2025, Vol. 10, Issue 12, Part A

Crop yield prediction is essential for agricultural planning, food security, and economic stability, particularly in the face of climate variability. This research paper explores the application of linear regression to predict maize crop yields based on environmental factors such as rainfall and temperature. Using synthetic data representative of real-world scenarios, we develop a multiple linear regression model that achieves a high R-squared value of 0.984, indicating strong predictive power. The model highlights the positive influence of both rainfall and temperature on yield. Limitations and future enhancements, including integration with more advanced machine learning techniques, are discussed. This study contributes to accessible predictive modeling for small-scale farmers.