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Plant Science Today (PST)

Research Articles

Early Access

Assessing the effectiveness of Artificial Neural Networks and PSLR models in predicting per capita food grain production in India

DOI
https://doi.org/10.14719/pst.7447
Submitted
26 January 2025
Published
15-04-2025
Versions

Abstract

Every living thing needs food. In addition to fostering social progress and economic expansion, agriculture is essential to our everyday existence. India has made great strides toward guaranteeing a sufficient supply of food since gaining its freedom. While India's population has tripled, food grain production has more than quadrupled. Consequently, there are now substantially more food grains available per individual. To meet the country's food needs, rice and wheat production is essential. Decision-makers need access to accurate forecasts to identify this need, put appropriate plans into place and allocate the required administrative resources. Based on artificial neural networks and PSLR, the per capita availability of food grain production in India was estimated. Indiastat and the FAO provided the historical data for the country from 1951 to 2022. To examine the food grain per capita availability (Kgs. Per Year), we used two effective analytical methodologies, artificial neural networks (ANNs) and Partial least squares (PLS) regression. The models' performances were compared using four relevant performance criteria to determine which model is best for future forecasting. The results show that, when it comes to accurately predicting the per capita number of dietary grains, ANN outperforms the PLSR model. For the ANN method, the values of MAE, MSE, RMSE and R2 were 3272.11, 1.748, 4194.28 and 0.956, in that order. The study discovered that PLS also functioned well, with very little difference between the two models' performance indicators.

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