Rice crop growth analysis using Auto Regressive Models

Abstract

Time series play a vital role in predicting and forecasting different types of agricultural applications with respect to different types of problems among successive units of observations. Time series forecasting techniques are applied in all areas of statistics, and one of the most important applications includes backscatter generating time-series data using advanced forecasting techniques. Agriculture is a major food sector in the world, and it is also a major income source for low-income people. In this paper, we present two aspects of the rice crop growing time series process. The first one is to identify different types of rice crop growing stages for backscatter datasets, and the second is to make a mathematical time series model for the generation of different data sets. The different operator techniques (DOT) method was introduced to identify different types of rice crop growing stages in a season. We proposed the DOT method for identification of different phenological stages for a short-term crop and adopted first and second-order auto-regressive models for prediction and forecasting of the generating backscatter time series observations. The measures of the quality fit are mean absolute percent error (MAPE), mean percent error (MPE), and mean absolute error (MAE).