Tomato identification and picking point location based on YOLOv5

The traditional tomato detection method of image segmentation is complex, and it is easy to be blocked by branches and leaves, fruit overlapping and other reasons, which affect the detection accuracy of fruit and the accurate positioning of picking points. This study proposes a fast identification and localization method of tomato based on YOLOv5 network. This method performs end-to-end detection by traversing the entire image with a single convolutional neural network, returning the class and location of the object. On the basis of YOLOv5, the regression box loss function is modified to improve the detection effect of tomato fruit, and the center point of the fruit boundary rectangle detected by YOLOv5 is used as the center point of tomato picking. The experimental results show that the average localization error of the proposed method is 1.379%, which is 1.867% lower than the traditional Hough method. The YOLOv5 method can effectively identify tomato fruits in natural environment. It can effectively detect tomatoes in overlapping, small targets, immature and other scenes, and perform more accurate positioning, laying a foundation for the tomato picking robot to select the best picking point.