Extraction and Recognition of Robotic Apple Picking Image Features Based on YOLOv5 Detection Models

Abstract

As China has emerged as one of the leading exporters of apples globally, the shortage of agricultural labor has posed a significant challenge for the apple industry's growth. To address the issue of image recognition for robotic apple picking in complex orchard settings, this paper combines computerized image processing and deep learning concepts to propose a detection model based on YOLOv5. By implementing image preprocessing techniques and optimizing the loss function, the study successfully achieves accurate extraction and recognition of apple image features. The experimental findings demonstrate the high performance and accuracy of the proposed method in apple picking tasks, offering valuable support for the advancement of robotic automated picking systems. Future research endeavors will focus on further refining the algorithm to enhance efficiency in real-world production settings. The improved OLOv5 Detection Models proposed in this article can be applied in fields such as industrial detection, intelligent traffic signal control, and sports events.