Abstract
The article demonstrates a method that combines the wavelet transform and machine learning methods to classify plants health using colored digital images. The input data for classifi cation is comprised of a built vector of Haralick texture features. The software was developed via the Python programming language to classify digital images with the multilevel discrete Daubechies wavelet transform and methods of classifi cation for machine learning, particularly classic logistic regression and perceptron. The effi ciency of this method for solving the problem of multiclass image classifi cation is demonstrated. The study concludes and assesses the prospects of the method.
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