Data mining for assessing soil fertility

Abstract

The study is devoted to the use of data mining to assess soil fertility, which is a modern and effective tool in agriculture and ecology. The method includes integrated approaches to data collection, processing and analysis aimed at determining soil fertility, its composition and potential for successful agricultural use. Using a variety of machine learning techniques and statistical models, researchers can predict crop yields, optimize fertilization and soil management strategies, and identify environmental and soil health risks. In particular, the use of the regression method makes it possible to build models that predict the values of fertile soil parameters based on available data. Using machine learning techniques such as Bayes' theorem and support vector machines (SVM), researchers can effectively estimate soil fertility, predict soil characteristics, and optimize agricultural practices. The results of the study demonstrate the high performance of the models in soil sample classification tasks, highlighting their potential for improving soil resource management and increasing crop yields. Such machine learning techniques provide powerful tools for agricultural workers and researchers, facilitating more precise and sustainable agriculture, which is essential for food security and ecosystem resilience.