Development, Integration, and Field Experiment Optimization of an Autonomous Banana-Picking Robot

Abstract

The high growth height and substantial weight of bananas present challenges for robots to harvest autonomously. To address the issues of high labor costs and low efficiency in manual banana harvesting, a highly autonomous and integrated banana-picking robot is proposed to achieve autonomous harvesting of banana bunches. A prototype of the banana-picking robot was developed, featuring an integrated end-effector capable of clamping and cutting tasks on the banana stalks continuously. To enhance the rapid and accurate identification of banana stalks, a target detection vision system based on the YOLOv5s deep learning network was developed. Modules for detection, positioning, communication, and execution were integrated to successfully develop a banana-picking robot system, which has been tested and optimized in multiple banana plantations. Experimental results show that this robot can continuously harvest banana bunches. The average precision of detection is 99.23%, and the location accuracy is less than 6 mm. The robot picking success rate is 91.69%, and the average time from identification to harvesting completion is 33.28 s. These results lay the foundation for the future application of banana-picking robots.

Keywords

• banana
• picking robot
• end-effector
• stalk detection
• harvesting optimization