Ensemble data mining methods for assessing soil fertility

Abstract

The application of ensemble data mining methods in assessing soil fertility and the use of methods such as random forest, gradient boosting and bagging to determine the level of soil fertility are examined in the article. Ensemble methods combine multiple machine learning models to improve the accuracy and stability of estimates. These methods consider various factors, including soil chemistry, climatic conditions, and historical crop yield data. The study also examines the application of the decision tree algorithm and such methods as random forest and bagging to estimate soil fertility. Performance results of these methods are provided using precision, recall, and F1-measure metrics. The results obtained show the high performance of ensemble methods in the task of classifying soil fertility levels. They have important implications for agricultural farms and research organizations that are working to improve soil management and increase crop yields.