Open AccessAnalysis of Wheat Samples Using the Calculation of Multifractal Spectrum
Author(s) -
Ivan Murenin,
AUTHOR_ID,
Natalia Ampilova,
AUTHOR_ID
Publication year - 2021
Publication title -
kompʹûternye instrumenty v obrazovanii
Language(s) - English
Resource type - Journals
eISSN - 2071-2359
pISSN - 2071-2340
DOI - 10.32603/2071-2340-2021-1-5-20
Subject(s) - principal component analysis , pattern recognition (psychology) , multifractal system , mathematics , artificial intelligence , support vector machine , random forest , feature vector , cluster analysis , outlier , similarity (geometry) , computer science , statistics , image (mathematics) , fractal , mathematical analysis
The computational analysis of wheat images to identify wheat varieties and quality has wide applications in agriculture and production. This paper presents an approach to the analysis and classication of images of wheat samples obtained by the method of crystallization with additives. In tests 3 concentration and 4 times for each concentration were used, such that each type of wheat was characterized by 12 images. We used the images obtained for 5 classes. All the images have similar visual characteristics, that makes it dicult to use statistical methods of analysis. The multifractal spectrum obtained by calculating the local density function was used as a classifying feature. The classication was performed on a set of 60 wheat images corresponding to 5 different samples (classes) by various machine learning methods such as linear regression, naive Bayesian classier, support vector machine, and random forest. In some cases, to reduce the dimension of the feature space the method of principal components was applied. To identify the relationships between wheat samples obtained at different concentrations, 3 different clustering methods were used. The classication results showed that the multifractal spectrum as classifying sign and using the random forest method in combination with the principal component analysis allow identifying wheat samples obtained by crystallization with additives, being the highest average classi- cation accuracy is 74 %.