  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.
