Multispectral Image Analysis Using Feature Extraction with Classification for Agricultural Crop Cultivation Based On 4G Wireless IOT Networks

Abstract

Land-use mapping and crop classification have both benefited greatly from the analysis of high-resolution remote sensing photos based on deep learning. This research proposes novel technique in multispectral image analysis based on feature extraction as well as classification utilizing DL architecture. The proposed model collects multispectral image based on agriculture crop cultivation by 4G IoT wireless networks. The input image has been processed for noise removal, smoothening and normalization. Then this processed image features have been extracted using stochastic Q-reinforcement neural network. the extracted features of multispectral image have been classified using discrete quantum convolutional architectures. Here the experimental analysis has been carried out for various agriculture crop cultivation-based dataset in terms of accuracy, precision, recall, F-1 score, RMSE, MAP and AUC.