Datasets Comparison

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.

Agricultural Science, Artificial Intelligence, Computer Vision, Machine Learning, Sustainable Agriculture, Precision Agriculture, Deep Learning

Creative Commons Attribution 4.0 International

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.

Agricultural Science, Artificial Intelligence, Computer Vision, Machine Learning, Sustainable Agriculture, Precision Agriculture, Deep Learning

Creative Commons Attribution 4.0 International