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Replication Data for: Comprehensive Nutrient Analysis in Agricultural Organic Amendments Through Non-Destructive Assays Using Machine Learning

World Agroforestry - Research Data Repository Dataverse OAI Archive

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Title Replication Data for: Comprehensive Nutrient Analysis in Agricultural Organic Amendments Through Non-Destructive Assays Using Machine Learning
 
Identifier https://doi.org/10.34725/DVN/YTJTZQ
 
Creator Towett, Erick K.
Drake, Lee B.
Acquah, Gifty E.
Haefele, Stephan M.
McGrath, Steve P.
Shepherd Keith D.
 
Publisher World Agroforestry - Research Data Repository
 
Description Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments.
 
Subject Agricultural Sciences
Spectroscopy
Machine Learning
Mid-infrared spectra
Portable X-ray fluorescence (pXRF) spectra
Organic amendments
Nutrient analysis
Analysis
 
Language English
 
Contributor Towett, Erick
World Agroforestry
Bill and Melinda Gates Foundation
UK Biotechnology and Biological Sciences Research Council
Water, Land and Ecosystems
 
Type Portable X-ray fluorescence (pXRF) Data
Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy data