Faster-RCNN Model Performance Evaluation for Rice Leaf Disease Detection

Xiaofan Shi's dataset provides performance evaluation results for various improved Faster-RCNN models tested on a rice leaf disease detection task. The data is stored in a 5.5 KB Excel file, indicating a small, focused set of experimental results. It was last updated in March 2026.

Use Cases

• Compare model performance metrics like detection accuracy and recognition speed across different Faster-RCNN architectures.
• Analyze the impact of specific improvements such as Roi Align, SENet attention modules, or feature scale fusion on evaluation scores.
• Benchmark the effectiveness of techniques for suppressing background information or handling quantization errors in the context of rice leaf disease detection.
• Assess trade-offs between model enhancements and computational metrics, potentially referenced in columns related to gradient magnitude or processing time.

Strengths

• Data is licensed under CC-BY-4.0, permitting open sharing and adaptation.
• Provides a direct comparison of multiple model variants on a specific test set for reproducibility.
• Includes evaluation of advanced techniques like attention modules and feature fusion specifically for agricultural computer vision.

Limitations

• The dataset is very small at 5.5 KB, containing only summary evaluation metrics, not raw images or annotations.
• Limited scope to a single application (rice leaf disease) and a single model family (Faster-RCNN).
• Unknown row count and specific column structure limit detailed statistical analysis.

Provenance

Source

Xiaofan Shi via figshare.

Collection Method

Results from experiments evaluating improved Faster-RCNN models on a test set for detecting rice leaf diseases.

Freshness

Last updated in March 2026, indicating recent or forward-dated model evaluation.