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

Uloženo v:

Podrobná bibliografie
Vydáno v:	Anais da Academia Brasileira de Ciências Ročník 97; číslo suppl 2; s. e20250075
Hlavní autoři:	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:	angličtina
Vydáno:	Brazil Academia Brasileira de Ciências 01.01.2025
Témata:	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 přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!