Abstract
This article presents an intelligent approach to the analysis of soil parameters using machine learning methods and statistical data analysis. Seasonal trends in changes in soil indicators were analyzed. The results of clustering revealed regions with similar parameters of humidity and air temperature. ARIMA and LSTM methods were used to predict the time series of soil moisture, light and air temperature parameters. An intelligent approach to the analysis of soil parameters demonstrates the effectiveness and prospects of using machine learning methods and data analysis in agriculture. Such an approach can be useful for improving land management, increasing crop yields and sustainable agricultural development. The study used the Python programming language, which has a rich set of libraries and modules for data analysis, which allows you to flexibly approach solving complex problems and create custom solutions that best suit the requirements of the project.
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