A Comparative Analysis of Neural Network Architectures for Predicting Indian Rice Production
Archives of Current Research International · pp. 273–279 · Published 7 May 2024
10.9734/acri/2024/v24i5702Abstract
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.
Cited by 1
Brahmacharimayum Preetiva, Abhishek Kumar Chaubey, Jonathan S. Singsit · Journal of Soil Science and Plant Nutrition · 2025
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