Crop Type Identification using Deep Learning Algorithms
Crop type identification plays a major role in producing and analyzing crop yield predictions. Crop yield prediction is important to understand food security so as to produce food to meet human needs. Sentinel-2 is a land monitoring constellation used to acquire optical images at a very high resolution so as to observe different crops for the crop yield. The crop types are being analyzed using hyper-spectral image data which is being collected by a ground scanner. The main aim to identify the crop yield is to monitor the agriculture which is a key task for producers and decision makers. To monitor this, a multi-temporal remote sensing data analysis is performed in an inexpensive method. Yeild predictions are necessary to be made so as to meet the expected domestic shortfall. To estimate this, a radar and an optical remote sensor are being used to predict the required growing situation. The main crop indicators used by the optical remote sensor includes biomass, height, leaf area and contents of the plant like, water, chlorophyll and nitrogen. The above indicators are necessary to monitor the entire growing season of the crops.