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

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.

Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods

Segmentation of structural parts of 3D models of plants is an important step for plant phenotyping, especially for monitoring architectural and morphological traits. This work introduces a benchmark for assessing the performance of 3D point-based deep learning methods on organ segmentation of 3D plant models, specifically rosebush models. Six recent deep learning architectures that segment 3D point clouds into semantic parts were adapted and compared. The methods were tested on the ROSE-X data set, containing fully annotated 3D models of real rosebush plants. The contribution of incorporating synthetic 3D models generated through Lindenmayer systems into training data was also investigated.