Remote sensing data and machine learning to predict yields of major crops on regional scale.

Lykhovyd, Pavlo, Oleg Shalar

dc.contributor.author	Lykhovyd, Pavlo, Oleg Shalar
dc.date.accessioned	2025-08-15T09:05:19Z
dc.date.available	2025-08-15T09:05:19Z
dc.date.issued	2025-04
dc.identifier.citation	Pavlo Lykhovyd, Raisa Vozhehova, Liudmyla Hranovska, Oleksandr Averchev, Anatolii Tomnytskyi, Nataliia Avercheva, Mariia Nikitenko, Serhii Haievskyi, Oleg Shalar. Remote sensing data and machine learning to predict yields of major crops on regional scale. / Modern Phytomorphology. 19, 2025. DOI: 10.5281/zenodo.200121. р. 141-146.	ru
dc.identifier.uri	http://hdl.handle.net/123456789/11242
dc.description.abstract	Remote sensing and machine learning tandem is a powerful tool for crop monitoring and yield prediction. Current study is devoted to the evaluation of the relationship between five remote sensing indicators, affecting crop yields, such as NDVI, NDMI, VHI, LST and PET, as well as establishing the connectivity of these indices with the yields of twelve major crops cultivated in Ukraine during the period 2015-2023. Yielding data were retrieved form official statistical bodies of Ukraine.	ru
dc.language.iso	en	ru
dc.publisher	Modern Phytomorphology. 19, 2025. DOI: 10.5281/zenodo.200121. р. 141-146.	ru
dc.subject	Land surface temperature, Modeling, Normalized difference moisture index, Normalized difference vegetation index, Potential evapotranspiration, Vegetation health index	ru
dc.title	Remote sensing data and machine learning to predict yields of major crops on regional scale.	ru
dc.type	Article	ru