Development of Machine Learning based Models for Multivariate Prediction of Wheat Crop Yield in Uttar Pradesh, India

Abstract

The consequences of climate change have a substantial impact on agricultural crop production and management. Predicting or forecasting crop yields well in advance would help farmers, agriculture corporations and government agencies manage risk and design suitable crop insurance plans. Ground survey is the traditional way of determining yield, which is subjective, time-consuming, and expensive. While Machine learning techniques make yield prediction less expensive, less time taking and more efficient. In this study, thirteen years of meteorological parameters and wheat yield data (2001-2013) of Uttar Pradesh were used to train and analyze three machine learning regression models viz. Support Vector Regression, Ordinary Least Squares, and Random Forest. Each model's performance was assessed using Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. Results revealed that the Random Forest model with a MAE of 0.258 t/ha, MSE of 0.096 t/ha and RMSE of 0.311 t/ha proved to be the best model in the yield prediction of wheat when results are statistically compared with others. Researchers and decision-makers can use the findings to estimate pre-harvest yields and to ensure food security.