A Multi-Class Real-Field Eggplant Leaf Disease Dataset for Computer Vision and Deep Learning Research

This dataset presents a comprehensive real-field collection of eggplant (Solanum melongena L.) leaf images designed to support research in computer vision, deep learning, and precision agriculture. The dataset was developed through extensive field surveys conducted in agricultural regions of Bangladesh between December 2025 and January 2026 under natural environmental conditions.

The dataset comprises 1,500 high-resolution RGB images categorized into three classes: Healthy leaves, Fungal-infected leaves, and Spider Mite-infected leaves, with 500 images per class. All images were captured directly from eggplant cultivation fields using smartphone cameras under diverse real-world conditions, including variations in illumination, background complexity, viewing angles, and plant growth stages. Such diversity enhances the robustness and generalization capability of machine learning models trained on this dataset.

The collected images were manually inspected, annotated, and organized into class-specific directories to facilitate supervised learning tasks. The dataset can be utilized for a wide range of agricultural artificial intelligence applications, including image classification, disease detection, transfer learning, feature extraction, and model benchmarking.

By providing a real-field, annotated, and diverse image collection, this dataset aims to serve as a valuable benchmark resource for developing intelligent and automated eggplant disease diagnosis systems, thereby contributing to sustainable agriculture and precision farming practices.

本数据集提供了一套全面的田间实拍茄子（Solanum melongena L.）叶片图像集，旨在支撑计算机视觉、深度学习及精准农业领域的相关研究。该数据集于2025年12月至2026年1月期间，在孟加拉国的农业产区通过大规模野外实地调研采集所得，所有采集工作均在自然环境条件下开展。
本数据集包含1500张高分辨率RGB图像，划分为3个类别：健康叶片、真菌侵染叶片、红蜘蛛（叶螨）侵染叶片，每类各500张。所有图像均使用智能手机摄像头直接在茄子种植田间拍摄，采集场景涵盖多样的真实田间环境，包括光照差异、背景复杂度变化、拍摄视角差异以及植株生长阶段差异。此类多样性可提升基于本数据集训练的机器学习模型的鲁棒性与泛化能力。
所采集的图像均经过人工核验、标注，并按类别分目录存储，以方便监督学习任务的开展。本数据集可应用于众多农业人工智能场景，涵盖图像分类、病害检测、迁移学习、特征提取以及模型基准测试等。
本数据集通过提供田间实拍、经标注且场景多样的图像集，旨在为智能自动化茄子病害诊断系统的研发提供优质基准资源，进而助力可持续农业与精准耕作实践的发展。