PCA- and PLSR-Based Machine Learning Model for Prediction of Urea-N Content in Heterogeneous Soils Using Near-Infrared Spectroscopy.

Damiano Crescini, Gabriele Mascialino, Nicola Moggia, Giordano Piubeni, Mauro Serpelloni, Emilio Sardini

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Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

6 authors.

Damiano CresciniDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.ORCID 0000-0001-8882-3789

Gabriele MascialinoDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.

Nicola MoggiaDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.ORCID 0009-0003-9176-075X

Giordano PiubeniDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.

Mauro SerpelloniDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.ORCID 0000-0001-6497-5876

Emilio SardiniDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.ORCID 0000-0001-8629-7316

Funding

European Union and MIUR 2022P3JY7N "New sensors and measurement techniques for underground monitoring and map-ping"

6 · The paper itself

Abstract

Determining the soil's nitrogen supply accurately and quickly is essential for effective agricultural management. This study explores the use of near-infrared (NIR) spectroscopy combined with spectral pre-processing techniques (such as Savitzky-Golay filtering) and partial least squares regression (PLSR) to assess soil nitrogen levels. Six soil types of varying compositions, treated with different levels of Urea-N fertilizer, were examined. Nitrogen-specific NIR peaks were identified, and regression models were consequently developed. Through a comparison of the performance of the models, the most effective model for nitrogen detection was selected. In calibration, the models performed well, with high R

Indexed as

data models and Urea-Npartial least squares regression (PLSR)reflectance spectroscopysensorsvarious soil

Identifiers

PMID40648429

DOI10.3390/s25134176

PMCPMC12252165

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