A Curated Dataset of Eggplant Leaves: Healthy Specimens and Six Major Disease Conditions
Description
This dataset comprises high-resolution images of eggplant (Solanum melongena) leaves, curated to support research in plant disease detection and classification using machine learning techniques. The images were collected under natural field conditions, providing a diverse set of images with varying lighting, angles, and backgrounds. This dataset is specifically designed for training machine learning models for plant disease detection, image classification, and precision agriculture applications. The dataset includes seven distinct classes of eggplant leaf conditions: 1. Healthy: Leaves that show no signs of disease or damage, representing a normal, healthy state of the plant. 2. Insect Pest: Leaves affected by insect pests, showing damage such as holes, discoloration, or deformation caused by feeding activities. 3. Leaf Spot: Leaves exhibiting various forms of leaf spot diseases, characterized by round or irregular lesions, often caused by fungal or bacterial pathogens. 4. Mosaic Virus: Leaves infected by mosaic viruses, displaying mottled or variegated patterns with yellow or light green patches, commonly associated with viral diseases. 5. Small Leaf: Leaves that are smaller than normal, resulting from environmental stress, nutrient deficiencies, or genetic factors leading to stunted growth. 6. White Mold: Leaves showing symptoms of white mold, with white, cotton-like fungal growth on the surface, caused by Sclerotinia spp. 7. Wilt: Leaves that appear droopy and dehydrated, exhibiting wilting, often due to root damage, water stress, or vascular infections. Each class contains 200 high-quality images, totaling 1,400 images in the dataset. The images are in JPG format with varying resolutions, making them suitable for training deep learning models to automatically detect and classify plant diseases. The dataset is intended for use in agricultural research, specifically in developing machine learning models for early disease detection and crop management.
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Institutions
- Daffodil International University