Improved Time Series Prediction Using LSTM Neural Network for Smart Agriculture Application

Abstract

Smart Agriculture is a solution to achieve precision in agriculture to rapidly increase agricultural production. From the smart agriculture system, data on the environmental conditions of plants is monitored. Prediction systems have also been developed using the Backpropagation Algorithm. However, it is not known whether Backpropagation is quite good compared to the other algorithms. Research has been conducted in other cases and it is found that the LSTM algorithm is capable of producing predictions better than Backpropagation. This paper discusses about the improvement of the predictions using LSTM compared to backpropagation which yields RMSE value of 0.8 for LSTM and 0.10 for backpropagation in smart agriculture applications.