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A Method for Identification of Basmati Rice grain of India and Its Quality Using Pattern Classification

By Harish S Gujjar


The research work deals with an approach to perform texture and morphological based retrieval on a corpus of Basmati rice grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The approach has been tested on sufficient number of basmati rice grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties of basmati rice seeds which are widely planted in India. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types. It is also to find the percentage purity of hulled basmati rice grain sample by image processing technique. Commercially the purity test of basmati rice sample is done according to the size of the grain kernel (full, half or broken). By image processing we can also identify any broken basmati rice grains. Here we discuss the various procedures used to obtain the percentage quality of basmati rice grains

Topics: Thresholding, Percentage Purity, Pixel area
Year: 2014
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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