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Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks

Published on October 2014 by Harish S Gujjar, M. Siddappa
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International Conference on Information and Communication Technologies
Foundation of Computer Science USA
ICICT - Number 1
October 2014
Authors: Harish S Gujjar, M. Siddappa
4f46d9cd-f67a-48fa-9d5f-f051ac8becd2

Harish S Gujjar, M. Siddappa . Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks. International Conference on Information and Communication Technologies. ICICT, 1 (October 2014), 12-17.

@article{
author = { Harish S Gujjar, M. Siddappa },
title = { Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks },
journal = { International Conference on Information and Communication Technologies },
issue_date = { October 2014 },
volume = { ICICT },
number = { 1 },
month = { October },
year = { 2014 },
issn = 0975-8887,
pages = { 12-17 },
numpages = 6,
url = { /proceedings/icict/number1/17959-1403/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Information and Communication Technologies
%A Harish S Gujjar
%A M. Siddappa
%T Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks
%J International Conference on Information and Communication Technologies
%@ 0975-8887
%V ICICT
%N 1
%P 12-17
%D 2014
%I International Journal of Computer Applications
Abstract

This paper deals with the classification of bulk food grain samples and detection of foreign bodies in food grains. A new method for inspecting food samples is presented, using ANN and segmentation to classify grain samples and detect foreign bodies that are not detectable using conventional methods easily. A BPNN based classifier is designed to classify the unknown grain samples. The algorithms are developed to extract color, texture and combined features are extracted from grains and after normalization presented to neural network for training purpose. The trained network is then used to identify the unknown grain type and it's quality in terms of pure/impure type. A Segmentation based detection model is developed to detect the foreign body in the impure grain samples. This model accepts an impure grain samples, pre-processes and segments the image using two different thresholds T1 and T2 to detect the foreign body in impure image. Finally the success rates are observed from both classification and foreign body detection models and are recorded.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks Classification Color Segmentation Texture Feature Extraction.