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

Pal Deka *

Department of Agricultural Statistics, Biswanath College of Agriculture, Biswanath Chariali-784176, Assam, India.

*Author to whom correspondence should be addressed.


Abstract

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.

Keywords: Multi-layer perceptron, artificial neural network, yield prediction, radial basis function


How to Cite

Deka, P. (2024). A Comparative Analysis of Neural Network Architectures for Predicting Indian Rice Production. Archives of Current Research International, 24(5), 273–279. https://doi.org/10.9734/acri/2024/v24i5702

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