Haievskyi
Department of Plant Sciences and Agricultural Engineering,, Kherson State Agrarian and Economic University, Kropyvnytskyi, UkrainePublications
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Research
Remote sensing data and machine learning to predict yields of major crops on regional scale
Author(s): Pavlo Lykhovyd*, Raisa Vozhehova, Liudmyla Hranovska, Oleksandr Averchev, Anatolii Tomnytskyi, Nataliia Avercheva, Mariia Nikitenko, Haievskyi and Oleg Shalar
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. Remote sensing data were calculated and generalized through the Google Earth Engine platform through the requests to the API in JavaScript. Correlation and regression analysis were performed using common methodologies, as well as more robust machine learning techniques like Random Forest and Gradient Boosting Regression were also applied for yield prediction. It was determined that the strongest correlati.. Read More»
DOI: 10.5281/zenodo.17994141