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Research on an improved RT-DETR-based model for rice disease detection
Yaojun Zhang1, Changqiang Shen1, Ying Xiong2
1School of Information Engineering, Xinyang Agriculture and Forestry University, Xinyang, Henan, China.
Plos One
|June 3, 2026
Summary
This study introduces ECL-RTDETR, an advanced model for detecting rice diseases. It significantly improves detection accuracy and speed while reducing computational costs, aiding smart agriculture.
Area of Science:
- Agricultural Science
- Computer Vision
- Artificial Intelligence
Background:
- Precise monitoring of rice diseases is crucial for global food security.
- Existing detection methods struggle with computational complexity, small targets, and robustness.
Purpose of the Study:
- To develop an enhanced RT-DETR model (ECL-RTDETR) for accurate and efficient rice disease detection and localization.
- To overcome limitations of current methods, including high computational load and loss of detailed information.
Main Methods:
- Utilized a lightweight EfficientViT backbone with a streamlined multi-head self-attention module for faster feature extraction.
- Incorporated the CARAFE upsampling operator to preserve fine-grained feature details.
- Replaced standard convolution with lightweight dynamic convolution (LDConv) for adaptive feature learning in complex conditions.
Main Results:
- ECL-RTDETR demonstrated a 0.7% improvement in mAP@0.5 compared to the baseline RT-DETR.
- Achieved a 22.2 FPS increase in detection speed.
- Reduced computational cost by 81.8 GFLOPs and parameters by 22.12M.
Conclusions:
- ECL-RTDETR offers superior accuracy, speed, and efficiency for intelligent rice disease detection.
- The model provides a robust solution for smart agriculture and sustainable food security.
- Advanced deep learning techniques enhance localization and identification of rice plant diseases.