Tracking and Counting Method for Tomato Fruits Scouting Robot in Greenhouse

Abstract

The quantity of tomatoes is closely related to their yield information, and a powerful inspection robot that can automatically count tomatoes is urgently necessary for the hot and harsh environment of greenhouses. With the continuous progress of computer vision technology, the use of deep learning algorithms for counting tomatoes can greatly improve the inspection speed of the inspection robot. This paper propose a tomato fruit counting method for greenhouse inspection robots, which tracks the position of tomatoes in the image by the spatial displacement information of the robot, while 3D depth filtering can effectively avoid the interference of background tomatoes on the counting. The main advantages of this method are: (1) it can realize the tracking of bunched fruits and the counting of single fruits at the same time; (2) it can avoid the interference of background tomatoes. The experimental results of the greenhouse showed that the accuracy rates of bunch and single fruit counting were higher than 84% and 86% respectively, which greatly improved the inspection speed compared with manual counting and basically meet the counting requirements of the current greenhouse.