Eggplant (Solanum melongena) is a major cash crop, yet field detection of its pests and diseases remains difficult because disease evidence is simultaneously occluded by foliage, blurred at lesion boundaries, and highly variable in scale. Fruit rot is especially challenging: the waxy epidermis and purple anthocyanin-rich surface reduce chromatic contrast, while infection spreads gradually from water-soaked tissue to necrotic tissue, producing diffuse borders between diseased and healthy regions. In this work, we reframe eggplant disease detection through a context-boundary-scale coupling principle, which states that accurate field detection should jointly model incomplete contextual cues, ambiguous lesion boundaries, and scale-varying symptom morphology rather than optimize these cues independently. ISA-YOLO is proposed as an implementation of this principle on top of YOLOv13 through coordinated context modeling, boundary-aware aggregation, and progressive multi-scale fusion. Experiments on two public datasets show that ISA-YOLO achieves 78.1 and 77.7% mAP at 30.66 and 31.74 FPS, outperforming mainstream detectors in overall trade-off between accuracy and speed. After pruning and quantization, inference speed increases to about 75 FPS while maintaining strong accuracy. These results indicate that the proposed principle provides an effective pathway for accurate and deployable eggplant pest and disease detection in smart agriculture.
Why it matches plant phenotyping methodsナスの病害・害虫を画像から検出するISA-YOLOモデルの開発と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フェノタイピング手法に該当する。
titleA high-performance detection model ISA-YOLO for eggplant pests and diseases.
Reproduction assets foundThe paper uses four public Roboflow image datasets (BISU, UTM, Papaya, Tomato) and states that supporting data and code are publicly available on Zenodo, all with explicit URLs in the Data availability section.Dataset · publicwas supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No newOpen asset ↗eggplant-disease-detectionlines:1509-1560Dataset · publicty
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No new data collection was performed for this research. The authors confirm that the use of these datasets in thOpen asset ↗eggplant-disease-detection-5fuqvlines:1509-1560Dataset · publicof the outcomes of the Provincial Undergraduate Training Program on Innovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these publicOpen asset ↗papaya-eswlk-r2ydylines:1509-1560Dataset · publicnovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and internatOpen asset ↗tomato-rottenlines:1509-1560Code · publicarch. The authors confirm that the use of these datasets in this study is fully compliant with their original licenses and ethical guidelines. The final images presented in the article accurately reflect the original data and meet community standards. The data and code supporting the conclusions of this article are available at https://zenodo.org/records/19425300 .
Declarations
Ethics approval and consent to participateOpen asset ↗Zenodo · 19425300lines:1509-1560Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Brinjal (eggplant) is a critical crop in South Asia, especially in Bangladesh, but its production is drastically affected by numerous diseases that inhibit yield and quality. Manual diagnosis of disease is time-consuming, subjective, and prone to errors, necessitating automated, scalable technology. To address these issues, this paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification using Simple Vision Transformer (ViT) with Patch Dropout and Contrastive Captioner (CoCa) methods. One new dataset of 1,823 field-harvested brinjal images encompassing five disease classes including Phomopsis Blight, Fruit and Shoot Borer, Fruit Cracking, Wet Rot, and Healthy samples were prepared through real-world agricultural data collection from Bangladesh. The approach includes extensive preprocessing, class balancing (under-sampling/oversampling), and resilient augmentation methods. The PD-ViCo model significantly improves classification performance under data imbalance with patch dropout regularization and CoCa-style aggregation, resulting in better generalization and robustness. On a range of imbalanced, under-sampled, and oversampled datasets, PD-ViCo achieved a classification accuracy of 99.12% and F1-score of 97.76%, outperforming both ViT and Swin Transformer across all key evaluation metrics. Explainability was also applied using Grad-CAM and Grad-CAM + + , generating visual explanations of model decisions and maintaining conformity to disease-affected regions in the images. These visualizations ensure the credibility of the model and its usability for real agricultural conditions. This study demonstrates that PD-ViCo is a highly accurate, interpretable, and lightweight model for multi-class brinjal disease diagnosis. Not only does it advance state-of-the-art in agricultural AI, but it also provides a valuable dataset and an understandable decision-making protocol that can be applied directly by farmers, agronomists, and agricultural technologists.
Why it matches plant phenotyping methods植物画像から病害状態を分類するモデル、データセット、説明可能性評価を中心に開発・検証しており、植物フェノタイピング手法として適格。
abstractthis paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification
Reproduction assets foundThe paper's own field-harvested brinjal disease image dataset (1,823 images, five classes) is publicly deposited on Mendeley Data, with an explicit availability statement and URL matching an allowed entry. No code or model checkpoint deposit is stated.Dataset · publicThe data utilized in this study is publicly accessible on Mendeley Data Repository at the following link: [ https://data.mendeley.com/datasets/ngc58fsxgd/1 ].Open asset ↗Mendeley Data · ngc58fsxgd/1lines:226-251Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Plant 3D reconstruction using optical imaging often suffers from incomplete point clouds due to viewpoint occlusion and sensor limitations. This incompleteness hinders accurate structural representation and subsequent feature extraction for plant analysis. To address these challenges, we propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion. A multi-scale point cloud generator (MSPG) that integrates local and global features from raw incomplete point clouds is used for MSDDG to reconstruct complete shapes. The dual-discriminators-a multi-view projected silhouette discriminator and a spatial distance discriminator-are designed to ensure geometric realism and spatial plausibility from multiple perspectives. To train MSDDG, we created the Plant4L dataset containing four plant species (sunflower, pumpkin, luffa, and eggplant) with high-quality 3D models augmented via 3D thin plate spline transformations and virtual occlusion simulation to generate incomplete point clouds and multi-view silhouettes. Experimental results on Plant4L demonstrate that MSDDG achieves superior completion performance, with Chamfer Distance (CD), Hausdorff Distance (HD), and Uniformity Chamfer Distance (UCD) all below 0.41. Comparative evaluations confirm MSDDG's superiority over previous point cloud completion methods. The application of MSDDG for 3D reconstruction from single view further validate its effectiveness in restoring occluded plant structures.
Why it matches plant phenotyping methods植物の不完全点群を補完して3D構造を再構成する手法を開発し、植物データセット上で比較評価・検証しており、表現型取得ワークフローが中心です。
abstractwe propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion.
Reproduction assets foundThe paper's data availability statement explicitly states that the source code and datasets (including the Plant4L point cloud completion dataset) are publicly available at the authors' GitHub repository.Code · publicThe source code and datasets used in this study are publicly available at https://github.com/Amuro-Aznable/MSCGPCN.git .Open asset ↗https://github.com/Amuro-Aznable/MSCGPCN.git · MSCGPCNlines:415-421Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Brinjal (Solanum melongena) or eggplant is one of the four most essential vegetable crops that are grown in Bangladesh and contribute significantly to the agricultural industry of the country. Brinjal supports the livelihood of numerous small farmers; however, brinjal is severely susceptible to various fruit diseases, which have serious impacts on yield quality and may cause considerable economic losses. While most existing plant disease datasets primarily focus on leaf-related disorders, only a limited number include fruit-related diseases and even those contain very few classes. This gap is significant because fruit diseases directly affect crop quality, market value, and overall yield. This is why we present here a new and comprehensive dataset that is unparalleled, exclusively for brinjal fruit diseases. This data set consists of 1823 high-quality, labelled images, across five distinct classes: Phomopsis Blight, Shoot and Fruit Borer, Fruit Cracking, Wet Rot, and Healthy Fruit. The images were collected from real farm conditions in numerous areas of Bangladesh to ensure a robust sample of varied environmental and farming practices impacting the growth of diseases. This dataset is designed with the unique aim to support plant disease research and enhance training of deep learning models for autonomous disease detection. Lastly, the dataset will allow early disease detection, enhancing crop management practice, reduction of losses, and increasing farmers' economic returns. The release of this dataset will encourage agricultural research as well as practical use in precision agriculture.
Why it matches plant phenotyping methodsナス果実の病徴を対象とした画像データセットの構築・公開が中心であり、植物の病害状態を画像から識別する再利用可能な表現型データ資源に該当する。
abstractwe present here a new and comprehensive dataset that is unparalleled, exclusively for brinjal fruit diseases.
Reproduction assets foundThe paper's brinjal fruit disease image dataset (1823 labeled images, five classes) is publicly deposited on Mendeley Data, and the authors' model training/augmentation code is publicly available on GitHub.Code · publicThe complete code, along with augmentation scripts and model development, is publicly available in our GitHub repository [12].Open asset ↗GitHubhtml-lines:299-357Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The Eggplant Leaf Disease Dataset was meticulously developed to address challenges in accurately identifying diseases that threaten eggplant crops, a vital agricultural resource worldwide. This dataset includes 3116 high-resolution images captured between March and May 2024 from two major agricultural regions in Bangladesh, representing real-world conditions. It comprises 10 distinct disease classes-Aphids, Cercospora Leaf Spot, Defect Eggplant, Flea Beetles, Fresh Eggplant, Fresh Eggplant Leaf, Leaf Wilt, Phytophthora Blight, Powdery Mildew, and Tobacco Mosaic Virus-making it the most comprehensive dataset for eggplant diseases to date. To enhance its utility, rigorous data augmentation techniques, including flipping, rotating, shearing, shifting, noise addition, and brightness adjustment, were applied. This expanded the dataset to 10,000 images, ensuring its robustness for machine learning applications. Expert annotations further enhance its quality, providing critical insights for precise disease classification. Our Proposed CBAM-EfficientNetB0 model had an amazing classification accuracy of 98.70 %, which was much better than the baseline architectures. ResNet50 only got 32.60 %, VGG16 got 73.00 %, and VGG19 got 68.00 %. The proposed model's better performance shows that combining channel and spatial attention through CBAM with EfficientNetB0's feature extraction abilities works well. This architecture does a good job of picking out the distinguishing features in eggplant leaf images, which makes it possible to accurately identify diseases. The dataset and model work together to make AI-powered early disease detection, automated monitoring, and decision support in precision agriculture possible. These tools help farmers use sustainable farming methods by making timely interventions, reducing the need for manual inspection, and increasing crop productivity and food security.
Why it matches plant phenotyping methodsナス葉の病害状態を画像から分類するデータセットと解析モデルを開発・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。
titleA comprehensive annotated image dataset for deep learning analysis of eggplant leaf diseases.
Reproduction assets foundThe paper's eggplant leaf disease image dataset (3116 annotated images, augmented to 10,000) is publicly deposited on Mendeley Data with a direct URL and DOI provided in the article.Dataset · publicSeed Certification Agency, Ministry of Agriculture, Bangladesh, for his invaluable feedback and cooperation .
Data source location
Town/City/Region: Dhaka, Musnshigonj and Jhenaidah Sadar.
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/5drkk544k8.1
Direct URL to data: https://data.mendeley.com/datasets/5drkk544k8/1Open asset ↗Mendeley Data · 10.17632/5drkk544k8.1lines:1-43Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
With the development of smart agriculture and the growth of the global population, vegetable production is facing the dual challenges of diversified planting environments and increased concealment of diseases. Eggplant, as an important economic crop, has its disease detection accuracy directly affecting yield and quality. However, traditional detection methods fail to effectively capture small diseased areas. To address this issue, this paper proposes an improved deep learning small target detection model-the Efficient Deep-fusion Detection Model (EDDet), which is specifically optimized for the recognition of small diseased spots in eggplant disease detection. In the detection network, we innovatively designed the Pinwheel Fusion Feature Extractor (PFFE) framework, replacing the standard convolutions of the first two layers with Pinwheel Convolutions (PConv). By using asymmetric padding and parallel convolution kernel design, the receptive field is effectively expanded, the ability to capture underlying features is enhanced, and the detection of small diseased areas in eggplants is more precise. In the feature fusion stage, this paper designs a Cross-layer Attention Module (CAM), including Cross-layer Channel Attention (CCA) and Cross-layer Spatial Attention (CSA), which can efficiently interact and fuse features of different scales without additional sampling, alleviating the information loss caused by semantic gaps. In addition, to solve the instability caused by IoU fluctuations in the bounding box regression process, the model introduces Scale-based Dynamic Loss (SD Loss), which dynamically adjusts the loss weight based on the size of the target. By adaptively adjusting the proportion of IoU-based loss and location constraint loss, more precise localization and stable regression of small diseased areas in eggplants are achieved. Experimental results demonstrate that EDDet achieves a notable improvement in mAP50 (85.4%), outperforming the baseline by 2.8%.Importantly, EDDet also Maintains excellent efficiency with only 2.75 M parameters, 9.1 GFLOPs, and a high inference speed of 288.3 FPS, which is 37.5 FPS higher than the baseline.These results highlight the model's strong potential for real-time deployment in complex agricultural scenarios where both precision and speed are critical.
Why it matches plant phenotyping methodsナス葉の病斑という植物の病害状態を画像から検出・局在化する深層学習手法を開発し、精度と速度を実験的に評価しており、表現型取得・抽出手法が中心である。
abstractthis paper proposes an improved deep learning small target detection model-the Efficient Deep-fusion Detection Model (EDDet), which is specifically optimized for the recognition of small diseased spots in eggplant disease detection.
Reproduction assets foundThe paper used a publicly available eggplant fruit disease dataset hosted on Roboflow, with an explicit public URL in the Data Availability statement; no author code or model checkpoints are stated as publicly available.Dataset · publicThis study analyzed a combination of publicly available datasets and data collected by the authors. The publicly available datasets can be accessed at https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection . If you wish to request the complete dataset, please email the corresponding authorOpen asset ↗bohol-island-state-university-vgjlb/eggplant-disease-detectionlines:1561-1651Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In the context of the rapid development of smart agriculture, the detection of crop diseases remains a critical and challenging task. The diversity in eggplant disease scales, disease edge features, and the complexity of planting backgrounds significantly impact disease detection effectiveness. To address these challenges, we propose an eggplant disease detection network with edge feature enhancement based on multi-scale learning. The overall network adopts a "backbone-neck-head" architecture: the backbone extracts features, the neck performs feature fusion, and a three-scale detection head produces the final predictions. First, we designed the Multi-scale Edge Information Enhance (CSP-MSEIE) module to extract features from different disease scales and highlight edge information to obtain richer target features. Second, the Multi-source Interaction Module (MSIM) and Dynamic Interpolation Interaction Module (DIIM) sub-modules were designed further to enhance the model's capacity for multi-scale feature representation. By leveraging dynamic interpolation and feature fusion strategies, these sub-modules significantly improved the model's ability to detect targets in complex backgrounds. Then, leveraging these sub-modules, we designed the Multi-scale Context Reconstruction Pyramid Network (MCRPN) to facilitate spatial feature reconstruction and hierarchical context extraction. This framework efficiently combines feature information across multiple levels, strengthening the model's ability to capture and utilize contextual details. Finally, we validated the effectiveness of the proposed model on two disease datasets. It is noteworthy that on the eggplant disease data, the proposed disease detection model achieved improvements of 4.7% and 7.2% in mAP50 and mAP50-95 metrics, respectively, and the model's frames per second (FPS) reached 270.5. This detection network provides an effective solution for the efficient detection of crop diseases.
Why it matches plant phenotyping methods植物病害の画像ベース検出ネットワークを開発し、病害状態の推定をデータセットで検証しており、表現型取得・抽出手法が中心である。
abstractwe propose an eggplant disease detection network with edge feature enhancement based on multi-scale learning.
Reproduction assets foundThe paper's two evaluation datasets are publicly available: the PlantDoc dataset and the Roboflow-hosted eggplant disease detection dataset, both explicitly linked in the data availability statement. No author code or model checkpoints are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://public.roboflow.com/object-detection/plantdoc/ ; https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection .
Author contributions
HS: Investigation, Formal Analysis, Methodology, Writing – original draft, Data curation, Conceptualization. RF: Formal Analysis, Data curation, Writing – review & editing, Methodology. DK: Writing – review & edOpen asset ↗PlantDoclines:371-486Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://public.roboflow.com/object-detection/plantdoc/ ; https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection .
Author contributions
HS: Investigation, Formal Analysis, Methodology, Writing – original draft, Data curation, Conceptualization. RF: Formal Analysis, Data curation, Writing – review & editing, Methodology. DK: Writing – review & editing, Funding acquisition, Formal Analysis, Supervision.
Conflict of interest
The authors declOpen asset ↗eggplant-disease-detectionlines:371-486Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This dataset on eggplant leaf diseases has been meticulously developed to provide a valuable resource for agricultural research and the advancement of automated disease detection systems. It comprises 4,089 high-resolution images of eggplant leaves, systematically categorized into six distinct classes: Healthy Leaf, Insect Pest Disease, Leaf Spot Disease, Mosaic Virus Disease, White Mold Disease, and Wilt Disease. The images were captured using smartphone cameras under controlled conditions with a consistent white background to ensure clarity and uniformity. To reflect real-world agricultural scenarios, data collection was conducted across multiple geographic locations and in varying lighting conditions. This approach enhances the dataset's diversity and applicability. The dataset underwent thorough manual labelling and preprocessing to ensure accuracy and consistency across all samples. Each image is clearly labelled according to its respective disease class, making the dataset readily usable for machine learning applications. The balanced representation of healthy and diseased leaves allows for comprehensive training and testing of classification models. Designed to support the development of machine learning models for the early detection and classification of eggplant diseases, this dataset holds significant reuse potential in various research domains. It is particularly suitable for applications in plant pathology, precision agriculture, and disease forecasting, where timely and accurate diagnosis is crucial. The dataset is freely available for academic and research purposes, making it a valuable resource for researchers and developers aiming to innovate in agricultural technology and crop management. With its robust design and practical focus, the dataset has the potential to drive advancements in sustainable farming practices and enhance agricultural productivity.
Why it matches plant phenotyping methodsナス葉の病害状態を画像から分類するための大規模データセットであり、植物病害表現型の取得・再利用可能な基盤が中心です。
titleA comprehensive image dataset for the identification of eggplant leaf diseases and computer vision applications.
Reproduction assets foundThe paper is a Data in Brief article describing an eggplant leaf disease image dataset (4,089 images, six classes) publicly deposited on Mendeley Data, plus an authors' GitHub repository containing the preprocessing code. Both are paper-specific, public, and directly actionable.Dataset · publiclant field in Char Keshabpur, Shibchar, Madaripur (Latitude: 23°21′32.9″N, Longitude: 90°11′48.5″E)
5.
Eggplant field in Daffodil Smart City, Khagan, Ashulia (Latitude: 23°52′37.6″N, Longitude: 90°19′16.2″E).
Data accessibility
Repository name: Mendeley Data.
Data identification number: 10.17632/d3ypkphghb.2
Direct URL to data: https://data.mendeley.com/datasets/d3ypkphghb/2
Access the dataset at https://data.mendeley.com/datasets/d3ypkphghb/2 and cite using Data ID 10.17632/d3ypkphghb.2 .
Related research articleOpen asset ↗Mendeley Data · 10.17632/d3ypkphghb.2lines:1-49Code · publicand facilitate classification tasks.
•
Classification: Images were organized into six predefined classes: Healthy Leaf, Insect Pest, Leaf Spot, Mosaic Virus, White Mold, and Wilt, forming a structured dataset ready for analysis.
4.5.
Code used for data preprocessing
GitHub Repository name: Data_Preprocessing
Direct URL of Code: https://github.com/paradoxicalProfessor/Data_Preprocessing
Limitations
The Eggplant Leaf Disease dataset has some limitations. It was collected from specific regions in Bangladesh, which may limit its applicability to other environments. Our dataset includes only six disease classes, which may not represent all eggplant diseases in different regions. Some disease clasOpen asset ↗Data_Preprocessinglines:223-258Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
Bangladesh's agricultural landscape is significantly influenced by vegetable cultivation, which substantially enhances nutrition, the economy, and food security in the nation. Millions of people rely on vegetable production for their daily sustenance, generating considerable income for numerous farmers. However, leaf diseases frequently compromise the yield and quality of vegetable crops. Plant diseases are a common impediment to global agricultural productivity, adversely affecting crop quality and yield, leading to substantial economic losses for farmers. Early detection of plant leaf diseases is crucial for improving cultivation and vegetable production. Common diseases such as Bacterial Spot, Mosaic Virus, and Downy Mildew often reduce vegetable plant cultivation and severely impact vegetable production and the food economy. Consequently, many farmers in Bangladesh struggle to identify the specific diseases, incurring significant losses. This dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones. The dataset includes images of vegetable leaves such as Bitter Gourd (2223 images), Bottle Gourd (1803 images), Eggplants (2944 images), Cauliflowers (1598 images), Cucumbers (1626 images), and Tomatoes (2449 images). Each vegetable class encompasses several common diseases that affect cultivation. By identifying early leaf diseases, this dataset will be invaluable for farmers and agricultural researchers alike.
Why it matches plant phenotyping methods植物葉の画像から健康状態と病徴を識別するデータセットを提供しており、植物の病害状態を観測する再利用可能な画像ベースのフェノタイピング資源が中心です。
abstractThis dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones.
Reproduction assets foundThe paper is a Data in Brief article describing a public smartphone image dataset of vegetable leaf diseases hosted on Mendeley Data, with an explicit direct URL matching an allowed URL.Dataset · publicRepository name: Mendeley Data
Data identification number: DOI: 10.17632/n67gctmjyj.3
Direct URL to data: https://data.mendeley.com/datasets/n67gctmjyj/3Open asset ↗Mendeley Data · 10.17632/n67gctmjyj.3lines:1-48Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
Why it matches plant phenotyping methods野菜の長さ・直径という植物器官形質を、ステレオカメラ、深度画像、キーポイント検出で非接触推定する手法が研究の中心であるため。
abstractThis work focuses on the problem of non-contact measurement for vegetables in agricultural automation.
Reproduction assets foundThe authors publicly released both the vegetable keypoint dataset (1600 COCO-format images with ROI boxes and six keypoints) and the implementation code for their size estimation method on GitHub. Labelme is a generic third-party annotation tool and is excluded.Code · publicThe implementation code of our size estimation method can be accessed on https://github.com/BourneZ130/VegetableDetection , accessed on 15 February 2022.Open asset ↗BourneZ130/VegetableDetectionlines:76-141