Recognition and Classification of White wholes (WW) grade Cashew kernel using Artificial Neural Networks

Narendra, V G and Hareesha, K S (2015) Recognition and Classification of White wholes (WW) grade Cashew kernel using Artificial Neural Networks. In: Conference of the International Journal of Food Science and Technology, 17th - 19th February 2015, Lincoln university, New Zealand.

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Abstract

This paper deals with a novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, includes 16 morphological features and also 24 colour features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 90.13%. This study also reveals that the combination of morphological and colour features outperforms rather using any one set of features separately to grade cashew kernels

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: White wholes (WW) grade cashew kernel images, feature extraction, Artificial Neural Networks, classification
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 14 Jan 2016 10:22
Last Modified: 14 Jan 2016 10:22
URI: http://eprints.manipal.edu/id/eprint/145066

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