Study on Recognition and Location Technology of Tomato in Facility Agriculture

Abstract

In order to recognize and detect tomatoes for providing accurate location information for tomato picking robot under the complex environment of facility greenhouse, the recognition and detection method based on YOLOV5 was adopted in this paper. The data enhancement method was used to improve the generalization ability of network model. The binocular camera was also used to collect images to match and calculate the central pixel of the detected tomatoes, according to the binocular ranging principle. At the same time, the parallax value of the detected tomatoes was compared with the real value in different environments. It is proved that the mAP of YOLOV5 method is 96%, the absolute value of stereo matching error is less than 3 pixels, and the matching time of single image is less than 10ms, which effectively improves the accuracy and efficiency of picking robot.