Name: Plant leaves image segmentation dataset
Creator: Zenodo
Published: 2025-01-20 20:35:47
License: 暂无描述

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https://zenodo.org/doi/10.5281/zenodo.14707857

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Overview: This repository contains high-resolution RGB images captured by field advisors and selected by experts for the dataset specifically designed for training two models to recognize Cercospora Leaf Spot Disease in beet and Brown Rust of Rye in rye. The dataset comprises 397 images and includes original photos along with binary masks for two categories: leaf segmentation and disease spot segmentation. Images were labeled using the Label Studio tool with expertise from the Institute of Plant Protection in Poznań, Poland. The dataset has been utilized to train CNN models for product avaiable at AI4EOSC marketplace that are integrated into the mobile application of eDWIN advisory platform. Dataset Composition: Each image comes with corresponding masks that highlight the leaves and specific disease spots. The dataset is organized into separate directories for each plant and type of segmentation: ├── beet/│ ├── disease_segmentation/│ │ ├── images/│ │ │ │ ├── sick_12.jpg│ │ │ │ ├── sick_13.jpg│ │ │ │ ├── sick_15.jpg│ │ │ │ ├── sick_17.jpg│ │ │ │ ├── sick_18.jpg│ │ │ │ └── ...│ │ └── masks/│ │ │ ├── sick_12.png│ │ │ ├── sick_13.png│ │ │ ├── sick_15.png│ │ │ ├── sick_17.png│ │ │ ├── sick_18.png│ │ │ └── ...│ └── leaves_segmentation/│ ├── images/│ │ │ ├── 1.jpg│ │ │ ├── 10.JPG│ │ │ ├── 11.JPG│ │ │ ├── 12.JPG│ │ │ ├── 13.JPG│ │ │ └── ...│ └── masks/│ │ ├── 1.png│ │ ├── 10.png│ │ ├── 11.png│ │ ├── 12.png│ │ ├── 13.png│ │ └── ...└── rye/ ├── disease_segmentation/ │ ├── images/ │ │ │ ├── sick_0.jpg │ │ │ ├── sick_102.jpg │ │ │ ├── sick_103.jpg │ │ │ ├── sick_104.jpg │ │ │ ├── sick_105.jpg │ │ │ └── ... │ └── masks/ │ │ ├── sick_0.png │ │ ├── sick_102.png │ │ ├── sick_103.png │ │ ├── sick_104.png │ │ ├── sick_105.png │ │ └── ... └── leaves_segmentation/ ├── images/ │ │ ├── IMG_0809.JPG │ │ ├── IMG_0810.JPG │ │ ├── IMG_0811.JPG │ │ ├── IMG_0812.JPG │ │ ├── IMG_0813.JPG │ │ └── ... └── masks/ │ ├── IMG_0809.png │ ├── IMG_0810.png │ ├── IMG_0811.png │ ├── IMG_0812.png │ ├── IMG_0813.png │ └── ... Usage: The dataset is structured to aid in the development and testing of models for agricultural disease detection. The following table represents structure of dataset_overview.csv: Plant Segmentation Category Filename beet disease_segmentation images sick_12.jpg beet disease_segmentation masks sick_12.jpg rye leaves_segmentation images sick_15.jpg rye leaves_segmentation masks sick_15.jpg File Numbering/Naming Scheme: The filenames for the images and masks correspond to the disease and leaf category they represent, with the RGB image names matching their respective binary masks. The naming convention is straightforward, reflecting the segmentation type and sequence number. Note: All data were recorded under various field conditions, and experts verified annotations to ensure the accuracy of labels. The dataset is continually updated with new data and annotations to enhance model robustness and accuracy.

### 数据集概览本仓库包含由田间技术顾问实地采集、经专家遴选审定的高分辨率RGB图像，本数据集专为训练两款识别模型而设计，分别用于识别甜菜尾孢叶斑病（Cercospora Leaf Spot Disease）以及黑麦褐锈病（Brown Rust of Rye）。数据集共计包含397张图像，涵盖原始照片以及两类二值掩码：叶片分割掩码与病害斑点分割掩码。图像标注工作借助Label Studio工具完成，并得到了波兰波兹南植物保护研究所的专业技术支持。本数据集已用于训练AI4EOSC marketplace上架的卷积神经网络（Convolutional Neural Network, CNN）模型，此类模型已集成至eDWIN咨询平台的移动端应用中。 ### 数据集构成每张图像均配有对应掩码，用于高亮显示叶片与特定病害斑点。本数据集按作物类型与分割任务类型分为独立目录，结构如下： ├── 甜菜（beet）/ │ ├── 病害分割（disease_segmentation）/ │ │ ├── images/ │ │ │ ├── sick_12.jpg │ │ │ ├── sick_13.jpg │ │ │ ├── sick_15.jpg │ │ │ ├── sick_17.jpg │ │ │ ├── sick_18.jpg │ │ │ └── ... │ │ └── masks/ │ │ ├── sick_12.png │ │ ├── sick_13.png │ │ ├── sick_15.png │ │ ├── sick_17.png │ │ ├── sick_18.png │ │ └── ... │ └── 叶片分割（leaves_segmentation）/ │ ├── images/ │ │ ├── 1.jpg │ │ ├── 10.JPG │ │ ├── 11.JPG │ │ ├── 12.JPG │ │ ├── 13.JPG │ │ └── ... │ └── masks/ │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ └── ... └── 黑麦（rye）/ ├── 病害分割（disease_segmentation）/ │ ├── images/ │ │ ├── sick_0.jpg │ │ ├── sick_102.jpg │ │ ├── sick_103.jpg │ │ ├── sick_104.jpg │ │ ├── sick_105.jpg │ │ └── ... │ └── masks/ │ ├── sick_0.png │ ├── sick_102.png │ ├── sick_103.png │ ├── sick_104.png │ ├── sick_105.png │ └── ... └── 叶片分割（leaves_segmentation）/ ├── images/ │ ├── IMG_0809.JPG │ ├── IMG_0810.JPG │ ├── IMG_0811.JPG │ ├── IMG_0812.JPG │ ├── IMG_0813.JPG │ └── ... └── masks/ ├── IMG_0809.png ├── IMG_0810.png ├── IMG_0811.png ├── IMG_0812.png ├── IMG_0813.png └── ... ### 使用场景本数据集旨在助力农业病害检测模型的开发与测试。下表为dataset_overview.csv的字段结构： | 作物 | 分割任务 | 类别 | 文件名 | | ---- | ---- | ---- | ---- | | beet | disease_segmentation | images | sick_12.jpg | | beet | disease_segmentation | masks | sick_12.jpg | | rye | leaves_segmentation | images | sick_15.jpg | | rye | leaves_segmentation | masks | sick_15.jpg | ### 文件编号与命名规则图像与掩码的文件名与其所代表的病害或叶片类别相对应，RGB图像文件名与其对应的二值掩码文件名一一匹配。本命名规则简洁直观，可反映分割任务类型与序列编号。 ### 备注所有数据均在多种田间环境下采集，专家已对标注结果进行核验以确保标签准确性。本数据集将持续更新新增数据与标注，以提升模型的鲁棒性与识别精度。

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