Simulation of LiDAR, unmanned ground vehicles, and neural networks for leaf area estimation in orchards

H. Murcia Moreno; S. Lacroix

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Simulation of LiDAR, unmanned ground vehicles, and neural networks for leaf area estimation in orchards

In: Precision agriculture '25

Authors:

H. Murcia Moreno

H. Murcia Moreno

hfmurciamo@laas.fr

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S. Lacroix

S. Lacroix

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Type:: Chapter

Pages:: 561–568

DOI:: https://doi.org/10.1163/9789004725232_073

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Dar Hadith al Hassania

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The leaf area index (LAI) is vital for assessing plant photosynthetic activity, crucial for optimising orchard management. This study presents a method to estimate leaf area density (LAD) variations and tree LAI using LiDAR data from unmanned ground vehicles (UGVs). Combining 3D tree reconstruction with neural network-based analysis of LiDAR penetration descriptors, the approach effectively estimates canopy parameters. The method was validated through simulation using diverse 3D canopy models, achieving performance metrics: RMSE=0.2 m²/m³, R²=0.95 for LAD and RMSE=0.17 m²/m², R²=0.84 for LAI. Results confirm the potential of LiDAR-based systems for precise orchard canopy monitoring.

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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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Simulation of LiDAR, unmanned ground vehicles, and neural networks for leaf area estimation in orchards

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Precision agriculture '25

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