Mathematical and economic analysis of perennial plantations based on neural networks

Modern agriculture requires the introduction of effective tools for analyzing perennial plantations, taking into account the high degree of uncertainty and complex relationships between agrobiological and economic parameters. The aim of the study is to develop methods of mathematical and economic analysis of perennial plantations based on neural networks. A deep learning model including biophysical, climatic and production-economic parameters was built. The model was trained on multi-year observation data on yields, meteorological conditions and financial results of farms. The results of neural network forecasting were compared with traditional economic and mathematical methods. It was found that the use of neural networks allows increasing the accuracy of yield forecasts by 20% and increasing the reliability of economic evaluation by 15% compared to classical regression models. The results obtained confirm the feasibility of integrating neural network algorithms into the decision support system in the management of perennial crops. The practical significance of the study lies in improving the efficiency of planning and resource management in the agricultural sector, as well as in creating a scientific basis for further improvement of digital farming methods.