Evaluation of deep learning convolutional neural network for crop classification

Abstract

In this paper, we have done exploratory experiments using deep learning convolutional neural network framework to classify crops into cotton, sugarcane and mulberry. In this contribution we have used Earth Observing-1 hyperion hyperspectral remote sensing data as the input. Structured data has been extracted from hyperspectral data using a remote sensing tool. An analytical assessment shows that convolutional neural network (CNN) gives more accuracy over classical support vector machine (SVM) and random forest methods. It has been observed that accuracy of SVM is 75 %, accuracy of random forest classification is 78 % and accuracy of CNN using Adam optimizer is 99.3 % and loss is 2.74 %. CNN using RMSProp also gives the same accuracy 99.3 % and the loss is 4.43 %. This identified crop information will be used for finding crop production and for deciding market strategies.