Research on the Tender Leaf Identification and Mechanically Perceptible Plucking Finger for High-quality Green Tea

TL;DR

This paper presents a tender leaf identification algorithm with over 92.8% accuracy and a mechanically perceptible plucking finger achieving a 92.5% success rate, advancing intelligent green tea harvesting technology.

Contribution

It introduces a novel tender leaf identification method and a feedback-controlled plucking finger, improving precision and quality in automated green tea harvesting.

Findings

Identification accuracy exceeds 92.8%.

Plucking success rate reaches 92.5%.

Design enables human-like plucking force control.

Abstract

BACKGROUND: Intelligent identification and precise plucking are the keys to intelligent tea harvesting robots, which are of increasing significance nowadays. Aiming at plucking tender leaves for high-quality green tea producing, in this paper, a tender leaf identification algorithm and a mechanically perceptible plucking finger have been proposed. RESULTS: Based on segmentation algorithm and color features, the tender leaf identification algorithm shows an average identification accuracy of over 92.8%. The mechanically perceptible plucking finger plucks tender leaves in a way that a human hand does so as to remain high quality of tea products. Though finite element analysis, we determine the ideal size of grippers and the location of strain gauge attachment on a gripper to enable the employment of feedback control of desired gripping force. Revealed from our experiments, the success rate of tender leaf plucking reaches 92.5%, demonstrating the effectiveness of our design. CONCLUSION: The results show that the tender leaf identification algorithm and the mechanically perceptible plucking finger are effective for tender leaves identification and plucking, providing a foundation for the development of an intelligent tender leaf plucking robot.