Lessons Learnt from Field Trials of a Robotic Sweet Pepper Harvester

TL;DR

This paper reports on field trials of a robotic sweet pepper harvester that combines vision algorithms and a novel end-effector to successfully detect, grasp, and harvest peppers in protected cropping environments, showing promising results.

Contribution

It introduces a new robotic harvesting system with innovative vision and end-effector designs, demonstrating effective autonomous sweet pepper harvesting in real-world conditions.

Findings

Successful detection, grasping, and detachment of sweet peppers in field trials

Significant improvement in harvesting success rates over existing methods

Potential for reducing labour costs and increasing crop quality

Abstract

In this paper, we present the lessons learnt during the development of a new robotic harvester (Harvey) that can autonomously harvest sweet pepper (capsicum) in protected cropping environments. Robotic harvesting offers an attractive potential solution to reducing labour costs while enabling more regular and selective harvesting, optimising crop quality, scheduling and therefore profit. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. We demonstrate a simple and effective vision-based algorithm for crop detection, a grasp selection method, and a novel end-effector design for harvesting. To reduce the complexity of motion planning and to minimise occlusions we focus on picking sweet peppers in a protected cropping environment where plants are grown on planar trellis structures. Initial field trials in protected cropping environments, with two cultivars, demonstrate the efficacy of this approach. The results show that the robot harvester can successfully detect, grasp, and detach crop from the plant within a real protected cropping system. The novel contributions of this work have resulted in significant and encouraging improvements in sweet pepper picking success rates compared with the state-of-the-art. Future work will look at detecting sweet pepper peduncles and improving the total harvesting cycle time for each sweet pepper. The methods presented in this paper provide steps towards the goal of fully autonomous and reliable crop picking systems that will revolutionise the horticulture industry by reducing labour costs, maximising the quality of produce, and ultimately improving the sustainability of farming enterprises.