Mango_Leaf_Bd_dataset

Description

The Mango Leaf Disease Dataset has been systematically developed to facilitate research in plant disease detection and classification using deep learning and image processing techniques. The dataset focuses on mango leaves, an essential part of agricultural production in tropical and subtropical regions, and includes both healthy samples and leaves affected by commohhn mango diseases. 1. Classes of the Dataset The dataset is organized into four distinct classes: 1. Healthy 2. Anthracnose 3. Gall Midges 4. Powdery Mildew 2. Dataset Composition • Original Images: o 500 images per class (total 2,000 original samples). o Image format: JPG. • Augmented Images: o To enhance variability and overcome data scarcity, augmentation techniques such as rotation, flipping, scaling, brightness adjustment, contrast variation, and Gaussian noise addition were applied. o 512 × 512 resolution: 1,500 images per class. o 256 × 256 resolution: 1,500 images per class. o Image format: JPG. 3. Final Dataset Size • 512 × 512 images: 6,000 samples (1,500 × 4 classes). • 256 ×ffd 256 images: 6,000 samples (1,500 × 4 classes). • Grand Total (original + augmented): 14,000 JPG images.

Steps to reproduce

Mango leaf images were captured under natural lighting conditions using a high-resolution digital camera.

Institutions

• Khwaja Yunus Ali University

  Sirajgonj

Categories

Licence