From simulation to field: a ground truth-free approach for 3D orchard monitoring

H. Murcia Moreno; S. Lacroix

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From simulation to field: a ground truth-free approach for 3D orchard monitoring

In: Precision agriculture '25

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H. Murcia Moreno

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S. Lacroix

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Type:: Chapter

Pages:: 553–560

DOI:: https://doi.org/10.1163/9789004725232_072

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Dar Hadith al Hassania

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Lack of annotated data as well as model transfer challenges limit accurate structural analysis of 3D orchards in field conditions by LiDAR and machine learning techniques. This study explores a scalable framework that trains deep learning models on synthetic labelled data. It then applies them to unlabelled real point clouds acquired with an unmanned ground vehicle in an apple orchard. Integrating classification, skeletonisation, and contrastive learning the framework segments trees, generates structural maps, and detects anomalies without ground-truth annotations. These results suggest potential contributions to flexible orchard monitoring by supporting variability analysis based on descriptors derived from the structural representations of trees.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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From simulation to field: a ground truth-free approach for 3D orchard monitoring

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Precision agriculture '25

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