S. Mavaddati; S. Mavaddati
Abstract
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 ...
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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 99% accuracy. Also, the proposed algorithm can detect the rice quality for different percentages of combination with other rice grains with 99.75% average accuracy.
H.5. Image Processing and Computer Vision
S. Mavaddati
Abstract
In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification ...
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In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts including sparse representation and dictionary learning techniques to yield over-complete models in this processing field. There are color-based, statistical-based and texture-based features to represent the structural content of rice varieties. To achieve the desired results, different features from recorded images are extracted and used to learn the representative models of rice samples. Also, sparse principal component analysis and sparse structured principal component analysis is employed to reduce the dimension of classification problem and lead to an accurate detector with less computational time. The results of the proposed classifier based on the learned models are compared with the results obtained from neural network and support vector machine. Simulation results, along with a meaningful statistical test, show that the proposed algorithm based on the learned dictionaries derived from the combinational features can detect the type of rice grain and determine its quality precisely.
