Design of intelligent sorting system of Pleurotus eryngii based on ROS and deep learning

In order to solve the problem of time-consuming, inefficiency and low accuracy of manual sorting of Pleurotus eryngii, this paper proposes an intelligent sorting method and robot intelligent sorting system for Pleurotus eryngii, which is dual-model parallel method of grading detection and grasp detection based on deep learning. The depth camera is used to collect the images of Pleurotus eryngii, and the robot is used as the sorting actuator. The intelligent sorting control software is designed based on ROS（Robot Operating System）, python and C++ language, and the monitoring and management system is designed based on PyQt. The test results show that the sorting system can automatically realize the grading detection of Pleurotus eryngii and the robot sorting and grasping. The sorting detection of single Pleurotus eryngii takes 18 ms, and the average accuracy of grading detection is 88.35%, the success rate of robot grasping is 98.33%, and the success rate of intelligent sorting is 88.35%, which confirms the feasibility and effectiveness of the system as a whole. It provides a new solution for the whole realization of intelligent sorting system of other agricultural products.