Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance
arXiv:2607.00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing. This work proposes a machine learning (ML) approach to calibrate predictive models for carbon (C) and nitrogen (N) content in Oxisols and Inceptisols, utilizing NIR spectral data acquired with a portable MyNIR device. Various preprocessing methods were evaluated, with the most effective being the Savitzky-Golay (SG) filter and a robust outlier removal method based o
The increasing maturity of machine learning techniques combined with accessible, portable spectroscopy devices is enabling more efficient and non-destructive agricultural analysis.
This research outlines a method to quantify critical soil nutrients like carbon and nitrogen quickly and affordably, which is vital for sustainable agriculture, climate modeling, and optimized land management.
The ability to rapidly and cost-effectively assess soil composition could significantly alter agricultural practices, moving towards more data-driven and precise nutrient management.
- · Agricultural technology companies
- · Farmers in developing regions
- · Environmental monitoring agencies
- · Precision agriculture sector
- · Traditional soil testing laboratories
- · Chemical fertilizer manufacturers (if optimization reduces demand)
More efficient and cost-effective soil nutrient analysis becomes widely accessible.
Improved soil health and agricultural yields due to better nutrient management, potentially influencing food security and carbon sequestration efforts.
The democratization of advanced agricultural diagnostics could empower small-scale farmers and accelerate sustainable land use practices globally.
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