Machine Learning in Agriculture Tomato Disease Classification Using Random Forest

Abstract

This research discusses the use of the Random Forest algorithm for classifying tomato diseases based on visual characteristics of leaves. The relevance of the study is due to the need to automate the process of diagnosing plant diseases in order to increase productivity and reduce costs. The stages of data processing, feature extraction, model training and the mathematical description of the Random Forest method are presented. The results obtained show high accuracy of classification and demonstrate the potential for introducing intelligent systems into the agricultural sector.