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Pepper-YOLO: an lightweight model for green pepper detection and picking point localization in complex environments
Yikun Huang1,2,3, Yulin Zhong1, Deci Zhong1,3
1School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou, China.
Frontiers in Plant Science
|January 15, 2025
Summary
This study introduces Pepper-YOLO, a lightweight AI model for green chili harvesting robots. It improves detection and localization accuracy in complex environments while reducing model size for low-cost deployment.
Area of Science:
- Agricultural Robotics
- Computer Vision
- Machine Learning
Background:
- Green chili harvesting is hindered by fruit-background color similarity and occlusion.
- High-accuracy detection models are often too complex for low-cost agricultural devices.
Purpose of the Study:
- To develop a lightweight, efficient AI model for simultaneous green chili detection and picking point localization.
- To enable deployment on resource-constrained agricultural hardware.
Main Methods:
- An improved lightweight Pepper-YOLO model based on YOLOv8n-Pose was developed.
- Incorporated a reversible dual pyramid structure with cross-layer connections for enhanced feature extraction.
- Utilized RepNCSPELAN4 for feature fusion and a C2fCIB module for optimized large-scale feature detection.
Main Results:
- Achieved 82.2% object detection accuracy and 88.1% harvesting point localization accuracy in complex scenes.
- Reduced model parameters by 38.3% and complexity by 28.9%, with a final model size of 4.3MB.
- Demonstrated superior parameter efficiency compared to state-of-the-art methods.
Conclusions:
- Pepper-YOLO offers high precision and real-time performance for green chili harvesting in challenging conditions.
- The model's lightweight design makes it suitable for deployment on low-cost agricultural robots.
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