Bayesian machine learning to quantify the uncertainty in nitrogen nutrition index using UAV-based imagery and weather data

T.S.T. Tanaka; R. Gislum

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Bayesian machine learning to quantify the uncertainty in nitrogen nutrition index using UAV-based imagery and weather data

In: Precision agriculture '25

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T.S.T. Tanaka

T.S.T. Tanaka takashi@agro.au.dk

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R. Gislum

R. Gislum
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Type:: Chapter

Pages:: 426–430

DOI:: https://doi.org/10.1163/9789004725232_054

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Dar Hadith al Hassania

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Nitrogen nutrition index (NNI) is commonly used to assess crop nitrogen (N) status, but uncertainty in predicted NNI using remote sensing technologies has not been fully addressed. This study quantified uncertainty in UAV-based NNI predictions for winter wheat by combining machine learning with Bayesian critical N dilution curves. A partial least squares regression model was developed using various covariates, including reflectance, vegetation indices, and weather data. Results showed significant uncertainty in predicted NNI, with the 97.5th percentile often exceeding one, highlighting the limitations of conventional point-based predictions. The combination of machine learning and Bayesian approach offers more reliable uncertainty analysis for better N management decisions.

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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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Bayesian machine learning to quantify the uncertainty in nitrogen nutrition index using UAV-based imagery and weather data

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Precision agriculture '25

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