  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 margin of error
(ME) of .03. The models were trained and tested using the Food and Agricultural
Organization of the United Nations data, along with the World Bank Climate
Change Data Catalog. Furthermore, each parameter was analyzed to understand how
varying factors could impact overall yield. We used unconventional models,
contrary to generally used Deep Learning (DL) and Machine Learning (ML) models,
combined with recently collected data to implement a unique approach in our
research. Existing scholarship would benefit from understanding the most
optimal model for agricultural research, specifically using the United Nations
data.
