Seeing the Fruit for the Leaves: Towards Automated Apple Fruitlet Thinning
Abstract
Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards. One of the primary challenges is maintaining skilled human operators capable of making precise fruitlet thinning decisions. Thinning requires accurately measuring the true crop load for individual apple trees to provide optimal thinning decisions on an individual basis. A challenging task due to the dense foliage obscuring the fruitlets within the tree structure. This paper presents the initial design, implementation, and evaluation details of the vision system for an automatic apple fruitlet thinning robot to meet this need. The platform consists of a UR5 robotic arm and stereo cameras which enable it to look around the leaves to map the precise number and size of the fruitlets on the apple branches. We show that this platform can measure the fruitlet load on the apple tree to with 84% accuracy in a real-world commercial apple orchard while being 87% precise.
Keywords
Cite
@article{arxiv.2302.09716,
title = {Seeing the Fruit for the Leaves: Towards Automated Apple Fruitlet Thinning},
author = {Ans Qureshi and Neville Loh and Young Min Kwon and David Smith and Trevor Gee and Oliver Bachelor and Josh McCulloch and Mahla Nejati and JongYoon Lim and Richard Green and Ho Seok Ahn and Bruce MacDonald and Henry Williams},
journal= {arXiv preprint arXiv:2302.09716},
year = {2023}
}
Comments
Accepted and Presented at the Australasian Conference on Robotics and Automation (ACRA 2022)