ICTACT Journal on Soft Computing

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A High Quality Embedded System for Assessing Food Quality using Histogram of Oriented Gradients


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1 Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, India
     

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A low cost high quality system for accessing quality of food samples by finding the presence of fungus is proposed. Most of the food items kept for long intervals will have fungal infection in them. The proposed system uses Histogram of Oriented Gradients algorithm along with Support Vector Machine classifier to detect the presence of fungus. The features of the food samples captured in real time using a webcam are extracted using Histogram of Oriented Gradients algorithm. The extracted features are given to SVM classifier which compares these features with the trained one and displays the quality of food samples. The algorithms are implemented using ARM Cortex A-53 processor. Experimental results indicate that very good sensitivity and specificity is obtained and the execution time of the algorithms implemented in ARM processor is much lesser compared to the results obtained using MATLAB software.

Keywords

Accuracy, Fungus Detection, Histogram of Oriented Gradients, OpenCV, Support Vector Machine.
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Abstract Views: 371

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  • A High Quality Embedded System for Assessing Food Quality using Histogram of Oriented Gradients

Abstract Views: 371  |  PDF Views: 1

Authors

S. Jayanthy
Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, India
S. Valli Ayswaria
Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, India
M. Vaishali
Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, India
P. Udhaya Poorani
Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, India

Abstract


A low cost high quality system for accessing quality of food samples by finding the presence of fungus is proposed. Most of the food items kept for long intervals will have fungal infection in them. The proposed system uses Histogram of Oriented Gradients algorithm along with Support Vector Machine classifier to detect the presence of fungus. The features of the food samples captured in real time using a webcam are extracted using Histogram of Oriented Gradients algorithm. The extracted features are given to SVM classifier which compares these features with the trained one and displays the quality of food samples. The algorithms are implemented using ARM Cortex A-53 processor. Experimental results indicate that very good sensitivity and specificity is obtained and the execution time of the algorithms implemented in ARM processor is much lesser compared to the results obtained using MATLAB software.

Keywords


Accuracy, Fungus Detection, Histogram of Oriented Gradients, OpenCV, Support Vector Machine.

References