Wheat Yield Prediction in India Using Principal Component Analysis-Multivariate Adaptive Regression Splines (PCA-MARS)

Abstract

Crop yield forecasting is becoming more essential in the current scenario when food security must be assured, despite the problems posed by an increasingly globalized community and other environmental challenges such as climate change and natural disasters. Several factors influence crop yield prediction, which has complex non-linear relationships. Hence, to study these relationships, machine learning methodologies have been increasingly adopted from conventional statistical methods. With wheat being a primary and staple food crop in the Indian community, ensuring the country’s food security is crucial. In this paper, we study the prediction of wheat yield for India overall and the top wheat-producing states with a comparison. To accomplish this, we use Multivariate Adaptive Regression Splines (MARS) after extracting the main features by Principal Component Analysis (PCA) considering the parameters such as area under cultivation and production for the years 1962–2018. The performance is evaluated by error analyses such as RMSE, MAE, and R². The best-fitted MARS model is chosen using cross-validation and user-defined parameter optimization. We find that the MARS model is well suited to India as a whole and other top wheat-producing states. A comparative result is obtained on yield prediction between India overall and other states, wherein the state of Rajasthan has a better model than other major wheat-producing states. This research will emphasize the importance of improved government decision-making as well as increased knowledge and robust forecasting among Indian farmers in various states.