EFFICIENT APPLICATION OF DRONE WITH SATELLITE DATA FOR EARLY-STAGE WHEAT DETECTION: FOR PRECISION AGRICULTURE MONITORING

Early-stage wheat detection is quite useful and important for monitoring national food security and crop management systems. However, it is difficult to differentiate early-stage wheat among the ploughed and bare lands using medium to low-resolution satellite data at a large scale. Further, the estimated wheat sowing area would be erroneous due to misclassifications of one class to the other class. On the other hand, drones provide high-resolution data at the field level. Hence, there is a need to fuse drone and satellite data for precision agriculture information at a large scale. Therefore, in this paper, we have explored the application of machine-learning classifiers for early-stage wheat detection using drone and sentinel 1A SAR data. Further, the area is estimated for each class and is compared with the area estimated from the high-resolution drone image. Both qualitative and quantitative analysis of the obtained classification results is carried out with the help of field survey data.