Comparison of Machine Learning Methods for Solving the Problem of Wheat Seeds Classification by Yield Properties

The use of data mining in agricultural production is gaining popularity. The results of the implementation of machine learning methods, namely, decision tree, support vector machine and the K-nearest neighbor for solving the problem of wheat seeds classification by yield properties, using bioelectric indicators of seeds are for the first time presented in the work. The effectiveness of the studied classifiers is presented by the accuracy indicators, the confusion matrix construction and training quality cross validation. The methods comparison results found that the decision tree method showed the best results in data classification. The method is quite simple in the model results understanding and interpretation and does not require additional data preparation. The experimental results showed relatively high accuracy (96%) for the sample with a noise component. There is no need to normalize data, add dummy variables or delete missed data. The K-nearest neighbor is also recommended for classifying seeds by yield properties. However, it is inferior in accuracy to decision trees. For sampling with noise the accuracy was 91%. The support vector machine is not a promising tool for solving this problem, although it is an extremely successful method for other areas.