Research on Detection Algorithm of Green Walnut in Complex Environment

Chenggui Yang, Zhengda Cai, Mingjie Wu, Lijun Yun (), Zaiqing Chen and Yuelong Xia
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Chenggui Yang: School of Information, Yunnan Normal University, Kunming 650500, China
Zhengda Cai: Yunnan Woody Oilseed (Walnut) Full Industry Chain Innovation Research Institute, Kunming 650500, China
Mingjie Wu: School of Information, Yunnan Normal University, Kunming 650500, China
Lijun Yun: School of Information, Yunnan Normal University, Kunming 650500, China
Zaiqing Chen: School of Information, Yunnan Normal University, Kunming 650500, China
Yuelong Xia: School of Information, Yunnan Normal University, Kunming 650500, China

Agriculture, 2024, vol. 14, issue 9, 1-22

Abstract: The growth environment of green walnuts is complex. In the actual picking and identification process, interference from near-background colors, occlusion by branches and leaves, and excessive model complexity pose higher demands on the performance of walnut detection algorithms. Therefore, a lightweight walnut detection algorithm suitable for complex environments is proposed based on YOLOv5s. First, the backbone network is reconstructed using the lightweight GhostNet network, laying the foundation for a lightweight model architecture. Next, the C3 structure in the feature fusion layer is optimized by proposing a lightweight C3 structure to enhance the model’s focus on important walnut features. Finally, the loss function is improved to address the problems of target loss and gradient adaptability during training. To further reduce model complexity, the improved algorithm undergoes pruning and knowledge distillation operations, and is then deployed and tested on small edge devices. Experimental results show that compared to the original YOLOv5s model, the improved algorithm reduces the number of parameters by 72.9% and the amount of computation by 84.1%. The mAP0.5 increased by 1.1%, the precision increased by 0.7%, the recall increased by 0.3%, and the FPS is 179.6% of the original model, meeting the real-time detection needs for walnut recognition and providing a reference for walnut harvesting identification.

Keywords: object detection; model; YOLOv5s; GhostNet; lightweight; walnut picking (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: 2024
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