Sapodilla Leaf Disease Detection and Classification Dataset

This dataset has been created to support the detection and classification of diseases affecting sapodilla leaves using image-based machine learning methods. Images were captured under real-world outdoor conditions with a smartphone, representing a variety of healthy and diseased leaves. The dataset provides a strong foundation for building AI models aimed at plant disease diagnosis, agricultural automation, and precision farming for sapodilla crops.

Original Dataset:
-Number of images: 1,778
-Data format: .jpg

Processed Dataset:
-Number of images: 1,778
-Data format: .jpg

Augmented Dataset:
-Number of images: 14,000
-Data format: .jpg

Augmentation Techniques:
1. Rotation, 2. Horizontal Flipping, 3. Vertical Flipping, 4. Brightness Enhancement, 5. Contrast Variation, 6. Image Blurring, 7. Shearing, 8. Zooming, 9. Noise Injection

Applications:
-Enables AI-based early detection of sapodilla leaf and fruit diseases to support better farm management.
-Useful for creating automated classification systems for tropical fruit crops.
-Supports research in precision farming by enabling image-based plant health assessment models.
-Provides a robust base for improving disease detection accuracy in real-world agricultural settings.