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Real-time peach detection method in complex environments based on improved YOLOv8 and multi-attention fusion
Shuanghong You1, Jinquan Li2, Lingyun Yao2
1Fruit Research Institute, Chongqing Academy of Agriculture Science, Chongqing, China.
Frontiers in Plant Science
|May 25, 2026
Summary
This study introduces Peach-YOLO, a lightweight deep learning model for automated peach picking. It achieves high accuracy and real-time performance in complex orchard conditions, improving detection efficiency.
Area of Science:
- Computer Vision
- Machine Learning
- Robotics
Background:
- Automated fruit picking requires accurate and efficient detection algorithms.
- Complex orchard environments present challenges like fruit overlap and occlusion.
Purpose of the Study:
- To develop a lightweight, high-precision peach detection model for automated picking.
- To enhance feature representation and reduce computational complexity.
Main Methods:
- Proposed Peach-YOLO model based on an improved YOLOv8n framework.
- Incorporated Receptive-Field Attention Convolution (RFAConv) and Convolution and Attention Fusion Module (CAFM).
- Utilized Coordinate Attention-guided high-level screening feature fusion pyramid network (CA-HSFPN) and Shape-IoU loss function.
Main Results:
- Peach-YOLO achieved 115.7 FPS inference speed with a 5.0 MB model size.
- Achieved mAP@0.5 of 82.2%, precision of 78.9%, and recall of 76.4%.
- Outperformed baseline YOLOv8n and other mainstream models in accuracy and efficiency.
Conclusions:
- Peach-YOLO offers a lightweight, high-precision, real-time solution for automated fruit picking.
- The model effectively addresses challenges in complex orchard environments.
- Demonstrates significant improvements in detection performance for robotic harvesting.
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