Prediction of soil fertility properties in Southern Brazil via proximal sensing

Abstract Although proximal sensing coupled with machine learning (ML) algorithms have been successful for characterizing soils, questions remain regarding their effectiveness under varied soil conditions. This study evaluated for the first time the efficiency of a portable X-ray fluorescence spectro...

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Bibliographic Details
Published in:	Anais da Academia Brasileira de Ciências Vol. 97; no. suppl 2; p. e20250075
Main Authors:	NACHTIGALL, STEFAN D., MANCINI, MARCELO, REIS, RENATA A., ARAÚJO, ELIAS FRANK DE, CARNEIRO, MARCO AURÉLIO C., CURI, NILTON, SILVA, SÉRGIO HENRIQUE GODINHO
Format:	Journal Article
Language:	English
Published:	Brazil Academia Brasileira de Ciências 01.01.2025
Subjects:	Algorithms Brazil Environmental Monitoring - methods Machine Learning MULTIDISCIPLINARY SCIENCES pedometrics Portable X-ray fluorescence Soil - chemistry soil properties soil variation Spectrometry, X-Ray Emission - methods Brazil soil properties Portable X-ray fluorescence pedometrics soil variation
ISSN:	0001-3765, 1678-2690, 1678-2690
Online Access:	Get full text
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