Tomato detection based on convolutional neural network for robotic application

The development of agricultural robots and the promotion of agricultural production automation are important means to alleviate the shortage of agricultural labor. Fruit and vegetable detection is a prerequisite for accurate harvesting by robots. It directly determines the efficiency and quality of harvesting operations. In order to meet the requirements of target positioning and recognition of tomato harvesting robots, this paper studies tomato recognition technology based on YOLOv3 convolutional neural network algorithm. And the tomato detection process of the YOLOv3 model is presented. The YOLOv3 model training dataset is constructed based on greenhouse tomato plants. The test results show that the model based on the YOLOv3 convolutional neural network has a better detection effect.