EDXRF and Machine Learning for Predicting Soil Fertility Attributes

EDXRF and Machine Learning for Predicting Soil Fertility Attributes

Soil fertility evaluation is fundamental for sustainable agricultural practices, often relying on conventional laboratory methods. These methods, while accurate, are labor-intensive, time-consuming, and require chemical reagents. Spectroscopic sensors, such as energy-dispersive X-ray fluorescence (...

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Bibliographic Details
Main Authors: José Vinícius Ribeiro, Felipe Rodrigues dos Santos, José Vitor de Oliveira Alves, Mariana Spinardi Fossaluza, Igor Marques Nogueira, José Francirlei de Oliveira, Graziela M. C. Barbosa, Marcelo Marques Lopes Müller, Renata Alesandra Borecki, Cristiano Andre Pott, Fábio Luiz Melquiades
Format: Article
Language:English
Published: Universidade Estadual de Londrina 2024-11-01
Series:Semina: Ciências Exatas e Tecnológicas
Subjects:
Soil fertility attributes
machine learning
PLS
EDXRF
Online Access:https://ojs.uel.br/revistas/uel/index.php/semexatas/article/view/51475

Internet

https://ojs.uel.br/revistas/uel/index.php/semexatas/article/view/51475

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