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

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.
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https://hal.inrae.fr/hal-03122633
Contributor : Olivier Dupre <>
Submitted on : Wednesday, January 27, 2021 - 10:52:51 AM
Last modification on : Friday, February 5, 2021 - 3:58:36 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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  • HAL Id : hal-03122633, version 1
  • ARXIV : 2012.13721

Citation

Mouad Zine-El-Abidine, Helin Dutagaci, Gilles Galopin, David Rousseau. Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds. 2021. ⟨hal-03122633⟩

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