Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds
This work provides a proof of feasibility for individual tree-level apple counting, which is a significant challenge for orchard management and yield estimation.
This paper developed a 3D color point cloud processing pipeline to count apples on individual trees in dense orchards. By aligning winter (leaf-off) and harvest period point clouds, the method successfully assigned apples to their respective trees with an accuracy rate higher than 95%.
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.