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High-Precision Stored-Grain Insect Pest Detection Method Based on PDA-YOLO
Fuyan Sun1,2,3, Zhizhong Guan1,2,3, Zongwang Lyu1,2,3
1Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.
Insects
|June 25, 2025
Summary
PDA-YOLO enhances stored-grain insect detection using advanced AI modules. This novel algorithm improves accuracy and speed, crucial for intelligent grain storage and food security.
Area of Science:
- Agricultural Science
- Computer Vision
- Artificial Intelligence
Background:
- Effective stored-grain insect pest detection is vital for preventing economic losses and ensuring food security.
- Current detection methods face challenges including high costs, environmental interference, and inconsistent performance.
Purpose of the Study:
- To develop an improved algorithm for stored-grain insect pest detection, addressing limitations of existing methods.
- To enhance accuracy, efficiency, and real-time performance in pest identification within grain storage management.
Main Methods:
- Proposed PDA-YOLO, an algorithm based on YOLO11n, integrating PoolFormer_C3k2 (PF_C3k2), Attention-based Intra-Scale Feature Interaction (AIFI), and Dynamic Multi-scale Aware Edge (DMAE) modules.
- Trained and tested the algorithm on 6200 images of five common stored-grain insect pests.
Main Results:
- PDA-YOLO achieved high performance metrics: mAP@0.5 of 96.6%, mAP@0.5:0.95 of 60.4%, and F1 score of 93.5%.
- Demonstrated a low computational cost (6.9 G) and fast mean detection time (9.9 ms per image).
- Outperformed mainstream detection algorithms in balancing accuracy, computational efficiency, and real-time capabilities.
Conclusions:
- PDA-YOLO offers a significant advancement for intelligent stored-grain insect pest detection.
- The algorithm provides a valuable reference for improving pest management in grain storage systems.
- PDA-YOLO effectively addresses the need for accurate, efficient, and rapid detection to support food security.
