2020/12/26 by Mouad Zine-El-Abidine, Helin Dutağacı, Helin Dutagaci +6
Agricultural and Biological Sciences · Computer Science · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Horticultural and Viticultural Research #Remote Sensing and LiDAR Applications #Smart Agriculture and AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2012.13721
arxiv created 2020/12/26 · openalex publication_date 2020/12/26 · arxiv updated 2020/12/29 · openalex created_date 2021/01/05 · openalex updated_date 2026/08/01
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.