Integrating Machine Learning Models for Linear and Exponential Regression to Predict Wheat Area, Productivity and Population
Integrating Machine Learning Models for Linear and Exponential Regression to Predict Wheat Area, Productivity and Population
Muhammad Islam1,4*, Farrukh Shehzad1, Muhammad Omar2, Abdul Qayyum3 and Rabia Siddiqui4
ABSTRACT
The true prediction of food crop with exact estimation of population growth can play a crucial role for evolving effective agricultural policy decisions for food concerns. This study integrated the efficacies of supervised machine learning (ML) algorithms using compound growth exponential regression models (CGREM) with linear regression models (LRM) to predict the wheat area, yield and population explosion. The historical data are collected from 1950 to 2020 and ML models are deployed using the 80% train and 20% test datasets. Various combinations of train test split have been applied to check the precisions of data partitions subset on the deployed models. ML model predict that the wheat area will rise up to 15%, 51.7%, wheat yield will grow up to 28%, 109.7% and population will rise up to 34.2%, 140.6%, respectively for the year 2030 and 2050. Population will upturn about 88.9% and 30.9% more than from wheat area and wheat yield up to 2050 and it might explode the food critiques in the region. It is concluded that wheat productivity must be raise to ensure the food demands. The results of this study demonstrated that the CGREM found to be superior comparing with benchmark LRM.
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