DESIGN AND EXPERIMENT OF RECOGNITION SYSTEM FOR COATED RED CLOVER SEEDS BASED ON MACHINE VISION

Abstract

While studying the coating theory, due to the lack of the support of the rapid identification and detection device for coated red clover seeds, for a long time, we have mainly relied on manual visual inspection to sort qualified coated seeds, only relying on human eyes to identify the cause of low efficiency, high wrong classification rate and high labor intensity. In order to identify the coated red clover seeds quickly and efficiently, a set of intelligent identification and detection system for coated red clover seeds was designed. First of all, by building a machine vision shooting platform to ensure that the light source and other shooting conditions are consistent, the images are transmitted to Vision Assistant 2018 for image processing. Secondly, two image processing algorithms are designed to process qualified coated seeds and damaged coated seeds respectively. Finally, an identification and detection algorithm is proposed, which uses LabVIEW2018 as the host computer to identify the qualified number and the damaged number. Taking red clover seeds as the test object, the test results show that the entire system takes about 1 second to collect and process a single image; the recognition accuracy of qualified coated seeds and damaged coated seeds is above 96% and 85%. The identification and detection system realizes the nondestructive detection of coated seeds, and provides theoretical basis and technical support for the later research on the optimal seed coating process, deepening the theoretical research of the coating machine and improving the degree of automation.