Applying random forest algorithm for automating classification of soil categories

The article discusses the creation of a machine learning model to solve the problem of soil classification using an ensemble of random decision trees (random forest) to automate the determination of soil categories with maximum accuracy based on the data available about them, including such characteristics as soil density, humidity, fractional composition and others. The user interface of the developed software and information system for conducting predictive analytics using the resulting model is also presented.