Machine learning for estimating leaf area index in almond orchards using high-resolution multispectral and point-cloud data

C. Minuesa; M. Quintanilla-Albornoz; A. Pelechá; J. Gené-Mola; L. Aparicio; J. Bellvert

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Machine learning for estimating leaf area index in almond orchards using high-resolution multispectral and point-cloud data

In: Precision agriculture '25

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C. Minuesa

C. Minuesa
cesar.minuesa@irta.cat

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M. Quintanilla-Albornoz

M. Quintanilla-Albornoz

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A. Pelechá

A. Pelechá

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J. Gené-Mola

J. Gené-Mola

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L. Aparicio

L. Aparicio

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J. Bellvert

J. Bellvert

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Type:: Chapter

Pages:: 569–577

DOI:: https://doi.org/10.1163/9789004725232_074

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Dar Hadith al Hassania

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In irrigation management, the leaf area index (LAI) is crucial to understanding the crop energy flux distribution. This study proposed an indirect method to estimate LAI by combining UAV-derived multispectral and point-cloud data with machine learning (ML) algorithms. Fractional intercepted photosynthetically active radiation (fiPAR) measurements from two almond orchards with varying ages and water regimes were used to develop models. Random forest (RF) and artificial neural network (ANN) were evaluated. Results showed ANN outperformed RF, achieving higher accuracy (R²=0.94, RMSE=0.07 m²/m²) on the test dataset and better generalization across the orchards.

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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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Machine learning for estimating leaf area index in almond orchards using high-resolution multispectral and point-cloud data

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Precision agriculture '25

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