A Lightweight Citrus Object Detection Method in Complex Environments

Qiurong Lv, Fuchun Sun (), Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li and Jie Zhou
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Qiurong Lv: School of Mechanical Engineering, Chengdu University, Chengdu 610106, China
Fuchun Sun: School of Mechanical Engineering, Chengdu University, Chengdu 610106, China
Yuechao Bian: School of Mechanical Engineering, Chengdu University, Chengdu 610106, China
Haorong Wu: School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China
Xiaoxiao Li: School of Mechanical Engineering, Chengdu University, Chengdu 610106, China
Xin Li: Shengzhong Water Conservancy Project Operation and Management Center of Sichuan Province, Nanchong 623300, China
Jie Zhou: School of Mechanical Engineering, Chengdu University, Chengdu 610106, China

Agriculture, 2025, vol. 15, issue 10, 1-23

Abstract: Aiming at the limitations of current citrus detection methods in complex orchard environments, especially the problems of poor model adaptability and high computational complexity under different lighting, multiple occlusions, and dense fruit conditions, this study proposes an improved citrus detection model, YOLO-PBGM, based on You Only Look Once v7 (YOLOv7). First, to tackle the large size of the YOLOv7 network model and its deployment challenges, the PC-ELAN module is constructed by introducing Partial Convolution (PConv) for lightweight improvement, which reduces the model’s demand for computing resources and parameters. At the same time, the Bi-Former attention module is embedded to enhance the perception and processing of citrus fruit information. Secondly, a lightweight neck network is constructed using Grouped Shuffle Convolution (GSConv) to simplify computational complexity. Finally, the minimum-point-distance-based IoU (MPDIoU) loss function is utilized to optimize the boundary return mechanism, which speeds up model convergence and reduces the redundancy of bounding box regression. Experimental results indicate that for the citrus dataset collected in a natural environment, the improved model reduces Params and GFLOPs by 15.4% and 23.7%, respectively, while improving precision, recall, and mAP by 0.3%, 4%, and 3.5%, respectively, thereby outperforming other detection networks. Additionally, an analysis of citrus object detection under varying lighting and occlusion conditions reveals that the YOLO-PBGM network model demonstrates good adaptability, effectively coping with variations in lighting and occlusions while exhibiting high robustness. This model can provide a technical reference for uncrewed intelligent picking of citrus.

Keywords: citrus; attention mechanism; machine vision; YOLOv7; deep learning (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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