| Naziv: | Oil yield prediction for sunflower hybrid selection using different machine learning algorithms |
Autori: | Cvejić, Sandra   ; Hrnjaković, Olivera ; Jocković, Milan ; Kupusinac, Aleksandar ; Doroslovački, Ksenija ; Gvozdenac, Sonja ; Jocić, Siniša ; Miladinović, Dragana |
Godina: | 2023 |
Publikacija: | Scientific Reports |
ISSN: | 2045-2322 Scientific Reports Pretraži identifikator |
Izdavač: | Nature Portfolio |
Tip rezultata: | Naučni članak |
Kolacija: | vol. 13 br. 1 str. 17611 |
DOI: | 10.1038/s41598-023-44999-3 |
WoS-ID: | 001086802300094 |
Scopus-ID: | 2-s2.0-85174453290 |
PMID: | 37848668 |
PMCID: | PMC10582183 |
URI: | http://fiver.ifvcns.rs/handle/123456789/3918
https://enauka.gov.rs/handle/123456789/839518 |
Projekat: | Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije, Ugovor br. 200032 (Naučni institut za ratarstvo i povrtarstvo, Novi Sad)
SmartSun - Creating climate smart sunflower for future challenges
CROPINNO - Stepping up scientific excellence and innovation capacity for climate-resilient crop improvement and production
COST Action CA18111: "Genome editing in plants - a technology with transformative potential"
CA19125 COST Action EPI-CATCH |
M-kategorija: | 21aM21a - Vodeći međunarodni časopis kategorije M21a |
