Machine Learning-Based Crop Yield Prediction: A Comparative Study of Regression Models in Precision Agriculture

Abstract

Precision agriculture, characterized by data-driven methodologies and
technological integration, has revolutionized modern farming practices. A central
element of precision agriculture involves predicting crop yields, empowering
farmers to make informed decisions regarding resource allocation, sustainability,
and profitability. Machine learning, with its ability to analyze intricate datasets,
holds the promise of improving the precision of crop yield predictions.
Nonetheless, the selection of the most suitable regression model remains a
fundamental challenge. In this study, we conduct an exhaustive comparative
examination of four regression models: Linear Regression, Decision Tree
Regression, Random Forest Regression, and Support Vector Regression, all of
which demonstrate potential in precision agriculture. Our evaluation is rooted in
a variety of metrics, including Mean Absolute Error (MAE), Mean Squared Error
(MSE), Root Mean Squared Error (RMSE), and R-squared (R²), providing insights
into the predictive capabilities of each model. Beyond predictive performance, we
explore aspects of model interpretability, resilience, scalability, and
computational efficiency, all of which are pivotal for practical implementation in
precision agriculture. Our findings serve as a valuable resource for farmers and
stakeholders in the precision agriculture field, aiding them in selecting the most
effective regression model for predicting crop yields. Furthermore, we identify
innovative research directions, encompassing real-time predictions, explainable
AI, hyperlocal insights, data fusion, and ethical considerations, paving the way
for the future of precision agriculture. This research contributes to the
advancement of sustainable and data-driven agricultural practices, addressing
the global demand for improved crop production.