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:111-131Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:1-44Code / dataset availability confirmedCrossref · checked 13 Sept 2026
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:1-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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:822-851Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:316-338Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:271-381Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:317-334Code / dataset availability confirmedarXiv · checked 15 Sept 2026
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:1-53Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:1-51Code / dataset availability confirmedCrossref · checked 6 Sept 2026
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:177-199Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:538-610Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:139-155Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:145-326Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:97-114Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
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:355-404Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:1-117Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
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:875-889Code / dataset availability confirmedOpenAlex · arXiv · checked 7 Sept 2026
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:1-39Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
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:729-756Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
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:57-72Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
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:337-344Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
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:27-64Code / dataset availability confirmedCrossref · checked 13 Sept 2026
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:103-113Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
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:394-409Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
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:153-237Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:1-48Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
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:1-53Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
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).
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:945-962