PCC-YOLO: A Fruit Tree Trunk Recognition Algorithm Based on YOLOv8

Yajie Zhang, Weiliang Jin, Baoxing Gu (), Guangzhao Tian, Qiuxia Li, Baohua Zhang and Guanghao Ji
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Yajie Zhang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Weiliang Jin: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Baoxing Gu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Guangzhao Tian: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Qiuxia Li: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Baohua Zhang: School of Smart Agriculture (Artificial Intelligence), Nanjing Agricultural University, Nanjing 210031, China
Guanghao Ji: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2025, vol. 15, issue 16, 1-20

Abstract: With the development of smart agriculture, the precise identification of fruit tree trunks by orchard management robots has become a key technology for achieving autonomous navigation. To solve the issue of tree trunks being hard to see against their background in orchards, this study introduces PCC-YOLO (PENet, CoT-Net, and Coord-SE attention-based YOLOv8), a new trunk detection model based on YOLOv8. It improves the ability to identify features in low-contrast situations by using a pyramid enhancement network (PENet), a context transformer (CoT-Net) module, and a combined coordinate and channel attention mechanism. By introducing a pyramid enhancement network (PENet) into YOLOv8, the model’s feature extraction ability under low-contrast conditions is enhanced. A context transformer module (CoT-Net) is then used to strengthen global perception capabilities, and a combination of coordinate attention (Coord-Att) and SENetV2 is employed to optimize target localization accuracy. Experimental results show that PCC-YOLO achieves a mean average precision (mAP) of 82.6% on a self-built orchard dataset (5000 images) and a detection speed of 143.36 FPS, marking a 4.8% improvement over the performance of the baseline YOLOv8 model, while maintaining a low computational load (7.8 GFLOPs). The model demonstrates a superior balance of accuracy, speed, and computational cost compared to results for the baseline YOLOv8 and other common YOLO variants, offering an efficient solution for the real-time autonomous navigation of orchard management robots.

Keywords: orchard management robot; YOLOv8; trunk detection; attention mechanism (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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