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This paper focuses on the problem that existing apple picking robots are unable to accurately recognize targets in complex and unstructured orchard environments. The target detection algorithm of the robot is optimized based on YOLOv5 and ResNet neural network model. First, considering the low resolution of the robot's own binocular camera, this paper adopts the RealSR model to process the image with super-resolution. Then, the YOLOv5 model is chosen as the optimized target detection algorithm, which can obtain the number, location, ripeness and quality information of apples in the image through training. Secondly, by comparing the VGG and ResNet models, which are widely used in the field of image classification, the ResNet model is finally chosen as the main body for recognizing and classifying different fruits, and its correct rate can reach more than 97%.
Yang et al. (Tue,) studied this question.