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Related Experiment Video

Updated: Sep 16, 2025

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

Damiano Crescini1, Gabriele Mascialino1, Nicola Moggia1

  • 1Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

Near-infrared (NIR) spectroscopy with multivariate models offers a rapid method for assessing soil nitrogen. The first derivative (FD) model showed superior predictive ability for nitrogen detection across diverse soil types.

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

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Accurate and rapid soil nitrogen assessment is critical for efficient agricultural management.
  • Traditional methods for soil nitrogen analysis can be time-consuming and labor-intensive.

Purpose of the Study:

  • To evaluate the effectiveness of near-infrared (NIR) spectroscopy combined with multivariate analysis for quantifying soil nitrogen.
  • To compare different spectral pre-processing techniques and regression models for optimal nitrogen detection.

Main Methods:

  • Utilized near-infrared (NIR) spectroscopy on six soil types with varying Urea-N fertilizer levels.
  • Applied spectral pre-processing techniques, including Savitzky-Golay filtering and derivative spectroscopy (first and second derivative).
  • Developed partial least squares regression (PLSR) models for nitrogen quantification and assessed model performance using calibration and validation.

Main Results:

  • Both first derivative (FD) and second derivative (SD) based PLSR models demonstrated high accuracy during calibration (R² > 0.9).
  • During validation, the FD model exhibited superior predictive ability (R² = 0.77, RPD = 2.06) compared to the SD model (R² = 0.65, RPD = 1.77).
  • The study achieved real-time online detection capability with low computational cost across multiple soil types.

Conclusions:

  • Near-infrared (NIR) spectroscopy, coupled with multivariate modeling (specifically FD-PLSR), is a promising tool for rapid and accurate soil nitrogen assessment.
  • The developed method offers advantages over traditional offline approaches, providing real-time data for improved agricultural management.
  • Validated model performance across diverse soil types, enhancing its practical applicability in various agricultural settings.