Forecasting of Rice Cultivation in India–A Comparative Analysis with ARIMA and LSTM-NN Models Kiran Kumar Paidipati 1,* and Arjun Banik 1 1 Department of Statistics, Pondicherry University, Puducherry-605014, India 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. Keywords: Food Security, Rice Cultivation, ARIMA and LSTM-NN Models. Received on 31 August 2019, accepted on 02 November 2019, published on 06 November 2019 Copyright © 2019 Kiran Kumar Paidipati and Arjun Banik licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi: 10.4108/eai.13-7-2018.161409 1. Introduction Global food security is one of the major concerns in the era of twenty first century. The land under cultivation is declining drastically whereas the demand for more food is increasing at an alarming rate. There is need for serious concern to increase the food production of major food crops like rice, wheat, and maize. To improve the overall food security situation in India, there have to analyze behavior of total production along with the total area, irrigated area and productivity of these major food crops. This may create a strategic development for the future food security. The agricultural and allied industry continues to play an important role in sustainable growth and development of the Indian economy. Agriculture accounts for considerable importance in India’s economic development, as it provides food for more than 1.25 billion people. *Corresponding Author: kirankumarpaidipati@gmail.com It generates employment for about 54.6% of the total population. Production of food grain covers the dominant part of the cropped area (nearly 65%) of Indian agriculture. In the world’s rice production, India stands second- largest producer after China and one of the largest consumers which accounts for 22.3% of global production. About 35% of net cropped area under paddy and about 50% of the farmers cultivate paddy every year. Farmer’s decision making on acreage under paddy depends on the future prices to be realized during harvest period. Rice has become a highly strategic and priority commodity for food security in India, majorly south and eastern states whereas the north and western states follows a feeding pattern of wheat and maize. An accurate estimate of crop size and overall risk helps farmer, agribusiness industries as well as policy makers in planning supply chain decisions like production scheduling. Business such as seeds, fertilizers, agrochemical and agricultural machinery plan production and marketing activities based on crop production estimates. 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 EAI Endorsed Transactions on Scalable Information Systems Research Article 1 EAI Endorsed Transactions on Scalable Information Systems 10 2019 - 01 2020 | Volume 7 | Issue 24 | e8