Abstract:
The prevention of disease transmission in plants is largely dependent on early
detection of pathogen infection. Plant diseases can be identified using machine learning
techniques before they fully manifest their symptoms. The more problems have been
solved, the more reliable systems have been built. This system developed the
agricultural field. Machine learning is a new area of study for agricultural analysis.
Machine learning is a new area of study for agricultural analysis. The use of machine
learning techniques in the sector of agriculture is the main topic of this study. Different
machine learning techniques are in use, such as k-Nearest Neighbors (k-NN), J48
Decision Trees, Nave Bayes and Decision Table for very recent applications of data
mining techniques in the agriculture field. This thesis properly classifies the problem
of soybean diseases. For this purpose, different types of machine learning techniques
were evaluated on soybean disease data sets. This thesis discusses the development of
an expert system to diagnose soybean disease using machine learning techniques. This
system implemented the K-folds cross validation method by using K value changes.
