Forecasting Agricultural Yields Using Artificial Intelligence Models

This paper examines the possibilities of applying data mining and artificial intelligence methods to forecast agricultural yields. The study analyzes meteorological data, the physical and chemical composition of the soil, crop varieties, and water resource utilization indicators as key factors. In the practical part, wheat yield data from Tashkent region for the years 2019–2024 were used to make forecasts using the Random Forest Regression model implemented in Python. According to the model results, the prediction accuracy reached R² = 0.87. The study demonstrates that yield forecasting can significantly improve resource management and agricultural planning efficiency.