Eggplant Leaf Disease Classification Dataset

Dataset Overview:
It contains 2991 high-resolution images of eggplant leaves.
Images were collected from the Changao, Paragram, Ashulia, Dhaka, and Narsingdi regions of Bangladesh.
Data was gathered between October 20 and November 2, 2024, over a period of 13 days.
Classes:
Cercospora: 628 images
Curl: 284 images
Flea Beetles: 84 images
Hadda Beetles: 530 images
Healthy: 188 images
LeafhopperJassids: 38 images
Magnesium Deficiency: 50 images
Phomposist Blast: 218 images
TMV (Tobacco Mosaic Virus): 356 images
Tobacco Caterpillar: 452 images
Verticillium Wilt: 163 images
Purpose:
Supports the advancement of automated agricultural disease detection systems.
It aims to assist in the early detection and management of eggplant leaf diseases.
Enables the development of reliable diagnostic tools using machine learning and image processing techniques.
Promotes better crop management, increased yield, and reduced pesticide use, contributing to sustainable agricultural practices.
Serves as a resource for developing and evaluating image-based disease recognition models and deep learning applications in agriculture.

数据集概览：本数据集收录了来自孟加拉国昌奥尔、帕拉格拉姆、阿舒利亚、达卡和纳辛迪地区的2991张茄子叶片的高分辨率图像。数据采集于2024年10月20日至11月2日，共计13天。类别包括：

- 疱疹病菌：628张图像
- 卷叶病：284张图像
- 跳甲：84张图像
- 食心虫：530张图像
- 健康：188张图像
- 叶蝉：38张图像
- 镁缺乏：50张图像
- 壳二孢菌叶枯病：218张图像
- 烟草花叶病毒（TMV）：356张图像
- 烟草螟：452张图像
- 茎腐病：163张图像。

目的：本数据集旨在支持自动化农业病害检测系统的进步，旨在协助早期检测和管理茄子叶片病害，并促进基于机器学习和图像处理技术的可靠诊断工具的开发。此外，它还推动了更优的作物管理、产量提升和农药使用减少，为可持续农业实践作出贡献。该数据集作为开发与评估基于图像的病害识别模型及农业领域深度学习应用的重要资源。