Development of an Adaptive Approach for Precision Agriculture Monitoring with Drone and Satellite Data

For better agricultural productivity and food management, there is an urgent need for precision agriculture monitoring at larger scales. In recent years, drones have been employed for precision agriculture monitoring at smaller scales, and for past few decades, satellite data are being used for land cover classification and agriculture monitoring at larger scales. The monitoring of agriculture precisely over a large scale is a challenging task. In this paper, an approach has been proposed for precision agriculture monitoring, i.e., the classification of sparse and dense fields, which is carried out using freely available satellite data (Landsat 8) along with drone data. Repeated usage of drone has to be minimized and hence an adaptive classification approach is developed, which works with image statistics of the selected region. The proposed approach is successfully tested and validated on different spatial and temporal Landsat 8 data.