Sapodilla Leaf Disease Detection and Classification Dataset
Description
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.
Files
Institutions
- Daffodil International University