3D multi-temporal reconstruction of proximal sensing data for vineyard monitoring

A. Dib; M. Mabrouk; F. Baldacci; J.P. Da Costa

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3D multi-temporal reconstruction of proximal sensing data for vineyard monitoring

In: Precision agriculture '25

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A. Dib

A. Dib
anna-louize.dib@u-bordeaux.fr

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M. Mabrouk

M. Mabrouk

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F. Baldacci

F. Baldacci

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J.P. Da Costa

J.P. Da Costa

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Type:: Chapter

Pages:: 445–452

DOI:: https://doi.org/10.1163/9789004725232_057

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Dar Hadith al Hassania

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Precision agriculture relies on continuous crop monitoring to optimize management, particularly in perennial crops like vineyards. This study explored the use of mobile robotics equipped with cameras to collect weekly RGB-D data in a vineyard in Bordeaux, France, enabling the tracking of key plant structures across the growing season. In particular, a novel algorithm, sliding sequential global/local registration (SS-GLR), was developed to align point clouds with sub-centimetric accuracy to reconstruct 3D models of vines at multiple phenological stages, addressing reconstruction challenges such as dense foliage and variable lighting changing from one week to another. The algorithm was validated using a homemade protocol, demonstrating its ability to generate precise 3D reconstructions. These findings open up new perspectives in multi-temporal vegetation monitoring for precision agriculture or phenotyping.

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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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3D multi-temporal reconstruction of proximal sensing data for vineyard monitoring

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Precision agriculture '25

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