- Conference Article
- 10.1117/12.3075577
Research on intelligent detection and analysis system for pesticide production based on deep learning
- Jun 18, 2025
- Jun Yang
In the era of smart agriculture and digital farming, quality inspection of pesticide products is a crucial step in the production process. The reliability and consistency of pesticide products directly impact the effectiveness of precision farming and intelligent pest management systems. However, traditional manual inspection methods face significant challenges due to their low efficiency and inconsistent standards. This paper presents an innovative defect detection system based on an improved YOLOv5 architecture, incorporating attention mechanisms and feature pyramid networks to enhance feature extraction capabilities. The system was tested on a comprehensive dataset of 52,000 images, including 38,000 normal samples and 14,000 defective samples. Experimental results demonstrate remarkable performance, with the system achieving a detection rate exceeding 90% for various defect types including damage, deformation, and bubbles. Operating at 60 frames per second, the system maintains high accuracy while meeting real-time production requirements. In actual production line applications, the system showed excellent stability across different environmental conditions, with performance degradation not exceeding 3% even under extreme conditions. This research provides an effective solution for automated quality control in pesticide production, contributing to the advancement of smart agriculture and sustainable farming practices.
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