Advances in computer vision technology for foods of animal and aquatic origin- a review

Computer vision system is an image processing/analysis technique which is used for objective evaluation of quality parameters. It is a rapid, economical, consistent inspection technique with great advantages in its objectiveness, efficiency and reliability. Computer vision system can perform many functions simultaneously in a food processing line such as segregation by species, by size, by visual quality attributes, determination of composition, evaluation of size, volume, measurement of shape parameters, and quantification of the meat colour, automated portioning and detection of defects. Computer vision considered as investigative tool to evaluate the functional properties and quality attributes such as shrinkage, texture and colour of cheddar and mozzarella cheese. This paper critically reviews the progress and application of computer vision technology in the food industry with a special emphasis on meat, poultry, seafood, and other foods. The authors have also explored the possible areas for further research and wider application the technique in food industry.

[1]  Gregory A. Baxes,et al.  Digital image processing - principles and applications , 1994 .

[2]  Achim Kohler,et al.  Sorting salted cod fillets by computer vision , 2002 .

[3]  Sundaram Gunasekaran,et al.  Using computer vision for food quality evaluation : Applications of immunobiosensors and bioelectronics in food sciences and quality control , 1994 .

[4]  Boaz Zion,et al.  Sorting fish by computer vision , 1999 .

[5]  T. P. McDonald,et al.  SEPARATING CONNECTED MUSCLE TISSUES IN IMAGES OF BEEF CARCASS RIBEYES , 1990 .

[6]  Emanuele Trucco,et al.  Visual Learning of Weight from Shape Using Support Vector Machines , 1998, BMVC.

[7]  Da-Wen Sun,et al.  Inspecting pizza topping percentage and distribution by a computer vision method , 2000 .

[8]  L. F. Pau,et al.  Vision Applications in the Fishing and Fish Product Industries , 1988, Int. J. Pattern Recognit. Artif. Intell..

[9]  I. Zayas,et al.  Wheat classification using image analysis and crush-force parameters , 1996 .

[10]  F J Tan,et al.  Assessment of fresh pork color with color machine vision. , 2000, Journal of animal science.

[11]  P. Wallin,et al.  Foreign body prevention, detection and control , 1998 .

[12]  G. H. Brusewitz,et al.  Asparagus Defect Inspection with Machine Vision , 1992 .

[13]  Bent Rønsholdt,et al.  EVALUATION OF IMAGE ANALYSIS AS A METHOD FOR EXAMINING CARCASS COMPOSITION OF RAINBOW TROUT , 2000 .

[14]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[15]  B. G. Batchelor,et al.  Machine vision for the food industry , 1993 .

[16]  A.J.M. Timmermans COMPUTER VISION SYSTEM FOR ON-LINE SORTING OF POT PLANTS BASED ON LEARNING TECHNIQUES , 1998 .

[17]  Katiganere Siddaramappa Hareesha,et al.  Quality Inspection and Grading of Agricultural and Food Products by Computer Vision- A Review , 2010 .

[18]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[19]  J. Lu,et al.  Evaluation of pork color by using computer vision. , 2000, Meat science.

[20]  Da-Wen Sun,et al.  Inspection and grading of agricultural and food products by computer vision systems—a review , 2002 .

[21]  James N. Ianelli,et al.  Estimating the size selectivity and catching efficiency of a survey bottom trawl for thornyheads, Sebastolobus spp. using a towed video camera sled , 2004 .

[22]  K. Heia,et al.  Detection of nematodes in cod (Gadus morhua) fillets by imaging spectroscopy. , 2007, Journal of food science.

[23]  Fredrik Manne,et al.  Rapid estimation of fat content in salmon fillets by colour image analysis , 2007 .

[24]  Yang Tao,et al.  INTERNAL INSPECTION OF DEBONED POULTRY USING X–RAY IMAGING AND ADAPTIVE THRESHOLDING , 2001 .

[25]  J. Tan,et al.  Beef Marbling and Color Score Determination by Image Processing , 1996 .

[26]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[27]  Bosoon Park,et al.  Co-occurrence matrix texture features of multi-spectral images on poultry carcasses , 2001 .

[28]  Richard K. Byler,et al.  Machine Vision Based Oyster Meat Grading and Sorting Machine , 1995 .

[29]  Y.-R. Chen,et al.  Intensified Multispectral Imaging System for Poultry Carcass Inspection , 1994 .

[30]  Jens Petter Wold,et al.  Non-Destructive Determination of Fat and Moisture in Whole Atlantic Salmon by Near-Infrared Diffuse Spectroscopy , 1997 .

[31]  Fred Godtliebsen,et al.  Ridge detection with application to automatic fish fillet inspection , 2009 .

[32]  Fred Wheaton,et al.  Image processing and pattern recognition for oyster hinge line detection. , 1990 .

[33]  Da-Wen Sun,et al.  Melting characteristics of cheese: analysis of effects of cooking conditions using computer vision technology , 2002 .

[34]  H. Yin,et al.  Image processing techniques for internal texture evaluation of French fries , 2004 .

[35]  Olivier Basset,et al.  Application of texture image analysis for the classification of bovine meat , 2000 .

[36]  K. A. Tarbell,et al.  A COMPUTER VISION SYSTEM FOR CHARACTERIZING CORN GROWTH AND DEVELOPMENT , 1991 .

[37]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[38]  Knut-Andreas Lie,et al.  Automatic Segmentation of Overlapping Fish Using Shape Priors , 2007, SCIA.

[39]  Valerie Jamieson Physics raises food standards , 2002 .

[40]  P. Tojeiro,et al.  OYSTER ORIENTATION USING COMPUTER VISION , 1991 .

[41]  Da-Wen Sun,et al.  Evaluation of the functional properties of Cheddar Cheese using a computer vision method , 2001 .

[42]  Dah-Jye Lee,et al.  Fast surface approximation for volume and surface area measurements using distance transform , 2003 .

[43]  Mauro Barni,et al.  Colour-based detection of defects on chicken meat , 1997, Image Vis. Comput..

[44]  Da-Wen Sun,et al.  Correlation between Cheese Meltability Determined with a Computer Vision Method and with Arnott and Schreiber Tests , 2002 .

[45]  Charles Baur,et al.  Computer vision and agricultural robotics for disease control : the Potato Operation , 1993 .

[46]  Dah-Jye Lee,et al.  Three-dimensional reconstruction for high-speed volume measurement , 2001, SPIE Optics East.

[47]  Mohammad-R. Akbarzadeh-T,et al.  Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp , 2009 .

[48]  Bosoon Park,et al.  Characterizing Multispectral Images of Tumorous, Bruised, Skin-torn, and Wholesome Poultry Carcasses , 1996 .

[49]  M. Gómez-Guillén,et al.  Use of image analysis to determine fat and connective tissue in salmon muscle , 1999 .

[50]  L. F. Pau,et al.  PDL-HM: morphological and syntactic shape classification algorithm , 2005, Machine Vision and Applications.

[51]  Haruhiko Murase,et al.  Three-dimensional Shape Recognition using a Charge-Simulation Method to Process Primary Image Features , 1998 .

[52]  Yoshiaki Shirai,et al.  Three-Dimensional Computer Vision , 1987, Symbolic Computation.

[53]  C. T. Morrow,et al.  Fourier-based Separation Technique for Shape Grading of Potatoes Using Machine Vision , 1995 .

[54]  Zhang Min,et al.  Colour vision in forest and wood engineering. , 2000 .

[55]  Frank Storbeck,et al.  Fish species recognition using computer vision and a neural network , 2001 .

[56]  Paulo Cortez,et al.  Lamb Meat Quality Assessment by Support Vector Machines , 2006, Neural Processing Letters.

[57]  Da-Wen Sun,et al.  Evaluation of the oiling off property of cheese with computer vision: Correlation with fat ring test , 2004 .

[58]  Philippe Loisel,et al.  Quality traits of brown trouts (Salmo trutta) cutlets described by automated color image analysis , 2004 .

[59]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .

[60]  Da-Wen Sun,et al.  Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision , 2007 .

[61]  Murat O. Balaban,et al.  Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue , 2010 .

[62]  N.J.C. Strachan Sea trials of a computer vision based fish species sorting and size grading machine , 1994 .

[63]  Yang Tao,et al.  DETECTING STEM AND SHAPE OF PEARS USING FOURIER TRANSFORMATION AND AN ARTIFICIAL NEURAL NETWORK , 2003 .

[64]  Sundaram Gunasekaran,et al.  Computer vision technology for food quality assurance , 1996 .

[65]  Sundaram Gunasekaran,et al.  Three-Dimensional Characteristics of Fat Globules in Cheddar Cheese , 1999 .

[66]  Dah-Jye Lee,et al.  Shape similarity measure using turn angle cross-correlation for oyster quality evaluation , 2010 .

[67]  S. Gunasekaran Nondestructive Food Evaluation: Techniques to Analyze Properties and Quality , 2000 .

[68]  Laurence T. Kell,et al.  A potential method for the differentiation between haddock fish stocks by computer vision using canonical discriminant analysis , 1995 .