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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Anais da Academia Brasileira de Ciências Jg. 97; H. suppl 2; S. e20250075
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	Brazil Academia Brasileira de Ciências 01.01.2025
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!