Abstract
In India, due to the blessing by the outbreak of the National Food Security Mission, the production of cereals such as wheat, rice etc, has increased in an alarming rate. In this Study, forecasting is done with the help Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM-NN) models on the basis of the historical data of rice cultivation from the year 1950-51 to 2017-18. The well fitted ARIMA models for the parameters such as Area under Cultivation (0,1,1), Production (0,1,1) and Yielding (2,2,1) are obtained from the significant spikes of their respective Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots. But, the models fitted with a supervised deep learning neural network known as LSTM-NN are found much better time series forecasting model than the ARIMA models. The performances of these models validated with the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. From the study, the LSTM-NN’s are more flexible and able to develop accurate models for predicting the behavior of agricultural parameters than the ARIMA models.
Highlights
Global food security is one of the major concerns in the era of twenty first century
Long Short– Term Memory (LSTM)–NN is a special type of RNN and, it is quite useful for time series forecasting
The model evaluation can be performed with the help of Mean absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), which are very useful in measuring the accuracy of the fitted model
Summary
Global food security is one of the major concerns in the era of twenty first century. Forecasting for the area under cultivation, agricultural production and yielding are the essential parameters for founding a support policy decision regarding the food security, effective land. Balanagammal et al (2000) applied ARIMA models to forecast five years for cultivable area, production, and productivity of various crops of the data during the 1956-57 to 1994-95 in Tamil Nadu. The need of the study is help for solving food security problems and development of various policy decisions for rice crop in Eastern India. Collection of Data such as Rice and Wheat of food security situation in SAARC countries through forecasting of area, production, The data for Area under Cultivation, Agricultural yield and total seed production. Production behavior and applied ARIMA models to forecast the area, production and yield of total food grains for policy makers to achieve the food and nutrition security in India
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