Comprehensive Mango Leaf Images Dataset for Multi-Class Disease Classification and Automated Plant Disease Detection

This dataset features high-resolution images of mango (Mangifera indica) leaves collected from the Rajbari and Pabna districts in Bangladesh. Each image was initially captured using a smartphone camera, then resized for efficient processing. A structured augmentation pipeline was applied to enhance the dataset, involving brightness, contrast and color adjustments, along with geometric transformations such as cropping, scaling, flipping, rotation and shifting.The final augmented dataset is categorized into six classes based on leaf health and disease status: Healthy, Anthracnose, Dried Leaves, Gall Midge, Powdery Mildew and Senescent Leaves. This diverse and well-labeled dataset serves as a valuable resource for training, testing and validating machine learning and deep learning models aimed at automated detection and classification of mango leaf diseases. It holds significant potential for supporting farmers and researchers in the development of effective crop management solutions.

Dataset Classes and Image Counts:
Anthracnose: 1000 images
Dried: 1000 images
Gall Midge: 1000 images
Healthy: 1000 images
Powdery Mildew: 1000 images
Senescent Leaves: 1000 images

Image Details:
Original image resolution: 4096 x 3072 pixels
Resized image resolution: 480 x 560 pixels
Image format: JPG
Color mode: RGB
Collection device: Smartphone camera
Location: Rajbari and Pabna districts, Bangladesh