Precision agriculture meets machine learning

Agriculture is a cornerstone of global food security, yet crop productivity is increasingly threatened by the complex interplay of climatic and agronomic factors. This chapter employs Random Forest Regression (RFR), a robust machine learning algorithm, to predict crop yield by analyzing key variables such as temperature, precipitation, solar radiation, fertilization, and irrigation. Using a dataset sourced from Kaggle, the model achieved an R² value of 0.9773 and a Root Mean Squared Error (RMSE) of 14,134.64 hg/ha, demonstrating its strong predictive capability. Feature importance analysis revealed that crop type and geographical region were the most influential factors, followed by average rainfall, pesticide usage, and temperature. The findings underscore the importance of localized and crop-specific agricultural strategies to enhance productivity and resilience in the face of climate change. This research contributes to the growing field of precision agriculture by providing data-driven insights for optimizing farming practices and ensuring sustainable food production.