Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data

Pejak, Branislav; Lugonja, Predrag; Antić, Aleksandar; Panić, Marko; Pandžić, Miloš; Alexakis, Emmanouil; Mavrepis, Philip; Zhou, Naweiluo; Marko, Oskar; Crnojević, Vladimir

Rezultati

eNauka > Rezultati > Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data

Naziv:	Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data
Autori:	Pejak, Branislav ; Lugonja, Predrag ; Antić, Aleksandar; Panić, Marko ; Pandžić, Miloš ; Alexakis, Emmanouil; Mavrepis, Philip; Zhou, Naweiluo; Marko, Oskar ; Crnojević, Vladimir
Godina:	2022
Publikacija:	Remote Sensing
ISSN:	2072-4292 Remote Sensing Pretraži identifikator
Izdavač:	MDPI
Tip rezultata:	Naučni članak
Kolacija:	vol. 14 br. 9 str. 1-22
DOI:	10.3390/rs14092256
WoS-ID:	000795433100001
Scopus-ID:	2-s2.0-85130175203
URI:	https://zenodo.org/record/6532266 https://enauka.gov.rs/handle/123456789/609094
Projekat:	ANTARES - Centre of Excellence for Advanced Technologies in Sustainable Agriculture and Food Security CYBELE - Fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics
M-kategorija:	21M21 - Rad u vrhunskom međ. časopisu

8
SCOPUS^TM

6
WEB OF SCIENCE^TM

Alt metrika

Dimensions

Pronađi DOI

Pronalaženje DOI u servisu CrossRef-u na osnovu meta-podataka u zapisu. Prikazaće se DOI (i metapodaci) čiji se podaci najviše poklapaju. OPREZ - to ne mora biti ta publikacija.

Unpaywall

Rezultati na eNauka su zaštićeni autorskim pravima i sva prava su zadržana, osim ako nije drugačije naznačeno.