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Machine learning enhances soil analysis for carbon and nitrogen quantification

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a machine learning approach using Near-Infrared (NIR) spectroscopy to quantify carbon and nitrogen content in Inceptisol and Oxisol soil types. The study evaluated various preprocessing techniques, with the Savitzky-Golay filter and a robust outlier removal method proving most effective. Ensemble learning models, including Partial Least Squares (PLS), Support Vector Regression (SVR), and Ridge, achieved an RPD greater than 2.0 with low overfitting, demonstrating the potential for rapid soil analysis to support sustainable agriculture. AI

IMPACT This research could lead to more efficient and environmentally friendly agricultural practices through faster soil analysis.

RANK_REASON The item is an academic paper detailing a new methodology for soil analysis using machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Machine learning enhances soil analysis for carbon and nitrogen quantification

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The item is an academic paper detailing a new methodology for soil analysis using machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Vinicius Herique Kieling, Guilherme Macedo Baggio, Felipe Augusto Bueno Rossi, Marco Antonio de Castro Barbosa, Dalcimar Casanova, Larissa Macedo dos Santos Tonial, Jefferson Tales Oliva ·

    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) appro…

  2. arXiv cs.LG TIER_1 English(EN) · Jefferson Tales Oliva ·

    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

    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…

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