In-Season Prediction Of Crop Types In The Us Great Plains Using Sequence Based Stochastic Models And Deep Learning

In this study, a convolutional neural network (CNN) is developed that predicts crop type in the US great plains using optical and active microwave remote sensing, historical crop type data and economic commodity crop price information. It includes a Markov chain model that predicts the prior probability of crop types from historical crop type sequences for each pixel. This prior and the remotely sensed imagery is fed through a CNN to produce the final crop type. The CNN is trained using imagery and crop type from the previous year. The field-scale accuracy of this method is found to be (8) over tilde5% using imagery from just the first 90 growing days.