In order to solve the problems of high cost,high labor intensity and slow speed of manual inspection in traditional agricultural production mode,a cherry tomatoes greenhouse inspection robot system was designed,which integrated automatic control technology,machine vision technology and deep learning technology to achieve accurate identification and counting of cherry tomatoes.The robot's automatic navigation function is realized by extracting the working path through machine vision and avoiding the obstacle by infrared sensor.An improved YOLOv4-tiny model was proposed to detect cherry tomatoes.The model added 52×52 feature layer and attention mechanism on the basis of YOLOv4-tiny to improve the recognition accuracy of small target detection.The test results show that the inspection robot can accurately work along the predetermined path when traveling at a speed of 0.2m/s.The average recognition accuracy of the improved YOLOv4-tiny-Tomato model for mature and immature cherry tomatoes in the greenhouse environment is 98.7%and 98.0%,and the F1 value reaches 96.71%and 95.8%,respectively.The real-time frame rate reaches 36 frames/s.This research can provide new ideas and technical support for intelligent inspection and fine management in agricultural field.
维普
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