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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.6, No.12, Nov. 2014

Evaluation of Shape and Color Features for Classification of Four Paddy Varieties

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Author(s)

Archana A. Chaugule, Suresh N. Mali

Index Terms

Color;Mean;Moments;Standard deviation;Variance;Shape features

Abstract

This research is aimed at evaluating the shape and color features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of shape and color features and neural network was done to classify four Paddy (Rice) grains, viz. Karjat-6, Ratnagiri-2, Ratnagiri-4 and Ratnagiri-24. Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and use them as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature set from the features was identified for accurate classification. The Shape-n-Color feature set outperformed in almost all the instances of classification.

Cite This Paper

Archana A. Chaugule, Suresh N. Mali,"Evaluation of Shape and Color Features for Classification of Four Paddy Varieties", IJIGSP, vol.6, no.12, pp.32-38, 2014.DOI: 10.5815/ijigsp.2014.12.05

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