Application research of outdoor cherry tomato recognition based on YOLOv10

To solve the problem of low recognition accuracy of outdoor orchard, it is greatly affected by outdoor light and other environments. In this paper, based on the deep learning neural network YOLOv9 algorithm, the cherry tomatoes in the outdoor orchard are accurately identified. In this paper, YOLOV5, YOLOV7, YOLOv8 YOLOV9, and YOLOv10 algorithms were used to detect mature cherry tomato fruits, immature cherry tomato fruits and damaged cherry tomato fruits. Compared with other YOLO series algorithms, YOLOv10 algorithm has higher detection accuracy and is less affected by outdoor light. Through the comparative test analysis, the mAP value of the model is 82.34%, the F1 value is 79.91%, the accuracy is 86.82%, the recall rate is 74.02%, the detection rate is 30.3 frames/s, and the model volume is 15.97 MB. It meets the requirements of outdoor detection and can provide identification basis for outdoor cherry tomato picking technology.