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. 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 correlation (0.93-0.96) is between such indices as NDVI and VHI, LST
and PET, VHI and PET. As for crop models, for most crops Random Forest algorithms provided the best overall quality of
fitting and prediction accuracy, followed by Gradient Boosting Regression. However, in case of crops with smaller datasets,
such as spring wheat, rapeseed and peas, linear regression provided better accuracy than more robust machine learning
methods. The study emphasizes the importance of using the tandem of machine learning and remote sensing in modern
agriculture to improve crop monitoring and yield prediction, contributing to sustainable agricultural practices and food
security
