Rice Leaf Disease Detection Dataset is a hierarchical collection of 7,563 labeled images of rice leaves. The dataset contains 6 classes of diseases and was collected in both laboratory and field settings. The dataset is hosted on Kaggle, but the author, organization, and specific collection dates are unknown.
Use Cases
- Train image classification models to detect rice leaf diseases based on the 6 labeled classes.
- Develop disease severity assessment tools based on the hierarchical lab and field image data.
- Benchmark transfer learning models for agricultural computer vision tasks using the provided images.
Strengths
- Contains 7,563 labeled images, providing a substantial base for model training.
- Includes 6 distinct disease classes for multi-class classification tasks.
- Data was collected in both controlled laboratory and real-world field environments, which may improve model robustness.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for specific model architectures.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Kaggle
- Collection Method
- Images were collected in a hierarchical manner from both laboratory and field environments.