Tea Leaf Diseases Dataset: Towards Accurate Field Diagnosis Using Image-Based Detection

The dataset comprises a total of 31,265 images, including 4,009 raw images and 27,256 augmented images, distributed across six categories. The raw dataset captures natural variability in leaf conditions under diverse environmental settings, while the augmented dataset enhances this variability by applying techniques such as rotation, scaling, flipping, and brightness adjustments to improve machine learning model generalizability. Category Distribution: Sunlight Scorching: Raw: 1,712 images Augmented: 4,009 images Total: 5,721 images Red Spider: Raw: 410 images Augmented: 4,746 images Total: 5,156 images Red Rust: Raw: 184 images Augmented: 4,849 images Total: 5,033 images Heliopeltis: Raw: 281 images Augmented: 4,825 images Total: 5,106 images Thrips: Raw: 176 images Augmented: 4,881 images Total: 5,057 images Normal (Healthy): Raw: 1,246 images Augmented: 3,946 images Total: 5,192 images Key Features: Diversity: The dataset captures variability in backgrounds, lighting, and growth stages, ensuring robust training and testing data. Augmentation: Includes 27,256 augmented images, generated using state-of-the-art techniques to simulate real-world variability. Category Coverage: Spans six distinct leaf conditions—ranging from pest-related damage (e.g., Red Spider, Heliopeltis) to environmental stress (e.g., Sunlight Scorching) and healthy leaves. Purpose: This dataset is designed to aid in the development of machine learning models capable of: Automated Leaf Condition Classification: Identifying specific conditions for improved crop health monitoring. Ecological Research: Supporting biodiversity studies by cataloging leaf conditions. Agricultural Applications: Enabling early detection of plant stressors, pests, and diseases to enhance sustainable farming practices. This combined raw and augmented dataset serves as a valuable resource for researchers, ecologists, and machine learning practitioners, contributing to advancements in agriculture, botany, and AI-driven ecological monitoring.