LGVM-YOLOv8n: A Lightweight Apple Instance Segmentation Model for Standard Orchard Environments

Wenkai Han, Tao Li, Zhengwei Guo, Tao Wu, Wenlei Huang, Qingchun Feng and Liping Chen ()
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Wenkai Han: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Tao Li: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Zhengwei Guo: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Tao Wu: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Wenlei Huang: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Qingchun Feng: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Liping Chen: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China

Agriculture, 2025, vol. 15, issue 12, 1-21

Abstract: Accurate fruit target identification is crucial for autonomous harvesting robots in complex orchards, where image segmentation using deep learning networks plays a key role. To address the trade-off between segmentation accuracy and inference efficiency, this study proposes LGVM-YOLOv8n, a lightweight instance segmentation model based on YOLOv8n-seg. LGVM is an acronym for lightweight, GSConv, VoVGSCSP, and M P D I o U , highlighting the key improvements incorporated into the model. The proposed model integrates three key improvements: (1) the GSConv module, which enhances feature interaction and reduces computational cost; (2) the VoVGSCSP module, which optimizes multi-scale feature representation for small objects; and (3) the M P D I o U loss function, which improves target localization accuracy, particularly for occluded fruits. Experimental results show that LGVM-YOLOv8n reduces computational cost by 9.17%, decreases model weight by 7.89%, and improves inference speed by 16.9% compared to the original YOLOv8n-seg. Additionally, segmentation accuracy under challenging conditions (front-light, back-light, and occlusion) improves by 3.28% to 4.31%. Deployment tests on an edge computing platform demonstrate real-time performance, with inference speed accelerated to 0.084 s per image and frame rate increased to 28.73 FPS. These results validated the model’s robustness and adaptability, providing a practical solution for apple-picking robots in complex orchard environments.

Keywords: deep learning; edge computing; apple harvesting; real-time inference (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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