Forecasting of Rice Cultivation in India–A Comparative Analysis with ARIMA and LSTM-NN Models

Paidipati, Kiran Kumar; Banik, Arjun

doi:10.4108/eai.13-7-2018.161409

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Moreover, the study has compared ARIMA, SutteARIMA, H-W, and NNAR with their MAPE and MSE values to suggest an appropriate prediction model. The ARIMA method was chosen because it is used in agriculture crop research and foodgrains predictions; for example, ARIMA was used to forecast food crop price [31] and Chinese food grain price [32]; ARIMA along with LSTM-NN models was used to forecast rice cultivation in India [33]. Studies have also predicted the amount of monsoon rainfall in Andhra Pradesh, India by using MANNs (Modular Artificial Neural Network).…”

Section: Research Gapmentioning

confidence: 99%

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

et al. 2023

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The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025.

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Section: Research Gapmentioning

confidence: 99%

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

et al. 2023

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“…From the overall summary of Table 13, it is observed that SVR Linear and SVR Polynomial kernels are the best models to predict the rice yield of overall India, and major states show a lower RMSE and MAE as compared to SVR RBF . When compared to advanced machine learning techniques, traditional methods for forecasting time series data, such as Autoregressive Integrated Moving Average (ARIMA) models, regression models, and other statistical models [23][24][25], which applied to agricultural production, did not yield good approximation values [4,[6][7][8]11,12,17,[26][27][28]. One of the drawbacks of conventional approaches is that the time series data must be in chronological order when fitting the models, which can be solved by advanced machine learning techniques that select data points at random and suit well-trained models.…”

Section: Svr With Different Kernels For Randomly Allocated Testing Da...mentioning

confidence: 99%

Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

et al. 2021

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The prediction of rice yields plays a major role in reducing food security problems in India and also suggests that government agencies manage the over or under situations of production. Advanced machine learning techniques are playing a vital role in the accurate prediction of rice yields in dealing with nonlinear complex situations instead of traditional statistical methods. In the present study, the researchers made an attempt to predict the rice yield through support vector regression (SVR) models with various kernels (linear, polynomial, and radial basis function) for India overall and the top five rice producing states by considering influence parameters, such as the area under cultivation and production, as independent variables for the years 1962–2018. The best-fitted models were chosen based on the cross-validation and hyperparameter optimization of various kernel parameters. The root-mean-square error (RMSE) and mean absolute error (MAE) were calculated for the training and testing datasets. The results revealed that SVR with various kernels fitted to India overall, as well as the major rice producing states, would explore the nonlinear patterns to understand the precise situations of yield prediction. This study will be helpful for farmers as well as the central and state governments for estimating rice yield in advance with optimal resources.

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“…The ARIMA Model was used to forecast the yearly rice output, consumption, imports, exports, and self-sufficiency of the Benin Republic [4]. Based on past rice cultivation data, the ARIMA model and long short-term memory neural network (LSTM-NN) models might be employed for prediction [5]. Artificial neural networks (ANN) and boosted tree regression could be used to forecast the upland rice yield under the climate change approach [6].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Neural Network Architectures for Predicting Indian Rice Production

Deka

2024

ACRI

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Rice (Oryza sativa) is one of the most important cereal crops in World and feeds more than a third of the world’s population. In Asian region, rice is a main source of nutrition and provides 30% to 70% of the daily calories for half of the world’s population. Here, in this study two different neural network models were used in prediction of rice production of India. It was observed that the accuracy score of Multi-layer perceptron neural network is better than Radial basis function in prediction of rice production. The loss/error value for Multi-layer perceptron (MLP) model is lower than Radial basis function (RBF) model. The relative error is found to be high for MLP.

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Forecasting of Rice Cultivation in India–A Comparative Analysis with ARIMA and LSTM-NN Models

Cited by 6 publications

References 2 publications

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

A Comparative Analysis of Neural Network Architectures for Predicting Indian Rice Production

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