Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction

arXiv:2312.02254v2 Announce Type: replace
Abstract: The prediction of crop yields internationally is a crucial objective in agricultural research. Thus, this study implements 6 regression models (Linear, Tree, Gradient Descent, Gradient Boosting, K Nearest Neighbors, and Random Forest) to predict crop yields in 37 developing countries over 27 years. Given 4 key training parameters, insecticides (tonnes), rainfall (mm), temperature (Celsius), and yield (hg/ha), it was found that our Random Forest Regression model achieved a determination coefficient (r2) of 0.94, with a …

abstract arxiv boosting cs.ai cs.lg developing countries forecasting global gradient innovations linear multivariate neighbors prediction random regression research study tree type