CANCELED: Enhancing crop type mapping in smallholder farms through image fusion of Sentinel-2 and UAV imagery

Crop-type mapping is crucial for precision agriculture, facilitating informed decision-making, particularly in regions where agriculture is a fundamental component of the economy. However, accurate crop-type classification impedes yield monitoring which is crucial for strategic planning. This paper presents a methodology for crop mapping, which involves integrating Sentinel-2 satellite imagery with high-resolution unmanned aerial vehicles (UAV) based images. By leveraging the multispectral bands of Sentinel-2 imagery and the finer spatial resolution of UAV-based images, our approach aims to advance the precision and efficacy of crop mapping models by enriching the information available for analysis. The integration of Sentinel-2 and UAV-based data substantially improves the identification of crop types. These findings underscore the potential of data fusion leveraging the strengths of individual sensors to help overcome weaknesses in alternative sensors. This research contributes to the advancement of precision agriculture methodologies and underscores the transformative impact of data fusion techniques in agricultural remote sensing applications.