A fast multi-source target recognition system for Dangshan pear based on lightweight “graph neural network - YOLOv5s”

China's Anhui Dangshan pear has a sweet and creamy taste and is a favorite product of consumers. However, it faces low economic added value and weak competitiveness, mainly due to the backwardness of post-production quality detection and grading and information management technology to address the above problems, the project integrates graph neural networks and YOLOv5s to construct a multi-source image detection system for the fruit to be tested combines graph neural networks to complete the comprehensive inversion of physicochemical properties, appearance and other physicochemical quality parameters utilizes the characteristics of YOLOv5s, which occupies little memory and has a fast recognition rate, to accelerate the rapid identification of the target fruit. The characteristics of YOLOv5s, which occupies little memory and has a fast recognition rate, are utilized to accelerate the rapid recognition of target fruits. Experimental results show that in the process of batch picture recognition, the average recognition rate of a single picture is about 0.02 seconds, and the recognition accuracy reaches 99.41 . At the same time to ensure that the production line fast and robust operation, the establishment of Dangshan pear multi-source information recognition system research and development, and steadily promote the quality and value-added fruit industry, and promote the rapid development of the regional economy.