Rice Classification with Fractal-based Features based on Sparse Structured Principal Component Analysis and Gaussian Mixture Model

سال انتشار: 1400
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 9، شماره: 2
کد COI اختصاصی: JR_JADM-9-2_010
زبان مقاله: انگلیسی
تعداد مشاهده: 286

نویسندگان

Department of Engineering and Technology, University of Mazandaran, Babolsar, Iran.

Sari Agricultural Sciences and Natural Resources University, Iran.

چکیده

Development of an automatic system to classify the type of rice grains is an interesting research area in the scientific fields associated with modern agriculture. In recent years, different techniques are employed to identify the types of various agricultural products. Also, different color-based and texture-based features are used to yield the desired results in the classification procedure. This paper proposes a classification algorithm to detect different rice types by extracting features from the bulk samples. The feature space in this algorithm includes the fractal-based features of the extracted coefficients from the wavelet packet transform analysis. This feature vector is combined with other texture-based features and used to learn a model related to each rice type using the Gaussian mixture model classifier. Also, a sparse structured principal component analysis algorithm is applied to reduce the dimension of the feature vector and lead to the precise classification rate with less computational time. The results of the proposed classifier are compared with the results obtained from the other presented classification procedures in this context. The simulation results, along with a meaningful statistical test, show that the proposed algorithm based on the combinational features is able to detect precisely the type of rice grains with more than ۹۹% accuracy. Also, the proposed algorithm can detect the rice quality for different percentages of combination with other rice grains with ۹۹.۷۵% average accuracy.

کلیدواژه ها

Rice classification, Wavelet packet transform, Fractal-based feature, Sparse structured principal component analysis, Gaussian mixture model

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