1,000 to 10,000 images of rice leaves categorized into multiple disease classes for agricultural pathology research. The dataset utilizes found annotations to support multi-class image classification tasks focused on crop health.
Use Cases
- Train image classification models to distinguish between different rice leaf diseases using the provided image samples
- Evaluate the performance of transfer learning models on agricultural datasets using the multi-class labels
- Build a mobile application for real-time rice disease identification based on the image-classification task category
Strengths
- Contains between 1,000 and 10,000 images of rice leaves
- Supports multi-class image classification tasks for agricultural disease detection
- Features 'found' annotations for identifying specific rice leaf pathologies