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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  • M.W. Tahir et al., “Detection of Fungus through an Optical Sensor System using the Histogram of Oriented Gradients”, IEEE Sensors, Vol. 17, No. 16, pp. 5341-5349, 2017.

  • J. Zhang, “Automatic Identification of Fungi in Microscopic Leucorrhea Images”, Journal of the Optical Society of America A, Vol. 34, pp. 1484-1489, 2017.

  • S. Gunasekaran, “Computer Vision Technology for Food Quality Assurance”, Trends in Food Science and Technology, Vol. 7, pp. 245-256, 1996.

  • V. Chelladurai et al., “Thermal Imaging for Detecting Fungal Infection in Stored Wheat”, Journal of Stored Products Research, Vol. 46, No. 3, pp. 174-179, 2010.

  • J.D. Tallada et al., “Detection of Fungus Infected Corn Kernels using Near Infrared Reflectance Spectroscopy and Colour Imaging”, Grain Marketing and Production Research Center, Vol. 54, No. 3, pp. 1151-1158, 2011.

  • E.A. Bauriegel et al., “Early Detection of Fusarium Infection in Wheat using Hyper-Spectral Imaging, Computers and Electronics in Agriculture, Vol. 75, No. 2, pp. 304-312, 2011.

  • U. Siripatrawan et al., “Rapid Detection of Escherichia Coli Contamination in Packaged Fresh Spinach using Hyperspectral Imaging”, Talanta, Vol. 85, No. 1, pp. 276-281, 2011.

  • G. Firrao et al., “Prediction of Milled Maize Fumonisin Contamination by Multispectral Image Analysis”, Journal of Cereal Science, Vol. 52, pp. 327-330, 2010.

  • A. Bhargava, “Fruits and Vegetables Quality Evaluation using Computer Vision: A Review”, Journal of King Saud University, Computer and Information Sciences, 2018.

  • G. Rajakumar, T. Ananth Kumar, T.S. Arun Samuel and E. Muthu Kumaran, “IOT Based Milk Monitoring System for Detection of Milk Adulteration”, International Journal of Pure and Applied Mathematics, Vol. 118, No. 9, pp. 21-32, 2018.

  • Aman Naimat, “2018 could be the Year AI Kills Spam and Redefines Data Science”, Available at: https://venturebeat.com/2018/04/01/food-and-beveragecpgenterprise-ai//,2018.

  • Adrie J.M. Beulens et al., “Possibilities for Applying Data Mining for Early Warning in Food Supply Networks”, Available at: ftp://ftp.iiasa.ac.at/pub/mda/Pubs/csm06/pres/beulens_pap.pdf.


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

Abstract Views: 450  |  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