Fusion of drone and satellite data for precision agriculture monitoring

In recent years, there is a strong need for large scale precision agriculture monitoring for improved productivity to meet the demands of growing population. At large scale, monitoring of agriculture precisely is a challenging task. Therefore, in this paper, a methodology has been proposed for precision agriculture monitoring i.e., to classify vegetation into sparse and dense vegetation classes by employing fusion of freely available satellite data (Landsat 8) and drone imagery. The methodology is able to successfully classify vegetation into the two classes and the results are verified with the help of drone image both visually, and quantitatively in the form of area estimation.