Climate-Driven Crop Yield Forecasting using Regression Model

The importance of crop yield prediction lies in accurate yield prediction in climate-resilient agriculture, precision farming, and sustainable food production systems. This paper introduces the multi-source environmental information-based and supervised regression modeling, applying to the current study a well-structured data-driven model to predict crop yields. The suggested methodology initiates with the multi-source data collection that combines the usage of IoT-driven soil sensors, meteorological measurements, satellite-generated vegetation indices, and past records of crop yields. The correlation analysis and ensemble-based importance ranking are the feature selection methods used to determine important predictors. Predictive performance can be improved by feature engineering strategies to give agronomic indicators such as growing degree days, cumulative rainfall indices, and the interaction variables of climatic factors. Random Forest Regressor, Support Vector Regressor, and Gradient Boosting Regressor are supervised regression models that are trained to predict values of continuous crop yield. Root Mean Square error, Mean Absolute error, and coefficient of determination are used as measures of model performance. It has been shown that ensemble-based models are useful in capturing the nonlinear interactions of the environment and enhancing prediction.