GuavaLeafVision: A Labeled Dataset of Guava Leaf Diseases for Image-Based Detection

This dataset supports research in plant pathology and computer vision by providing a labeled image collection of guava leaf diseases, captured under real-world agricultural conditions. The original dataset includes 1,050 high-resolution images representing seven categories: Caterpillars, Cutting Weevil, Die Back, Healthy, Mealybug Pests, Red Rust, and Yellow Spot. Each class contains 150 images, ensuring class balance.
The images were captured using two high-end smartphones (Samsung Galaxy S21+ and iPhone 11) in two guava gardens in Ashulia, Savar, Dhaka, Bangladesh. The original images have a resolution of 3024x4032 pixels, later resized to 480x480 for initial processing.

To enhance generalizability and model robustness, the dataset was augmented using the following techniques:
• Image Resizing: All images resized to 224x224 pixels.
• Normalization: Pixel values scaled to the [0, 1] range.
• Data Augmentation: Applied rotation, shearing, and flipping to increase variability and simulate real-life conditions.
This augmentation expanded the dataset to 8,400 images, increasing the sample size and representation of disease diversity. It is ideal for training and evaluating deep learning models for plant disease classification, particularly in resource-constrained and agro-environmental contexts.

Number of Classes: 7
Original Images: 1,050
Augmented Images: 8,400
Total Images: 9,450
Image Format (Original): .jpg
Image Format (Augmented): .png
Dimensions: 480x480 (Original)
Dimensions: 224x224 (Augmented)