Prediction of international rice production using long short term memory and machine learning models
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Date
2025
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Abstract
Rice, a staple food source globally, is in high demand and production across
the world. Its consumption varies in different countries, with each nation
having its unique way of incorporating rice into its diet. Recognizing the
global nature of rice, its production is a crucial aspect of ensuring its
availability, agriculture forecasting, economic stability, and food security.
By predicting its production, we can develop a global plan for its production
and stock, thereby preventing issues like famine. This paper proposes
machine learning (ML) and deep learning (DL) models like linear
regression, ridge regression, random forest (RF), adaptive boosting
(AdaBoost), categorical boosting (CatBoost), extreme gradient boosting
(XGBoost), gradient boosting, decision tree, and long short-term memory
(LSTM) to predict international rice production. A total of nine ML and one
DL models are trained and tested on the international dataset, which contains
the rice production details of 192 countries over the last 62 years. Notably,
linear regression and the LSTM algorithm predict rice production with the
highest percentage of R-squared (R2), 98.40% and 98.19%, respectively.
These predictions and the developed models can play a vital role in resolving
crop-related international problems, uniting the global agricultural
community in a common cause.