Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds

TL;DR

This paper introduces a 3D color point cloud processing pipeline that accurately assigns apples to individual trees in dense orchards by leveraging winter branch structure and harvest period apple localization, achieving over 95% accuracy.

Contribution

The paper presents a novel method for assigning apples to trees in dense orchards using 3D point clouds, combining winter branch delineation with harvest period apple localization.

Findings

Achieves over 95% accuracy in apple-to-tree assignment.

Demonstrates feasibility of using 3D point clouds for orchard management.

Provides suggestions for pipeline improvements.

Abstract

We propose a 3D color point cloud processing pipeline to count apples on individual apple trees in trellis structured orchards. Fruit counting at the tree level requires separating trees, which is challenging in dense orchards. We employ point clouds acquired from the leaf-off orchard in winter period, where the branch structure is visible, to delineate tree crowns. We localize apples in point clouds acquired in harvest period. Alignment of the two point clouds enables mapping apple locations to the delineated winter cloud and assigning each apple to its bearing tree. Our apple assignment method achieves an accuracy rate higher than 95%. In addition to presenting a first proof of feasibility, we also provide suggestions for further improvement on our apple assignment pipeline.