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Crop Pest Identification and Real-Time Monitoring System Design Based on Improved YOLOv8s.

Qiang Gao1,2, Chongchong Shi1,2, Yu Ji2,3

  • 1School of Information Engineering, Xi'an University, Xi'an 710065, China.

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Summary
This summary is machine-generated.

This study enhances the YOLOv8s model for crop pest detection by adding a lightweight attention mechanism and feature enhancement. The improved model shows better accuracy and efficiency for real-time pest monitoring.

Keywords:
crop pest detectionfeature enhancement moduleimproved YOLOv8s modellightweight attention mechanismsystem design

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Crop pest detection faces challenges with existing YOLOv8 model accuracy and adaptability.
  • Efficient and accurate pest identification is crucial for crop protection and yield optimization.

Purpose of the Study:

  • To improve the detection accuracy and deployment adaptability of the YOLOv8s model for crop pest detection.
  • To develop a real-time pest monitoring system based on an optimized YOLOv8s model.

Main Methods:

  • Incorporated a lightweight attention mechanism and a feature enhancement module into the YOLOv8s architecture.
  • Evaluated the improved model on a self-constructed pest dataset and the IP102 dataset.
  • Developed a real-time pest monitoring system utilizing the enhanced model.

Main Results:

  • The improved YOLOv8s model achieved higher mean Average Precision (mAP) on both datasets (e.g., +0.6% mAP0.5 and +0.8% mAP0.5-0.95 on the self-constructed dataset).
  • Model parameters were reduced from 11.1 million to 10.2 million, with a slight increase in inference speed (249.76 FPS vs. 225.38 FPS).
  • Demonstrated high recognition accuracy for pests in various states, outperforming the original YOLOv8s model on the IP102 dataset (e.g., +2.6% Precision).

Conclusions:

  • The proposed enhancements effectively improve YOLOv8s performance for crop pest detection.
  • The optimized model offers a valuable reference for accurate and real-time crop pest identification systems.
  • The study highlights the potential of lightweight attention and feature enhancement for agricultural AI applications.