Innovative Design of an Experimental Jasmine Flower Automated Picker System Using Vertical Gripper and YOLOv5

Abstract

Background: Recently, there has been a growing demand for the mechanization of flower harvesting to enhance harvesting efficiency. Therefore, the purpose of the current research was to design a jasmine flower automated picker system (JFAPS). The picking system incorporates a gripper that moves along the third vertical axis using an Intel depth camera and the You Only Look Once (YOLO-V5) deep learning system to locate and detect the flowers. Results: For different design cross-sections, it was observed that the least safe factor of design safety was considered high enough to marginalize any mechanical failure potential. Furthermore, according to the prediction box, the flowers’ center point on the pixel plane was detected, while the real vertical position of the flowers was computed using a deep learning system. Consequently, the gripper moves down to pick the flowers and convey them to the storage system. In these conditions, the detection method’s average precision and recall of flowers were 100% and 90%, respectively. Conclusions: The JFAPS was balanced and efficient in detecting flowers. Therefore, future efforts will be directed at evaluating this system and confirming its efficacy in collecting flowers on an experimental farm.