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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Anais da Academia Brasileira de Ciências Ročník 97; číslo suppl 2; s. e20250075
Hlavní autori:	NACHTIGALL, STEFAN D., MANCINI, MARCELO, REIS, RENATA A., ARAÚJO, ELIAS FRANK DE, CARNEIRO, MARCO AURÉLIO C., CURI, NILTON, SILVA, SÉRGIO HENRIQUE GODINHO
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Brazil Academia Brasileira de Ciências 01.01.2025
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!