Site-specifi c sunfl ower yield forecasting based on spatial analysis and machine learning

The study focuses on the development of an intelligent yield forecasting system using satellite data, geospatial data and climate indicators. The introduction of modern information technologies, in particular machine learning and big data analysis methods, provides agricultural professionals with strategic advantages, reducing the risks of excessive pesticide use and promoting sustainable agricultural development. This study aims to optimize desiccant application in sunflower cultivation by modeling potential yield losses based on data obtained during the growing season. The use of digital solutions is relevant for crop production, as it increases the accuracy of forecasts and the efficiency of management decisions, while reducing costs and increasing the productivity of agrophytocenoses.