Seeing the Fruit for the Leaves: Robotically Mapping Apple Fruitlets in a Commercial Orchard

TL;DR

This paper presents an automated vision system for mapping apple fruitlets in orchards, enabling accurate crop load measurement to support thinning and improve orchard management amid labor shortages.

Contribution

It introduces a novel robotic platform capable of two-sided scanning of apple trees, achieving high accuracy in fruitlet counting and size estimation.

Findings

Two-sided scans yield 81.17% counting accuracy.

System estimates fruitlet size within 5.9% RMSE.

Platform effectively maps fruitlets in dense foliage.

Abstract

Aotearoa New Zealand has a strong and growing apple industry but struggles to access workers to complete skilled, seasonal tasks such as thinning. To ensure effective thinning and make informed decisions on a per-tree basis, it is crucial to accurately measure the crop load of individual apple trees. However, this task poses challenges due to the dense foliage that hides the fruitlets within the tree structure. In this paper, we introduce the vision system of an automated apple fruitlet thinning robot, developed to tackle the labor shortage issue. This paper presents the initial design, implementation,and evaluation specifics of the system. The platform straddles the 3.4 m tall 2D apple canopy structures to create an accurate map of the fruitlets on each tree. We show that this platform can measure the fruitlet load on an apple tree by scanning through both sides of the branch. The requirement of an overarching platform was justified since two-sided scans had a higher counting accuracy of 81.17 % than one-sided scans at 73.7 %. The system was also demonstrated to produce size estimates within 5.9% RMSE of their true size.