Mature tomato recognition and location algorithm based on binocular vision and deep learning

Abstract

This paper proposes a method combining binocular vision and deep learning to identify and locate ripe tomatoes in greenhouses. First, the CBAM attention mechanism module is added to the YOLO V3 model to improve the robustness of the YOLO V3 model to the greenhouse environment, and then the tomato results identified by the improved YOLOV3 CBAM are fused with the three-dimensional information obtained by the binocular stereo camera. to obtain the threedimensional position information of the tomato fruit. After testing, the model has an accuracy of 89.15% for tomato recognition, the AP is 86.17%, and the F1 value is 82%. The relative error of the tomato fruit positioning is less than 1.5%. Finally, the model was arranged in the greenhouse to test the tomato picking robot, which verifies the practicability of the method.