and productivity. However, the existing approaches for disease recognition are characterised by a high rate of misinterpretation. Due to the high requirement for high strawberry productivity, relying on conventional recognition techniques primarily based on personal expertise and visual inspection is insufficient to address these challenges. Hence, it has become essential to develop more efficient approaches for accurately detecting strawberry diseases, along with providing detailed disease descriptions and suitable control measures. This work presents a clustering‐based Deep Learning (DL) model for strawberry disease recognition. Initially, the input images are normalised, and the affected regions are segmented by the Fuzzy C Means (FCM) clustering. Finally, the categorisation of different diseases is classified using the DL model Adaptive Deep Residual Network (ADRN). The ADRN is the integration of the Deep Residual Network (DRN) and the Reptile Search Optimizer (RSO). The analysis is evaluated on the Strawberry Disease Detection Dataset and attained better accuracy and precision of 0.991 and 0.995, respectively.
Why it matches plant phenotyping methodsイチゴ病害の症状領域を画像から分割・認識する手法を開発・評価しており、植物の病害状態を直接推定することが研究の中心である。
abstractThis work presents a clustering‐based Deep Learning (DL) model for strawberry disease recognition.
This paper investigates the application of a VR-controlled robotic system for yield monitoring in strawberry farming within a greenhouse environment. The study aims to evaluate the effectiveness of the system in identifying and counting ripe strawberries, categorized by size (small and large) and variety (Seascape and Albion), and compares with the obtained results by an onsite human expert. We designed experiments, in a controlled environment agriculture center, and conducted in two trials. The yield monitoring performance of the developed robotic system was evaluated based on two primary metrics of cycle completion times and fruit detection accuracy, 32 strawberry plants which grew 336 ripe fruits. In the first experiment, the system achieved detection rates of 63 % for small strawberries and 72 % for large strawberries, with cycle completion times ranging from 12.5 to 16 s. In the second experiment, improvements were observed, with detection rates increasing to 74 % for both sizes and cycle completion times reduced to between 11.9 and 15.7 s. The developed robotic system demonstrated high accuracy and efficiency, particularly with larger strawberries. However, some limitations were identified, including challenges related to occlusion. These findings suggest that while the VR-controlled robotic system has the potential to complement and even surpass traditional yield monitoring methods managed by human experts, further refinements are necessary. Future research should focus on optimizing the system’s performance and adapting the system to broader applications in agriculture.
Why it matches plant phenotyping methodsVR制御ロボットによるイチゴ果実の検出・計数とサイズ分類を、検出精度および処理時間で評価しており、果実収量関連形質の取得方法と技術性能が研究の中心である。
abstractThis paper investigates the application of a VR-controlled robotic system for yield monitoring in strawberry farming within a greenhouse environment.
StrawberryGreenhouseFruitClassificationGrowth / development / phenology
Strawberries, known for their economic significance and rich nutritional value, are cultivated extensively worldwide. However, a host of workers need to be employed every year to identify and categorize the developmental stages of the strawberries in the greenhouses, which is not only time-consuming, inefficient, increasing the cultivation cost, but also difficult to guarantee the classification accuracy. Meanwhile, affected by the complicated background, occlusions, and color interference, the features of strawberries are proven challenging to be extracted via the traditional neural networks due to serious gradient disappearance. Therefore, an improved CBAM-ResNet34- based classification evaluation method for developmental processes of greenhouse strawberries is investigated. The procedure of this method is as follows: firstly, the developmental stages of greenhouse strawberries are classified by experts into four stages: Stage I (initial stage), Stage II (green and white fruit stage), Stage III (early ripening stage), and Stage IV (fully ripe stage). The 627, 640, 604, and 340 strawberry images for these four stages are captured. Subsequently, the images are divided into training, validation, as well as testing sets and then undergo image pre- processing, expansion, and augmentation. Whereafter, the 7×7 convolution kernel in the first layer of the network is replaced by three consecutive 3×3 convolution cores to eliminate the redundant weights and unnecessary model parameters, and the BasicBlocks configuration is adjusted. Finally, the CBAM attention mechanism is added to each BasicBlock so as to pinpoint the spatial position of the strawberries and extract their major features such as shape, size, and color. Comparison experiments with the conventional deep neural networks LeNet5, AlexNet, VGG16, ResNet18, ResNet34, and every improved part of CBAM-ResNet34 demonstrated that when the learning rate is 0.001, the Dropout rate is 0.3, and the Adam’s weight decay parameter is 0.001, the accuracies for validation and testing sets can reach to 92.36% and 87.56% with F1 scores of 0.92, 0.87, 0.85 and 0.88.
Why it matches plant phenotyping methodsイチゴの発育段階という植物状態を画像から分類するCBAM-ResNet34手法を開発し、比較実験と精度評価を行っており、フェノタイピング手法が研究の中心である。
abstractan improved CBAM-ResNet34- based classification evaluation method for developmental processes of greenhouse strawberries is investigated.
Introduction Detecting strawberry growth stages is crucial for optimizing production management. Precise monitoring enables farmers to adjust management strategies based on the specific growth needs of strawberries, thereby improving yield and quality. However, dense planting patterns and complex environments within greenhouses present challenges for accurately detecting growth stages. Traditional methods that rely on large-scale equipment are impractical in confined spaces. Thus, the development of lightweight detection technologies suitable for portable devices has become essential. Methods This paper presents SGSNet, a lightweight deep learning model designed for the fast and accurate detection of various strawberry growth stages. A comprehensive dataset covering the entire strawberry growth cycle is constructed to serve as the foundation for model training and testing. An innovative lightweight convolutional neural network, named GrowthNet, is designed as the backbone of SGSNet, facilitating efficient feature extraction while significantly reducing model parameters and computational complexity. The DySample adaptive upsampling structure is employed to dynamically adjust sampling point locations, thereby enhancing the detection capability for objects at different scales. The RepNCSPELAN4 module is optimized with the iRMB lightweight attention mechanism to achieve efficient multi-scale feature fusion, significantly improving the accuracy of detecting small targets from long-distance images. Finally, the Inner-IoU optimization loss function is applied to accelerate model convergence and enhance detection accuracy. Results Testing results indicate that SGSNet performs exceptionally well across key metrics, achieving 98.83% precision, 99.45% recall, 99.14% F1 score, 99.50% mAP@0.5, and a loss value of 0.3534. It surpasses popular models such as Faster R-CNN, YOLOv10, and RT-DETR. Furthermore, SGSNet has a computational cost of only 14.7 GFLOPs and a parameter count as low as 5.86 million, demonstrating an effective balance between high performance and resource efficiency. Discussion Lightweight deep learning model SGSNet not only exceeds the mainstream model in detection accuracy, but also greatly reduces the need for computing resources and is suitable for portable devices. In the future, the model can be extended to detect the growth stage of other crops, further advancing smart agricultural management.
Why it matches plant phenotyping methodsイチゴの生育ステージという植物状態を画像から検出する軽量深層学習モデルを開発し、データセット構築と性能評価を行っており、表現型取得・推定手法が中心である。
abstractThis paper presents SGSNet, a lightweight deep learning model designed for the fast and accurate detection of various strawberry growth stages.
Fruit cells’ shape generally reflects the physiological state and quality of the fruit, and indirectly dictates its economics. In this study, a new bio-microscope including three independent and orthogonal channels of opto-electromechanical microscopic observation systems was developed to obtain the three views (e.g., front view, top view, side view) of a single fruit cell using tomato and strawberry at two ripening stages as fruit samples. The obtained three-view images were used to reconstruct the 3D real shape of a single cell based on the 3D geometrical modelling method using Solidworks CAD design software and then compared with the actual geometric size. The average relative errors for the major diameter, minor diameter 1, minor diameter 2, projection perimeter and projection area were 4.04 %, 6.25 %, 5.71 %, 1.69 % and 3.79 %, respectively. This good accuracy makes the newly developed bio-microscope together with the proposed 3D geometrical modelling method a promising 3D shape reconstruction technology for a single fruit cell to extract real and detailed cell morphology information. Furthermore, this method can find applications in other fields such as human and animal cells where soft particles’ 3D shape analysis is important.
Why it matches plant phenotyping methods果実細胞の3D形状・形態を取得する顕微鏡と再構成手法の開発および精度検証が研究の中心であり、植物の形態形質を直接推定している。
abstracta new bio-microscope including three independent and orthogonal channels of opto-electromechanical microscopic observation systems was developed to obtain the three views
StrawberryFruitClassificationCountingTrackingGrowth / development / phenology
Accurately counting fruit in orchards is a critical step for effective digital farming management. However, the variability in fruit size, overlapping shadows, and light interference present significant challenges to applying computer vision during the strawberry growth phase. To address these challenges, we propose StraTracker, a multi-object tracking (MOT) algorithm specifically designed to identify and count strawberries at various growth stages. StraTracker transforms the counting task into a frame-by-frame tracking problem, integrating both motion and appearance features. The algorithm is composed of three key components: a strawberry detector based on YOLOv8n, a feature association module, and a dual-area counting (DC) module. First, the strawberry detector accurately recognizes five growth stages, achieving an average accuracy of 91.93 % at 38.3 FPS. Next, the feature association module, incorporating the Feature Slicing Attention (FSA) and Adaptive Kalman Filtering (AKF) modules, mitigates issues such as light interference, impractical tracking frames, and ID switching (IDs). As a result, StraTracker achieves a Multi-Object Tracking Accuracy (MOTA) of 83.28 % and a Higher-Order Tracking Accuracy (HOTA) of 77.26 %, with only 259 IDs, outperforming existing baseline models. Finally, the DC module categorizes fruit counts based on the unique IDs assigned during tracking. The algorithm’s coefficient of determination (R2 = 0.91) and GEH of 2.33 indicate a strong correlation between predicted and actual counts. In conclusion, StraTracker offers a promising solution for farmers to optimize planting strategies and develop more precise harvesting plans.
Why it matches plant phenotyping methodsイチゴ果実数という植物器官の形質を、画像検出・多対象追跡・成長段階認識で自動抽出する手法を開発し、精度検証しているため、方法中心の植物フェノタイピング研究である。
abstractwe propose StraTracker, a multi-object tracking (MOT) algorithm specifically designed to identify and count strawberries at various growth stages.
Strawberry (Fragraria x ananassa) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (gₛ) and photosynthesis (A) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (gₛ and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms.
Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習を用いて、イチゴ植物の感染初期の生理的ストレスと萎凋病重症度を推定する手法が研究の中心であるため。
abstractThe combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants.
Strawberry holds significant economic value, but the laborious and time-consuming process of evaluating phenotypic traits in numerous germplasm resources during breeding poses a challenge. Prior studies relied on manual image collection within a single laboratory background, making it difficult to achieve automatic image collection and precise segmentation in complex field environments. However, accurate segmentation of plant organs is crucial for reliable phenotyping. In this research, we collected strawberry images at three growth stages (vegetative, flowering, and fruiting) using mobile phones and a high-resolution industrial camera mounted on our self-developed robot. Next, we designed an improved semantic segmentation model specifically tailored for strawberry plants, named Strawberry Segment Model (SSM), based on the Segment Anything Model. To address the uneven sample distribution problem, we enhanced the loss function and introduced a multi-loss approach combined with the class weight, resulting in improved detection performance. The comparative results demonstrated that SSM achieved state-of-the-art segmentation performance on the mobile phone image set, with a mean Intersection over Union (mIoU) of 80.20 %. We updated the model for the industrial camera on the robot, and achieved 75.81 % mIoU with only 10 % of the new data, striking a balance between performance and cost. Additionally, we mitigated uneven illumination using the Contrast Limited Adaptive Histogram Equalization and employed a Support Vector Machine model to classify 90 germplasm resources. The accuracy rates were 100 % for leaves and flowers, and 92.59 % for fruits. Overall, this study introduces novel equipment and methods for automated phenotypic analysis, supporting breeding investigations.
Why it matches plant phenotyping methodsイチゴの生殖質を対象に、ロボット搭載カメラによる画像取得、器官セグメンテーション、色表現型の分類を開発・評価しており、表現型取得手法が研究の中心である。
titleDetection of color phenotype in strawberry germplasm resources based on field robot and semantic segmentation
Modern, automated quality control systems for speciality crops utilise computer vision together with a machine learning paradigm exploiting large datasets for learning efficient crop assessment components. To model anomalous visuals, data augmentation methods are often developed as a simple yet powerful tool for manipulating readily available normal samples. State-of-the-art augmentation methods embed arbitrary “structural” peculiarities in normal images to build a classifier of these artefacts (i.e., pretext task), enabling self-supervised representation learning of visual signals for anomaly detection (i.e., downstream task). In this paper, however, we argue that learning such structure-sensitive representations may be suboptimal for agricultural anomalies (e.g., unhealthy crops) that could be better recognised by a different type of visual element like “colour”. To be specific, we propose Channel Randomisation (CH-Rand)—a novel data augmentation method that forces deep neural networks to learn effective encoding of “colour irregularities” under self-supervision whilst performing a pretext task to discriminate channel-randomised images. Extensive experiments are performed across various types of speciality crops (apples, strawberries, oranges, and bananas) to validate the informativeness of learnt representations in detecting anomalous instances. Our results demonstrate that CH-Rand’s representations are significantly more reliable and robust, outperforming state-of-the-art methods (e.g., CutPaste) that learn structural representations by over 43% in Area Under the Precision–Recall Curve (AUC–PR), particularly for strawberries. Additional experiments suggest that adopting the L∗a∗b∗ colour space and “curriculum” learning in the pretext task — gradually disregarding channel combinations for unrealistic outcomes — further improves downstream-task performance by 16% in AUC–PR. In particular, our experiments employ Riseholme-2021, a novel speciality crop dataset consisting of 3.5K real strawberry images gathered in situ from the real farm, along with the Fresh & Stale public dataset. All our code and datasets are made publicly available online to ensure reproducibility and encourage further research in agricultural technologies.
Why it matches plant phenotyping methods作物画像から異常・不健全状態を検出する画像解析手法を開発し、複数作物で検証しているため、植物状態の取得・推定が研究の中心である。
abstractwe propose Channel Randomisation (CH-Rand)—a novel data augmentation method that forces deep neural networks to learn effective encoding of “colour irregularities” under self-supervision
Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R 2 ), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm 3 , respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant's canopy volumes, which is crucial for implementing site-specific management of strawberry crops.
Why it matches plant phenotyping methodsUAV RGB画像とCNNを用いてイチゴ個体のキャノピー体積を推定する手法を開発・評価しており、植物形質の取得と技術検証が中心である。
abstractthis study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera.
Abstract: Preserving plant health and early detection of diseases are crucial in modern agriculture. Artificial intelligence techniques, particularly deep learning networks, are employed for this purpose. In this study, disease recognition was conducted using leaf images from various plant species. The study encompassed important agricultural products such as apples, strawberries, grapes, corn, peppers, and potatoes among the plant species considered. Among the deep learning networks, popular architectures like AlexNet, Vgg16, MobileNetV2, and Inception were compared. The Inception V3 model achieved the highest success rate of 92%, followed by the AlexNet architecture with a success rate of 91%. Among these networks, the InceptionV3 model yielded the best results. The InceptionV3 model effectively learned from plant leaf images and accurately distinguished between diseased and healthy leaves. These findings demonstrate that AI-based systems can be efficiently utilized for disease recognition and prevention in the agriculture sector. In this study, the performance of the InceptionV3 model in disease recognition on plant leaves was analyzed in detail, emphasizing the role of deep learning networks in agricultural applications.
Why it matches plant phenotyping methods植物葉画像から健全・罹病状態を推定する深層学習手法を比較・評価しており、植物病害表現型の取得・分類が研究の中心です。
abstractIn this study, disease recognition was conducted using leaf images from various plant species.
Reproduction assets foundThe paper's plant-disease classification experiments were performed on the public New Plant Diseases Dataset (Kaggle), which the authors explicitly state is openly accessible via a Kaggle URL. This is a paper-specific, public, actionable phenotype image dataset. No author analysis code or trained models are reported asDataset · publicsector. Suggestions for
future research include the use of larger and more
diverse datasets and the application of federated learning
techniques, which can improve the performance of the
model and provide security.
Dataset Access: The dataset used in this study is open
and can be accessed from the relevant source link.
Access:
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset.Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetpdf-raw-page:11 lines:97-114Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model's target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.
Why it matches plant phenotyping methodsイチゴ葉の病害状態を画像から自動検出するYOLOv8改良モデルを開発し、精度と計算量を比較評価しており、植物病害フェノタイピング手法が中心である。
abstractTo automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed.
Strawberries are a commonly used agricultural product in the food industry. In the traditional production model, labor costs are high, and extensive picking techniques can result in food safety issues, like poor taste and fruit rot. In response to the existing challenges of low detection accuracy and slow detection speed in the assessment of strawberry fruit maturity in orchards, a CR-YOLOv9 multi-stage method for strawberry fruit maturity detection was introduced. The composite thinning network, CRNet, is utilized for target fusion, employing multi-branch blocks to enhance images by restoring high-frequency details. To address the issue of low computational efficiency in the multi-head self-attention (MHSA) model due to redundant attention heads, the design concept of CGA is introduced. This concept aligns input feature grouping with the number of attention heads, offering the distinct segmentation of complete features for each attention head, thereby reducing computational redundancy. A hybrid operator, ACmix, is proposed to enhance the efficiency of image classification and target detection. Additionally, the Inner-IoU concept, in conjunction with Shape-IoU, is introduced to replace the original loss function, thereby enhancing the accuracy of detecting small targets in complex scenes. The experimental results demonstrate that CR-YOLOv9 achieves a precision rate of 97.52%, a recall rate of 95.34%, and an mAP@50 of 97.95%. These values are notably higher than those of YOLOv9 by 4.2%, 5.07%, and 3.34%. Furthermore, the detection speed of CR-YOLOv9 is 84, making it suitable for the real-time detection of strawberry ripeness in orchards. The results demonstrate that the CR-YOLOv9 algorithm discussed in this study exhibits high detection accuracy and rapid detection speed. This enables more efficient and automated strawberry picking, meeting the public's requirements for food safety.
Why it matches plant phenotyping methodsイチゴ果実の成熟度という植物形質を画像から検出する手法を開発し、精度・再現率・mAP・速度で評価しており、フェノタイピング手法が研究の中心である。
abstracta CR-YOLOv9 multi-stage method for strawberry fruit maturity detection was introduced.
In recent years, hyperspectral imaging combined with machine learning techniques has garnered significant attention for its potential in assessing fruit maturity. This study proposes a method for predicting strawberry fruit maturity based on the harvest time. The main features of this study are as follows. 1) Selection of wavelength band associated with strawberry growth season; 2) Extraction of efficient parameters to predict strawberry maturity 3) Prediction of internal quality attributes of strawberries using extracted parameters. In this study, experts cultivated strawberries in a controlled environment and performed hyperspectral measurements and organic analyses on the fruit with minimal time delay to facilitate accurate modeling. Data augmentation techniques through cross-validation and interpolation were effective in improving model performance. The four parameters included in the model and the cumulative value of the model were available for quality prediction as additional parameters. Among these five parameter candidates, two parameters with linearity were finally identified. The predictive outcomes for firmness, soluble solids content, acidity, and anthocyanin levels in strawberry fruit, based on the two identified parameters, are as follows: The first parameter, p s , demonstrated RMSE performances of 1.0 N, 2.3 %, 0.1 %, and 2.0 mg per 100 g fresh fruit for firmness, soluble solids content, acidity, and anthocyanin, respectively. The second parameter, p 3 , showed RMSE performances of 0.6 N, 1.2 %, 0.1 %, and 1.8 mg per 100 g fresh fruit, respectively. The proposed non-destructive analysis method shows the potential to overcome the challenges associated with destructive testing methods for assessing certain internal qualities of strawberry fruit.
Why it matches plant phenotyping methodsイチゴ果実の内部品質形質を、ハイパースペクトル画像と機械学習で非破壊推定する手法の開発・性能評価が研究の中心である。
abstractThis study proposes a method for predicting strawberry fruit maturity based on the harvest time.
Gallic acid (GA) is one of the main phenolic components naturally occurring in many plants and foods and has been a subject of increasing interest owing to its antioxidant and anti-mutagenic properties. This study introduces a novel flexible sensor designed for in situ detecting GA in plant leaves. The sensor employs a laser-induced graphene (LIG) flexible electrode, enhanced with MXene and molybdenum disulfide (MoS 2 ) nanosheets. The MXene/MoS 2 /LIG flexible sensor not only demonstrates exceptional mechanical properties, covering a wide detection range of 1-1000 μM for GA, but also exhibits remarkable selectivity and stability. The as-prepared sensor was successfully applied to in situ determination of GA content in strawberry leaves under salt stress. This innovative sensor opens an attractive avenue for in situ measurement of metabolites in plant bodies with flexible electronics.
Why it matches plant phenotyping methods植物葉内の代謝物をin situ測定する柔軟センサーを開発し、検出性能を評価した研究であり、植物状態の取得法が中心である。
abstractThis study introduces a novel flexible sensor designed for in situ detecting GA in plant leaves.
StrawberryField / plotLeafClassificationWater status / transpiration
In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring leaf wetness duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida's Plant Science Research and Education Unit (PSREU) in Citra, FL, USA, and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.
Why it matches plant phenotyping methods高解像度画像と深層学習で葉面濡れ時間という植物状態を推定する手法を開発し、手動観察と比較検証しているため、方法が中心である。
abstractthis study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs).
In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible-near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500-978 nm) and NIR (908-1676 nm) data collected from 180 samples of "Tochigi iW1 go" white strawberries and 150 samples of "Tochigi i27 go" red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R 2 p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R 2 p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system.
Why it matches plant phenotyping methods可視・近赤外分光とPLSRによりイチゴ果実の糖度を非破壊推定する手法を評価しており、植物器官の品質・成熟状態の取得方法が研究の中心です。
abstractvisible-near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC.
Introduction In strawberry farming, phenotypic traits (such as crown diameter, petiole length, plant height, flower, leaf, and fruit size) measurement is essential as it serves as a decision-making tool for plant monitoring and management. To date, strawberry plant phenotyping has relied on traditional approaches. In this study, an image-based Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely “YOLOv4” and “U-net” integrated into a single system. We aimed to create the most suitable DL-based tool with enhanced robustness to facilitate digital strawberry plant phenotyping directly at the natural scene or indirectly using captured and stored images. Methods Our SPT was developed primarily through two steps (subsequently called versions) using image data with different backgrounds captured with simple smartphone cameras. The two versions (V1 and V2) were developed using the same DL networks but differed by the amount of image data and annotation method used during their development. For V1, 7,116 images were annotated using the single-target non-labeling method, whereas for V2, 7,850 images were annotated using the multitarget labeling method. Results The results of the held-out dataset revealed that the developed SPT facilitates strawberry phenotype measurements. By increasing the dataset size combined with multitarget labeling annotation, the detection accuracy of our system changed from 60.24% in V1 to 82.28% in V2. During the validation process, the system was evaluated using 70 images per phenotype and their corresponding actual values. The correlation coefficients and detection frequencies were higher for V2 than for V1, confirming the superiority of V2. Furthermore, an image-based regression model was developed to predict the fresh weight of strawberries based on the fruit size (R2 = 0.92). Discussion The results demonstrate the efficiency of our system in recognizing the aforementioned six strawberry phenotypic traits regardless of the complex scenario of the environment of the strawberry plant. This tool could help farmers and researchers make accurate and efficient decisions related to strawberry plant management, possibly causing increased productivity and yield potential.
Why it matches plant phenotyping methods画像ベースでイチゴの複数形質を抽出・測定する深層学習ツールを開発し、精度と実測値との相関を検証しており、植物表現型取得手法が研究の中心である。
abstractIn this study, an image-based Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely “YOLOv4” and “U-net” integrated into a single system.
Reproduction assets foundThe authors publicly released the strawberry image datasets, annotations, and trained YOLOv4/U-net deep-learning models (SPT V1/V2) on GitHub, and deployed the V2 tool as a web service. Both are paper-specific, public, and actionable.Dataset · publicThe images, annotation results and DL models subjected to V1 and V2 of STP are available at https://github.com/kist-smartfarm/SPT .Open asset ↗kist-smartfarm/SPTlines:355-404Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.
Why it matches plant phenotyping methods植物画像から病徴を分類するCNN-SVM手法の開発・比較・検証が中心であり、植物病害状態の画像ベース表現型推定に該当する。
abstractThis research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans.
StrawberryField / plotLeafClassificationWater status / transpiration
In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring Leaf Wetness Duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida's Plant Science Research and Education Unit (PSREU) in Citra, Florida, USA and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.
Why it matches plant phenotyping methods高解像度画像と深層学習により葉面濡れ状態・葉面濡れ時間を推定する手法を開発し、手動観測と比較検証しており、植物状態の取得手法が研究の中心です。
abstractthis study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs).
AppleStrawberryField / plotLiDAR / point cloudFruitSegmentation
The accurate segmentation of fruit phenotypes in the field is of great significance for agricultural automation in the 3D scene. Although the existing fruit segmentation based on 3D point cloud has made great progress, in the complex field environment, due to lighting, leaf occlusion, shooting angle and other problems, the point cloud obtained by depth camera often has the problem of multiple voids and discrete points, which seriously affects the accurate segmentation of fruit phenotype. This paper proposes a embedding subnetwork FSDnet based on density-based feature extraction and feature propagation and embeds it in the novel segmentation networks, which effectively improves the segmentation accuracy of the point cloud phenotype in multi-hole and multi-discrete fruits, including (1) The density-based point cloud feature extraction and feature propagation theory is proposed to alleviate the problem of perception degradation in fruit edge point caused by discrete points and holes caused by imcomplete point cloud in the agriculture scene. (2) A density-adaptive embedding semantic segmentation framework FSDnet is proposed, and embedding the classical point cloud neural network can significantly improve the segmentation accuracy of the fruit phenotypes with multiple holes and discrete points in the traditional network. (3) This paper made a strawberry dataset and tested the designed new neural network on both strawberry and apple filed dataset. After FSDnet is embedded on different novel net, almost all net have been improved. We verified the performance of FSDnet in different density states in agricultural scenarios, mitigated the negative impact of density on segmentation accuracy, proving that it can adapt to different point cloud density in agricultural scenarios in comparison between Gaussian density and other two traditional density schemes, Gaussian density reduces the computational traffic (0.58G) of the network while maintaining similar performance to the other two densities, proving the superiority of assuming a Gaussian density.
Why it matches plant phenotyping methods3D点群から果実表現型を分割・抽出する手法を開発し、イチゴ・リンゴデータセットで性能検証しているため、植物フェノタイピング手法が中心である。
abstractThe accurate segmentation of fruit phenotypes in the field is of great significance for agricultural automation in the 3D scene.
This paper proposes a study conducted on various techniques that can be employed for the early detection of plant diseases. With exponential growth in the global population, there is a dire need for the detection and prevention of various types of plant diseases such as Mosaic virus in Solanum Lycopersicon (tomato), bacterial spot in Fragaria Ananassa (strawberry), late and early blight in Solanum Tuberosum (potato), huanglongbing in Citrus sinensis (orange), and Isariopsis leaf spot in Vitis vinifera (grapes). These diseases generally lead to lower yields and hence less profit. In the last two decades, there has been rapid development in the fields of image processing and deep learning. Various models of deep learning can be used for plant disease detection. The main objective is that as soon as plant leaf disease appears, there should be one device to monitor the symptoms and detect them over a large field with as much accuracy as possible. This study compares the deep learning models Resnet, MobileNet, and inceptionV3 that are implemented on a large dataset taken from the Kaggle repository. We implemented the models using Google Colaboratory tools, which provide us with Python’s Jupyter notebook that runs on the Google cloud server. The GPU “Tesla T4” and CPU “Intel Xenon” were used during training, validation, and testing respectively. The training and validation accuracy of the InceptionV3 model was 98.78% and 93.94%, respectively. MobileNet classified various plant diseases with training and validation accuracies of 99.57% and 97.31. Similarly, for ResNet, the training accuracy was found to be around 99.62% and the validation accuracy was 97.16%. We hope that this work will provide a helpful resource for other researchers working in the field of agriculture to detect various types of crop diseases. Future work and some challenges still faced are also discussed in this study.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を比較評価しており、病害状態の表現型推定と手法検証が研究の中心である。
abstractThis paper proposes a study conducted on various techniques that can be employed for the early detection of plant diseases.
Reproduction assets foundThe paper's plant-disease classification experiments are built entirely on two public leaf-image datasets: the augmented New Plant Diseases Dataset from Kaggle (87.9k RGB leaf images, 38 classes) and the original PlantVillage-Dataset on GitHub. Both are explicitly cited with public URLs and directly constitute the phenDataset · publicWe used the New Plant Disease Dataset (augmented) [18],
which can be found in the Kaggle repository.Open asset ↗Kagglepdf-raw-page:3 lines:1-117Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Among the technological tools used in precision agriculture, the convolutional neural network (CNN) has shown promise in determining the nutritional status of plants, reducing the time required to obtain results and optimizing the variable application rates of fertilizers. Not knowing the appropriate amount of nitrogen to apply can cause environmental damage and increase production costs; thus, technological tools are required that identify the plant’s real nutritional demands, and that are subject to evaluation and improvement, considering the variability of agricultural environments. The objective of this study was to evaluate and compare the performance of two convolutional neural networks in classifying leaf nitrogen in strawberry plants by using RGB images. The experiment was carried out in randomized blocks with three treatments (T1: 50%, T2: 100%, and T3: 150% of recommended nitrogen fertilization), two plots and five replications. The leaves were collected in the phenological phase of floral induction and digitized on a flatbed scanner; this was followed by processing and analysis of the models. ResNet-50 proved to be superior compared to the personalized CNN, achieving accuracy rates of 78% and 48% and AUC of 76%, respectively, increasing classification accuracy by 38.5%. The importance of this technique in different cultures and environments is highlighted to consolidate this approach.
Why it matches plant phenotyping methodsRGB画像からイチゴ葉の窒素栄養状態を分類するCNNを比較評価しており、植物形質推定手法の技術的検証が中心です。
abstractThe objective of this study was to evaluate and compare the performance of two convolutional neural networks in classifying leaf nitrogen in strawberry plants by using RGB images.
Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study, two separate sets of strawberry images, along with their associated diseases, were collected and subjected to resizing and augmentation. Subsequently, a combined dataset consisting of nine classes was utilized to fine-tune three distinct pretrained models: vision transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first five and last five layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the vision transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.
Why it matches plant phenotyping methodsイチゴ画像から病害と成熟度を分類する画像解析手法を開発・比較し、植物の状態推定が研究の中心であるため。
abstractthe analysis revealed that the vision transformer model exhibited superior performance in strawberry ripeness and disease classification.
Reproduction assets foundThe paper's strawberry disease/quality image dataset (the merged Afzaal-derived and StrawDI-derived images used for fine-tuning ViT, MobileNetV2, and ResNet18) is openly deposited by the authors on OSF, per the Data Availability Statement and reference 15. No author analysis code or trained model checkpoints are statedDataset · publicData Availability Statement: The data presented in this study are openly available in OSF at
10.17605/OSF.IO/EJ5QV reference number https://osf.io/ej5qv/ (accessed on 13 February 2023).Open asset ↗OSF · 10.17605/OSF.IO/EJ5QVpdf-page:14 lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation. Results The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general. Discussion The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration.
Why it matches plant phenotyping methodsイチゴ病害の画像から病斑・病害状態をセグメンテーションする手法を開発・比較しており、植物の病害表現型の取得が中心である。
abstractMiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection
Reproduction assets foundThe paper's phenotyping analysis is based on a public Kaggle strawberry disease image dataset (2,450 raw images, augmented to 4,574) explicitly linked in the data availability statement. No author analysis code or trained model checkpoints are deposited.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/usmanafzaal/strawberry-disease-detection-dataset .Open asset ↗Kaggle · usmanafzaal/strawberry-disease-detection-datasetlines:875-889Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published1 Jun 2024Indonesian Journal of Electrical Engineering and Computer ScienceCited by 1 · OpenAlex ↗
Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.
Why it matches plant phenotyping methods植物葉の病害を画像から分類するTransformerベースの手法を提案し、複数データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation.
Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.
Why it matches plant phenotyping methods3D再構成・意味分割・RANSACによるイチゴ収量推定システムを開発し、精度評価まで行っており、植物形質（収量）の取得・推定法が中心である。
abstractthis paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map.
Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study two separate sets of strawberry images, along with their associated diseases, were collected and subjected to, resizing, and augmentation. Subsequently, a combined dataset consisting of 9 classes was utilized to fine-tune three distinct pre-trained models: Vision Transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first 5 and last 5 layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the Vision Transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.
Why it matches plant phenotyping methodsイチゴ画像から病害と成熟度を分類するコンピュータビジョン手法が研究の中心であり、複数モデルの性能比較・評価も実施しているため、植物フェノタイピング手法として含める。
titleClassification Of Strawberry Diseases and Quality Using Different Machine Learning Methods
Monitoring and modeling the growth of strawberries at the individual fruit level can open up new opportunities for yield prediction, fruit grading and supply chain optimization. However, existing strawberry growth models mainly focus on plot or plant level and can not simulate the growth of individual fruits, and existing computer vision (CV)-based studies primarily focus on instant tasks but lack the reasoning capabilities required for dynamic growth simulations. In this study, we developed a novel knowledge-guided CV framework, named KGCV-strawberry, to simulate the growth of strawberry fruits on an individual basis. We conducted two consecutive years of greenhouse experiments with intensive measurements to develop and test the framework. The KGCV-strawberry framework consists of two components: first, the fruit trait detector (acting as the “eye”) that interprets bounding boxes and biophysical traits of individual fruits from raw images, and second, the fruit growth simulator (acting as the “brain”) that uses the estimated traits to predict fruit growth. We employed a hybrid training approach for KGCV-strawberry, where the fruit trait detector was trained by ground observations and the fruit growth simulator was trained by synthetic data generated by the S-shape fruit growth curves. The KGCV-strawberry is designed to be able to dynamically assimilate observations (e.g., image sequences) such that the fruit growth simulator infers growth curve parameters from fruit size sequences. We tested the KGCV-strawberry by ground fruit trait measurements, with a case study showing the RMSE of diameter estimation decreased by 74 % as the sequence of observations expanded from 1 to 6. For the yield prediction task, we observed a reduction in the RMSE from 3.58 to 2.01 g and an increase in R² from 0.25 to 0.73 as more images were assimilated into the framework. Additionally, the RMSE for predicting the remaining growing degree days (GDD) until maturity saw a significant reduction from 71.87 °C·day to 39.30 °C·day, accompanied by an increase in R² from 0.11 to 0.61. Although the best prediction is achieved near maturity, the prediction accuracy is acceptable two weeks before fruit maturity. Additionally, we conducted a comparison between KGCV-Strawberry and a process-based model for predicting plant-level yields. KGCV-Strawberry exhibited superior performance in capturing yield dynamics for each harvest. These findings highlight the potential of applying this framework for precise management optimization of individual fruits in intelligent strawberry farming.
Why it matches plant phenotyping methods個々のイチゴ果実の画像から形態形質を抽出し、生育軌跡・収量・成熟までの期間を予測するコンピュータビジョン手法を開発・検証しており、表現型取得と解析が研究の中心である。
abstractwe developed a novel knowledge-guided CV framework, named KGCV-strawberry, to simulate the growth of strawberry fruits on an individual basis.
Crop growth status detection is significant in agriculture and is vital in planting planning, crop yield, and reducing the consumption of fertilizers and workforce. However, little attention has been paid to detecting the growth status of each crop. Accuracy remains a challenging problem due to the small size of individual targets in the image. This paper proposes an object detection model, HR-YOLOv8, where HR means High-Resolution, based on a self-attention mechanism to alleviate the above problem. First, we add a new dual self-attention mechanism to the backbone network of YOLOv8 to improve the model’s attention to small targets. Second, we use InnerShape(IS)-IoU as the bounding box regression loss, computed by focusing on the shape and size of the bounding box itself. Finally, we modify the feature fusion part by connecting the convolution streams from high resolution to low resolution in parallel instead of in series. As a result, our method can maintain a high resolution in the feature fusion part rather than recovering high resolution from low resolution, and the learned representation is more spatially accurate. Repeated multiresolution fusion improves the high-resolution representation with the help of the low-resolution representation. Our proposed HR-YOLOv8 model improves the detection performance on crop growth states. The experimental results show that on the oilpalmuav dataset and strawberry ripeness dataset, our model has fewer parameters compared to the baseline model, and the average detection accuracy is 5.2% and 0.6% higher than the baseline model, respectively. Our model’s overall performance is much better than other mainstream models. The proposed method effectively improves the ability to detect small objects.
Why it matches plant phenotyping methods作物の生育状態・成熟度という植物状態を画像から検出するYOLOv8改良法を開発し、複数データセットで性能評価しており、表現型取得・推定手法が中心である。
abstractThis paper proposes an object detection model, HR-YOLOv8, where HR means High-Resolution, based on a self-attention mechanism to alleviate the above problem.
The strawberry appearance is an essential standard for judging the quality, so it is crucial to accurately identify the strawberry appearance quality for intelligent picking. This study proposed a new strawberry appearance quality detection based on unsupervised deep learning. Firstly, using deep learning (Resnet18, Resnet50, and Resnet101) to extract the strawberry image feature information. And using the t-SNE (t-distribution stochastic neighbor embedding) to reduce the feature vectors’ dimension. Finally, the unsupervised learning method (Gaussian Mixture Model) was used to cluster strawberries’ feature points. The results showed that: (1) the clustering performance based on Resnet101 was effective in 2-dimensional space, the cluster accuracy was 94.89%, and the validation accuracy was 91.79%. (2) The clustering method based on Resnet50 had good performance in the 3-dimensional space, the cluster accuracy was 96.10%, and the validation accuracy was 93.08%. (3) The accuracy of deep features plus RF (random forest) was 95.00% under limited data. Thus this method will promote intelligent picking strawberry equipment and it will overcome the supervised learning drawback that divides image datasets according to prior knowledge.
Why it matches plant phenotyping methodsイチゴ果実画像から外観品質を推定・分類する深層学習とクラスタリング手法が研究の中心であり、植物器官の状態を定量化するフェノタイピング手法に該当する。
abstractThis study proposed a new strawberry appearance quality detection based on unsupervised deep learning.
Automated phenotyping of plants for breeding and plant studies promises to provide quantitative metrics on plant traits at a previously unattainable observation frequency. Developers of tools for performing high-throughput phenotyping are, however, constrained by the availability of relevant datasets on which to perform validation. To this end, we present a spatio-temporal dataset of 3D point clouds of strawberry plants for two varieties, totalling 84 individual point clouds. We focus on the end use of such tools - the extraction of biologically relevant phenotypes - and demonstrate a phenotyping pipeline on the dataset. This comprises of the steps, including; segmentation, skeletonisation and tracking, and we detail how each stage facilitates the extraction of different phenotypes or provision of data insights. We particularly note that assessment is focused on the validation of phenotypes, extracted from the representations acquired at each step of the pipeline, rather than singularly focusing on assessing the representation itself. Therefore, where possible, we provide \textit{in silico} ground truth baselines for the phenotypes extracted at each step and introduce methodology for the quantitative assessment of skeletonisation and the length trait extracted thereof. This dataset contributes to the corpus of freely available agricultural/horticultural spatio-temporal data for the development of next-generation phenotyping tools, increasing the number of plant varieties available for research in this field and providing a basis for genuine comparison of new phenotyping methodology.
Why it matches plant phenotyping methods植物の3D点群データセットを提供し、セグメンテーション・骨格化・追跡による表現型抽出パイプラインと、その定量的検証手法を中心に扱っているため。
abstractThis comprises of the steps, including; segmentation, skeletonisation and tracking, and we detail how each stage facilitates the extraction of different phenotypes or provision of data insights.
Reproduction assets foundThe paper's LAST-Straw dataset (84 strawberry plant point clouds with semantic/instance annotations and ground-truth stem skeletons) and supplementary graph-matching code are both publicly available via author-provided URLs in the data availability statement.Code · publicSupplementary code
for graph matching can be accessed via https://github.com/LCAS/GraphMatching3D.Open asset ↗LCAS/GraphMatching3Dpdf-page:31 lines:1-39Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2024Computers and Electronics in Agriculture.
With the wave of agricultural modernization, deep learning technology has brought revolutionary changes to the vision system of strawberry picking robots. Yet, the morphological diversity of strawberries, small and dense targets, and high overlap scenes make the detection and ripeness classification of strawberries a great challenge. To solve these problems, we introduce a new task-aligned one-stage object detection (TOOD) mechanism. Firstly, we incorporate the Swin-B (Swin-Base) transformer module to enhance the feature extraction performance in the backbone network. Secondly, we replace the original feature pyramid network (FPN) with CARAFE-FPN, which utilizes advanced upsampling methods to enhance detection at different scales. A multi-scale training (MST) approach is applied to capture the small targets effectively. Additionally, the Augmentations library is utilized for dataset augmentation to enhance the model’s generalization. Lastly, we refine the task alignment learning head and propose simple anchor alignment metric (S-aam) to reduce the impact of parameters on network performance for finding the optimal solutions. We collected a complex strawberry image dataset of more than 90,000 instances to test the method effectiveness in detecting strawberry ripeness. The results show that our model achieves 74.1% average precision (AP), 93.9% AP₅₀, and 84.1% AP₇₅, respectively. Our model shows the superior detection performance compared to most of models with fewer parameters and lower FLOPs. In addition, our model obtained the highest accuracy in detecting small strawberry targets. To prove the generalization of the model, we also verify it on COCO dataset, and the results show that the performance has been enhanced by 0.7% compared to the baseline. In summary, our proposed methods can be used to accurately identify ripe strawberries, which has the potential to be applied in strawberry picking robot system.
Why it matches plant phenotyping methodsイチゴ画像から成熟度という植物状態を推定する物体検出手法を開発し、専用データセットで性能評価しているため、植物フェノタイピング手法が中心である。
abstractTo solve these problems, we introduce a new task-aligned one-stage object detection (TOOD) mechanism.
Autonomous field robots are being developed for picking of fruit, where each fruit needs to be individually graded and handled. There is therefore a need for rapid and non-destructive sensing to measure critical fruit quality parameters. In this article we report how total soluble solids (TSS), a measure for total sugar content, can be measured in strawberries in the field by non-contact near-infrared (NIR) interaction spectroscopy. A specially designed prototype system working in the wavelength range 760-1080 nm was tested for this purpose. This novel instrument was compared with a commercial handheld NIR reflection instrument working in the range 900-1600 nm. The instruments were calibrated in the lab using data collected from 200 strawberries of two varieties and tested in a strawberry field on 50 berries in 2022 and 100 berries in 2023. Both systems performed well during calibration with root mean square errors of cross validation for TSS around 0.49 % and 0.57 %, for interaction and reflection, respectively. For prediction of TSS in new berries in 2023, the interaction system was superior, with a prediction error of 1.0 % versus 8.1 % for the reflection system, most likely because interaction probes deeper into the berries. The results suggest that interaction measurements of average TSS are more robust and would most likely require less calibration maintenance compared to reflection measurements. The non-contact feature is important since it reduces the spread of diseases and physical damage to the berries.
Why it matches plant phenotyping methodsイチゴ果実の糖度（TSS）という植物形質を非接触NIRで測定するプロトタイプを開発・比較検証しており、センシング手法が研究の中心である。
abstractwe report how total soluble solids (TSS), a measure for total sugar content, can be measured in strawberries in the field by non-contact near-infrared (NIR) interaction spectroscopy.
In crop cultivation, particularly in controlled environmental agriculture, light quality is one of the most critical factors affecting crop growth and harvest. Many scholars have studied the effects of light quality on strawberry traits, but they have used relatively simple light components and considered only a small number of light qualities and traits in each experiment, and the results were not complete or objective. In order to comprehensively investigate the effects of different light qualities from 350 nm to 1000 nm on strawberry traits to better predict the future growth trend of strawberries under different light qualities, we proposed a new approach. We introduced Spearman's rank correlation coefficient to handle complex light quality variations and multiple traits, preprocessed the cultivation data through the CEEDMAN method, and predicted them using the Informer network. We took 500 strawberry plants as samples and cultivated them in 72 groups of dynamically changing light qualities. Then, we recorded the growth changes and formed training and testing sets. Finally, we discussed the correlation between light quality and plant trait changes in consistency with current studies, and the proposed prediction model achieved the best performance in the prediction task of nine plant traits compared with the comparison models. Thus, the validity of the proposed method and model was demonstrated.
Why it matches plant phenotyping methods植物9形質の将来予測を目的に、データ前処理法とInformer予測モデルを中心的に提案・評価しており、形質取得・推定手法が実験の単なる routine 測定ではない。
abstractpreprocessed the cultivation data through the CEEDMAN method, and predicted them using the Informer network.
The capability of robotic fruit-harvesting systems to accurately assess the ripeness of fruits is crucial for fulfilling the diverse standards of the market and the preferences of consumers. Existing studies involving fruit ripeness estimation mainly focus on detecting of ripe strawberries as one class or classifying of ripeness into several stages, such as overripe, ripe, and unripe. Current harvesting robots also lack the ability to determine the ripeness of the back of fruit with respect to the robots. This paper proposes a lightweight convolutional neural network (CNN) regression model for ripeness quantification based on the internal image sensing system of the gripper with full-view coverage of the fruit for strawberry-harvesting robots. A gripper internal sensing system was developed using two RGB cameras that could provide full-view fruit coverage for a more accurate estimation of fruit ripeness. Four base CNN networks capable of feature learning were used for feature extraction, followed by the utilization of newly added dense layers that produced a regressed value to represent the strawberry ripeness. However, the base networks were cumbersome and relatively slow due to their complex structures. To simplify this, a new MiniNet with fewer convolutional layers was proposed to reduce the model size and inference time. All models were trained via two loss functions, mean square error and Huber loss. The results showed that the models trained via Huber loss performed better. An Xception model trained on Huber loss showed the best performance with a mean absolute error of 4.0% and an average inference time of 42.5 ms. Of all the models, the new MiniNet was the most lightweight and fastest model while maintaining high performances (an mean absolute error of 4.8% and an inference time of 6.5 ms for Huber loss trained model). The proposed method may be also applicable to other fruit-harvesting systems.
Why it matches plant phenotyping methodsイチゴ果実の熟度という植物器官の状態を、グリッパー内蔵カメラとCNNで定量推定する取得・解析手法を開発し、精度と推論速度を評価しているため、植物フェノタイピング手法が中心である。
abstractThis paper proposes a lightweight convolutional neural network (CNN) regression model for ripeness quantification based on the internal image sensing system of the gripper with full-view coverage of the fruit for strawberry-harvesting robots.
The impact of a coating solution containing chitosan, lignocellulosic nanofibers (LCNF), and raspberry leaf extract on strawberry quality was assessed. Strawberries were coated, inoculated with Aspergillus flavus (a major aflatoxin producer), and stored at 5 ± 1 °C for 16 days. An automated fruit quality evaluation system was developed, and daily images were taken for analysis. ImageJ software was used to evaluate colour, size loss, and fungal damage. Coating solution significantly reduced fruit size loss, with coated strawberries showing 18.4 ± 12.8% size loss after 16 days, compared to 30.2 ± 8.4% in the control group and 25 ± 10.7% in uncoated strawberries. Regarding colour, lower colour changes were observed in coated fruit compared to the control during storage (p ≤ 0.05). Fungal damage did not significantly differ between treatments during storage (p > 0.05). Furthermore, the study demonstrated that image analysis and visual estimation methods for assessing fruit fungal damage yielded similar results, with a strong correlation (R² = 0.87). This suggests that the proposed image analysis methodology is effective for the assessment of fungal damage in strawberries compared to visual estimation. Chitosan-LCNF-raspberry extract coating solution shows promise in enhancing strawberry quality by reducing size loss during storage and managing fungal development.
Why it matches plant phenotyping methodsイチゴの色、サイズ損失、真菌被害を画像から評価する自動システムを開発し、視覚評価との一致度も検証しており、画像ベースの表現型取得が中心的な方法貢献である。
abstractAn automated fruit quality evaluation system was developed, and daily images were taken for analysis.
Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400-1000 nm) and short-wave infrared (SWIR) (935-1720 nm) for predicting four strawberry quality attributes (firmness-FF, total soluble solid content-TSS, titratable acidity-TA, and dry matter-DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R 2 = 0.959), DM (R 2 = 0.947), and TA (R 2 = 0.877), whereas good prediction was observed for FF (R 2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R 2 = 0.924 for DM; R 2 = 0.898 for TSS; R 2 = 0.953 for TA; R 2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R 2 = 0.942 for DM; R 2 = 0. 981 for TSS; R 2 = 0.976 for TA; R 2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product's marketability.
Why it matches plant phenotyping methods2種類のハイパースペクトル画像システムを用いてイチゴ果実の品質形質を非破壊推定し、ANNモデル、性能評価、データ融合を検証しており、フェノタイピング手法が中心である。
abstractThis research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems
Background Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. Results Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. Conclusions This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.
Why it matches plant phenotyping methodsイチゴ葉の病害症状をハイパースペクトル画像から直接推定し、特徴抽出と分類器を開発・評価しているため、植物表現型取得法が中心である。
abstractBased on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases.
Stolons are an essential nutritional organ of strawberry seedlings, whose quantity and vigorous growth directly impact the quality of the seedling cultivation. Accurately detecting stolon is essential for increasing productivity in seedling factories. To alleviate these issues of low recognition accuracy of stolon caused by complex backgrounds, diverse growth types and low proportion of effective information in the bounding box, we propose a Stolon-YOLO for visual recognition of stolon for the first time. In our method, improvements are focused on two aspects: HorBlock-decoupled head and Stem Block feature enhancement module. Firstly, a new decoupled head is designed by introducing the HorBlock, which enables the interaction of high-order spatial information and enhances the precision for locating stolons. Sequential gⁿConv of the HorBlock realizes better global modeling for incomplete and curved stolon to improve detection efficiency. Then, the Stem Block is used to reinforce feature expression and reduce dimensionality. We fuse the processing results of the Stem Block with the output of HorBlock in a residual form. Experimental results demonstrate that the Stolon-YOLO achieves 92.5% in precision, 89.7% in recall rate, 91.1% in F1 score and 88.5% in average precision for stolon detection, which outperforms the standard YOLOv7 by 3% in recall rate and 3.4% in average precision. Compared with YOLOX, SSD, CenterNet and Faster R-CNN, the Stolon-YOLO increases recall rate by 8.3%, 27.4%, 17%, and 3.5%, and increases AP by 12.2%, 28.9%, 2.4%, and 7.5%, respectively. Meanwhile, the Stolon-YOLO achieves a frame rate of 50.25 FPS, meeting real-time detection demands. The above results highlight that this study provides a fast and effective method for identifying the stolon of strawberry seedlings in glass greenhouses.
Why it matches plant phenotyping methodsイチゴ苗の匍匐茎を画像から検出するYOLOベース手法を開発し、複数手法との性能比較とリアルタイム性評価を行っており、植物器官の表現型取得が中心である。
abstractwe propose a Stolon-YOLO for visual recognition of stolon for the first time
StrawberryFruitObject detectionGrowth / development / phenology
Identifying the ripeness of strawberries can be challenging due to their complex growth environment, interference from light intensity, and shading caused by strawberry aggregation. To address these issues, this study aims to develop an algorithm for accurately detecting and classifying ripe strawberries. This study proposed a novel LS-YOLOv8s model for detecting and grading the ripeness of strawberries, which is based on the YOLOv8s deep learning algorithm and incorporates the LW-Swin Transformer module. To improve the performance of the model, two new random variables were introduced in the contrast enhancement process to control the enhancement effect. The dataset was expanded from 1089 to 7515 images, which increased the diversity of the data and reduced the risk of over fitting the model. Additionally, the Swin Transformer module was added to the TopDown Layer2 during the feature fusion stage to capture long distance dependencies in the input data and improve the generalization capability of the model with the use of a multi-headed self-attention mechanism. Finally, a more efficient feature fusion network was achieved by introducing a residual network with learnable parameters and scaled normalization into the original residual structure of the Swin Transformer. To evaluate the effectiveness of LS-YOLOv8s for strawberry ripeness detection, we collected a dataset of strawberry images from a strawberry planting base. The dataset was split using the 5-fold cross-validation approach, which improved the model evaluation process. Experimental results showed that LS-YOLOv8s better than other models, with a 1.6 %, 33.5 %, and 3.4 % improvement in mAP0.5 on the validation set compared to YOLOv5s, CenterNet, and SSD, respectively. Moreover, LS-YOLOv8s achieved better detection precision and speed than YOLOv8m with only approximately 51.93 % of the number of parameters used, achieving 94.4 % detection precision and 19.23fps detection speed, improving by 0.5 % and 6.56fps, respectively. The LS-YOLOv8s model can provide reliable theoretical support for detecting strawberry targets, evaluating their ripeness, and automating the strawberry picking process for orchard management.
Why it matches plant phenotyping methodsイチゴ画像から成熟度という植物器官の状態を推定するYOLOベース手法を開発・評価しており、表現型取得・抽出手法が中心である。
abstractThis study proposed a novel LS-YOLOv8s model for detecting and grading the ripeness of strawberries
To investigate the effect of water stress on strawberry seedlings, a chlorophyll-fluorescence-image-acquisition system was developed. Strawberry seedlings of uniform growth were selected for grouped water-stress incubation experiments; the collected chlorophyll-fluorescence images of leaves were converted to red-green-blue (RGB), hue-saturation-value (HSV), and hue-saturation-intensity (HSI) color spaces and analyzed for water and chlorophyll contents measured at the same time for 14 consecutive days. The results indicate that the analysis and prediction of plant stress conditions can be effectively conducted using the channel components of the color-space model and the channel component ratios, which provide a reference for promoting agricultural development.
Why it matches plant phenotyping methods水ストレス評価のための蛍光画像取得システムを開発し、色空間チャネルから植物ストレス状態を予測する画像解析手法を中心に扱っている。
abstracta chlorophyll-fluorescence-image-acquisition system was developed
Published23 Nov 20232023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)Cited by 4 · OpenAlex ↗
India, being an agriculture-centric nation, historically relied on traditional farming methods that often resulted in crop losses and significant financial setbacks for farmers. In the present era, however, the incorporation of technology in agriculture has led to a rise in crop value, still, there is a great scope of research. One of the key reasons of the production of low-quality crops is the presence of infections or diseases. This research paper focuses on the application of machine learning algorithms, specifically Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and MobileNetV2, for plant disease detection in apple, strawberry, and grape leaves. The dataset used comprises 29,327 images, and various performance metrics were employed to evaluate the models' accuracies. According to the findings, the SVM model performs best with 82%, 98% and 72% accuracy for apple, strawberry, and grape crop respectively. Also, the precision, recall and F-score values are significantly high for CNN. So, among three considered model in the experiment for identification of disease in apple while for grapes and strawberry CNN works better than other two models.
Why it matches plant phenotyping methods葉画像から植物病害を推定する機械学習手法を比較・評価しており、植物の病徴状態の取得・判定が研究の中心です。
abstractThis research paper focuses on the application of machine learning algorithms, specifically Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and MobileNetV2, for plant disease detection in apple, strawberry, and grape leaves.
Strawberry detection and ripeness classification are important concerns in robotic harvesting, as precondition for efficient and nondestructive picking. However, the different sizes and overlap of strawberries complicate detection, and research on strawberry ripeness classification is lacking. Therefore, this study proposes the red color ratio as a new parameter for strawberry ripeness quantification. This parameter considers the proportion of the strawberry red area as a key factor. First, an adaptive strawberry feature augmentation network (ASFA-net) is proposed to generate masks for the strawberries. ASFA-net uses the shifted window (Swin) Transformer as the backbone network for strawberry feature extraction and employs a feature pyramid network along with the proposed strawberry feature adaptive fusion module, to augment the features. The proposed decoupled head network is then applied to generate the final results. Second, the red region within the mask of ripe strawberries is segmented based on hue, saturation, and value (HSV) to calculate the proportion of the red area of individual strawberries. ASFA-net was verified on a home-made strawberry dataset. The results show that ASFA-net can detect strawberries accurately and efficiently with mean average precision of 95.91 ± 0.64 % and mean intersection over union of 90.15 ± 1.49 %. The strawberry ripeness classification method exhibited good performance with an accuracy of 95.94 ± 1.40 %, false positive rate of 3.09 ± 0.46 %, and false negative rate of 4.22 ± 1.68 %. The purpose of this study is to establish a method for simultaneous strawberry detection and ripeness classification in a facility environment, to provide a new reference method for the vision system of robots.
Why it matches plant phenotyping methodsイチゴ果実の画像から赤色面積比を抽出して成熟度を定量化する手法を開発・検証しており、植物器官の状態推定が研究の中心である。
abstractTherefore, this study proposes the red color ratio as a new parameter for strawberry ripeness quantification.
Anthracnose diseases, caused by Colletotrichum spp., are considered to be among the most destructive diseases that have a significant impact on the global production of strawberries. These diseases alone can cause up to 70% yield loss in North America. Colletotrichum spp. causes several disease symptoms on strawberry plants, including root, fruit, and crown rot, lesions on petioles and runners, and irregular black spots on the leaf. In many cases, a lower level of infection on foliage remains non-symptomatic (quiescent), posing a challenge to growers as these plants can be a significant source of inoculum for the fruiting field. Reliable detection methods for quiescent infection should play an important role in preventing infected plants' entry into the production system or guiding growers to take appropriate preventative measures to control the disease. This review aims to examine both conventional and emerging approaches for detecting anthracnose disease in the early stages of the disease cycle, with a focus on newly emerging techniques such as remote sensing, especially using unmanned aerial vehicles (UAV) equipped with multispectral sensors. Further, we focused on the acutatum species complex, including the latest taxonomy, the complex life cycle, and the epidemiology of the disease. Additionally, we highlighted the extensive spectrum of management techniques against anthracnose diseases on strawberries and their challenges, with a special focus on new emerging sustainable management techniques that can be utilized in organic strawberry systems.
Why it matches plant phenotyping methodsイチゴ植物の病徴・感染状態を検出する手法のレビューであり、UAV搭載マルチスペクトルセンサーによるリモートセンシングを中心に扱うため、植物病害フェノタイピング手法のレビューに該当する。
abstractThis review aims to examine both conventional and emerging approaches for detecting anthracnose disease in the early stages of the disease cycle, with a focus on newly emerging techniques such as remote sensing, especially using unmanned aerial vehicles (UAV) equipped with multispectral sensors.
Managing the nutritional status of strawberry plants is critical for optimizing yield. This study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield. Partial least squares regression (PLSR) models were developed to estimate nutrient concentrations. The determination coefficient of prediction (R 2 P ) and ratio of performance to deviation (RPD) were used to evaluate prediction accuracy, which often proved to be greater for leaves, flowers, and unripe fruit than for ripe fruit. The prediction accuracies for N concentration were R 2 P = 0.64, 0.60, 0.81, and 0.30, and RPD = 1.64, 1.59, 2.64, and 1.31, for leaves, flowers, unripe fruit, and ripe fruit, respectively. Prediction accuracies for Ca concentrations were R 2 P = 0.70, 0.62, 0.61, and 0.03, and RPD = 1.77, 1.63, 1.60, and 1.15, for the same respective plant parts. Yield and fruit mass only had significant linear relationships with the Difference Vegetation Index (R 2 = 0.256 and 0.266, respectively) among the eleven vegetation indices tested. Hyperspectral imaging showed potential for estimating nutrient status in strawberry crops. This technology will assist growers to make rapid nutrient-management decisions, allowing for optimal yield and quality.
Why it matches plant phenotyping methodsイチゴの葉・花・果実の栄養状態と収量という植物形質を、ハイパースペクトル画像とPLSRで推定・検証しており、表現型取得法の適用と性能評価が中心である。
abstractThis study evaluated the potential of hyperspectral imaging (400-1,000 nm) to estimate nitrogen (N), phosphorus (P), potassium (K), and calcium (Ca) concentrations in strawberry leaves, flowers, unripe fruit, and ripe fruit and to predict plant yield.
Introduction Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する深層学習手法を開発しており、植物の病徴・病害状態の取得と推定が研究の中心であるため。
abstractThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases.
Reproduction assets foundThe paper's plant leaf disease classification experiments are built entirely on two public Kaggle image datasets explicitly cited by the authors: the PlantVillage Dataset (eight-class experiment) and the Plant Disease Prediction Dataset (three-class experiment). No author code, trained model, or supplementary deposit (Dataset · publicWe verified the effectiveness and robustness of the DeepPlantNet model by using images from the publicly available Kaggle “PlantVillage Dataset” dataset ( Dataset : https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ).Open asset ↗Kaggle · abdallahalidev/plantvillage-datasetlines:355-366Dataset · publicWe validated our model using another common, publicly accessible Kaggle dataset, “Plant Disease Prediction Dataset,” to assess and estimate the generalizability and performance of the DeepPlantNet model ( Dataset : https://www.kaggle.com/datasets/shuvranshu/plant-disease-prediction-dataset ).Open asset ↗Kaggle · shuvranshu/plant-disease-prediction-datasetlines:729-756Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Sept 2023Computers and Electronics in Agriculture.
The occurrence of strawberry diseases has a huge impact on the yield and quality of strawberry fruits, resulting in huge economic losses. Real-time and effective identification and diagnosis of strawberry disease is an essential step for strawberry disease prevention. Machine learning-based methods are widely used in strawberry disease identification tasks, but these methods require expertise to design proper strawberry disease feature descriptors. Deep-learning methods have remarkably improved the capability of feature extraction. However, the strawberry disease with complex backgrounds brings great challenges for accurate feature extraction, which leads to poor recognition results of strawberry disease under complex backgrounds. In this paper, an improved transformer-based strawberry disease identification method is proposed to achieve precise and fast recognition of multiple classes of strawberry diseases. First, a multi-classes strawberry disease dataset has been constructed with 5369 images and 12 types of common strawberry disease. To increase the diversity of samples under complex backgrounds, various data augmentation strategies are introduced into the strawberry disease recognition method. Then, Multi-Head Self-Attention (MSA) is used to capture feature dependencies over long distances of strawberry disease images by leveraging the self-attention mechanism. To improve the recognition efficiency, the spatial convolutional self-attention-based transformer (SCSA-Transformer) is proposed to reduce the parameters of the transformer network. The experimental results validated on the constructed strawberry disease dataset demonstrate that the recognition accuracy of the proposed method can achieve 99.10%, which outperforms other methods. Besides, we also observe that the parameters of the classification model are reduced compared with other methods, which effectively improves the recognition efficiency of strawberry diseases.
Why it matches plant phenotyping methodsイチゴ葉・植物体の病害状態を画像から識別する深層学習手法を開発し、専用データセットで性能検証しており、植物病害フェノタイピング手法が中心である。
abstractan improved transformer-based strawberry disease identification method is proposed to achieve precise and fast recognition of multiple classes of strawberry diseases
Chilling injury can adversely affect strawberry bud differentiation, pollen vitality, fruit yield, and quality. Photosynthesis is a fundamental process that sustains plant life. However, different strawberry varieties exhibit varying levels of cold adaptability. Quantitatively evaluating the physiological activity of the photosynthetic system under low-temperature chilling injury remains a challenge. In this study, we investigated the effects of different levels of chilling stress on twenty photosynthetic fluorescence parameters in strawberry plants, using short-day strawberry variety "Toyonoka" and day-neutral variety "Selva" as representatives. Three dynamic chilling treatment levels (20/10 °C, 15/5 °C, and 10/0 °C) and three durations (3 days, 6 days, and 9 days) were applied to each variety. WUE, LCP, Y(II), qN, SIFO2-B and rSIFO2-B were selected as crucial indicators of strawberry photosynthetic physiological activity. Subsequently, we constructed a comprehensive score to assess the strawberry photosynthetic system under chilling injury and established a hyperspectral inversion model for stress quantification. The results indicate that the short-day strawberry "Toyonoka" exhibited a recovery effect under continuous 20/10 °C treatment, while the day-neutral variety "Selva" experienced progressively worsening stress levels across all temperature groups, with stress severity higher than that in "Toyonoka". The BPNN model for the comprehensive assessment of the strawberry photosynthetic system under chilling injury showed optimal performance. It achieved a stress level prediction accuracy of 71.25% in 80 validation samples, with an R 2 of 0.682 when fitted to actual results. This study provides scientific insights for the application of canopy remote sensing diagnostics of strawberry photosynthetic physiological chilling injury in practical agricultural production.
Why it matches plant phenotyping methodsイチゴの低温ストレスによる光合成生理状態を、蛍光指標とハイパースペクトル反転モデルで定量化し、予測性能も検証しているため、植物フェノタイピング手法が中心である。
abstractwe constructed a comprehensive score to assess the strawberry photosynthetic system under chilling injury and established a hyperspectral inversion model for stress quantification.
Object detection in remote sensing images is one of the most critical computer vision tasks for various earth observation applications. Previous studies applied object detection models to orthomosaic images generated from the SfM (Structure-from-Motion) analysis to perform object detection and counting. However, some small objects that are occluded from the vertical view but observable in raw images from the oblique views cannot be detected in the orthomosaic image, leading to an occlusion issue that cannot be resolved with the traditional orthophoto-based approach. Taking strawberry detection as a case study, the objective of this study is to detect small objects directly from multi-view raw images. Firstly, an object-detection model (Faster R-CNN in this study) was applied to each raw image to identify strawberry fruit and flower objects. Each unique strawberry object on the ground can be detected multiple times in the raw images because images have forward- and side overlap. To find the unique objects from the step one detection results, an improved FaceNet model was proposed to combine the image and position information to calculate the feature distance between those objects, and a clustering algorithm was used to associate the cluster with each unique strawberry using the object distance output from the FaceNet model, from which the final position and number of strawberry fruits and flowers were obtained. Compared with the orthomosaic image alone, this approach using multi-view images effectively solved the occlusion problem and improved overall recognition accuracy of strawberry flowers, unripe fruits, and ripe fruits from 76.28% to 96.98%, 71.64% to 99.09%, and 69.81% to 97.17%, respectively, highlighting the potential of multi-view stereovision (MVS) in small object detection.
Why it matches plant phenotyping methodsイチゴの花・果実をマルチビュー画像から検出・重複排除して位置と個数を推定する手法が研究の中心であり、植物器官の表現型計測に該当する。
abstractthe objective of this study is to detect small objects directly from multi-view raw images
Plant diseases pose a significant threat to agricultural productivity and food security in Bangladesh. In this research, we address the challenge of timely and accurate plant disease detection through the application of transfer learning with deep neural models. We curated a diverse dataset comprising 18 categories of plant leaf images, including Bell pepper Bacterial spot, Bell pepper Healthy, Peach Healthy, Potato Early Blight, Rice Leaf Blast, Rice Healthy, Rice Brown Spot, Potato Healthy, Peach Bacterial spot, Corn Blight, Potato Late blight, Corn Healthy, Tomato Bacterial spot, Strawberry Leaf Scorch, Tomato Early blight, Tomato Early blight, Strawberry Healthy, and Tomato Healthy. The dataset represents the most prevalent plant diseases observed in the Bangladeshi context. We employed three state-of-the-art deep learning algorithms, EfficientNetV2M, VGG-19, and NASNetLarge, to develop robust plant disease detection models. Through transfer learning, these pre-trained models were fine-tuned on our specialized dataset to adapt them for the task at hand. The performance evaluation revealed impressive results, with EfficientNetV2M achieving an accuracy rate of 99%, VGG-19 achieving 93%, and NASNetLarge attaining 83% accuracy. The high accuracy of EfficientNetV2M showcases its exceptional capability in accurately classifying plant diseases prevalent in Bangladesh. The success of these deep neural models in detecting various plant diseases signifies their potential in revolutionizing plant disease management and enhancing agricultural practices. Our research contributes valuable insights into the effective use of transfer learning for plant disease detection and emphasizes the significance of dataset curation for improved model performance. The developed models hold promise in providing timely and precise disease diagnosis to farmers and agricultural professionals, thereby facilitating prompt interventions and minimizing crop losses. Future research can explore the integration of these deep neural models into practical agricultural tools, enabling real-time disease detection and offering substantial benefits to the agricultural industry in Bangladesh.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習モデルの開発・性能評価が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractwe address the challenge of timely and accurate plant disease detection through the application of transfer learning with deep neural models.
Strawberry is the most cultivated berry fruit globally and it is really appreciated by consumers because of its characteristics, mainly bioactive compounds with antioxidant properties. During the breeding process, it is important to assess the quality characteristics of the fruits for a better selection of the material, but the conventional approaches involve long and destructive lab techniques. Near infrared spectroscopy (NIR) could be considered a valid alternative for speeding up the breeding process and is not destructive. In this study, a total of 216 strawberry fruits belonging to four different cultivars have been collected and analyzed with conventional lab analysis and NIR spectroscopy. In detail, soluble solid content, acidity, vitamin C, anthocyanin, and phenolic acid have been determined. Partial least squares discriminant analysis (PLS-DA) models have been developed to classify strawberry fruits belonging to the four genotypes according to their quality and nutritional properties. NIR spectroscopy could be considered a valid non-destructive phenotyping method for monitoring the nutritional parameters of the fruit and ensuring the fruit quality, speeding up the breeding program.
Why it matches plant phenotyping methodsイチゴ果実の栄養・品質形質をNIR分光で非破壊推定し、従来分析との比較とPLS-DAモデル構築を行う方法中心の研究である。
abstractNIR spectroscopy could be considered a valid non-destructive phenotyping method for monitoring the nutritional parameters of the fruit and ensuring the fruit quality, speeding up the breeding program.
Our laboratory at MIT has been interested over the past few years in new techniques to facilitate the transfer of chemical information from living organisms, specifically plants, animals and humans, for applications ranging from precision agriculture to precision medicine. This presentation will discuss recent advances on this topic. As tool towards this end, fluorescent nanosensors hold the potential to revolutionize life sciences and medicine. However, their adaptation and translation into the in vivo environment is fundamentally hampered by unfavourable tissue scattering and intrinsic autofluorescence. Here we develop wavelength-induced frequency filtering (WIFF) whereby the fluorescence excitation wavelength is modulated across the absorption peak of a nanosensor, allowing the emission signal to be separated from the autofluorescence background, increasing the desired signal relative to noise, and internally referencing it to protect against artefacts. Using highly scattering phantom tissues, an SKH1-E mouse model and other complex tissue types, we show that WIFF improves the nanosensor signal-to-noise ratio across the visible and near-infrared spectra up to 52-fold. This improvement enables the ability to track fluorescent carbon nanotube sensor responses to riboflavin, ascorbic acid, hydrogen peroxide and a chemotherapeutic drug metabolite for depths up to 5.5 ± 0.1 cm when excited at 730 nm and emitting between 1,100 and 1,300 nm, even allowing the monitoring of riboflavin diffusion in thick tissue. As an application, nanosensors aided by WIFF detect the chemotherapeutic activity of temozolomide transcranially at 2.4 ± 0.1 cm through the porcine brain without the use of fibre optic or cranial window insertion. The ability of nanosensors to monitor previously inaccessible in vivo environments will be important for life-sciences research, therapeutics and medical diagnostics. Also towards this overall objective, our laboratory at MIT has been interested in exploring the relatively new interface between living plants and non-biological nanostructures to impart the former with new and enhanced functions, which we call Plant Nanobionics. We have developed a theory of subcellular uptake and kinetic trapping of a wide range of nanoparticles, validated in-vivo in living plants. Confocal visible and near infrared fluorescent microscopy and single particle tracking of Gold-Cystein-AF405 (GNP-Cys-AF405), Streptavidin-Quantum Dot (SA-QD), Dextran and Poly(acrylic acid) nanoceria, and various polymer-wrapped SWCNT, including lipid-PEG-SWCNT, chitosan-SWCNT and (AT)15-SWCNT, were used to demonstrate that particle size and the magnitude, but not the sign, of the zeta potential are key in determining whether a particle is spontaneously and kinetically trapped within chloroplasts or the cytosol. We develop a mathematical model of this Lipid Exchange Envelope Penetration (LEEP) mechanism, which agrees well with observations of this size and zeta potential dependence. As an application, we rationally designed a chitosan-complexed single-walled carbon nanotube (SWNT) as nanocarriers to selectively deliver plasmid DNA (pDNA) to chloroplasts of different plant species without external biolistic or chemical aid. We demonstrate chloroplast-targeted transgene delivery and expression in living mature arugula (Eruca sativa) and watercress (Nasturitium officinale) plants in planta and in isolated Arabidopsis thaliana mesophyll protoplasts. Another application of nanoparticles and nanotechnology to plant sciences is in the form of biochemical sensors that operate in planta and across diverse species. Using non-destructive optical nanosensors, we find that the spatial and temporal H2O2 concentration immediately post-wounding follows a simple logistic waveform for six dicot plant species: lettuce (Lactuca sativa), arugula (Eruca sativa), spinach (Spinacia oleracea), strawberry blite (Blitum capitatum), sorrel (Rumex acetosa), and Arabidopsis thaliana, ranked in order of wave speed from 0.44 to 3.10 cm/min. The H2O2 wave tracks the concomitant surface potential wave measured electrochemically for the series of plants. We show that the plant NADPH oxidase RbohD, glutamate receptor-like channels (GLR3.3 and GLR3.6) are all critical to the propagation of the H2O2 waveform upon wounding. Our findings highlight the utility of a new type of nanosensor probe that is species-independent and capable of real-time, spatial and temporal biochemical measurements in planta.
Why it matches plant phenotyping methods植物体内の化学状態を非破壊・リアルタイムに測定するナノセンサーと信号処理法を開発し、創傷後のH2O2波の空間・時間特性を植物で実証しており、植物フェノタイピング手法が中心である。
abstractUsing non-destructive optical nanosensors, we find that the spatial and temporal H2O2 concentration immediately post-wounding follows a simple logistic waveform for six dicot plant species
Introduction In closed-loop soilless cultivation, the main nutrient sinks are nutrients retained either by the crop or in spent growing media. Measurement of nutrients in spent growing media and in the aboveground vegetative plant biomass at crop termination can be a tool for assessing and optimizing nutrient efficiency. The first aim of this study was to test the potential of near-infrared reflectance spectroscopy (NIRS) to forecast the various nutrient contents in strawberry leaves, which would then allow for assessment of crop nutrient status and total nutrient uptake by strawberry plants. The second aim was to test NIRS as a high throughput technique for assessing the N, K, Ca, Mg and organic matter (OM) content and the pH, EC and C:N and C:P ratios for a dataset of composts, plant fibers and spent growing media. The NIRS prediction model for fast screening of the total nutrient contents in spent growing media was compared with a single extraction method. Methods A database with 369 dried and ground strawberry leaf samples with known contents of N, P, K, Ca, and Mg were scanned using NIRS. The database covered a range of leaf contents of 6-35 g N/kg dry matter (DM), 0.7-6.3 g P/kg DM and 2-29 g K/kg DM. A dataset of 458 samples of different types of materials used in growing media was validated with a dataset of 109 samples. Results Validation for the strawberry leaves indicated potential for this application, with R 2 values of 0.90 or higher for N, K and Ca, and R 2 values higher than 0.85 for P and Mg. Validation for the dataset of composts, plant fibers and spent growing media also indicated the potential for this application, with R 2 values of 0.90 or higher for organic matter, and with R 2 values of 0.85 or higher for total Ca, pH and C:N. A first test indicated potential for the calibration based on fresh samples of compost, plant fiber as well as spent growing media or dried (not ground) samples. Discussion Use of NIRS on fresh samples would eliminate the need for drying and grinding the samples and would reduce screening time. The ammonium acetate extraction is a reliable alternative to NIRS for fast screening of the total P, K, Ca, and Mg contents in composts, plant fibers and spent growing media.
Why it matches plant phenotyping methodsイチゴ葉の栄養含量・栄養状態をNIRSで推定する手法を開発・検証しており、植物形質取得が中心的な貢献である。
abstractThe first aim of this study was to test the potential of near-infrared reflectance spectroscopy (NIRS) to forecast the various nutrient contents in strawberry leaves, which would then allow for assessment of crop nutrient status and total nutrient uptake by strawberry plants.
As a fruit with high economic value, strawberry has a short ripeness period, and harvesting at an incorrect time will seriously affect the quality of strawberries, thereby reducing economic benefits. Therefore, the timing of its harvesting is very demanding. A fine ripeness recognition can provide more accurate crop information, and guide strawberry harvest management more timely and effectively. This study proposes a fine recognition method for field strawberry ripeness that combines deep learning and image processing. The method is divided into three stages: In the first stage, self-calibrated convolutions are added to the Mask R-CNN backbone network to improve the model performance, and then the model is used to extract the strawberry target in the image. In the second stage, the strawberry target is divided into four sub-regions by region segmentation method, and the color feature values of B, G, L, a and S channels are extracted for each sub-region. In the third stage, the strawberry ripeness is classified according to the color feature values and the results are visualized. Experimental results show that with the incorporation of self-calibrated convolutions into the Mask R-CNN, the model's performance has been substantially enhanced, leading to increased robustness against diverse occlusion interferences. As a result, the final average precision (AP) has improved to 0.937, representing a significant increase of 0.039 compared to the previous version. The strawberry ripeness classification effect is the best on the SVM classifier, and the accuracy under the combined channel BGLaS reaches 0.866. The classification results are better than common manual feature extraction methods and AlexNet, ResNet18 models. In order to clarify the role of the region segmentation method, the contribution of different sub-regions to each ripeness is also explored. The comprehensive results demonstrate that the proposed method enables the evaluation of six distinct ripeness levels of strawberries in the complex field environment. This method can provide accurate decision support for strawberry refined planting management.
Why it matches plant phenotyping methodsイチゴ果実の画像から熟度という植物状態を抽出・分類する画像解析手法を開発し、性能評価まで行っており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a fine recognition method for field strawberry ripeness that combines deep learning and image processing.
We developed a method for the early on-site detection of strawberry anthracnose using a portable Raman system with multivariate statistical analysis algorithms. By using molecular markers based on Raman spectra, the proposed method can detect anthracnose in strawberry stems 3 days after exposure to Colletotrichum gloeosporioides. A fiber-optic probe was applied for the portable Raman system, and the acquisition time was 10 s. We found that the molecular markers were closely related to the following subjects: i) an increase in amide III and fatty acids of C. gloeosporioides invading strawberry stems (Raman bands at 1180-1310 cm -1 ) and ii) a decrease in metabolites in strawberry plants, such as phenolic compounds and terpenoids (Raman bands at 760, 800, and 1523 cm -1 ). We also found that the increased fluorescence background caused by various chromophores within the invading C. gloeosporioides could serve as a marker. A two-dimensional cluster plot obtained by principal component analysis (PCA) showed that the three groups (control, fungal infection, and pathogen) were distinguishable. The linear discriminant analysis (LDA)-based prediction algorithm could identify C. gloeosporioides infection with a posterior probability of over 40%, even when no symptoms were visible on the inoculated strawberry plants.
Why it matches plant phenotyping methods携帯型ラマン分光と統計解析を用いて、症状出現前のイチゴ茎における炭疽病感染状態を検出する手法の開発が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractWe developed a method for the early on-site detection of strawberry anthracnose using a portable Raman system with multivariate statistical analysis algorithms.
Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a 'spectral-spatial' feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective 'spectral-spatial' latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.
Why it matches plant phenotyping methods果実の病害・損傷・低温障害などの状態をハイパースペクトル画像から検出する手法を開発・評価しており、植物器官の状態推定が研究の中心です。
abstractIn this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed.
Reproduction assets foundThe paper's SSAD code implementation and learned models are publicly available on GitHub. The fruit hyperspectral datasets are paper-specific but only available on request from the corresponding author.Code · publicThe code implementation and learned models of SSAD are available at https://github.com/YisenLiu-Intelligent-Sensing/SSAD accessed on 18 May 2022.Open asset ↗YisenLiu-Intelligent-Sensing/SSADlines:57-72Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published10 Jul 2023Jurnal Ilmiah Teknik Elektro Komputer dan InformatikaCited by 26 · OpenAlex ↗
Strawberry is a plant that has many benefits and a high risk of being attacked by pests and diseases. Diseases in strawberry plants can cause a decrease in the quality of fruit production and can even cause crop failure. Therefore, a method is needed to assist farmers in identifying the types of diseases in strawberry plants. Currently, there are many methods to assist farmers in identifying types of disease in plants, including strawberry plants. In this study, a system is proposed to be able to detect strawberry plant diseases by classifying the disease based on healthy and diseased strawberry leaf images. The proposed system is the Convolutional Neural Network (CNN) algorithm using MobileNetV3-Large and EfficientNet-B0 models to train pre-processed datasets. The results of this study obtained the best accuracy reaching 92.14% using the MobileNetV3-Large architecture with the hyperparameter optimizer RMSProp, epochs 70, and learning rate 0.0001. The percentage of the evaluation model using MobileNetV3-Large for precision, recall, and F1-Score achieved 92.81%, 92.14%, and 92.25%. Whereas in the EfficientNet-B0 architecture, the best accuracy results only reach 90.71% with the hyperparameter optimizer Adam, 70 epochs, and a learning rate of 0.003. Then, the precision, recall, and F1-scores for EfficientNet-B0 reached 92.65%, 90.00%, and 90.37%. Overall, it presents fairly good results in classifying strawberry leaf plant disease. Furthermore, in future work, it needs to obtain higher accuracy by generating more datasets, trying other augmentation techniques, and proposing a better model.
Why it matches plant phenotyping methodsイチゴ葉画像から植物病害状態をCNNで分類する手法が研究の中心であり、植物の病害表現型を直接推定しているため含める。
abstracta system is proposed to be able to detect strawberry plant diseases by classifying the disease based on healthy and diseased strawberry leaf images.
Plant architecture plays a major role in flowering and therefore in crop yield. Attempts to visualize and analyse strawberry plant architecture have been few to date. Here, we developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry. We applied this software to six seasonal strawberry varieties whose plants were exhaustively described monthly at the node scale. Results showed that the architectural pattern of the strawberry plant is characterized by a decrease of the module complexity between the zeroth-order module (primary crown) and higher-order modules (lateral branch crowns and extension crowns). Furthermore, for each variety, we could identify traits with a central role in determining yield, such as date of appearance and number of branches. By modeling the spatial organization of axillary meristem fate on the zeroth-order module using a hidden hybrid Markov/semi-Markov mathematical model, we further identified three zones with different probabilities of production of branch crowns, dormant buds, or stolons. This open-source software will be of value to the scientific community and breeders in studying the influence of environmental and genetic cues on strawberry architecture and yield.
Why it matches plant phenotyping methodsイチゴ植物体の時空間的な構造形質を取得・解析するオープンソースソフトウェアを開発しており、表現型取得・解析手法が研究の中心である。
abstractwe developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry.
Reproduction assets foundThe paper's strawberry architectural phenotype data (MTG-encoded plant descriptions) are publicly deposited in the authors' GitHub repository, and the OpenAlea.Strawberry analysis/visualization software is open-source on GitHub with a Docker image for deployment. The data.inrae.fr deposits contain only a demonstration,Dataset · publicAll data are available at Github: https://github.com/openalea/strawberry/tree/master/share/dataOpen asset ↗https://github.com/openalea/strawberry/tree/master/share/datalines:406-468Code · publicFirst, OpenAlea.Strawberry is an open-source Python package ( https://github.com/openalea/strawberry ), available in the OpenAlea platformOpen asset ↗https://github.com/openalea/strawberrylines:337-344Plant phenotyping relevance match · UnverifiedCrossref · checked 8 Sept 2026
Currently, estimations of strawberry productivity are conducted manually, which is a laborious and subjective process. The use of more efficient and precise estimation methods would result in better crop management. The objective of this study was to assess the performance of two regression algorithms-Linear Regression and Support Vector Machine—in estimating the average weight and number of fruits and the number of leaves on strawberry plants, using multispectral images obtained by a remotely piloted aircraft (RPA). The experiment, which was conducted in the experimental area of the Botany Laboratory at the Federal University of Uberlândia-Monte Carmelo Campus (Universidade Federal de Uberlândia, Campus Monte Carmelo), was carried out using a randomized block design with six treatments and four replications. The treatments comprised six commercial strawberry varieties: San Andreas, Albion, PR, Festival, Oso Grande, and Guarani. Images were acquired on a weekly basis and then preprocessed to extract radiometric values for each plant in the experimental area. These values were then used to train the production prediction algorithms. During the same period, data on the average fruit weight, number of fruits per plant, and number of leaves were collected. The total fruit weight in the field was 48.08 kg, while the linear regression (LR) and Support Vector Machine (SVM) estimates were 48.04 and 43.09 kg, respectively. The number of fruits obtained in the field was 4585, and the number estimated by LR and SVM algorithms was 4564 and 3863, respectively. The number of leaves obtained in the field was 10,366, and LR and SVM estimated 10,360 and 10,171, respectively. It was concluded that LR and SVM can estimate strawberry production and the number of fruits and leaves using multispectral unmanned aerial vehicle (UAV) images. The LR algorithm was the most efficient in estimating production, with 99.91% accuracy for average fruit weight, 99.55% for the number of fruits and 99.94% for the number of leaves. SVM exhibited 89.62% accuracy for average fruit weight, 84.26% for the number of fruits, and 98.12% for the number of leaves.
Why it matches plant phenotyping methodsマルチスペクトル画像から果実重量、果実数、葉数を推定する機械学習手法を開発・性能評価しており、植物形質の取得・推定が研究の中心である。
abstractThe objective of this study was to assess the performance of two regression algorithms-Linear Regression and Support Vector Machine—in estimating the average weight and number of fruits and the number of leaves on strawberry plants, using multispectral images obtained by a remotely piloted aircraft (RPA).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
Plant phenotyping and production management are emerging fields to facilitate Genetics, Environment, & Management (GEM) research and provide production guidance. Precision indoor farming systems (PIFS), vertical farms with artificial light (aka plant factories) in particular, have long been suitable production scenes due to the advantages of efficient land utilization and year-round cultivation. In this study, a mobile robotics platform (MRP) within a commercial plant factory has been developed to dynamically understand plant growth and provide data support for growth model construction and production management by periodical monitoring of individual strawberry plants and fruit. Yield monitoring, where yield = the total number of ripe strawberry fruit detected, is a critical task to provide information on plant phenotyping. The MRP consists of an autonomous mobile robot (AMR) and a multilayer perception robot (MPR), i.e., MRP = the MPR installed on top of the AMR. The AMR is capable of traveling along the aisles between plant growing rows. The MPR consists of a data acquisition module that can be raised to the height of any plant growing tier of each row by a lifting module. Adding AprilTag observations (captured by a monocular camera) into the inertial navigation system to form an ATI navigation system has enhanced the MRP navigation within the repetitive and narrow physical structure of a plant factory to capture and correlate the growth and position information of each individual strawberry plant. The MRP performed robustly at various traveling speeds with a positioning accuracy of 13.0 mm. The temporal-spatial yield monitoring within a whole plant factory can be achieved to guide farmers to harvest strawberries on schedule through the MRP's periodical inspection. The yield monitoring performance was found to have an error rate of 6.26% when the plants were inspected at a constant MRP traveling speed of 0.2 m/s. The MRP's functions are expected to be transferable and expandable to other crop production monitoring and cultural tasks.
Why it matches plant phenotyping methods個体ごとのイチゴ果実数・収量を定期取得する移動ロボット型フェノタイピング基盤を開発し、位置精度と収量監視誤差を評価しているため、方法が中心的です。
abstracta mobile robotics platform (MRP) within a commercial plant factory has been developed to dynamically understand plant growth and provide data support for growth model construction and production management by periodical monitoring of individual strawberry plants and fruit.
Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The "harvest-first, analysis-second" method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice's total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.
Why it matches plant phenotyping methodsイチゴ果実の糖度（Brix）を非破壊・現場取得データから推定する手法を開発・検証し、複数モデルの精度をベンチマークしているため、植物形質取得が中心である。
abstractIn this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies.
The efficiency of photosynthesis in strawberry plants is measured to maintain the quality and quantity of strawberries produced. The latest method used to measure the photosynthetic status of plants is chlorophyll fluorescence imaging (CFI), which has the advantage of obtaining plant spatiotemporal data non-destructively. This study developed a CFI system to measure the maximum quantum efficiency of photochemistry (Fv/Fm). The main components of this system include a chamber for plants to adapt to dark environments, blue LED light sources to excite the chlorophyll in plants, and a monochrome camera with a lens filter attached to capture the emission spectra. In this study, 120 pots of strawberry plants were cultivated for 15 days and divided into four treatment groups: control, drought stress, heat stress, and a combination of drought and heat stress, resulting in Fv/Fm values of 0.802 ± 0.0036, 0.780 ± 0.0026, 0.768 ± 0.0023, and 0.749 ± 0.0099, respectively. A strong correlation was found between the developed system and a chlorophyll meter (r = 0.75). These results prove that the developed CFI system can accurately capture the spatial and temporal dynamics resulting from the response of strawberry plants to abiotic stresses.
Why it matches plant phenotyping methodsイチゴの乾燥・高温ストレスに対する光合成状態を画像化するCFIシステムを開発し、クロロフィルメーターとの相関で検証しており、表現型取得法が研究の中心である。
abstractThis study developed a CFI system to measure the maximum quantum efficiency of photochemistry (Fv/Fm).
Plant secondary metabolites exhibit various horticultural traits. Simple and rapid analysis methods for evaluating these metabolites are in demand in breeding and consumer markets dealing with horticultural crops. We applied probe electrospray ionization (PESI) to evaluate secondary metabolite levels in horticultural crops. PESI does not require pre-treatment and separation of samples, which makes it suitable for high-throughput analysis. In this study, we targeted anthocyanins, one of the primary pigments in horticultural crops. Eighty-one anthocyanins were detected in approximately 3 minutes in the selected reaction-monitoring mode. Tandem mass spectrometry (MS/MS) could adequately distinguish between the fragments of anthocyanins and flavonols. Probe sampling, an intuitive method of sticking a probe directly to the sample, could detect anthocyanins qualitatively on a micro-area scale, such as achenes and receptacles in strawberry fruit. Our results suggest that PESI/MS/MS can be a powerful tool to characterize the profile of anthocyanins and compare their content among cultivars.
Why it matches plant phenotyping methodsPESI/MS/MSによるアントシアニン量・プロファイルの高速取得法を開発・適用し、品種比較や果実器官の局所評価に用いているため、植物形質の化学的フェノタイピングが中心である。
abstractWe applied probe electrospray ionization (PESI) to evaluate secondary metabolite levels in horticultural crops.
StrawberryMultispectral / hyperspectralFruitPhysiological trait estimationSegmentationVisualization / data management
In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913-2166 nm) is developed. NIR-HSI data collected from 180 samples of "Tochigi iW1 go" white strawberries are investigated. In order to recognize the pixels corresponding to the flesh and achene on the surface of the strawberries, principal component analysis (PCA) and image processing are conducted after smoothing and standard normal variate (SNV) pretreatment of the data. Explanatory partial least squares regression (PLSR) analysis is performed to develop an appropriate model to predict Brix reference values. The PLSR model constructed from the raw spectra extracted from the flesh region of interest yields high prediction accuracy with an RMSEP and R2p values of 0.576 and 0.841, respectively, and with a relatively low number of PLS factors. The Brix heatmap images and violin plots for each sample exhibit characteristics feature of sugar content distribution in the flesh of the strawberries. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of white strawberries.
Why it matches plant phenotyping methods近赤外ハイパースペクトル画像からイチゴ果実の糖含量分布を推定・可視化する画像解析および回帰モデルが研究の中心であり、植物器官の品質形質を直接取得するフェノタイピング手法に該当する。
abstractan approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913-2166 nm) is developed
Ubiquitous sensor networks collecting real-time data have been adopted in many industrial settings. This paper describes the second stage of an end-to-end system integrating modern hardware and software tools for precise monitoring and control of soil conditions. In the proposed framework, the data are collected by the sensor network distributed in the soil of a commercial strawberry farm to infer the ultimate physicochemical characteristics of the fruit at the point of harvest around the sensor locations. Empirical and statistical models are jointly investigated in the form of neural networks and Gaussian process regression models to predict the most significant physicochemical qualities of strawberry. Color, for instance, either by itself or when combined with the soluble solids content (sweetness), can be predicted within as little as 9% and 14% of their expected range of values, respectively. This level of accuracy will ultimately enable the implementation of the next phase in controlling the soil conditions where data-driven quality and resource-use trade-offs can be realized for sustainable and high-quality strawberry production.
Why it matches plant phenotyping methods土壌センサーデータと機械学習を用いて収穫時のイチゴ果実形質（色、可溶性固形分など）を推定し、予測精度も評価しているため、果実形質推定手法が中心である。
abstractthe data are collected by the sensor network distributed in the soil of a commercial strawberry farm to infer the ultimate physicochemical characteristics of the fruit at the point of harvest
Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field.
Why it matches plant phenotyping methodsイチゴ葉の病斑を画像から抽出・識別するCNN手法を開発し、圃場データセットで比較評価しているため、植物病害状態の画像ベース表現型計測が中心である。
Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed. A dataset of apple leaves samples was used for training and optimization. Another set of grape and strawberry leaf samples was used as an extra testing dataset. Then, supervised convolutional neural networks were adopted for semantic segmentation. Moreover, the possibility of weakly supervised models for disease spot segmentation was also explored. Grad-CAM combined with ResNet-50 (ResNet-CAM), and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation (WSLSS), was designed. They were trained using image-level annotations (healthy versus diseased) to reduce the cost of annotation work. Results showed that the supervised DeepLab achieved the best performance (IoU = 0.829) on the apple leaf dataset. The weakly supervised WSLSS achieved an IoU of 0.434. When processing the extra testing dataset, WSLSS realized the best IoU of 0.511, which was even higher than fully supervised DeepLab (IoU = 0.458). Although there was a certain gap in IoU between the supervised models and weakly supervised ones, WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure. Furthermore, the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.
Why it matches plant phenotyping methods病斑分布という植物の病害表現型を対象に、教師あり・弱教師ありセマンティックセグメンテーション手法を開発・評価し、データセットも提供しているため、方法が中心的である。
abstractIn this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning.
Reproduction assets foundThe authors contributed a diseased-leaf dataset with pixel-level annotations (used directly for this paper's segmentation experiments) and deposited it publicly on Mendeley Data, with a Baidu Pan mirror. Source datasets (Plant Village, diseased apple leaves) are cited prior public datasets, not paper-specific assets,;Dataset · publicWe uploaded the images and the corresponding annotation to the Mendeley Data repository ( https://data.mendeley.com/datasets/tsfxgsp3z6 ).Open asset ↗Mendeley Data · tsfxgsp3z6lines:27-64Dataset · publicThe dataset is also available at https://pan.baidu.com/s/1y7K2dVpfkQ3HVOU1qEeChQ (password: ecff).Open asset ↗lines:27-64Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Marssonina blotch infect the entire tree and black rot creates the possibility for fungicides to infect the neighborhood fruits thereby creating the entire fruit basket infected. Marssonina blotch and black rot are the most commonly occurring infections on fruits. Scab is discovered as the other category of fruit infection caused by a fungus infecting even leaves that cause cracks and immature stage of fruits and leaves. The preprocessing steps involve the process of segmentation, filtration and haar cascade defect training using the training data set consisting of 2500 defect records on apple, strawberry, grapefruit, cucumber, kiwi, lime, mango and guava. Deep learning and Stacking ensemble learning is used to diagnose the infections on fruits by using the CNN algorithm to create fine grained chunk visuals for disease prediction. The system is constructed to identify color of the fruit so that the system is able to predict the fruit image on all the possible colors for example on apple both red and green color apples are trained. The overall accuracy of prediction recorded by the proposed work is 97.8% which proves to hit the required efficiency of the diagnosis system to prevent the infection on fruits and increase the productivity and marketing strategy. Blotch prediction accuracy is observed as 97.5%, scab as 95.89 %and rot prediction accuracy is recorded to 98.2%.
Why it matches plant phenotyping methods果実画像から病害状態を分類・診断する画像解析手法が研究の中心であり、植物の病害表現型を直接推定しているため。
abstractDeep learning and Stacking ensemble learning is used to diagnose the infections on fruits by using the CNN algorithm to create fine grained chunk visuals for disease prediction.
Strawberry (Fragaria × ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm-900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyll-fluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants' early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSR b ), and the red-edge chlorophyll index (CI REDEDGE ), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage.
Why it matches plant phenotyping methodsハイパースペクトル画像から葉の蛍光指標を抽出し、イチゴの熱・水ストレスという植物状態を推定するワークフローを開発・評価しており、表現型取得と解析手法が中心である。
abstractThe hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm-900 nm.
Abstract Plant disease detection has a huge impact on plant farming. Early diagnosis of plant illness can help control disease spread and reduce loss. It is a soil-borne disease that affects leaves of plants. In the current research, the emphasis is on the early diagnosis and prevention of plant leaf disease. In this paper, strawberry, tomato, pepper bell, and potato disease detection network (STPP- DDN) based on Faster R-CNN and multi-task learning. STPP-ddn is developed which leverages attention mechanisms in feature extraction. STPP-DDN detects disease based on plant symptoms. Unlike other approaches for diagnosing disease from the total plant look, the STPP-DDN automatically classifies the petioles and young leaves. A large dataset with number of photos divided into various groups is constructed to serve as a basis for analyzing and testing our proposed technique. Each image also includes a label that indicates whether or not the plants has been affected. With the proposed STPP-DDN, we achieved a mAP of 77:54% on object detection of 4 categories and 99:95% accuracy for strawberry verticillium wilt detection.
Why it matches plant phenotyping methods植物病徴を画像から検出・分類する深層学習手法を開発し、データセットと精度評価を提示しており、植物の疾病状態の表現型取得が中心です。撤回表示はあるものの、内容はスクリーニング対象に該当します。
abstractSTPP-DDN detects disease based on plant symptoms.
Reproduction assets foundThe withdrawn preprint states that the plant leaf image datasets used for its disease-detection experiments (potato, tomato, pepper bell, and strawberry) are freely available on open-source platforms, with explicit public URLs given in footnotes: a Kaggle plant disease dataset and a GitHub strawberry verticillium wilt.Dataset · publicthese datasets are freely available on open source plat- forms; potato, tomato, pepper bell 1 strawberry 2 .Open asset ↗lines:103-113Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Nov 2022Computers and Electronics in Agriculture.
Bruising is one of the major defects of strawberries. Strawberry bruised areas are often visible as flattened, sunken, and discolored. Postharvest strawberries can be bruised by compression, impact, or vibration forces during harvesting, transportation, and packinghouse operations. These bruises make strawberries more vulnerable to rot or disease, thus shortening their shelf life. Bruises can also cause consumers to abandon purchase decisions. Therefore, inspecting strawberry bruises is an important procedure for commercial strawberry farms to guarantee the high quality of strawberries sent to the market. A novel technique was developed in this study to detect bruises on strawberry images captured by a color camera under incandescent and ultraviolet (UV) light. Mask Region-Based Convolutional Neural Networks (Mask R-CNN), a deep learning method, was utilized for the strawberry bruise detection. Four bruise severities were classified according to the estimated bruise ratio of every single strawberry (Minor Bruise, Light Bruise, Moderate Bruise, and Severe Bruise). The results showed that Mask R-CNN could accurately detect whether a strawberry is bruised or not under both lighting conditions. However, the strawberry bruise severity classification accuracy of the proposed machine vision technique was improved significantly under UV light compared to incandescent light. For whole strawberry bruise detection under UV light, the F1 score for detecting strawberry bruises was 0.99, and the highest F1 score for whole strawberry bruise severity classification was 0.92 for Minor Bruise. This strawberry bruise detection system can assist farmers in detecting bruised strawberries on the processing line or an automated strawberry harvester, improve the quality of strawberries that are sent to the market, and avoid shortening the shelf life of fresh strawberries.
Why it matches plant phenotyping methodsイチゴ画像から打撲の有無と重症度（打撲率）を推定する画像ベース手法を開発・評価しており、植物状態の取得が中心である。
abstractA novel technique was developed in this study to detect bruises on strawberry images captured by a color camera under incandescent and ultraviolet (UV) light.
Strawberry powdery mildew (PM) is the main disease affecting the yield and quality of strawberries in recent years, which always appears on the back side of leaves in the early stage. Traditional methods of disease detection are labor-intensive and time-consuming. In this paper, we proposed a computer vision algorithm for strawberry leaf PM and infected leaves (IL) detection in complex background. Then we additionally proposed the estimation index of strawberry leaf PM for disease assessment. The original YOLOv4 backbone and neck are replaced by the proposed backbone and neck with depthwise convolution and hybrid attention mechanism, and the improvement can be made to decrease the size of the model and retain the performance. By combining the proposed backbone and neck, four new network structures are designed and evaluated, and the best one was named DAC-YOLOv4. Compared with YOLOv4, the mean average precision (mAP) of DAC-YOLOv4 reaches 72.7%, while the size is greatly compressed. To confirm the effectiveness of the proposed model, we compare DAC-YOLOv4 with five algorithms, and experimentally show that DAC-YOLOv4 performs well. We also deploy the algorithm on the Jetson Xavier NX and Jetson Nano, and the speed of DAC-YOLOv4 is 43 and 20 FPS, respectively, which can meet the real-time detection requirements. In summary, the experimental results indicate that the DAC-YOLOv4 proposed in this paper has good performance in strawberry leaf PM detection on the embedded platform, and the method to attain the disease index provides a solution for the early detection and prevention of strawberry PM.
Why it matches plant phenotyping methodsイチゴ葉のうどんこ病と感染葉を画像から検出し、病害指数を推定するコンピュータビジョン手法を開発・比較・組込み実装しており、植物病害状態の取得が中心である。
abstractIn this paper, we proposed a computer vision algorithm for strawberry leaf PM and infected leaves (IL) detection in complex background.
Detecting early signs of plant diseases and pests is important to preclude their progress and minimize the damages caused by them. Many methods are developed to catch signs of diseases and pests from plant images with deep learning techniques, however, detecting early signs is still challenging because of the lack of datasets to train subtle changes in plants. To solve these challenges, we built an automatic data acquisition system for the accumulation of a large dataset of plant images and trained an ensemble model to detect targeted plant diseases and pests. After obtaining 13,393 plant image data, our ensemble model shows a decent detection performance with an average of AUPRC 0.81. Also, this data acquisition and the detection process can be applied to other plant anomalies with the collection of additional data.
Why it matches plant phenotyping methods植物画像から病害・害虫感染の状態を早期検出する自動画像取得システムとアンサンブルモデルを開発しており、植物の状態推定手法が中心である。
abstractwe built an automatic data acquisition system for the accumulation of a large dataset of plant images and trained an ensemble model to detect targeted plant diseases and pests.
Modeling plant canopy biophysical parameters at the individual plant level remains a major challenge. This study presents a workflow for automatic strawberry canopy delineation and biomass prediction from high-resolution images using deep neural networks. High-resolution (5 mm) RGB orthoimages, near-infrared (NIR) orthoimages, and Digital Surface Models (DSM), which were generated by Structure from Motion (SfM), were utilized in this study. Mask R-CNN was applied to the orthoimages of two band combinations (RGB and RGB-NIR) to identify and delineate strawberry plant canopies. The average detection precision rate and recall rate were 97.28% and 99.71% for RGB images and 99.13% and 99.54% for RGB-NIR images, and the mean intersection over union ( mIoU ) rates for instance segmentation were 98.32% and 98.45% for RGB and RGB-NIR images, respectively. Based on the center of the canopy mask, we imported the cropped RGB, NIR, DSM, and mask images of individual plants to vanilla deep regression models to model canopy leaf area and dry biomass. Two networks (VGG-16 and ResNet-50) were used as the backbone architecture for feature map extraction. The R 2 values of dry biomass models were about 0.76 and 0.79 for the VGG-16 and ResNet-50 networks, respectively. Similarly, the R 2 values of leaf area were 0.82 and 0.84, respectively. The RMSE values were approximately 8.31 and 8.73 g for dry biomass analyzed using the VGG-16 and ResNet-50 networks, respectively. Leaf area RMSE was 0.05 m 2 for both networks. This work demonstrates the feasibility of deep learning networks in individual strawberry plant extraction and biomass estimation.
Why it matches plant phenotyping methods個体レベルのイチゴ画像からキャノピーを自動抽出し、葉面積と乾物バイオマスを推定する深層学習ワークフローを開発・評価しており、植物表現型取得・推定が中心である。
abstractThis study presents a workflow for automatic strawberry canopy delineation and biomass prediction from high-resolution images using deep neural networks.
Fruit cells' shape generally reflects the physiological state and quality of the fruit, and indirectly dictates its economics. In this study, a new bio-microscope including three independent and orthogonal channels of opto-electromechanical microscopic observation systems was developed to obtain the three views (e.g., front view, top view, side view) of a single fruit cell using tomato and strawberry at two ripening stages as fruit samples. The obtained three-view images were used to reconstruct the 3D real shape of a single cell based on the 3D geometrical modelling method using Solidworks CAD design software and then compared with the actual geometric size. The average relative errors for the major diameter, minor diameter 1, minor diameter 2, projection perimeter and projection area were 4.04 %, 6.25 %, 5.71 %, 1.69 % and 3.79 %, respectively. This good accuracy makes the newly developed bio-microscope together with the proposed 3D geometrical modelling method a promising 3D shape reconstruction technology for a single fruit cell to extract real and detailed cell morphology information. Furthermore, this method can find applications in other fields such as human and animal cells where soft particles' 3D shape analysis is important.
Why it matches plant phenotyping methodsトマトとイチゴの単一果実細胞について、3D形状を取得・再構成する顕微鏡と幾何モデリング手法を開発し、実寸法との誤差で精度検証しているため、植物表現型取得法が中心である。
abstracta new bio-microscope including three independent and orthogonal channels of opto-electromechanical microscopic observation systems was developed to obtain the three views (e.g., front view, top view, side view) of a single fruit cell
Background: Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected leaves and healthy leaves are very similar, making the early identification of strawberry anthracnose and gray mold still challenging. Results Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices for early detection of strawberry leaf diseases. The CARS algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and vegetation indices, respectively. Three machine learning models, BPNN, SVM and ELM, were developed for the early identification of strawberry anthracnose and gray mold, using spectral fingerprint features, vegetation index features and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and vegetation index features had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers were 97.78%, 94.44%, and 93.33%, respectively. This indicates that the fused features approach proposed in this study can effectively improve the early detection performance of strawberry leaf diseases. Conclusions This study provides a basis for the development of a rapid online detection and real-time monitoring system for fruit diseases.
Why it matches plant phenotyping methodsイチゴ葉の病徴という植物状態を、ハイパースペクトル画像と特徴抽出・機械学習で早期推定する手法が研究の中心である。
abstractBased on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices for early detection of strawberry leaf diseases.
The prediction and early detection of physiological disorders based on the nutritional conditions and stress of plants are extremely vital for the growth and production of crops. High-throughput phenotyping is an effective nondestructive method to understand this, and numerous studies are being conducted with the development of convergence technology. This study analyzes physiological disorders in plant leaves using hyperspectral images and deep learning algorithms. Data on seven classes for various physiological disorders, including normal, prediction, and the appearance of symptom, were obtained for strawberries subjected to artificial treatment. The acquired hyperspectral images were used as input for a convolutional neural network algorithm without spectroscopic preprocessing. To determine the optimal model, several hyperparameter tuning and optimizer selection processes were performed. The Adam optimizer exhibited the best performance with an F1 score of ≥0.95. Moreover, the RMSProp optimizer exhibited slightly similar performance, confirming the potential for performance improvement. Thus, the novel possibility of utilizing hyperspectral images and deep learning algorithms for nondestructive and accurate analysis of the physiological disorders of plants was shown.
Why it matches plant phenotyping methods植物葉の生理障害状態をハイパースペクトル画像とCNNで非破壊推定し、モデルの最適化・性能評価まで行うため、フェノタイピング手法が中心です。
abstractHigh-throughput phenotyping is an effective nondestructive method to understand this
For continual learning in the process of plant disease recognition it is necessary to first distinguish between unknown diseases from those of known diseases. This paper deals with two different but related deep learning techniques for the detection of unknown plant diseases; Open Set Recognition (OSR) and Out-of-Distribution (OoD) detection. Despite the significant progress in OSR, it is still premature to apply it to fine-grained recognition tasks without outlier exposure that a certain part of OoD data (also called known unknowns) are prepared for training. On the other hand, OoD detection requires intentionally prepared outlier data during training. This paper analyzes two-head network included in OoD detection models, and semi-supervised OpenMatch associated with OSR technology, which explicitly and implicitly assume outlier exposure, respectively. For the experiment, we built an image dataset of eight strawberry diseases. In general, a two-head network and OpenMatch cannot be compared due to different training settings. In our experiment, we changed their training procedures to make them similar for comparison and show that modified training procedures resulted in reasonable performance, including more than 90% accuracy for strawberry disease classification as well as detection of unknown diseases. Accurate detection of unknown diseases is an important prerequisite for continued learning.
Why it matches plant phenotyping methodsイチゴ病害の画像に基づく未知病害検出手法を比較・評価しており、植物の病害状態を推定する方法が研究の中心である。
abstractThis paper deals with two different but related deep learning techniques for the detection of unknown plant diseases; Open Set Recognition (OSR) and Out-of-Distribution (OoD) detection.
The strawberry plant has three life stages: seedling, blooming, and crop. It needs different acclimatization conditions in these life stages. A dataset consisting of 10,000 photographs of the strawberry plant was prepared. Using this dataset, classification in convolutional neural networks was performed in Matrix Laboratory (MATLAB). Nine different algorithms were used in this process. They were realized in ResNet101 architecture, and the highest accuracy rate was 99.8%. A low-resolution camera was used while growing strawberry plants in the application greenhouse. Every day at 10:00, a picture of the strawberry plant was taken. The captured image was processed in ResNet101 architecture. The result of the detection process appeared on the computer screen and was sent to the microcontroller via a USB connection. The microcontroller adjusted air-conditioning in the greenhouse according to the state of the strawberry plant. For this, it decided based on the data received from the temperature, humidity, wind direction, and wind speed sensors outside the greenhouse and the temperature, humidity, and soil moisture sensors inside the greenhouse. In addition, all data from the sensors and the life stage of the plant were displayed with a mobile application. The mobile application also provided the possibility for manual control. In the study, the greenhouse was divided into two. Strawberries were grown with the hybrid system on one side of the greenhouse and a normal system on the other side of the greenhouse. This study achieved 9.75% more crop, had a 4.75% earlier crop yield, and required 8.59% less irrigation in strawberry plants grown using the hybrid system.
Why it matches plant phenotyping methodsイチゴの生育段階という植物状態を画像と深層学習で分類し、温室制御へ接続するシステムを開発・評価しており、表現型取得手法が研究の中心である。
abstractUsing this dataset, classification in convolutional neural networks was performed in Matrix Laboratory (MATLAB).
High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet-visible-near infrared region (350-1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics-genomics approach in strawberry breeding.
Why it matches plant phenotyping methodsキャノピー反射スペクトルによる粉状うどんこ病抵抗性の表現型推定と、ゲノム予測への統合・独立検証が研究の中心であり、単なる形質測定ではない。
abstractRemote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection.
In winter and spring, for greenhouses with larger areas and stereoscopic cultivation, distributed light environment regulation based on photosynthetic rate prediction model can better ensure good crop growth. In this paper, strawberries at flowering-fruit stage were used as the test crop, and the LI-6800 portable photosynthesis system was used to control the leaf chamber environment and obtain sample data by nested photosynthetic rate combination experiments under temperature, light and CO2 concentration conditions to study the photosynthetic rate prediction model construction method. For a small-sample, nonlinear real experimental data set validated by grey relational analysis, a photosynthetic rate prediction model was developed based on Support vector regression (SVR), and the particle swarm algorithm (PSO) was used to search the influence of the empirical values of parameters, such as the penalty parameter C, accuracy ε and kernel constant g, on the model prediction performance. The modeling and prediction results show that the PSO-SVR method outperforms the commonly used algorithms such as MLR, BP, SVR and RF in terms of prediction performance and generalization on a small sample data set. The research in this paper achieves accurate prediction of photosynthetic rate of strawberry and lays the foundation for subsequent distributed regulation of greenhouse strawberry light environment.
Why it matches plant phenotyping methodsイチゴの光合成速度という植物生理形質を、実測データとPSO-SVRで予測するモデルの構築・性能比較が論文の中心であり、形質推定手法に該当する。
abstracta photosynthetic rate prediction model was developed based on Support vector regression (SVR)
Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.
Why it matches plant phenotyping methodsイチゴ病害を画像からリアルタイム検出するコンピュータビジョン手法をIoTプラットフォームの中心機能として開発・実装し、農場で評価しているため、植物病害状態のフェノタイピング手法に該当する。
abstracta computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time.
Reproduction assets foundThe paper provides an authors' public GitHub repository for the proposed IoT/edge phenotyping platform (sensor collection, YOLO v5 disease detection, Isolation Forest ML) and points to a public Kaggle strawberry disease detection dataset used for the computer vision model.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/usmanafzaal/strawberry-disease-detection-dataset (accessed on 14 June 2022).Open asset ↗https://www.kaggle.com/datasets/usmanafzaal/strawberry-disease-detection-datasetlines:394-409Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Pests and diseases are the two primary reasons for poor crop yields. Farmers have traditionally relied on manual methods to identify pests and diseases, which is time-consuming and costly. The Internet and pervasiveness of camera-enabled mobile devices, however, have made image acquisition more convenient and cheaper than ever before, and have launched a wave of research into how to use deep learning models to recognize pests and diseases in field. However, the datasets used in these studies were customized for only one or a few crop types. ImageNet pre-trained models were usually adopted to obtain high accuracy, regardless of the attributes of the target image datasets. A more comprehensive image dataset of crop pests and diseases was created. Transfer learning based on this disease and pest image dataset (TLDP) was compared with ImageNet pre-training. From experiments, we observed that TLDP has a similar effect to ImageNet pre-training. In addition, the performance of transfer learning largely depended on model performance on the source image dataset. To further improve the accuracy of TLDP, a novel convolutional neural network backbone called Decoupling-and-Attention network (DANet) was developed. DANet trained with the TLDP method achieved the highest classification accuracy on a strawberry pests and diseases image dataset (96.79%), followed by ImageNet pre-trained ResNet-50 (96.56%). In terms of computational cost, DANet was only a quarter of ResNet-50. The pre-trained DANet was also tested on other open pests and diseases image datasets. It still shows comparable performance to ImageNet pre-trained models.
Why it matches plant phenotyping methods作物病害・害虫画像データセットとDANetモデルを開発し、植物の病害状態を画像から分類する手法を比較・検証しており、表現型取得・推定が中心である。
abstractA more comprehensive image dataset of crop pests and diseases was created.
Disease has a significant impact on strawberry quality and yield, and deep learning has become an important approach for the detection of crop disease. To address the problems of complex backgrounds and small disease spots in strawberry disease images from natural environments, we propose a new Faster R_CNN architecture. The multiscale feature fusion network is composed of ResNet, FPN, and CBAM blocks, and it can effectively extract rich strawberry disease features. We built a dataset for strawberry leaves, flowers and fruits, and the experimental results showed that the model was able to effectively detect healthy strawberries and seven strawberry diseases under natural conditions, with an mAP of 92.18% and an average detection time of only 229 ms. The model is compared with Mask R_CNN and YOLO-v3, and we find that our model can guarantee high accuracy and fast detection operational requirements. Our method provides an effective solution for crop disease detection and can improve farmers' management of the strawberry growing process.
Why it matches plant phenotyping methodsイチゴの葉・花・果実画像から病害状態を検出する深層学習手法を開発し、データセット構築、比較評価、精度・速度検証を行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose a new Faster R_CNN architecture.
Why it matches plant phenotyping methodsイチゴ果実の可溶性固形分という植物器官特性を、NIR分光法で非破壊推定する予測モデルを評価・検証しており、表現型取得法が研究の中心である。
abstractThe present study aimed to evaluate the predictive capacity of NIRS technology, as a possible alternative to conventional methodology, for the analysis of the main organoleptic parameters of strawberries (Fragaria × ananassa Duch.).
Plants are complex organisms subject to variable environmental conditions, which influence their physiology and phenotype dynamically. We propose to interpret plants as reservoirs in physical reservoir computing. The physical reservoir computing paradigm originates from computer science; instead of relying on Boolean circuits to perform computations, any substrate that exhibits complex non-linear and temporal dynamics can serve as a computing element. Here, we present the first application of physical reservoir computing with plants. In addition to investigating classical benchmark tasks, we show that Fragaria × ananassa (strawberry) plants can solve environmental and eco-physiological tasks using only eight leaf thickness sensors. Although the results indicate that plants are not suitable for general-purpose computation but are well-suited for eco-physiological tasks such as photosynthetic rate and transpiration rate. Having the means to investigate the information processing by plants improves quantification and understanding of integrative plant responses to dynamic changes in their environment. This first demonstration of physical reservoir computing with plants is key for transitioning towards a holistic view of phenotyping and early stress detection in precision agriculture applications since physical reservoir computing enables us to analyse plant responses in a general way: environmental changes are processed by plants to optimise their phenotype.
Why it matches plant phenotyping methods植物の葉厚センサーを用いた物理リザバーコンピューティングを提案・実証し、光合成速度や蒸散速度などの生理形質推定とストレス早期検出への応用を中心に扱うため、植物フェノタイピング手法として適格。
abstractHere, we present the first application of physical reservoir computing with plants.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated/analysed (leaf thickness sensor traces, environmental variables, gas exchange data) on Zenodo and the analysis data/code on a public GitHub repository, both with exact URLs matching allowed_urls.Dataset · publicDatasets generated and/or analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.4264624 .Open asset ↗Zenodo · 10.5281/zenodo.4264624lines:153-237Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.
Why it matches plant phenotyping methodsRGB/HSV/HSL画像から機械学習でイチゴ果実のTSSとpHを非破壊推定する方法の開発が主目的であり、植物形質の取得・推定手法が中心である。
abstractthe main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries.
The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases' spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops. To that end, here we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet). (1) In order to fully extract the color features from images of diseased strawberry leaves, this paper constructed a color feature path at the front end of the network. The color feature information in the image was then extracted mainly through a color correlogram. (2) Likewise, to fully extract the texture features from images, a texture feature path at the front end of the network was built; it mainly extracts texture feature information by using an area compensation rotation invariant local binary pattern (ACRI-LBP). (3) To enhance the model's ability to extract detailed features, for the main frame, this paper proposed a multidirectional attention mechanism (MDAM). This MDAM can allocate weights in the horizontal, vertical, and diagonal directions, thereby reducing the loss of feature information. Finally, in order to solve the problems of gradient disappearance in the network, the ELU activation function was used in the main frame. Experiments were then carried out using a database we compiled. According to the results, the highest recognition accuracy by the network used in this paper for six types of strawberry leaf diseases and normal leaves is 95.79%, with an F1 score of 95.77%. This proves the introduced method is effective at detecting strawberry leaf diseases.
Why it matches plant phenotyping methodsイチゴ葉の病徴を画像から検出・分類する深層学習手法を開発し、認識精度とF1スコアで評価しており、植物状態の取得・抽出法が中心である。
abstracthere we proposed a detection framework for strawberry leaf diseases based on a dual-channel residual network with a multi-directional attention mechanism (MDAM-DRNet).
There has been substantial research that has achieved significant advancements in plant disease detection based on deep object detection models. However, with unknown diseases, it is difficult to find a practical solution for plant disease detection. This study proposes a simple but effective strawberry disease detection scheme with unknown diseases that can provide applicable performance in the real field. In the proposed scheme, the known strawberry diseases are detected with deep metric learning (DML)-based classifiers along with the unknown diseases that have certain symptoms. The pipeline of our proposed scheme consists of two stages: the first is object detection with known disease classes, while the second is a DML-based post-filtering stage. The second stage has two different types of classifiers: one is softmax classifiers that are only for known diseases and the K -nearest neighbor ( K -NN) classifier for both known and unknown diseases. In the training of the first stage and the DML-based softmax classifier, we only use the known samples of the strawberry disease. Then, we include the known ( a priori ) and the known unknown training samples to construct the K -NN classifier. The final decisions regarding known diseases are made from the combined results of the two classifiers, while unknowns are detected from the K -NN classifier. The experimental results show that the DML-based post-filter is effective at improving the performance of known disease detection in terms of mAP. Furthermore, the separate DML-based K -NN classifier provides high recall and precision for known and unknown diseases and achieve 97.8% accuracy, meaning it could be exploited as a Region of Interest (ROI) classifier. For the real field data, the proposed scheme achieves a high mAP of 93.7% to detect known classes of strawberry disease, and it also achieves reasonable results for unknowns. This implies that the proposed scheme can be applied to identify disease-like symptoms caused by real known and unknown diseases or disorders for any kind of plant.
Why it matches plant phenotyping methodsイチゴの病徴を画像から検出・分類する手法の開発と実フィールドでの性能評価が研究の中心であり、植物の病害状態を直接推定するため。
abstractThis study proposes a simple but effective strawberry disease detection scheme with unknown diseases that can provide applicable performance in the real field.
Multi-stage strawberry fruits detection is one of the important clues to estimate crop yields and assist robotic picking in modern agricultural production. However, it is difficult for detecting strawberries due to their small size, foreground-foreground class imbalance, and complex natural environment. Many works focus on how to detect fruits while ignoring multi-stage fruit detecting problems. In this paper, we propose DSE-YOLO (Detail-Semantics Enhancement You Only Look Once) to detect multi-stage strawberries. In DSE-YOLO, DSE (Detail-Semantics Enhancement) module is designed for detecting small fruits and distinguishing different stages of the fruit with higher accuracy, which utilize pointwise convolution and dilated convolution to extract various detail and semantics features in the horizontal and vertical dimensions. Exponentially Enhanced Binary Cross Entropy (EBCE) and Double Enhanced Mean Square Error (DEMSE) loss function are constructed to focus on small fruits, which can deal with foreground-foreground class imbalance problem. Experiments conducted on datasets demonstrate the superiority of DSE-YOLO over state-of-the-arts. The detection results had a mAP value of 86.58% and an F₁-Score value of 81.59%, which demonstrates the effectiveness of the proposed model. Especially, DSE-YOLO can almost detect every stage of strawberry fruit accurately in the natural scene, which can provide an important theoretical basis and premise for automatic picking and monitoring system.
Why it matches plant phenotyping methodsイチゴ果実の生育段階という植物器官の状態を画像から識別するYOLO手法を開発・評価しており、単なる収穫対象の位置検出を超えたフェノタイピング手法が中心である。
abstractIn this paper, we propose DSE-YOLO (Detail-Semantics Enhancement You Only Look Once) to detect multi-stage strawberries.
Societal Impact Statement Strawberry breeders are faced with increasing demands by propagators, growers, retailers and consumers for particular agronomic traits. This and the volume of plants requiring assessment during selection constrain breeders to rapid and qualitative rating methods. High‐throughput systems for assessing these traits automatically could indicate which families, or individual genotypes, should be singled out for further, more thorough evaluation, thus significantly increasing the selection intensity and accuracy. This review assesses the current status of and future potential for automated phenotyping in strawberry crops, highlighting key advances and the gaps which need to be addressed to facilitate the development of such technology. Summary Automated image‐based phenotyping has become widely accepted in crop phenotyping, particularly in cereal crops, yet few traits used by breeders in the strawberry industry have been automated. Early phenotypic assessment remains largely qualitative in this area since the manual phenotyping process is laborious and domain experts are constrained by time. Precision agriculture, facilitated by robotic technologies, is increasing in the strawberry industry, and the development of quantitative automated phenotyping methods is essential to ensure that breeding programs remain economically competitive. In this review, we investigate the external morphological traits relevant to the breeding of strawberries that have been automated and assess the potential for automation of traits that are still evaluated manually, highlighting challenges and limitations of the approaches used, particularly when applying high‐throughput strawberry phenotyping in real‐world environmental conditions.
Why it matches plant phenotyping methodsイチゴ育種における自動・画像ベース高スループット表現型計測を中心に、既存手法、課題、将来展望をレビューしているため。
abstractThis review assesses the current status of and future potential for automated phenotyping in strawberry crops
The aim of this work is to present an embedded vision system for the strawberry crop named “berryIP Embedded”. We developed a complete solution, considering hardware with sensors, camera and wi-fi in an embedded platform, sending information to software to collect weather data and to determine the leaf area by image manipulation techniques. This software also presents crop weather and image results in a graphical user interface, allowing the system operation for distance and generating statistical data for crop analysis. We used a indoor greenhouse at the University of Passo Fundo to validate the equipment. Results suggested our cost-effective system that could be used in practice by researchers, allowing an effective monitoring of the crop. Data collections were performed during the 21 days, and the data obtained were statistically analyzed. A comparison was executed between the manual method of estimating leaf area of Albion culture, through prediction equations, and the proposed method of image processing, showing that data measured by the platform does not exceed 10% variation. Pearson’s coefficient showed a strong correlation (ρ=0.96) between leaf area and accumulated temperature during the period.
Why it matches plant phenotyping methodsイチゴ葉面積を画像処理で推定する組込み型ビジョンシステムを開発し、手法比較と温室内検証を行っており、植物形質取得が研究の中心です。
abstractWe developed a complete solution, considering hardware with sensors, camera and wi-fi in an embedded platform, sending information to software to collect weather data and to determine the leaf area by image manipulation techniques.
Gray mold disease is one of the most frequently occurring diseases in strawberries. Given that it spreads rapidly, rapid countermeasures are necessary through the development of early diagnosis technology. In this study, hyperspectral images of strawberry leaves that were inoculated with gray mold fungus to cause disease were taken; these images were classified into healthy and infected areas as seen by the naked eye. The areas where the infection spread after time elapsed were classified as the asymptomatic class. Square regions of interest (ROIs) with a dimensionality of 16 × 16 × 150 were acquired as training data, including infected, asymptomatic, and healthy areas. Then, 2D and 3D data were used in the development of a convolutional neural network (CNN) classification model. An effective wavelength analysis was performed before the development of the CNN model. Further, the classification model that was developed with 2D training data showed a classification accuracy of 0.74, while the model that used 3D data acquired an accuracy of 0.84; this indicated that the 3D data produced slightly better performance. When performing classification between healthy and asymptomatic areas for developing early diagnosis technology, the two CNN models showed a classification accuracy of 0.73 with regards to the asymptomatic ones. To increase accuracy in classifying asymptomatic areas, a model was developed by smoothing the spectrum data and expanding the first and second derivatives; the results showed that it was possible to increase the asymptomatic classification accuracy to 0.77 and reduce the misclassification of asymptomatic areas as healthy areas. Based on these results, it is concluded that the proposed 3D CNN classification model can be used as an early diagnosis sensor of gray mold diseases since it produces immediate on-site analysis results of hyperspectral images of leaves.
Why it matches plant phenotyping methodsイチゴ葉のハイパースペクトル画像から健康・感染・無症状領域を分類し、CNNモデルの開発と性能評価を行うことが中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstracthyperspectral images of strawberry leaves that were inoculated with gray mold fungus to cause disease were taken; these images were classified into healthy and infected areas as seen by the naked eye.
Abstract Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi‐view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer‐grade components for capturing, calibrating, and reconstructing the 3D form of small‐to‐moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R 2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground‐truth object and the reconstructed models was >0.97 and root‐mean square error (RMSE) was 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, five grapes, seven peppers, and four Bosc and two red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.
Why it matches plant phenotyping methods果実形状の3D取得・再構成システムを開発し、基準物体との比較で精度検証しているため、植物表現型取得法が中心である。
abstractWe present a complete system constructed of consumer‐grade components for capturing, calibrating, and reconstructing the 3D form of small‐to‐moderate sized fruits and tubers.
Counting crop seedlings is a time-demanding activity involved in diverse agricultural practices like plant cultivating, experimental trials, plant breeding procedures, and weed control. Unmanned Aerial Vehicles (UAVs) carrying RGB cameras are novel tools for automatic field mapping, and the analysis of UAV images by deep learning methods can provide relevant agronomic information. UAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study. Five locations were monitored at different growth stages, and the crop number per plot was automatically predicted by using a fully convolutional network (FCN) pipeline. Our FCN-based approach is a single model for jointly determining both the exact stem location of crop and weed plants and a pixel-wise plant classification considering crop, weed, and soil. To determinate the approach performance, predicted crop counting was compared to visually assessed ground truth data. Results show that UAV-based counting of sugar-beet plants delivers forecast errors lower than 4.6%, and the main factors for performance are related to the intra-row distance and the growth stage. The pipeline’s extension to other crops is possible; the errors of the predictions are lower than 4% under practical field conditions for maize and strawberry fields. This work highlight the feasibility of automatic crop counting, which can reduce manual effort to the farmers.
Why it matches plant phenotyping methodsUAV画像と深層学習パイプラインによって作物個体数を自動推定する手法が研究の中心であり、複数作物・圃場で地上真値と比較検証しているため、植物フェノタイピング手法に該当する。
abstractUAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study.
Botrytis fruit rot and anthracnose are fungal diseases of strawberry. These diseases are a significant contributor to yield losses, requiring farmers to use fungicides frequently to prevent them. The proliferation of botrytis and anthracnose is directly linked to the duration of the presence of free water on the plant canopy, which is generally defined as leaf wetness duration (LWD). LWD is an important measure in determining the risk for these diseases to develop in the strawberry crop. By accurately measuring LWD, the risk of disease can be calculated more accurately, and specific fungicide application recommendations can be given to the farmers. This reduces the frequency with which fungicide is applied and ultimately reduces costs for farmers. There is no standard method to detect leaf wetness, but leaf wetness sensors are widely used for that purpose. These wetness sensors are difficult to calibrate and not very accurate, which reduces their reliability. The objective of this study was to find a better alternative to the commonly used leaf wetness sensors. This study implemented color and thermal imaging-based approaches as a solution to the problem of leaf wetness detection in strawberry plants. The proposed method used deep learning and computer vision techniques to detect leaf wetness from color and thermal images. The deep learning model was highly accurate in detecting wetness when compared with the visual observation of the images. It was also found that leaf wetness could be detected with a high degree of accuracy using deep learning with color images. In the future, using the findings of this study, a portable device can be developed to replace the commonly used wetness sensor with a more reliable imaging-based device.
Why it matches plant phenotyping methodsイチゴ葉の濡れ状態（LWD）をカラー・熱画像と深層学習で検出し、既存センサーと比較する手法開発・検証が研究の中心である。
abstractThe objective of this study was to find a better alternative to the commonly used leaf wetness sensors.
Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.
Why it matches plant phenotyping methodsイチゴ葉の病害状態を画像から推定する深層学習・マシンビジョン手法を開発・比較し、実時間フィールド条件で検証しており、植物表現型取得が中心である。
abstractdeep learning models were used to identify the leaf scorch disease in strawberry plants.
In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.
Why it matches plant phenotyping methods植物葉画像から病害の同定と病害度を推定する深層学習画像解析手法を開発し、複数手法および既存アルゴリズムと性能比較しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves.
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · checked 15 Sept 2026
Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi-view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer-grade components for capturing, calibrating, and reconstructing the 3D form of small-to-moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R 2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground-truth object and the reconstructed models was > 0.97 and root-mean square error (RMSE) was 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, 5 grapes, 7 peppers, and 4 Bosc and 2 red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.
Why it matches plant phenotyping methods果実・塊茎の3D形状を取得・再構成する低コスト高スループット画像フェノタイピングシステムの開発と精度検証が研究の中心である。
abstractWe present a complete system constructed of consumer-grade components for capturing, calibrating, and reconstructing the 3D form of small-to-moderate sized fruits and tubers.
Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%.
Why it matches plant phenotyping methodsイチゴ病害の症状を画像からインスタンスセグメンテーションし、データセットとMask R-CNN手法を開発・評価しているため、植物状態の取得手法が中心である。
abstractwe introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions.
This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.
Why it matches plant phenotyping methods深層学習によるイチゴのキャノピー delineation（植物形態・被覆の抽出）を中心とした画像解析ワークフローであり、植物フェノタイピング手法として実質的です。
abstractThis publication presents a guide to image analysis
Reproduction assets foundThe article's Data Availability section provides a public Google Drive link containing the strawberry canopy imagery, canopy boundary shapefiles, and training data used in the Mask RCNN phenotyping workflow.Dataset · publicdel is trained
using examples related to the target application. We used
10,273 canopies to train the model, but we believe that the
model can be trained with fewer canopies and still produce
reasonably reliable results.
Data Availability
Data used for this article is available to readers and can
be found at the following link: https://drive.google.com/file/d/19_NXehuBrBdE64Ejkao-8eYminYZ9rOL.Citations
Abd-Elrahman, A., Z. Guan, C. Dalid, V. Whitaker, K. Britt,
B. Wilkinson, and A. Gonzalez. 2020. “Automated Canopy
Delineation and Size Metrics Extraction for Strawberry Dry
Weight Modeling Using Raster Analysis of High-Resolution
Imagery.” Remote Sensing 12 (21): 3632.
Ammirato, P., and A. C.Open asset ↗19_NXehuBrBdE64Ejkao-8eYminYZ9rOLpdf-raw-page:5 lines:1-48Plant phenotyping relevance match · UnverifiedOpenAlex · checked 8 Sept 2026
Chlorophyll fluorescence is interesting for phenotyping applications as it is rich in biological information and can be measured remotely and non-destructively. There are several techniques for measuring and analysing this signal. However, the standard methods use rather extreme conditions, e.g., saturating light and dark adaption, which are difficult to accommodate in the field or in a greenhouse and, hence, limit their use for high-throughput phenotyping. In this article, we use a different approach, extracting plant health information from the dynamics of the chlorophyll fluorescence induced by a weak light excitation and no dark adaption, to classify plants as healthy or unhealthy. To evaluate the method, we scanned over a number of species (lettuce, lemon balm, tomato, basil, and strawberries) exposed to either abiotic stress (drought and salt) or biotic stress factors (root infection using Pythium ultimum and leaf infection using Powdery mildew Podosphaera aphanis). Our conclusions are that, for abiotic stress, the proposed method was very successful, while, for powdery mildew, a method with spatial resolution would be desirable due to the nature of the infection, i.e., point-wise spread. Pythium infection on the roots is not visually detectable in the same way as powdery mildew; however, it affects the whole plant, making the method an interesting option for Pythium detection. However, further research is necessary to determine the limit of infection needed to detect the stress with the proposed method.
Why it matches plant phenotyping methods弱光励起・暗順応なしのクロロフィル蛍光動態から植物の健康状態やストレスを推定する手法を提案し、複数種・生物的/非生物的ストレスで評価しているため、植物フェノタイピング手法が中心である。
abstractChlorophyll fluorescence is interesting for phenotyping applications as it is rich in biological information and can be measured remotely and non-destructively.
BlueberryStrawberryFruitClassificationGrowth / development / phenology
Automated technologies for quality inspection of fruits have attracted great interest in the food industry. The development of nondestructive mechanisms to assess the quality of individual fruit prior to sale may lead to an increase in overall product quality, value, and consequently, producer competitiveness. However, the existing methods have limitations. Herein, a texture sensor based on highly sensitive hair-like cilia receptors, to allow a quick quality evaluation of fruit is proposed. The texture sensor consists of up to 100 magnetized nanocomposite cilia attached to a chip with magnetoresistive sensors in a full Wheatstone bridge architecture. In this paper we demonstrate the use of ciliary sensors in scanning fruits (blueberries and strawberries) in different maturation stages. The contact of the cilia with the fruit skin provided qualitative information about its texture in terms of ripeness stage. Less mature fruits exhibited, on average, a highest peak voltage of 0.14 mV for blueberries and 0.12 mV for strawberries, while overripe fruits exhibited 0.58 mV and 0.56 mV, respectively. The results were confirmed by sensorial assessment of the fruit freshness, and therefore attesting the application potential of the sensing technology for fruit quality control.
Why it matches plant phenotyping methods果実の成熟度・テクスチャを直接推定する磁気式センサーを開発し、ブルーベリーとイチゴで検証しており、植物器官の状態取得が中心である。
abstractHerein, a texture sensor based on highly sensitive hair-like cilia receptors, to allow a quick quality evaluation of fruit is proposed.
The symptoms and biophysical processes occurring in garden strawberry plants when they are affected by the dominant type of disease (up to 80%) caused by pathogenic fungi have been described. The ineffectiveness of the visual assessment of the degree of damage to strawberry diseases by a conventional 5-point scale or as a percentage of the leaf plate area affected by fungi, with the involvement of qualified specialists, has been shown. To create diagnostic tools that allow early detection of fungal diseases of garden strawberries, one of the methods of computer vision was proposed by counting image pixels in the space of color channels of red, green and blue (R, G, B), which makes it possible to determine the degree of fungal diseases affecting an individual plant leaf. The algorithm includes capturing an image with a digital camera by focusing on a plant leaf placed on a substrate with a uniform background providing a contrasting selection of the object; converting a color image to black and white; dividing the image between areas with necrotic spots and healthy areas of the plant leaf by masking and removing pixels; counting the number of pixels in these two areas and calculating their ratio. Information about a computer software for determining the degree of damage to a strawberry leaf by garden fungal diseases has been given. Java programming language (operating system Android Studio 3.4.1) was used as a language for the development of the logical part of the information system. In order to build a graphical interface, the software facilitating the development and integration of various modules of the LibGDX software project was used. The proposed algorithm is implemented for a personal computer and can be installed on a smartphone in the form of a software application, with the help of which any agricultural producer can carry out early diagnosis of fungal plant diseases.
Why it matches plant phenotyping methodsイチゴ葉の病斑面積比を画像処理で定量化し、植物病害の程度を推定する手法とスマートフォンソフトウェアの開発が中心であるため含める。
abstractone of the methods of computer vision was proposed by counting image pixels in the space of color channels of red, green and blue (R, G, B), which makes it possible to determine the degree of fungal diseases affecting an individual plant leaf.
Gray mold (Botrytis cinerea) is one of the main diseases that affect strawberries. Severity assessments of the disease in fruits have been mainly performed without any aid. Although there has been a rating scale already published, there are no standard area diagrams (SADs) that could be used. Therefore, our objective was to develop and validate a set of SADs to assess severity of gray mold in strawberry fruit. The set of SADs has seven levels of disease severity (0, 8, 28, 46, 77, 82 and 100%) and they were validated by five experienced raters and five inexperienced raters in plant disease assessment. Photos of 50 strawberry fruits were provided to raters for gray mold severity assessment, which was performed in three stages: i) without any aid; ii) with a rating scale previously published; and iii) with the SADs. Lin’s concordance correlation coefficient (LCCC) of estimated and actual severity was improved with the use of SADs (0.94) compared to the rating scale (0.74) and assessment without aid (−0.07). Peer comparison of all raters showed that 87% of the coefficient of determination from the regression models between actual and estimated severity were greater than 0.81 with the SADs, while only 37% and 9% of the values were higher than 0.81 with the rating scale and without any aid, respectively. Hence, there was greater consistency of disease assessment among raters. The set of SADs proposed in this study can improve accuracy and precision to estimate severity of gray mold in strawberry fruit.
Why it matches plant phenotyping methodsイチゴ果実の灰色かび病重症度を測定する標準面積図を開発・検証しており、植物病害状態の表現型取得法が研究の中心である。
abstractTherefore, our objective was to develop and validate a set of SADs to assess severity of gray mold in strawberry fruit.
Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry ( Fragaria x ananassa ) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.
Why it matches plant phenotyping methodsイチゴ果実画像から形態・色形質を自動抽出するパイプラインの開発と応用が研究の中心であり、遺伝解析も含む実質的な画像ベース植物フェノタイピングである。
abstractHere, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry ( Fragaria x ananassa ) images.
In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeNet-MOD2. Data augmentation was carried out from among 1450 healthy and infected leaf images to prevent overfitting and to consider the various shapes and direction of the leaves in the field. A total of eight clockwise rotations (0°; the original data, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) was performed to generate 11,600 data points. Overall, the six DL algorithms that were used in this study showed on average of >92% in classification accuracy (CA). ResNet-50 gave the highest CA of 98.11% in classifying the healthy and infected leaves; however, considering the computation time, AlexNet had the fastest processing time, at 40.73 s, to process 2320 images with a CA of 95.59%.When considering the memory requirements for hardware deployment, SqueezeNet-MOD2 would be recommended for PM detection on strawberry leaves with a CA of 92.61%.
Why it matches plant phenotyping methodsイチゴ葉の画像から病害状態（健全・うどんこ病感染）を推定する深層学習手法を比較・評価しており、植物フェノタイピング手法が中心である。
titleA deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves
The quality characteristics and consumer acceptability of strawberries depend on their ripening stages. Pelargodinin-3-glucoside (P3G) is the main anthocyanin in strawberries and changes during postharvest distribution. Hyperspectral imaging technology (HSI), which covers the ranges of visible-near infrared (VIS-NIR) and shortwave infrared (SWIR) light, was used to predict the P3G of strawberries for two types of harvest maturity, half maturity (HM) and full maturity (FM) during distribution periods. Spectral data was extracted from hyperspectral images of strawberries at the two stages of maturity and partial least square (PLS) regression was used to develop the prediction model of P3G. The highest prediction accuracy was 86% in HM using the SWIR region. Meaningful wavelengths to develop the PLS model were approximately 970, 1,153, 1,303, and 1,445 nm for moisture, carbohydrates, and anthocyanin. Moreover, the spatial information of P3G accumulation can be also analyzed using HSI. The overall results indicated that HSI could be used for the prediction of P3G with different harvest maturity of strawberry. Additionally, this technology could provide the information of distribution periods of strawberry by monitoring changes of pigment of strawberries.
Why it matches plant phenotyping methodsイチゴ果実のアントシアニン量と空間分布を、ハイパースペクトル画像とPLS回帰で非破壊推定する手法が研究の中心であり、植物形質の取得・推定方法に該当する。
abstractpartial least square (PLS) regression was used to develop the prediction model of P3G
Fruit shape of cultivated strawberry ( Fragaria × ananassa Duch.) is an important breeding target. To detect genomic regions associated with this trait, its quantitative evaluation is needed. Previously we created a multi-parent advanced-generation inter-cross (MAGIC) strawberry population derived from six founder parents. In this study, we used this population to quantify fruit shape. Elliptic Fourier descriptors (EFDs) were generated from 2 969 two-dimensional binarized fruit images, and principal component (PC) scores were calculated on the basis of the EFD coefficients. PC1-PC3 explained 96% of variation in shape and thus adequately quantified it. In genome-wide association study, the PC scores were used as phenotypes. Genome wide association study using mixed linear models revealed 2 quantitative trait loci (QTLs) for fruit shape. Our results provide a novel and effective method to analyze strawberry fruit morphology; the detected QTLs and presented method can support marker-assisted selection in practical breeding programs to improve fruit shape.
Why it matches plant phenotyping methods画像解析と楕円フーリエ記述子によりイチゴ果実形状を定量化する手法が研究の中心であり、育種への応用も示しているため。
titleStrawberry fruit shape: quantification by image analysis and QTL detection by genome-wide association analysis.
Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.
Why it matches plant phenotyping methodsイチゴの形態・構造・生理特性や病害などを対象に、リモートセンシングと機械学習によるフェノタイピング手法を体系的に扱うレビューであり、方法論が中心である。
abstractIn recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning.
Detecting plant diseases in the earliest stages, when remedial intervention is most effective, is critical if damage crop quality and farm productivity is to be contained. In this paper, we propose an improved vision-based method of detecting strawberry diseases using a deep neural network (DNN) capable of being incorporated into an automated robot system. In the proposed approach, a backbone feature extractor named PlantNet, pre-trained on the PlantCLEF plant dataset from the LifeCLEF 2017 challenge, is installed in a two-stage cascade disease detection model. PlantNet captures plant domain knowledge so well that it outperforms a pre-trained backbone using an ImageNet-type public dataset by at least 3.2% in mean Average Precision (mAP). The cascade detector also improves accuracy by up to 5.25% mAP. The results indicate that PlantNet is one way to overcome the lack-of-annotated-data problem by applying plant domain knowledge, and that the human-like cascade detection strategy effectively improves the accuracy of automated disease detection methods when applied to strawberry plants.
Why it matches plant phenotyping methodsイチゴ植物の病害を画像から検出する深層学習手法を開発・比較しており、植物の病害状態を推定する方法が研究の中心です。
abstractwe propose an improved vision-based method of detecting strawberry diseases using a deep neural network (DNN)
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.
Why it matches plant phenotyping methods植物のリン栄養状態を hyperspectral imaging と機械学習で非侵襲推定する手法が研究の中心であり、分類器の開発と評価も行っている。
abstractThe measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment.
Abstract Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.
Why it matches plant phenotyping methods植物葉のリン栄養状態を非侵襲的に推定するため、ハイパースペクトル画像と機械学習分類器を中心的に開発・適用しており、植物フェノタイピング手法に該当する。
abstractThe measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment.
The strawberry ( Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30-40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases-leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.
Why it matches plant phenotyping methodsCNNによるイチゴ葉・果実の病徴画像認識手法を開発しており、植物の病害状態推定が研究の中心であるため。
abstractWe developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model.
The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second.
Why it matches plant phenotyping methods植物葉の病害状態を画像から自動推定する深層学習・クラウド実装を開発・評価しており、植物フェノタイピング手法が中心です。
abstractAutomated identification and classification of plant leaf diseases is, therefore, essential
Reproduction assets foundThe paper's Data Availability statement explicitly links a public Kaggle plant-disease image dataset used for training/testing and an authors' GitHub code repository. The TensorFlow plant_village catalog URL is a generic mirror of the same public dataset rather than a paper-specific deposit.Dataset · publicData Availability: Dataset is available from the below link: https://www.kaggle.com/emmarex/plantdiseaseOpen asset ↗kaggle · emmarex/plantdiseaselines:123-130Code · publicGithub Code Repo Link: https://github.com/umairnawazz/Plant-Disease-DetectionOpen asset ↗github · umairnawazz/Plant-Disease-Detectionlines:123-130Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
ABSTRACT Automatizing phenotype measurement is needed to increase plant breeding efficiency. Morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semi-automatically obtained. Yet, fruit morphology evaluation can be boosted by resorting to fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry images. The pipeline segments, classifies and labels the images, extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier Elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominant effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and is moderately heritable. Although we study the strawberry species, the algorithm can be applied to other fruits, as shown in the GitHub repository https://github.com/lauzingaretti/DeepAFS .
Why it matches plant phenotyping methodsイチゴ果実の画像から形態・色彩形質を自動抽出するパイプラインの開発が研究の中心であり、遺伝解析はその応用である。
abstractHere, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry images.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicFigure 2. Data analysis workflow (available at https://github.com/lauzingaretti/DeepAFS ). The input are all the
segmented internal and external fruit images.Open asset ↗lauzingaretti/DeepAFSpdf-page:5 lines:1-53Plant phenotyping relevance match · UnverifiedOpenAlex · checked 9 Sept 2026
Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.
Why it matches plant phenotyping methodsイチゴのキャノピーを画像から自動 delineation し、面積・体積・高さなどの形質を抽出する手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractThis study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis
StrawberryMultispectral / hyperspectralFruitPhysiological trait estimationWater status / transpiration
Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.
Why it matches plant phenotyping methodsイチゴ果実の水分含量という植物器官の形質を、ハイパースペクトル計測とBPニューラルネットワークで非破壊推定する手法の開発・性能評価が中心である。
abstractThis study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture.
In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on AWS DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using AWS DeepLens on average took 0.349s, providing disease information to the user in less than a second.
Why it matches plant phenotyping methods植物葉の病徴を画像から自動検出・分類する手法と、AWS DeepLens/SageMakerによるリアルタイム実装・評価が研究の中心であり、植物病害状態のフェノタイピングに該当します。
abstractOur proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases
Fruit and vegetable harvest efficiency depends on the mechanization and automation of production. The available literature lacks the results of research on the applicability of pneumatic end effectors among grippers for the robotic harvesting of strawberries. To determine their practical applications, a series of tests was performed. They included the determination of the morphological indicators of the strawberry, fruit suction force, the real stress exerted by fruit suckers and the degree of fruit damage. The fruits' morphological indicators included the relationships between the weight and geometrical dimensions of the tested fruit, the equivalent diameter, and the sphericity coefficient. The fruit suction force was determined on a stand equipped with a vacuum pump, and control and measurement instruments, as well as a MTS 2 testing machine. The necrosis caused by tissue damage to the fruits by suction cup adhesion was assessed by counting the necrosis surface areas using the LabView programme. The assessment of the necrosis was conducted immediately upon the test's performance, after 24 and after 72h. The stress values were calculated by referring the values of the suction forces obtained to the surface of the suction cup face. The tests were carried out with three constructions of suction cups and three positions of suction cup faces on the fruits' surface. The research shows that there is a possibility for using pneumatic suction cups in robotic picking heads. The experiments performed indicate that the types of suction cups constructions, and the zones and directions of the suction cups' application to the fruit significantly affect the values of the suction forces and stresses affecting the fruit. The surface areas of the necrosis formed depend mainly on the time that elapses between the test and their assessment. The weight of strawberry fruit in the conducted experiment constituted from 13.6% to 23.1% of the average suction force.
Why it matches plant phenotyping methodsイチゴ収穫用の空気圧エンドエフェクタを技術評価し、果実形態、吸引力、応力、損傷（壊死面積）を測定・比較している。収穫対象の単なる検出ではなく、果実状態の取得と装置性能評価が中心である。
abstractThey included the determination of the morphological indicators of the strawberry, fruit suction force, the real stress exerted by fruit suckers and the degree of fruit damage.
Abstract Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery ( Apium graveolens L., cv. Neon), sugar beet ( Beta vulgaris L., cv. Tapir) and strawberry ( Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.
Why it matches plant phenotyping methods植物のリン栄養状態を非侵襲的に推定するため、ハイパースペクトル画像と機械学習による分類手法を中心的に適用・評価している。
abstractThe measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment.
Strawberry shape uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters 'by eye' and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry shape uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A "regular shape" QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a potential candidate for marker-assisted breeding, while the potential to implement genomic selection is explored. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry shape uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification and unlocked the ability to accurately select for uniform berries.
Why it matches plant phenotyping methodsイチゴ果実の形状均一性を対象に3D画像法を開発し、自動指標と手動評価を比較して形質定量の精度を検証しているため、フェノタイピング手法が中心です。
abstractIn this study, a 3D-imaging approach was developed to characterise berry shape uniformity.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
The automated harvesting of strawberry brings benefits such as reduced labor costs, sustainability, increased productivity, less waste, and improved use of natural resources. The accurate detection of strawberries in a greenhouse can be used to assist in the effective recognition and location of strawberries for the process of strawberry collection. Furthermore, being able to detect and characterize strawberries based on field images is an essential component in the breeding pipeline for the selection of high-yield varieties. The existing manual examination method is error-prone and time-consuming, which makes mechanized harvesting difficult. In this work, we propose a robust architecture, named "improved Faster-RCNN," to detect strawberries in ground-level RGB images captured by a self-developed "Large Scene Camera System." The purpose of this research is to develop a fully automatic detection and plumpness grading system for living plants in field conditions which does not require any prior information about targets. The experimental results show that the proposed method obtained an average fruit extraction accuracy of more than 86%, which is higher than that obtained using three other methods. This demonstrates that image processing combined with the introduced novel deep learning architecture is highly feasible for counting the number of, and identifying the quality of, strawberries from ground-level images. Additionally, this work shows that deep learning techniques can serve as invaluable tools in larger field investigation frameworks, specifically for applications involving plant phenotyping.
Why it matches plant phenotyping methodsイチゴ果実の検出・計数・品質（plumpness）評価を目的とした撮像システムと深層学習手法を開発し、植物フェノタイピングへの応用として技術評価しているため、方法が中心的である。
abstractwe propose a robust architecture, named "improved Faster-RCNN," to detect strawberries in ground-level RGB images captured by a self-developed "Large Scene Camera System."
The study extracts representative features to train a model with supervised machine learning (ML) to detect powdery mildew (Sphaerotheca macularis f. sp. fragariae) on the strawberry leaves. Powdery mildew (PM) is a fungal disease that greatly affects the production of strawberry and usually infects under conditions of warming temperatures and high humidity. In this research, we report robust models to detect PM using image processing and ML technologies. Three feature extraction techniques (histogram of oriented gradients; HOG, speeded-up robust features; SURF, and gray level co-occurrence matrix; GLCM) and two supervised ML (artificial neural network; ANN and support vector machine; SVM) were implemented using MATLAB. Images were augmented to 1016 images using a four different angle rotation technique to simulate strawberry leaf bundles in the real field. The classification accuracy (CA) to detect PM was highest at 94.34% with a combination of ANN and SURF with 908 × 908 image resolution and with SVM and GLCM at 88.98% with 908 × 908 image resolution. In terms of the extraction time for real-time processing, HOG takes the shortest time to extract features in both ANN and SVM.
Why it matches plant phenotyping methodsイチゴ葉の画像からうどんこ病状態を抽出・分類する画像処理および機械学習手法が研究の中心であり、植物病害表現型の測定法に該当する。
abstractThe study extracts representative features to train a model with supervised machine learning (ML) to detect powdery mildew (Sphaerotheca macularis f. sp. fragariae) on the strawberry leaves.
Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400-1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (R p 2 ) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with R p 2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with R p 2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries.
Why it matches plant phenotyping methodsイチゴのSSC、pH、ビタミンCをハイパースペクトル画像と多変量解析で非破壊推定し、重要変数選択と分布マップ生成まで行う手法開発が中心である。
abstractto develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods
Abstract Background Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit shape is an inherently multi-dimensional, continuously variable trait and not adequately described by a single categorical or quantitative feature. Morphometric approaches enable the study of complex, multi-dimensional forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC). We use these human-recognizable shape categories to select quantitative features extracted from multiple morphometric analyses that are best fit for genetic dissection and analysis. Results We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered 4 principal shape categories, and inferred progression using PPKC. We extracted 68 quantitative features from digital images of strawberries using a suite of morphometric analyses and multivariate statistical approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68% to 99% for the newly created phenotypic variables for describing a shape. Conclusions Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches that we applied in strawberry should apply to other fruits, vegetables, and specialty crops.
Why it matches plant phenotyping methodsイチゴ果実のデジタル画像から多次元形状形質を抽出・分類・順序付ける手法を開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC).
The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing-in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction-the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical-physical properties of the samples, finding remarkable applications in the agro-food market.
Why it matches plant phenotyping methodsイチゴ果実の品質形質（可溶性固形分・硬度）をNIR分光とPLS回帰で非破壊推定し、モデル性能を検証しているため、植物形質取得法が中心です。
abstractThe PLS model for the soluble solids content prediction was the best performing-in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications.
An accurate and robust strawberry flower representation and detection scheme is a key step to enable the reliable forecasting of fruit yield for use in precision agricultural applications. A state-of-the-art deep-level object detection framework which processes images through several layers using a region-based convolutional neural network (R-CNN) was developed to visually represent the instances of strawberry flowers in outdoor fields and improve the detection accuracy. A modified version of the visual geometry group 19 (VGG19) architecture, which had 47 layers, was used to represent the multiple scales of strawberry flower image features. The networks were trained entirely on 400 strawberry flower images and tested on another 100 images. Different region-based object detection methods, including the R-CNN, Fast R-CNN and Faster R-CNN, were used to represent the strawberry flower instances. The Faster R-CNN model achieved a better performance than the R-CNN and Fast R-CNN in detecting the instances and had a lower execution time. The detection accuracy of the Faster R-CNN model was 86.1%, which was higher than those of the R-CNN and Fast R-CNN models (63.4% and 76.7%, respectively). The experimental results showed the effectiveness of the deep-level Faster R-CNN framework for representing the strawberry flower instances under various camera view-points, different distances to flowers, overlaps, complex background illumination, blur, etc. The system developed for automatic and accurate strawberry flower detection provides an important and significant solution that enables subsequent applications to estimate the strawberry yield in outdoor fields.
Why it matches plant phenotyping methodsイチゴ花という植物器官の画像取得・検出手法を開発し、複数の検出法を比較検証しているため、花数・開花状態の推定に用いる植物フェノタイピング手法が中心です。
abstractA state-of-the-art deep-level object detection framework which processes images through several layers using a region-based convolutional neural network (R-CNN) was developed to visually represent the instances of strawberry flowers in outdoor fields and improve the detection accuracy.
StrawberryTomatoFruitLeafRootPhysiological trait estimationWater status / transpiration
Plant water stable isotopes (δ 18 O, δ 2 H) have been used in eco-hydrological, biogeochemical and hydrological studies to e.g., quantify terrestrial water fluxes or to determine plant water sources. Current plant water extraction methods for isotope measurements are either expensive, labor-intensive or can lead to isotopic fractionation. Recent studies employed a new, extraction-free measurement method that was originally developed for the analysis of isotopes in sediment pore water: the water-vapor equilibrium method. It still needs to be tested if this method can be reliably used for isotope analysis of plant samples and how to best prepare the samples. Therefore, we investigated the effects of various preparation steps when measuring the plant water stable isotopes using this new method. We chose tomato and strawberry plants and prepared roots, shoots, leaves and fruits by either grinding or cutting them into pieces. Further, the necessary sample amount and the effect of equilibration time was evaluated. We investigated the effect of the preparation steps on mean values, standard deviations and a measurement device-specific value (LWV) that indicates a negative impact of volatile organic compounds (VOC) on reported isotope values. Results showed that an equilibration time longer than 24 hours is not advisable as the relationship between δ 18 O and δ 2 H of all plant samples worsened with R² declining from 0.97 to a minimum of 0.16. Additionally, the LVW indicated the influence of VOC with progressing equilibration time. Optimum amounts of plant material for roots were 3 g while for all other plant parts 5 g was necessary. In contrast to cut samples, kinetic fractionation effects were observed for grinded samples which could also be apparent fractionation effects because of the observed changes in LWV indicative of VOC interferences. For both plants the successive enrichment of the irrigation water from roots to leaves was observed. Fruits showed differences in their isotopic composition of the water stored inside the fruit compared to the water in the skin, with the inside water closer to the applied irrigation water. The intersection of the dual-isotope plot of all measured plant samples with the local meteoric water line was close to the applied irrigation water, making it theoretically possible to acquire information about the plant source water and enrichment factors in future studies when using the water-vapor equilibration method. From the findings of this study protocols can be established for sample preparation and plant water stable isotope analysis using the water-vapor equilibrium method.
Why it matches plant phenotyping methods植物試料の水安定同位体測定法について、試料調製、必要量、平衡時間、干渉および分画影響を検証し、植物の水状態・水源推定に使えるプロトコル確立を目指しているため、測定法が中心です。
abstractIt still needs to be tested if this method can be reliably used for isotope analysis of plant samples and how to best prepare the samples.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
The metabolic activity in plants or fruits is associated with volatile organic compounds (VOCs), which can help identify the different diseases. P-ethylphenol has been demonstrated as one of the most important VOCs released by the Phytophthora cactorum (P. cactorum) infected strawberries. In this study, a bioelectronic nose based on a gas biosensor array and signal processing model was developed for the noninvasive diagnostics of the P. cactorum infected strawberries, which could overcome the limitations of the traditional spectral analysis methods. The gas biosensor array was fabricated using the single-wall carbon nanotubes (SWNTs) immobilized on the surface of field-effect transistor, and then non-covalently functionalized with different single-strand DNAs (ssDNA) through π–π interaction. The characteristics of ssDNA-SWNTs were investigated using scanning electron microscope, atomic force microscopy, Raman, UV spectroscopy, and electrical measurements, indicating that ssDNA-SWNTs revealed excellent stability and repeatability. By comparing the responses of different ssDNA-SWNTs, the sensitivity to P-ethylphenol was significantly higher for the s6DNA-SWNTs than other ssDNA-SWNTs, in which the limit of detection reached 0.13% saturated vapor of P-ethylphenol. However, s6DNA-SWNTs can still be interfered with by other VOCs emitted by the strawberries in the view of poor selectivity. The bioelectronic nose took advantage of the different sensitivities of different gas biosensors to different VOCs. To improve measure precision, all ssDNA-SWNTs as a gas biosensor array were applied to monitor the different VOCs released by the strawberries, and the detecting data were processed by neural network fitting (NNF) and Gaussian process regression (GPR) with high accuracy.
Why it matches plant phenotyping methods感染イチゴの病態を非侵襲的に推定するバイオ電子鼻・ガスセンサーアレイと信号処理モデルの開発が研究の中心であり、植物の病害状態を測定する方法に該当する。
abstracta bioelectronic nose based on a gas biosensor array and signal processing model was developed for the noninvasive diagnostics of the P. cactorum infected strawberries
Strawberry uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a strong candidate for marker-assisted breeding. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification. This tool has allowed us to illustrate the use of advanced image analysis towards the breeding of greater uniformity in strawberry.
Why it matches plant phenotyping methodsイチゴ果実の均一性を定量化する3D画像解析手法を開発し、自動指標と手動評価を比較しているため、表現型取得・抽出法が研究の中心である。
abstractIn this study, a 3D-imaging approach was developed to characterise berry uniformity.
Abstract Autonomous plant alive status monitoring and corresponding localization of individual plant are two important tasks in precision agriculture. In this study, two new methods that are crucial to such robotic operations are proposed. First, a low cost and light scene invariant approach is proposed to differentiate green and yellow leaves using distinct color-ratio index ranges. Second, based on the relative pixel information of neighboring plants, an extended Kalman filter is used to determine plant positions. Such a differential style localization method is shown to be capable of achieving a similar centimeter level accuracy as light detection and ranging (LIDAR) or real-time kinematic-global positioning system (RTK-GPS) based approaches, but with a much lower upfront and maintenance cost. These two new methods are successfully validated in a nearby commercial field.
Why it matches plant phenotyping methods植物の生存状態（緑葉・黄葉によるalive status）を画像から判定する新規手法を開発し、圃場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す。
abstracta low cost and light scene invariant approach is proposed to differentiate green and yellow leaves using distinct color-ratio index ranges
Improving the accuracy of harvest timing predictions offers an opportunity to sustainably improve soft fruit farming. Fruits are perishable, high-value and seasonal, and prices are typically time-sensitive. Harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. We have developed and tested a novel framework for linking mesoscale weather forecasts to local crop microclimates using embedded autonomous sensors to produce bespoke phenological predictions, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR) irradiance. Fleeces were added to a subset of the plants to generate additional within-polytunnel variation. Trigonometric models transformed weather station data, which showed a relatively low agreement with polytunnel air temperature (R² = 0.6) and RH (R² = 0.5), into more accurate polytunnel-specific predictions for temperature and RH (both R² = 0.8). Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic conditions. After 10,000 iterations, machine learning adequately optimised the coefficients of these curves, including RH and air temperature into the fitted equation. Dataloggers measuring environmental data in-situ could infer model parameters using iterative training for novel fruit cultivars growing in different locations without a-priori phenological information. Reliance on manually measured yield data is a current limitation but if high-throughput technologies emerge then this process could be entirely automated. We have demonstrated that this framework can be used to predict fruit timing. Predictions could be refined and updated as frequently as new data becomes available, which in this case would be every eight minutes. This approach represents a step-forward in developing bespoke phenological predictions to inform grower decisions.
Why it matches plant phenotyping methodsネットワーク化マイクロセンサー、気象モデル、機械学習を統合し、イチゴの収量・フェノロジーを予測する枠組み自体を開発・検証しており、植物形質の取得・推定が中心である。
abstractWe have developed and tested a novel framework for linking mesoscale weather forecasts to local crop microclimates using embedded autonomous sensors to produce bespoke phenological predictions, using strawberries as the model crop.
Abstract Background The assessment of nitrogen status non-destructively in strawberry was performed to indicate its growth and provide guidance for precise management of N fertilizer using near-infrared reflectance (NIR) spectroscopy with leaf spectral reflectance. The Leaf soil plant analysis development (SPAD) value was thought as an indicator data that indirectly reflects nitrogen status in strawberry. However, the variation of cultivars would lead to differences in the cell structure and light scattering and/or reflection effects of strawberry leaves when assessed strawberry N status, which caused that no single leaf SPAD threshold value or same N demand can be applied for all strawberry cultivars. As a result, accurate detection of SPAD values and N status in strawberries with multiple cultivars is still challenging.Results In this study, Individual-cultivar model, hybrid-cultivar model and multi-cultivar model were developed for the determination of SPAD values, and the performance of the models in lessening the impact of cultivar variation were studied and compared. Individual-cultivar model was constructed on the basis of the single cultivar of strawberry leaf; hybrid-cultivar model was developed by merging the spectrum reflectance data and SPAD values of all studied leaf samples, and multi-cultivar model was built in combination with cultivar identification, individual-cultivar models, and model search strategy.Conclusion The results indicated that multi-cultivar model was more superior to the other two models for SPAD values estimation of strawberry leaves from different cultivars, with the overall Rp and RMSEP value respectively being 0.966 and 0.468. We demonstrate that the leaf N content per strawberry is profoundly affected by cultivar variation, and establishing a multi-cultivar model might be more useful in monitoring nitrogen status and guiding N fertilization recommendations for different strawberry cultivars.
Why it matches plant phenotyping methods携帯型NIR分光と品種認識・多変量モデルにより、イチゴ葉のSPAD値および窒素状態を非破壊推定する手法を開発・比較しており、表現型取得と解析手法が研究の中心である。
abstractThe assessment of nitrogen status non-destructively in strawberry was performed
StrawberryFlowerLeafStem / branchClassificationGrowth / time-series analysisGrowth / development / phenology
Plant development studies often generate data in the form of multivariate time series, each variable corresponding to a count of newly emerged organs for a given development process. These phenological data often exhibit highly structured patterns, and the aim of this study was to identify such patterns in cultivated strawberry. Six strawberry genotypes were observed weekly for their course of emergence of flowers, leaves, and stolons during 7 months. We assumed that these phenological series take the form of successive phases, synchronous between individuals. We applied univariate multiple change-point models for the identification of flowering, vegetative development, and runnering phases, and multivariate multiple change-point models for the identification of consensus phases for these three development processes. We showed that the flowering and the runnering processes are the main determinants of the phenological pattern. On this basis, we propose a typology of the six genotypes in the form of a hierarchical classification. This study introduces a new longitudinal data modeling approach for the identification of phenological phases in plant development. The focus was on development variables but the approach can be directly extended to growth variables and to multivariate series combining growth and development variables.
Why it matches plant phenotyping methodsイチゴの花・葉・ランナーの出現時系列から発育フェーズを同定する変化点モデルを開発・適用しており、植物表現型の抽出手法が研究の中心である。
abstractWe applied univariate multiple change-point models for the identification of flowering, vegetative development, and runnering phases, and multivariate multiple change-point models for the identification of consensus phases for these three development processes.
Background Shape is a critical element of the visual appeal of strawberry fruit and determined by both genetic and non-genetic factors. Current fruit phenotyping approaches for external characteristics in strawberry rely on the human eye to make categorical assessments. However, fruit shape is multi-dimensional, continuously variable, and not adequately described by a single quantitative variable. Morphometric approaches enable the study of complex forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the principal progression of k clusters (PPKC). We use these human-recognizable shape categories to select features extracted from multiple morphometric analyses that are best fit for genome-wide and forward genetic analyses. Results We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered four principal shape categories, and inferred progression using PPKC. We extracted 67 quantitative features from digital images of strawberries using a suite of morphometric analyses and multi-variate approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68.9 – 99.3% for the newly created, genetically correlated phenotypic variables describing a shape. Conclusions Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches we applied in strawberry should apply to other fruits, vegetables, and specialty crops.
Why it matches plant phenotyping methodsイチゴ果実画像から形状を定量化・分類する画像解析、機械学習、形態計測、PPKC手法の開発が研究の中心であり、再利用可能な表現型特徴量を生成している。
abstractwe developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the principal progression of k clusters (PPKC).
Verticillium dahliae is a highly detrimental pathogen of soil cultivated strawberry (Fragaria × ananassa). Breeding of Verticillium wilt resistance into commercially viable strawberry cultivars can help mitigate the impact of the disease. In this study we describe novel sources of resistance identified in multiple strawberry populations, creating a wealth of data for breeders to exploit. Pathogen-informed experiments have allowed the differentiation of subclade-specific resistance responses, through studying V. dahliae subclade II-1 specific resistance in the cultivar ‘Redgauntlet’ and subclade II-2 specific resistance in ‘Fenella’ and ‘Chandler’. A large-scale low-cost phenotyping platform was developed utilising automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials. The images were used to calculate disease susceptibility for infected plants through the normalized difference vegetation index score. The automated disease scores showed a strong correlation with the manual scores. A co-dominant resistant QTL; FaRVd3D, present in both ‘Redgauntlet’ and ‘Hapil’ cultivars exhibited a major effect of 18.3 % when the two resistance alleles were combined. Another allele, FaRVd5D, identified in the ‘Emily’ cultivar was associated with an increase in Verticillium wilt susceptibility of 17.2%, though whether this allele truly represents a susceptibility factor requires further research, due to the nature of the F1 mapping population. Markers identified in populations were validated across a set of 92 accessions to determine whether they remained closely linked to resistance genes in the wider germplasm. The resistant markers FaRVd2B from ‘Redgauntlet’ and FaRVd6D from ‘Chandler’ were associated with resistance across the wider germplasm. Furthermore, comparison of imaging versus manual phenotyping revealed the automated platform could identify three out of four disease resistance markers. As such, this automated wilt disease phenotyping platform is considered to be a good, time saving, substitute for manual assessment.
Why it matches plant phenotyping methods自動無人車両と近赤外画像を用いた圃場病害表現型計測プラットフォームを開発し、手動評価との相関で検証しているため、表現型取得法が研究の中心です。
abstractA large-scale low-cost phenotyping platform was developed utilising automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials.
Reproduction assets foundThe paper used the authors' public Crosslink tool for linkage map generation (GitHub URL given in a footnote), and the Frontiers supplementary material contains paper-specific phenotype data (per-genotype AUDPC disease scores, validation set individuals, linkage maps). The bioRxiv preprint is the article itself and is;Supplement · publicFIGURE S7
Relative Area Under the Disease Progression Curve (AUDPC) for each of the seven phenotyping events illustrating the phenotypic range of disease symptoms.Open asset ↗lines:945-962Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 10 Sept 2026
StrawberryGreenhouseFruitGrowth / time-series analysisYield / biomass estimationGrowth / development / phenologyWater status / transpirationYield / yield components
Predicting harvest timing is a key challenge to sustainably develop soft fruit farming and reduce food waste. Soft fruits are perishable, high-value and seasonal, and sales prices are typically time-sensitive. In addition, fruit harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. A novel approach for predicting soft fruit phenology and yields was developed and tested, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR). Fleeces were added to a subset of the plants to generate additional within-polytunnel variation. Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic growing conditions. After 10,000 iterations, machine learning revealed that RH was the optimal factor informing the coefficients of these curves, perhaps because it is an integrative metric of air temperature and water status. Trigonometric models transformed weather forecasts, which showed a relatively low agreement with polytunnel air temperature (R 2 = 0.6) and RH (R 2 = 0.5) measurements, into more accurate polytunnel-specific predictions for temperature and RH (both R 2 = 0.8). We present a framework for using machine-learning techniques to calculate curve coefficients and parametrise coupled weather models which can predict fruit yields and timing to a greater degree of accuracy that previously possible. Dataloggers measuring environmental and yield data could infer model parameters using iterative training for novel fruit varieties or crop types growing in different locations without a-priori phenological information. At this stage in the development of artificial intelligence and networked microsensors, this is a step forward in generating bespoke phenological prediction models to inform and support growers.
Why it matches plant phenotyping methodsネットワーク化マイクロセンサー、気象モデル、機械学習を統合し、イチゴの収量とフェノロジーを予測する枠組み自体が中心的に開発・検証されているため、植物フェノタイピング手法として含める。
abstractA novel approach for predicting soft fruit phenology and yields was developed and tested, using strawberries as the model crop.
Strawberry plants have been facing a significant proportion of diseases during cultivation, scattered throughout the field, emphasizing the need for proper diseases management. Powdery mildew is one of the major fungal strawberry disease which is typically responsible for approximately 30–70% loss of yields. The aim of this study was to develop a machine vision based artificial cloud lighting condition system for detecting strawberry powdery mildew leaf disease. The artificial cloud lighting condition system was developed consisting of custom software, two µEye colour cameras, a black cloth cover, real time kinematics-global positioning system and a ruggedized laptop computer and mounted on a mobile platform. The custom software was developed in C# programming language. The colour co-occurrence matrix based texture analysis was used to extract image features and discriminant analysis (quadratic) for classification. The study proposed mobile platform of artificial cloud lighting condition for image acquisition is beneficial. It showed higher detection accuracies of 95.26%, 95.45% and 95.37% for recall, precision and F-measure, respectively compared to 81.54%, 72% and 75.95% of recall, precision and F-measure, respectively with acquired images at natural cloud lighting condition. The feature selection results suggested the PM_GHSI feature model was best fit for this study. This study also revealed that the image acquisition speed (1.5 km h−1) and working depth (300 mm) are suitable for strawberry powdery mildew disease detection in real-time field condition.
Why it matches plant phenotyping methodsイチゴ葉のうどんこ病という植物状態を対象に、人工雲照明・移動撮像プラットフォーム・画像特徴抽出・分類器を開発し、自然光条件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。
abstractThe aim of this study was to develop a machine vision based artificial cloud lighting condition system for detecting strawberry powdery mildew leaf disease.
In protected strawberry ( Fragaria × ananassa Duch.) cultivation, environmental control based on the process of photosynthate translocation is essential for optimizing fruit quality and yield, because the process of photosynthate translocation directly affects dry matter partitioning. We visualized photosynthate translocation to strawberry fruits non-invasively with 11 CO 2 and a positron-emitting tracer imaging system (PETIS). We used PETIS to evaluate real-time dynamics of 11 C-labeled photosynthate translocation from a 11 CO 2 -fed leaf, which was immediately below the inflorescence, to individual fruits on an inflorescence in intact plant. Serial photosynthate translocation images and animations obtained by PETIS verified that the 11 C-photosynthates from the source leaf reached the sink fruit within 1 h but did not accumulate homogeneously within a fruit. The quantity of photosynthate translocation as represented by 11 C radioactivity varied among individual fruits and their positions on the inflorescence. Photosynthate translocation rates to secondary fruit were faster than those to primary or tertiary fruits, even though the translocation pathway from leaf to fruit was the longest for the secondary fruit. Moreover, the secondary fruit was 25% smaller than the primary fruit. Sink activity ( 11 C radioactivity/dry weight [DW]) of the secondary fruit was higher than those of the primary and tertiary fruits. These relative differences in sink activity levels among the three fruit positions were also confirmed by 13 C tracer measurement. Photosynthate translocation rates in the pedicels might be dependent on the sink strength of the adjoining fruits. The present study established 11 C-photosynthate arrival times to the sink fruits and demonstrated that the translocated material does not uniformly accumulate within a fruit. The actual quantities of translocated photosynthates from a specific leaf differed among individual fruits on the same inflorescence. To the best of our knowledge, this is the first reported observation of real-time translocation to individual fruits in an intact strawberry plant using 11 C-radioactive- and 13 C-stable-isotope analyses.
Why it matches plant phenotyping methodsPETISによる非侵襲的な光合成産物移行のリアルタイム画像化が研究の中心で、果実への移行速度・蓄積という植物生理形質を定量・評価している。
abstractWe visualized photosynthate translocation to strawberry fruits non-invasively with 11 CO 2 and a positron-emitting tracer imaging system (PETIS).
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · checked 15 Sept 2026
1.0 Abstract Verticillium dahliae is a highly detrimental pathogen of soil cultivated strawberry ( Fragaria x ananassa ). Breeding of Verticillium wilt resistance into commercially viable strawberry cultivars can help mitigate the impact of the disease. In this study we describe novel sources of resistance identified in biparental strawberry populations, creating a wealth of data for breeders to exploit. Pathogen-informed experiments have allowed the differentiation of subclade-specific resistance responses, through studying V. dahliae subclade II-1 specific resistance in the cultivar ‘Redgauntlet’ and subclade II-2 specific resistance in ‘Fenella’ and ‘Chandler’. A large-scale low-cost phenotyping platform was developed utilising automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials. The images were used to calculate disease susceptibility for infected plants through the normalized difference vegetation index score. The automated disease scores showed a strong correlation with the manual scores. A co-dominant resistant QTL; FaRVd3D , present in both ‘Redgauntlet’ and ‘Hapil’ cultivars exhibited a major effect of 18.3 % when the two resistance alleles were combined. Another allele, FaRVd5D , identified in the ‘Emily’ cultivar was associated with an increase in Verticillium wilt susceptibility of 17.2%, though whether this allele truly represents a susceptibility factor requires further research, due to the nature of the bi-parental cross. Markers identified in bi-parental populations were validated across a set of 92 accessions to determine whether they remained closely linked to resistance genes in the wider germplasm. The resistant markers FaRVd2B from ‘Redgauntlet’ and FaRVd6D from ‘Chandler’ were associated with resistance across the wider germplasm. Furthermore, comparison of imaging versus manual phenotyping revealed the automated platform could identify three out of four disease resistance markers. As such, this automated wilt disease phenotyping platform is considered to be a good, time saving, substitute for manual assessment.
Why it matches plant phenotyping methods自動車両と近赤外画像を用いた圃場病害表現型プラットフォームの開発・手動評価との比較検証が中心であり、植物病害状態の定量的推定を行っているため。
abstractA large-scale low-cost phenotyping platform was developed utilising automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials.
Strawberries in California have a $2 billion direct economic impact on the state; however, they are currently produced based only on uniform field management techniques. Creating yield maps of strawberries could allow for variable-rate and site-specific applications of inputs, which could improve productivity and reduce environmental pollution. This paper presents the development and application of an instrumented strawberry-picking cart for yield mapping. During manual harvest of strawberries planted on raised beds, pickers walk inside the furrows, pick fruit from the beds on both sides, and deposit them into a tray placed on a picking cart. A ‘smart’ picking cart, similar to the standard carts, has been designed and instrumented with several types of sensors including load cells, a real-time kinematic global positioning system (RTK GPS), a microcontroller, and an inertial measurement unit (IMU). This instrumented cart serves two purposes: to work in sync with tray-transporting robots during robot-aided strawberry harvesting, and to create yield maps of strawberry fields. A yield map for an approximately 300m2 plot of a strawberry field in Salinas, California, was generated after the cart was calibrated. During the yield-monitoring experiment, 13.5 trays, each with a capacity of about 4.2kg of strawberries, were filled with fruit. The mean prediction accuracy of the mass of full trays measured by the load cells was calculated to be 4.8%.
Why it matches plant phenotyping methodsイチゴ収量という植物形質を測定するセンサー搭載収穫カートを開発し、校正と測定精度評価、収量マップ作成まで行っており、表現型取得手法が中心である。
abstractThis paper presents the development and application of an instrumented strawberry-picking cart for yield mapping.
StrawberryStereoThermalLeafVisualization / data managementStress response / tolerance
Freezing in plants can be monitored using infrared (IR) thermography, because when water freezes, it gives off heat. However, problems with color contrast make 2-dimensions (2D) infrared images somewhat difficult to interpret. Viewing an IR image or the video of plants freezing in 3 dimensions (3D) would allow a more accurate identification of sites for ice nucleation as well as the progression of freezing. In this paper, we demonstrate a relatively simple means to produce a 3D infrared video of a strawberry plant freezing. Strawberry is an economically important crop that is subjected to unexpected spring freeze events in many areas of the world. An accurate understanding of the freezing in strawberry will provide both breeders and growers with more economical ways to prevent any damage to plants during freezing conditions. The technique involves a positioning of two IR cameras at slightly different angles to film the strawberry freezing. The two video streams will be precisely synchronized using a screen capture software that records both cameras simultaneously. The recordings will then be imported into the imaging software and processed using an anaglyph technique. Using red-blue glasses, the 3D video will make it easier to determine the precise site of ice nucleation on leaf surfaces.
Why it matches plant phenotyping methods植物の凍結部位・進行を可視化する3D赤外線動画の取得・処理手法を開発・実証しており、植物状態の計測法が中心である。
abstractIn this paper, we demonstrate a relatively simple means to produce a 3D infrared video of a strawberry plant freezing.
In strawberry production farms, shape and size classification of harvested strawberry fruits is very important phase before packing and sending to the market. However, it is not only very labour-intensive but also time-consuming task for farmers. Computer vision-based automatic strawberry grading systems are capable to overcome this labour-intensive and time-consuming process. In this work, a simple and efficient image processing algorithm for automatic strawberry shape and size estimation and classification is presented. Being different from other existing methods in literature, the current method is based on the geometrical properties of 'right kite' and 'simple kite' which resemble to strawberry fruit shape. The proposed method is used to estimate diameter, length and apex angle from two-dimensional images of strawberry fruits. Then, these parameters are used as input data to a 3-layer neural network for class-A, B, C and D classification. The performance of proposed method is tested for a total of 337 strawberry samples with and without calyx occlusion. The results show that the accuracies for diameter and length estimations are 94% and 93% respectively for strawberries without calyx occlusion and 94% and 89% for that with calyx occlusion. The classification accuracy is between 94 and 97% and the average processing time for one strawberry (one piece) is below 0.45–0.5 s.
Why it matches plant phenotyping methodsイチゴ果実の画像から形状・サイズ形質を推定し分類する画像処理手法を開発・評価しており、植物表現型の取得が研究の中心である。
abstracta simple and efficient image processing algorithm for automatic strawberry shape and size estimation and classification is presented.
Published30 May 2018The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 66 · OpenAlex ↗
Abstract. Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.
Why it matches plant phenotyping methodsイチゴ果実の形状という植物器官形質をデジタル画像から抽出し、記述子と機械学習で分類する手法が研究の中心であるため。
abstractIn this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification
Strawberry plants produce one of the highest quantities of soft fruits in the UK. The plants are grown in fields and glass houses where the environment is hard to completely control. There are a variety of biotic and abiotic stresses that affect the plants' production rate, lifespan and the quality of the fruit. It is possible to mitigate these stresses, but first they must be detected as early as possible. Drought, or water deficit, is an environmental stress which impacts the plants' productivity. The stress can be monitored by looking for visible signs, but detection by a person can occur too late. Using technology could improve the detection before a person can see the signs. One such technology is hyperspectral imaging. Hyperspectral imaging has the potential to detect certain features in plants by examining their reflectance spectrum. In this work, a spectral range from visible to near-infrared will be used to record reflectance changes from plants during drought experiments. The data collected in this thesis comprises strawberry plants undergoing drought conditions. Initial inspection of the data suggests that a difference in reflectance may exist throughout this time period, but the data is noisy and ... (continues)
Why it matches plant phenotyping methodsイチゴの乾燥ストレスという植物状態を、可視〜近赤外ハイパースペクトル画像の反射スペクトルから早期検出する手法が研究の中心であるため。
titleEarly detection of stress in strawberry plants using hyperspectral image analysis
Background Accurate and quantitative phenotypic data in plant breeding programmes is vital in breeding to assess the performance of genotypes and to make selections. Traditional strawberry phenotyping relies on the human eye to assess most external fruit quality attributes, which is time-consuming and subjective. 3D imaging is a promising high-throughput technique that allows multiple external fruit quality attributes to be measured simultaneously. Results A low cost multi-view stereo (MVS) imaging system was developed, which captured data from 360° around a target strawberry fruit. A 3D point cloud of the sample was derived and analysed with custom-developed software to estimate berry height, length, width, volume, calyx size, colour and achene number. Analysis of these traits in 100 fruits showed good concordance with manual assessment methods. Conclusion This study demonstrates the feasibility of an MVS based 3D imaging system for the rapid and quantitative phenotyping of seven agronomically important external strawberry traits. With further improvement, this method could be applied in strawberry breeding programmes as a cost effective phenotyping technique.
Why it matches plant phenotyping methodsイチゴ果実の形質抽出を目的に、MVS 3D画像システムと専用ソフトウェアを開発し、手動評価との一致性も検証しており、フェノタイピング手法が研究の中心である。
abstractA low cost multi-view stereo (MVS) imaging system was developed, which captured data from 360° around a target strawberry fruit.
Anthracnose crown rot (ACR) is one of the major diseases affecting strawberry crops grown in warm climates and causes huge yield losses each year. ACR is caused by the fungus Colletotrichum. Since this airborne disease spreads rapidly, detection at the early stage of infection is critical. The objective of this study was to investigate the feasibility of detecting ACR in strawberry at its early stage under field conditions using spectroscopy technology. Hyperspectral data were collected in-field using a mobile platform on three categories of strawberry plants: infected but asymptomatic, infected and symptomatic, and healthy. As a comparison, indoor data were also collected from the same three categories of strawberry plants under a controlled laboratory setup. Three classification models, stepwise discriminant analysis (SDA), Fisher discriminant analysis (FDA), and the k-Nearest Neighbor (kNN) algorithms, were investigated for their potential to differentiate the three infestation categories. Thirty-three spectral vegetation indices (SVIs) were calculated as inputs using selected spectral bands in the visible (VIS) and near infrared (NIR) regions to train classification models. The mean classification accuracies of in-field tests for the three infestation categories were 71.3%, 70.5%, and 73.6% for SDA, FDA, and kNN, respectively. These accuracies were approximately 15–20% lower than those of the indoor tests. The low accuracy (15.4%) of classifying healthy leaves in-field using the kNN model was possibly due to the training datasets being unbalanced. After the adjustment of sample sizes of each category, the accuracies of kNN improved greatly, especially for the healthy and symptomatic categories. Overall, SDA was the optimal classifier for both indoor and in-field tests for detection strawberry ACR. However, kNN performed better for asymptomatic leaves in the field in the case of balanced sample sizes of each category.
Why it matches plant phenotyping methodsイチゴ植物の感染状態・症状をハイパースペクトル計測と分類モデルで推定する手法の開発・評価が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractThe objective of this study was to investigate the feasibility of detecting ACR in strawberry at its early stage under field conditions using spectroscopy technology.
Expansins play an important role in cell wall metabolism and fruit softening. Determination of expansin activity is a challenging problem since it depends on measuring cell wall properties by using ad hoc extensometers, a fact that has strongly restricted its study. Then, the objective of the work was to adapt a methodology to measure cell wall creep and expansin activity using a commercial texture meter, equipped with miniature tensile grips and an ad hoc cuvette of easy construction.It was possible to measure hypocotyls acid growth and expansin activity in a reliable and reproducible way, using a commercial texture meter, common equipment found in laboratories of food science or postharvest technology. Expansin activity was detected in protein extracts from cucumber hypocotyls, tomato and strawberry fruits, and statistical differences in expansin activity were found in both fruit models at different ripening stages.The possibility of measuring expansin activity following this adapted protocol with a commercial texture meter could contribute to ease and increase the analysis of expansin in different systems, leading to a better understanding of the properties of these proteins under different experimental conditions.
Why it matches plant phenotyping methods市販テクスチャーアナライザーを用いて細胞壁クリープとエクスパンシン活性を測定する手法を適応・検証しており、植物の生理状態を取得する方法が中心である。
abstractthe objective of the work was to adapt a methodology to measure cell wall creep and expansin activity using a commercial texture meter
The genetic control of the switch between seasonal and perpetual flowering has been deciphered in various perennial species. However, little is known about the genetic control of the dynamics of perpetual flowering, which changes abruptly at well-defined time instants during the growing season. Here, we characterize the perpetual flowering pattern and identify new genetic controls of this pattern in the cultivated strawberry. Twenty-one perpetual flowering strawberry genotypes were phenotyped at the macroscopic scale for their course of emergence of inflorescences and stolons during the growing season. A longitudinal analysis based on the segmentation of flowering rate profiles using multiple change-point models was conducted. The flowering pattern of perpetual flowering genotypes takes the form of three or four successive phases: an autumn-initiated flowering phase, a flowering pause, and a single stationary perpetual flowering phase or two perpetual flowering phases, the second one being more intense. The genetic control of flowering was analysed by quantitative trait locus mapping of flowering traits based on these flowering phases. We showed that the occurrence of a fourth phase of intense flowering is controlled by a newly identified locus, different from the locus FaPFRU, controlling the switch between seasonal and perpetual flowering behaviour. The role of this locus was validated by the analysis of data obtained previously during six consecutive years.
Why it matches plant phenotyping methodsイチゴの開花動態を表現型として取得し、開花率プロファイルを変化点モデルで分節化して連続的な開花相を抽出する解析手法が、遺伝解析の基盤として中心的に用いられている。
abstractTwenty-one perpetual flowering strawberry genotypes were phenotyped at the macroscopic scale for their course of emergence of inflorescences and stolons during the growing season.
A hyperspectral imaging system covering two spectral ranges (380–1030 nm and 874–1734 nm) was applied to evaluate strawberry ripeness. The spectral data were extracted from hyperspectral images of ripe, mid-ripe and unripe strawberries. The optimal wavelengths were obtained from spectra of 441.1–1013.97 and 941.46–1578.13 nm by loadings of principal component analysis (PCA). Pattern texture features (correlation, contrast, entropy and homogeneity) were extracted from the images at optimal wavelengths. Support vector machine (SVM) was used to build classification models on full spectral data, optimal wavelengths, texture features and the combined dataset of optimal wavelengths and texture features, respectively. SVM models using combined datasets performed best among all datasets. SVM models using datasets from hyperspectral images at 441.1–1013.97 nm performed better with classification accuracy over 85%. The overall results indicated that hyperspectral imaging could be used for strawberry ripeness evaluation, and data fusion combining spectral information and spatial information showed advantages in strawberry ripeness evaluation.
Why it matches plant phenotyping methodsイチゴ果実の熟度という植物器官の状態を、ハイパースペクトル画像・テクスチャ特徴・SVMで推定する手法が研究の中心であり、スペクトルと空間情報の融合も評価しているため。
abstractA hyperspectral imaging system covering two spectral ranges (380–1030 nm and 874–1734 nm) was applied to evaluate strawberry ripeness.
Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90 % (NHRH ≥90 %), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE <2 h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7 h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.
Why it matches plant phenotyping methodsイチゴ葉の濡れ時間という植物状態を推定するモデルを比較・検証し、センサー精度や地域間性能、病害警告への実用性を評価しており、表現型取得手法が研究の中心である。
abstractThe objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements.
In order to establish new multispectral indexes for automatic identification of strawberry ripeness, hyperspectral imaging technology was applied in this paper. Eight indexes: Ind1=R730+R640-2×R680, Ind2=R680/(R640+R730), Ind3=R675/R800, IAD=log10(R720/R670), I1=R650/R550, I2=R650/R450, I3=R650/(R450+R550), I4=2×R650-(R550+R450) were calculated by extracting average spectral of strawberry samples and their identification effects of strawberry samples in three ripening stages(mature, nearly mature and immature) were judged with Fisher linear discriminant(FLD). The result showed that the identification effects of linear discriminant analysis model based on index I4 was the best among 8 indexes and the identification accuracy of modeling and prediction set was 90% and 91. 67% respectively. Three wavelengths (535, 675, 980 nm) related to strawberry ripeness were extracted based on average spectral of strawberry samples and 4 new indexes were established based on these three wavelengths: i1=2×R675- (R980+R535), i2=R675/(R980+R535), i3= (R675-R535)/(R675+R535), i4=[R675- (R535+R980)]/[R675+(R535+R980)]. The identification effects was judged with FLD and the results showed that the effects of linear discriminant analysis models based on i1, i2, i4 were better than index I4 and the identification accuracy of modeling and prediction set was 95.83%，95.83%，95.83% and 95%，95%，96.67% respectively. In conclusion, new established indexes i1, i2, i4 could be used in automatic identification of strawberry ripeness.
Why it matches plant phenotyping methodsハイパースペクトル画像からスペクトル指標を抽出し、イチゴの熟度という植物器官の状態を自動判別する手法を開発・評価しており、表現型取得・推定が中心である。
abstractIn order to establish new multispectral indexes for automatic identification of strawberry ripeness, hyperspectral imaging technology was applied in this paper.
Hyperspectral imaging provides comprehensive spectral and spatial information about observed objects. This technology has been applied to many fields, such as geology, mining, surveillance and agriculture. Strawberry qualities have been examined using hyperspectral imaging in several studies. However, none of the previous literature presented a non-destructive method for diagnosing the infection stages of anthracnose, a devastating disease for strawberries. This study examined strawberry foliar anthracnose using three different hyperspectral imaging analyzing methods: spectral angle mapper (SAM), stepwise discriminant analysis (SDA) and self-developed correlation measure (CM). Three different infection stages, including healthy, incubation and symptomatic stages, were investigated using these methods. The incubation stage is a stage at which the symptoms are still not yet visible. The three infection stage classification results were promising, with a classification accuracy of approximately 80%. For two infection stage classification (healthy and symptomatic stages), an average accuracy of high 80% was attained. In fact, an average accuracy of 93% was achieved by SDA for two-stage classification. This study not only proves the feasibility of hyperspectral imaging for diagnosing strawberry foliar anthracnose infection, but also identifies a smaller set of significant wavelengths at which similar classification performance was accomplished. For significant wavelength selection, partial least squares (PLS) regression is an standard wavelength selection method and it was applied to be compared with SDA and CM. Wavelengths of 551, 706, 750 and 914nm formed the multispectral imaging observing bands that showed an accuracy of 80% when classifying the three infection stages. Therefore, using either hyperspectral or multispectral imaging to detect anthracnose infected foliar areas is more practical and efficient than classic destructive methods. In particular, early detection (the incubation stage), something that cannot be seen via naked eyes, reaches 80% classification accuracy with both SDA and CM. Strawberry farmers could profit greatly from this technology.
Why it matches plant phenotyping methodsイチゴ葉の病徴・感染段階という植物状態を、ハイパースペクトル／マルチスペクトル画像と分類手法で非破壊推定する方法が研究の中心であり、精度評価と波長選択も行っている。
abstractThis study examined strawberry foliar anthracnose using three different hyperspectral imaging analyzing methods: spectral angle mapper (SAM), stepwise discriminant analysis (SDA) and self-developed correlation measure (CM).