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Gıda Biliminde Bilgisayarla Görü Teknolojisi

Yıl 2018, Cilt: 8 Sayı: 1, 403 - 409, 01.01.2018

Öz

Bilgisayarla görü; elde edilen görüntü veya görüntü sırasında bir nesne hakkında teorik ve algoritmik tabanda otomatik olarak yararlı bilgi çıkarmaya yarayan bir bilim dalıdır. Bilgisayarla görü sistemleri; gıda endüstrisinde gıda yüzeyinde kusurların belirlenmesinde, bozulmanın tespit edilmesinde ve gıdaların kalite muayenelerinde günden güne yaygınlaşarak kullanılmaktadır. Özellikle bu sistemler, ham ve işlenmiş gıdaların çeşitli kalite özelliklerini belirleyen insanların yerini almaktadır. Bilgisayarla görü teknolojisi; hızlı, kesin ve tutarlı sonuçlar vermesi ve diğerlerine oranla daha az maliyetli olması nedeniyle gıda endüstrisinde önemli rol oynar. Günümüzde bilgisayarla görü sistemleri; gerçek zamanlı kalite derecelendirilmesi ve kontolünde gıda işleme birimlerinin ayrılmaz bir parçası olarak görülmektedir. Kalite gereksinimlerine uyumlu kalmak ve artan üretim miktarlarına karşılık gerçek zamanlı olarak akan veriyi işleyebilmek üzere etkin ve yeni teknikler geliştirilmelidir. Yakın gelecekte tamamen otomatik ve robotlar tarafından kontrol edilen üretim, gıda üreticileri arasında anahtar bir teknoloji olacaktır. Ayrıca, gelişen mobil donanım ve yazılım teknolojileri sayesinde tüketiciler, gıdaların kalitelerini kendileri de kontrol edebileceklerdir

Kaynakça

  • Alirezaei, M., Zare, D., Nassiri, SM. 2013. Application of Computer Vision for Determining Viscoelastic Characteristics of Date Fruits. J of Food Eng., 118(3):326-332.
  • Amigo, JM., Babamoradi, H., Elcoroaristizabal, S. 2015. Hyperspectral Image Analysis. A tutorial. Analytica Chimica Acta., 896:34-51.
  • Arakeri, MP., Lakshmana, L. 2016. Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture Industry. Proc Comput Sci., 79:426-433.
  • Barbin, D.F., Mastelini, SM., Barbon, S., Campos, GFC., Barbon, APAC., Shimokomaki, M. 2016. Digital Image Analyses as An Alternative Tool for Chicken Quality Assessment. Biosystems Eng., 144: 85-93.
  • Barzegar, M., Zare, D., Stroshine, R. L. 2015.An Integrated Energy and Quality Approach to Optimization of Green Peas Drying in a Hot Air Infrared-Assisted Vibratory Bed Dryer. J of Food Eng., 166: 302-315.
  • Chaugule, A., Mali, SN. 2014. Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties. J. Eng., 2014: 1-8.
  • Cheng, JH., Sun, DW. 2015. Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method. Food and Bioprocess Technol., 8(5):951-959.
  • Chmiel, M., Słowiński, M., Dasiewicz, K., Florowski, T. 2016. Use of Computer Vision System (CVS) for Detection of PSE Pork Meat Obtained from M.semimembranosus. LWT - Food Sci and Technol., 65: 532-536.
  • Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., Blasco, J. 2011. Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables. Food and Bioprocess Technol., 4(4): 487-504.
  • Daugaard, SB., Adler-Nissen, J., Carstensen, JM. 2010.New Vision Technology for Multidimensional Quality Monitoring of Continuous Frying of Meat. Food Control., 21(5):626-632.
  • Donis-González, IR., Guyer, DE., Pease, A. 2016.ostharvest Noninvasive Assessment of Undesirable Fibrous Tissue in Fresh Processing Carrots Using Computer Tomography Images. J. Food Eng., 190:154-166.
  • Dowlati, M., de la Guardia, M., Mohtasebi, SS. 2012. Application of Machine-Vision Techniques to Fish-Quality Assessment. TrAC Trends in Analyt Chem., 40: 168-179.
  • Dowlati, M., Mohtasebi, SS., Omid, M., Razavi, SH., Jamzad, M., de la Guardia, M. 2013. Freshness Assessment of Gilthead Sea Bream (Sparus aurata) by Machine Vision Based on Gill and Eye Color Changes. J. Food Eng., 119(2):277-287.
  • Dutta, M. K., Singh, A., Ghosal, S. 2015. A Computer Vision Based Technique for Identification of Acrylamide In Potato Chips. Comput and Electron in Agric., 119:40-50.
  • Ghasemi-Varnamkhasti, M., Goli, R., Forina, M., Mohtasebi, SS., Shafiee, S., Naderi-Boldaji, M. 2016. Application of Image Analysis Combined With Computational Expert Approaches for Shrimp Freshness Evaluation. Int. J. Food Properties, 19(10): 2202-2222.
  • Girolami, A., Napolitano, F., Faraone, D., Braghieri, A. 2013. Measurement of Meat Color Using a Computer Vision System. Meat Sci., 93(1):111-118.
  • He, HJ., Wu, D., Sun, DW. 2014. Potential of Hyperspectral Imaging Combined with Chemometric Analysis for Assessing and Visualising Tenderness Distribution in Raw Farmed Salmon Fillets. J of Food Eng., 126:156-164.
  • Huang, M., Wang, Q., Zhang, M., Zhu, Q. 2014. Prediction of Color and Moisture Content for Vegetable Soybean During Drying Using Hyperspectral Imaging Technology. J. Food Eng., 128:24-30.
  • Ivorra, E., Sánchez, A.J., Camarasa, JG., Diago, M.P., Tardaguila, J. 2015.Assessment of Grape Cluster Yield Components Based on 3D Descriptors Using Stereo Vision. Food Control., 50: 273-282.
  • Jackman, P., Sun, DW. 2013. Recent Advances in Image Processing Using Image Texture Features for Food Quality Assessment. Trends in Food Sci & Technol., 29(1)35-43.
  • Jha, SN., Narsaiah, K., Jaiswal, P., Bhardwaj, R., Gupta, M., Kumar, R., Sharma, R. 2014. Nondestructive Prediction of Maturity of Mango Using Near Infrared Spectroscopy. J. Food Eng., 124:152-157.
  • Jinorose, M., Prachayawarakorn, S., Soponronnarit, S. 2014.A Novel Image-Analysis Based Approach to Evaluate Some Physicochemical and Cooking Properties of Rice Kernels. J. Food Eng.,124: 184-190.
  • Kamruzzaman, M., Makino, Y., Oshita, S. 2016. Online Monitoring of Red Meat Color Using Hyperspectral Imaging. Meat Sci., 116: 110-117.
  • Kamruzzaman, M., ElMasry, G., Sun, DW., Allen, P. 2013. Non-Destructive Assessment of Instrumental and Sensory Tenderness of Lamb Meat Using NIR Hyperspectral Imaging. Food Chem.,141(1): 389-396.
  • Keresztes, JC., Goodarzi, M., Saeys, W. 2016. Real-Time Pixel Based Early Apple Bruise Detection Using Short Wave Infrared Hyperspectral Imaging in Combination with Calibration and Glare Correction Techniques. Food Control., 66: 215-226.
  • Khoshroo, A., Arefi, A., Masoumiasl, A., Jowkar, GH. 2014. Classification of Wheat Cultivars Using Image Processing and Artificial Neural Networks. Agric. Communications, 2(1):17-22.
  • Kiani, S., Minaei, S., Ghasemi-Varnamkhasti, M. 2016.Fusion of Artificial Senses as A Robust Approach to Food Quality Assessment. J. Food Eng., 171:230-239.
  • Liu, D., Ma, J., Sun, DW., Pu, H., Gao, W., Qu, J., Zeng,, XA. 2014.Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging. Food Bioprocess Technol., 7(11):3100 - 3108.
  • López-Maestresalas, A., Keresztes, JC., Goodarzi, M., Arazuri, S., Jarén, C., Saeys, W. 2016.Non-Destructive Detection of Blackspot in Potatoes by Vis-NIR and SWIR Hyperspectral Imaging. Food Control., 70:229-241.

Computer Vision Technology On Food Science

Yıl 2018, Cilt: 8 Sayı: 1, 403 - 409, 01.01.2018

Öz

Computer vision is a science that extracts useful information about an object from an observed image or image sequence automatically by analyzing in theoretical and algorithmic bases. Computer vision systems are increasingly used for detection of the surface defects, contamination, and quality inspection of the foods in the food industry. Essentially, such systems take the place of human inspectors to assess the various quality characteristics of raw and ready-to-eat foods. Computer vision technology plays a key role by giving rapid, precise, and consistent results as well as having relatively low cost. Today, computer vision systems are considered as an indispensable part of food processing units for real-time quality assessment and control. Effective techniques will be developed to process image stream data in real time to meet increased production amounts and comply with quality requirements. Robot-controlled and fully automated production will be key technology about quality assurance for competitive food producers in near future. Also, consumers will be able to check the quality of their products by themselves in the near future thanks to developing mobile hardware and software technologies.

Kaynakça

  • Alirezaei, M., Zare, D., Nassiri, SM. 2013. Application of Computer Vision for Determining Viscoelastic Characteristics of Date Fruits. J of Food Eng., 118(3):326-332.
  • Amigo, JM., Babamoradi, H., Elcoroaristizabal, S. 2015. Hyperspectral Image Analysis. A tutorial. Analytica Chimica Acta., 896:34-51.
  • Arakeri, MP., Lakshmana, L. 2016. Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture Industry. Proc Comput Sci., 79:426-433.
  • Barbin, D.F., Mastelini, SM., Barbon, S., Campos, GFC., Barbon, APAC., Shimokomaki, M. 2016. Digital Image Analyses as An Alternative Tool for Chicken Quality Assessment. Biosystems Eng., 144: 85-93.
  • Barzegar, M., Zare, D., Stroshine, R. L. 2015.An Integrated Energy and Quality Approach to Optimization of Green Peas Drying in a Hot Air Infrared-Assisted Vibratory Bed Dryer. J of Food Eng., 166: 302-315.
  • Chaugule, A., Mali, SN. 2014. Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties. J. Eng., 2014: 1-8.
  • Cheng, JH., Sun, DW. 2015. Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method. Food and Bioprocess Technol., 8(5):951-959.
  • Chmiel, M., Słowiński, M., Dasiewicz, K., Florowski, T. 2016. Use of Computer Vision System (CVS) for Detection of PSE Pork Meat Obtained from M.semimembranosus. LWT - Food Sci and Technol., 65: 532-536.
  • Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., Blasco, J. 2011. Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables. Food and Bioprocess Technol., 4(4): 487-504.
  • Daugaard, SB., Adler-Nissen, J., Carstensen, JM. 2010.New Vision Technology for Multidimensional Quality Monitoring of Continuous Frying of Meat. Food Control., 21(5):626-632.
  • Donis-González, IR., Guyer, DE., Pease, A. 2016.ostharvest Noninvasive Assessment of Undesirable Fibrous Tissue in Fresh Processing Carrots Using Computer Tomography Images. J. Food Eng., 190:154-166.
  • Dowlati, M., de la Guardia, M., Mohtasebi, SS. 2012. Application of Machine-Vision Techniques to Fish-Quality Assessment. TrAC Trends in Analyt Chem., 40: 168-179.
  • Dowlati, M., Mohtasebi, SS., Omid, M., Razavi, SH., Jamzad, M., de la Guardia, M. 2013. Freshness Assessment of Gilthead Sea Bream (Sparus aurata) by Machine Vision Based on Gill and Eye Color Changes. J. Food Eng., 119(2):277-287.
  • Dutta, M. K., Singh, A., Ghosal, S. 2015. A Computer Vision Based Technique for Identification of Acrylamide In Potato Chips. Comput and Electron in Agric., 119:40-50.
  • Ghasemi-Varnamkhasti, M., Goli, R., Forina, M., Mohtasebi, SS., Shafiee, S., Naderi-Boldaji, M. 2016. Application of Image Analysis Combined With Computational Expert Approaches for Shrimp Freshness Evaluation. Int. J. Food Properties, 19(10): 2202-2222.
  • Girolami, A., Napolitano, F., Faraone, D., Braghieri, A. 2013. Measurement of Meat Color Using a Computer Vision System. Meat Sci., 93(1):111-118.
  • He, HJ., Wu, D., Sun, DW. 2014. Potential of Hyperspectral Imaging Combined with Chemometric Analysis for Assessing and Visualising Tenderness Distribution in Raw Farmed Salmon Fillets. J of Food Eng., 126:156-164.
  • Huang, M., Wang, Q., Zhang, M., Zhu, Q. 2014. Prediction of Color and Moisture Content for Vegetable Soybean During Drying Using Hyperspectral Imaging Technology. J. Food Eng., 128:24-30.
  • Ivorra, E., Sánchez, A.J., Camarasa, JG., Diago, M.P., Tardaguila, J. 2015.Assessment of Grape Cluster Yield Components Based on 3D Descriptors Using Stereo Vision. Food Control., 50: 273-282.
  • Jackman, P., Sun, DW. 2013. Recent Advances in Image Processing Using Image Texture Features for Food Quality Assessment. Trends in Food Sci & Technol., 29(1)35-43.
  • Jha, SN., Narsaiah, K., Jaiswal, P., Bhardwaj, R., Gupta, M., Kumar, R., Sharma, R. 2014. Nondestructive Prediction of Maturity of Mango Using Near Infrared Spectroscopy. J. Food Eng., 124:152-157.
  • Jinorose, M., Prachayawarakorn, S., Soponronnarit, S. 2014.A Novel Image-Analysis Based Approach to Evaluate Some Physicochemical and Cooking Properties of Rice Kernels. J. Food Eng.,124: 184-190.
  • Kamruzzaman, M., Makino, Y., Oshita, S. 2016. Online Monitoring of Red Meat Color Using Hyperspectral Imaging. Meat Sci., 116: 110-117.
  • Kamruzzaman, M., ElMasry, G., Sun, DW., Allen, P. 2013. Non-Destructive Assessment of Instrumental and Sensory Tenderness of Lamb Meat Using NIR Hyperspectral Imaging. Food Chem.,141(1): 389-396.
  • Keresztes, JC., Goodarzi, M., Saeys, W. 2016. Real-Time Pixel Based Early Apple Bruise Detection Using Short Wave Infrared Hyperspectral Imaging in Combination with Calibration and Glare Correction Techniques. Food Control., 66: 215-226.
  • Khoshroo, A., Arefi, A., Masoumiasl, A., Jowkar, GH. 2014. Classification of Wheat Cultivars Using Image Processing and Artificial Neural Networks. Agric. Communications, 2(1):17-22.
  • Kiani, S., Minaei, S., Ghasemi-Varnamkhasti, M. 2016.Fusion of Artificial Senses as A Robust Approach to Food Quality Assessment. J. Food Eng., 171:230-239.
  • Liu, D., Ma, J., Sun, DW., Pu, H., Gao, W., Qu, J., Zeng,, XA. 2014.Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging. Food Bioprocess Technol., 7(11):3100 - 3108.
  • López-Maestresalas, A., Keresztes, JC., Goodarzi, M., Arazuri, S., Jarén, C., Saeys, W. 2016.Non-Destructive Detection of Blackspot in Potatoes by Vis-NIR and SWIR Hyperspectral Imaging. Food Control., 70:229-241.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Article
Yazarlar

Duygu Balpetek Külcü Bu kişi benim

Yayımlanma Tarihi 1 Ocak 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 8 Sayı: 1

Kaynak Göster

APA Külcü, D. B. (2018). Computer Vision Technology On Food Science. Karaelmas Fen Ve Mühendislik Dergisi, 8(1), 403-409.
AMA Külcü DB. Computer Vision Technology On Food Science. Karaelmas Fen ve Mühendislik Dergisi. Ocak 2018;8(1):403-409.
Chicago Külcü, Duygu Balpetek. “Computer Vision Technology On Food Science”. Karaelmas Fen Ve Mühendislik Dergisi 8, sy. 1 (Ocak 2018): 403-9.
EndNote Külcü DB (01 Ocak 2018) Computer Vision Technology On Food Science. Karaelmas Fen ve Mühendislik Dergisi 8 1 403–409.
IEEE D. B. Külcü, “Computer Vision Technology On Food Science”, Karaelmas Fen ve Mühendislik Dergisi, c. 8, sy. 1, ss. 403–409, 2018.
ISNAD Külcü, Duygu Balpetek. “Computer Vision Technology On Food Science”. Karaelmas Fen ve Mühendislik Dergisi 8/1 (Ocak 2018), 403-409.
JAMA Külcü DB. Computer Vision Technology On Food Science. Karaelmas Fen ve Mühendislik Dergisi. 2018;8:403–409.
MLA Külcü, Duygu Balpetek. “Computer Vision Technology On Food Science”. Karaelmas Fen Ve Mühendislik Dergisi, c. 8, sy. 1, 2018, ss. 403-9.
Vancouver Külcü DB. Computer Vision Technology On Food Science. Karaelmas Fen ve Mühendislik Dergisi. 2018;8(1):403-9.