Deep Predictive Models Based on IoT and Remote Sensing Big Time Series for Precision Agriculture

Abstract

Managing time series data generated by the intelligent objects around us requires cleaning and processing techniques, as well as a prediction model with high accuracy and low complexity. This study attempts to fuse climate time series data (Temperature, Humidity, Wind speed and Rainfall) and remote sensing data to predict rice yield. We performed statistical analysis of the data, additive decomposition, and measured the correlation between the different variables. We checked the stationarity of the data by using the Augmented Dickey-Fuller ADF test to apply statistical prediction models based on AutoRegression and moving averages for univariate series such as ARIMA (Autoregressive Integrated Moving Average) and for multivariate series such as Vector AutoRegression VAR and Vector Autoregressive Moving Average VARMAX. In addition, we applied a non-parametric model such as K-Nearest Neighbors KNN and Recurrent Neural Network models such as Long short-term memory LSTM and Gated recurrent unit GRU for rice yield prediction. The best yield estimation is achieved using LSTM with a Root Mean Square Error RSME error of 0.100.