Results
eNauka >
Results >
High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data
| Title: | High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data | Authors: | Ranđelović, Predrag   ; Đorđević, Vuk ; Miladinović, Jegor ; Prodanović, Slaven ; Ćeran, Marina ; Vollmann, Johann |
Issue Date: | 2023 | Publication: | Plant Methods | ISSN: | 1746-4811 Plant Methods Search Idenfier |
Type: | Article | Collation: | vol. 19 br. 1 str. 89 | DOI: | 10.1186/s13007-023-01054-6 | WoS-ID: | 001093871700002 | Scopus-ID: | 2-s2.0-85169119775 | PMID: | 37633921 | PMCID: | PMC10463513 | URI: | https://enauka.gov.rs/handle/123456789/787552 https://fiver.ifvcns.rs/handle/123456789/4064 http://fiver.ifvcns.rs/handle/123456789/4064 |
Project: | Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200032 (Naučni institut za ratarstvo i povrtarstvo, Novi Sad) ECOBREED (Increasing the efficiency and competitiveness of organic crop breeding) |
Metadata source: | (Preuzeto iz CrossRef-a) Ranđelović, Predrag | M-category: | 21aM21a |
Items in eNauka are protected by copyright, with all rights reserved, unless otherwise indicated.