Eggplant Dataset: A Comprehensive Dataset for Agricultural Research and Disease Detection

Published: 10 July 2024| Version 1 | DOI: 10.17632/5drkk544k8.1


This dataset is a valuable resource for researchers and practitioners in agriculture, machine learning, and computer vision, focusing on classifying diseases that affect eggplant plants. The dataset comprises two main categories: the Original Dataset and the Augmented Dataset. These categories contain images depicting various conditions of eggplant leaves and fruits, encompassing diseases such as Cercospora Leaf Spot, Flea Beetles, Phytophthora Blight, Powdery Mildew, Tobacco Mosaic Virus, among others. By including augmented images generated through techniques like flip, rotation, noise addition, shift, brightness adjustment, and zoom, this dataset supports robust algorithm development and evaluation. Researchers can leverage these datasets to train and validate machine learning models for accurate disease classification and early detection in eggplant plants. The dataset aims to accelerate progress in agricultural technology, crop protection, and sustainable farming practices through innovative applications of computer vision and machine learning. The dataset initially includes 3,116 original high-resolution images depicting various eggplant diseases, meticulously annotated for precise classification. Additionally, 10,000 augmented images were generated from these originals, expanding the dataset while maintaining an image size suitable for detailed analysis. Folder Structure: Augmented Dataset: Number of datasets: 10,000 Data format: .jpg Original Dataset: Number of datasets: 3,116 Data format: .jpg This comprehensive dataset provides a foundational tool for advancing research in eggplant plant pathology, facilitating the development of AI-driven solutions for disease management and crop enhancement.



Daffodil International University


Agricultural Science, Computer Vision, Machine Learning, Plant Diseases, Eggplant, Deep Learning