Abstract

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.

中文翻译：

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机器学习方法解决小麦种子产量特性分类问题的比较

摘要

数据挖掘在农业生产中的使用正日益普及。首次提出了利用种子的生物电指标解决基于种子产量特性的小麦种子分类问题的决策树，支持向量机和K-近邻机器学习方法的实施结果。通过精度指标，混淆矩阵的构建和训练质量的交叉验证来证明所研究分类器的有效性。方法比较结果表明，决策树方法在数据分类中表现最好。该方法在模型结果的理解和解释上非常简单，不需要额外的数据准备。实验结果表明，带有噪声成分的样品具有较高的准确度（96％）。无需标准化数据，添加虚拟变量或删除丢失的数据。还建议将K最近的邻居用于按产量属性对种子进行分类。但是，其准确性不如决策树。对于有噪声的采样，精度为91％。支持向量机不是解决该问题的有前途的工具，尽管它在其他领域是非常成功的方法。