Predicting Crop Yields in the Southern Regions of Russia with Artificial Intelligence Tools

The article discusses modern approaches to predicting crop yields in the agricultural regions of southern Russia using artificial intelligence technologies (neural networks). The relevance of this topic is due to the high importance of the southern regions (Krasnodar Territory, Stavropol Territory, Rostov Region, etc.) in Russia’s food security, and the need for prompt and accurate crop forecasting. T he purpose of this work is to develop, apply and evaluate models for predicting crop yields in southern Russia using artificial intelligence methods based on various types of neural networks. Methodology and tools of neural network algorithms application (LSTM, CNN, MLP) are considered to predict crop yields based on data from 2020 to 2025, including statistical indicators of crop yields, meteorological data, and vegetation indices (NDVI). T he article presents the results of modeling, which demonstrate the advantage of the LSTM model in terms of prediction accuracy compared to other models. The results section includes graphs and tables that illustrate the actual and predicted crop yields, as well as a comparative analysis of the model errors.