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Apple Leaf Disease Detection Based on Improved YOLOv11 with DSSA Mechanism
Yuanyuan Zhang1, Jiya Tian1, Duanyang Zhang1
1College of Information Engineering, Xinjiang Institute of Technology, Aksu 843000, China.
Plants (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces an improved YOLOv11 model with a Dual Sparse Selection Attention (DSSA) module for accurate apple leaf disease detection. The enhanced model significantly improves precision and recall for identifying diseases like black rot, rust, and scab in orchards.
Area of Science:
- Agricultural Science
- Computer Vision
- Plant Pathology
Background:
- Visual inspection of apple leaf diseases is subjective and inefficient for large-scale orchards.
- Accurate and rapid disease identification is crucial for effective orchard management and crop yield.
Purpose of the Study:
- To develop an advanced deep learning model for precise and automated apple leaf disease detection.
- To improve the accuracy and efficiency of identifying common apple leaf diseases.
Main Methods:
- An improved YOLOv11 model was developed, incorporating a Dual Sparse Selection Attention (DSSA) module.
- The DSSA module was embedded in the YOLOv11 backbone to enhance feature extraction and reduce background interference.
- A tailored training strategy with an optimized learning rate and optimizer was employed.
- Experiments were conducted on a dataset of 7594 images covering black rot, rust, scab, and healthy apple leaves.
Main Results:
- The proposed model achieved high performance metrics: 0.973 precision, 0.978 recall, 0.991 mAP50, and 0.949 mAP50-95.
- The model outperformed existing YOLOv8, YOLOv9, YOLOv10, and vanilla YOLOv11 models.
- A practical Qt-based visualization system was developed for orchard deployment.
Conclusions:
- The improved YOLOv11 model with DSSA offers a reliable solution for intelligent apple leaf disease detection.
- This technology supports smart orchard management by enabling rapid and accurate disease identification.
- The developed system has practical applications for real-time disease monitoring in agricultural settings.
