A Model for Wheat Yield Prediction to Reduce the Effect of Climate Change Using Support Vector Regression
DOI:
https://doi.org/10.21015/vtse.v12i2.1855Abstract
Recent changes in the climatic conditions have significantly threatened the food security globally. Increasing in temperature adversely affected different crops in Pakistan particularly Wheat crop. Mostly farmer’s crop wheat in District Khairpur but yield is not predicted yet. Therefore, famers are unable to estimate the effects of climate changes. This research work introduces a novel framework for the development of wheat yield prediction model using Support Vector Regression. The model incorporates four predictor variables: temperature, rainfall, humidity and pH value of soil. The essential wheat yield data obtained from official departments, websites, and scholarly publications. Five datasets are created from the gathered data in order evaluate the suggested wheat prediction model. For the creation of dataset, some preprocessing operations such as handling missing values and outlier’s detection are applied to the collected raw data. Experiments performed using simple linear and multiple linear regression models. By dividing the dataset in 70% and 30%, model training and testing performed respectively. The conducted research illustrated that multiple linear regression model provide desired outcomes.
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