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