This research proposes an algorithm to implement feature extraction technique using wavelet, and use the extracted coefficients to represent the image for classification of Grains. A total of 75 Wavelet features were extracted from the high-resolution images of paddy grains. The wavelet features were employed along with ANN to identify paddy varieties. This research is aimed at comparing Single-level discrete 2-D wavelet transform and Multilevel 2-D wavelet decomposition, using ANN for discriminating Indian Paddy Varieties and also evaluate variety-wise classification of individual grains. An evaluation of the classification accuracy of wavelet features and ANN was done to classify four Paddy (Rice) grains, viz. Karjat-6(K6) and Ratnagiri-2(R2), Ratnagiri-4(R4) and Ratnagiri-24(R24). All feature models were tested for their ability to classify these cereal grains and the most suitable feature was identified from the Wavelet features for accurate classification. Single-level discrete 2-DWT gave the best classification using ANN and more accuracy can be obtained by increasing the levels of decomposition.

Author Name: Archana Chaugule

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Keywords: Energy Multilevel 2-DWT Neural network and Single-level Discrete 2-DWT

ISSN: 2320-5407

EISSN: 2320-5407

EOI/DOI: 10.21474/IJAR01/10963

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