Seeing the Fruit for the Leaves: Towards Automated Apple Fruitlet Thinning

TL;DR

This paper introduces a vision system using stereo cameras and a robotic arm to accurately count and size apple fruitlets, enabling automated thinning and addressing labor shortages in orchard management.

Contribution

The paper presents the design, implementation, and evaluation of an automated vision system for apple fruitlet thinning using a robotic platform with stereo cameras.

Findings

Achieved 84% accuracy in measuring fruitlet load in real orchards.

System demonstrated 87% precision in fruitlet size estimation.

Enabled automated thinning decisions reducing reliance on skilled labor.

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.