# Intelligent identification of rice leaf diseases via improved faster-RCNN with multi-feature scale fusion

## TL;DR

This paper introduces an improved Faster-RCNN model for more accurate and efficient detection of rice leaf diseases using advanced AI techniques.

## Contribution

The novel Faster-RCNN-Pro model integrates SENet attention modules, multi-feature scale fusion, ROI Align, and a balanced L1 loss function for disease detection.

## Key findings

- The improved model achieves better detection accuracy and robustness in identifying rice leaf diseases.
- The model significantly reduces misjudgment rates compared to existing methods.
- Multi-feature scale fusion enhances the utilization of micro-target features for better recognition.

## Abstract

Many Artificial Intelligence and Machine Learning technologies have been applied to detect rice diseases. These approaches are either unable to identify the diseases or have a slow recognition speed. Therefore, an improved Faster-RCNN (Faster-RCNN-Pro) model is proposed to overcome these issues. First, SENet attention modules are embedded in the backbone of Faster-RCNN to enhance confidence of objects that are difficult to recognize by enhancing key image information and suppressing background information. Second, structure of the feature extraction network and RPN are improved by using multi-feature scale fusion to increase the utilization of micro-target features. Third, the quantization error introduced in the process of pooling the region of interest is then eliminated by ROI Align. Finally, a balanced L1 loss function is designed to effectively reduce the imbalance between samples with a large gradient that are difficult to learn, and samples with a small gradient that are easy to learn. The experiment results show that the improved model has a better detection accuracy and robustness in recognizing the fine features of rice leaf diseases. Therefore, the application of this model to the intelligent identification of rice leaf disease can significantly improve the accuracy and reduce the misjudgment rate.