Kalinka Kaloyanova 1, 2, a) and Pavel Genevski 1, b)
1 Sofia University “St. Kliment Ohridski”, Faculty of Mathematics and Informatics
2 Institute of Mathematics and Informatics, Bulgarian Academy of Sciences,
a) Corresponding author: kkaloyanova@fmi.uni-sofia.bgb)genevski@fmi.uni-sofia.bg
Abstract. Monitoring sunflower crops in the early stages of germination is essential for profitable crop management. Traditional manual, ground-based methods are la-borious, subjective, and error-prone. This study explores the applicability of different multispectral data sources to tasks related to sunflower monitoring. A collection of multispectral and RGB images was captured using a UAV and processed. The data collection establishes a foundational framework, enabling additional analyses to be conducted based on applied mathematical models and machine learning algorithms to provide field coverage research in supporting advanced agronomic decision-mak-ing.
Keywords: Multispectral imaging, Precision agriculture, UAV, Satellite, Earth ob-servation, Sunflower