Visual quality detection of aquatic products using machine vision

Abstract Aquatic products are popular among consumers and their visual quality used to be detected manually for sorting, grading, species classification and freshness assessment. Machine vision, as a non-destructive method, has been used in external quality detection of aquatic products for its efficiency, objectiveness, consistency and reliability. Quite a number of researches have highlighted its potential for visual quality detection of fishes, fish filets and some other aquatic products (i.e. shrimp, oyster, and scallop). This review introduced detecting methods based on measurement of size, shape, and color using machine vision systems. Size measurement (i.e. length and area) was usually taken for sorting and grading, while shape was measured for species classification with the integration of size information. Color information was studied for analysis of fish filets, fish muscle, fish skin and shrimp, and for color changes of specially treated fish. Machine vision systems used for measuring size, shape, and color was described, including improvements of cameras, illumination settings, image processing and analysis methods, and experimental results as well. With the development in these areas, machine vision technique may achieve higher accuracy and efficiency, and wider application in visual quality detection of aquatic products. Besides, advantages and limitations of these machine vision systems were discussed, with recommendation on future developments.

[1]  Dah-Jye Lee,et al.  An efficient shape analysis method for shrimp quality evaluation , 2012, 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV).

[2]  J. B. Luten,et al.  Colour measurement on skin during storage of wet and frozen fish. , 2003 .

[3]  Stein Ove Østvik,et al.  High-speed weight estimation of whole herring (Clupea harengus) using 3D machine vision. , 2011, Journal of food science.

[4]  M. Balaban,et al.  Quantifying nonhomogeneous colors in agricultural materials. Part II: comparison of machine vision and sensory panel evaluations. , 2008, Journal of food science.

[5]  Shahrul Azman Mohd Noah,et al.  Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network , 2010 .

[6]  Bruce A. Welt,et al.  Comparison of Minolta colorimeter and machine vision system in measuring colour of irradiated Atlantic salmon. , 2009 .

[7]  M. Balaban,et al.  Determination of count and uniformity ratio of shrimp by machine vision , 1995 .

[8]  M. Balaban,et al.  Quantifying nonhomogeneous colors in agricultural materials part I: method development. , 2008, Journal of food science.

[9]  E Misimi,et al.  Computer vision-based sorting of Atlantic salmon (Salmo salar) fillets according to their color level. , 2007, Journal of food science.

[10]  E Misimi,et al.  Quality grading of Atlantic salmon (Salmo salar) by computer vision. , 2008, Journal of food science.

[11]  Da-Wen Sun,et al.  Computer vision technology for food quality evaluation , 2008 .

[12]  Dah-Jye Lee,et al.  Shape analysis for an automatic oyster grading system , 2004, SPIE Optics East.

[13]  R. K. Byler,et al.  GEOMETRIC AND PHYSICAL PROPERTIES OF RAW OYSTER MEAT AS RELATED TO GRADING , 1990 .

[14]  Yu-Teng Liang,et al.  Machine Vision-Based Automatic Raw Fish Handling and Weighing System of Taiwan Tilapia , 2009, IEA/AIE.

[15]  K. Miyashita,et al.  Handbook of seafood quality, safety and health applications. , 2010 .

[16]  John E. W. Mayhew,et al.  3D Model Recognition from Stereoscopic Cues , 1991 .

[17]  S. Robson,et al.  An analysis of the calibration stability and measurement accuracy of an underwater stereo-video system used for shellfish surveys. , 2000 .

[18]  Jiang Shu-yi.  Automatic measurement of size and location in scallop based on image processing , 2012 .

[19]  Lindsay G. Ross,et al.  Effect of temperature on growth and survival of Chirostoma estor estor, Jordan 1879, monitored using a simple video technique for remote measurement of length and mass of larval and juvenile fishes , 2002 .

[20]  Zayde Alçiçek,et al.  Development and application of “The Two Image” method for accurate object recognition and color analysis , 2012 .

[21]  Norval J. C. Strachan,et al.  Length measurement of fish by computer vision , 1993 .

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

[23]  W. S. Otwell,et al.  The Influence of Carbon Monoxide and Filtered Wood Smoke on Fish Muscle Color , 2007 .

[24]  Peter Corke,et al.  Automatic grading and packing of prawns , 1993 .

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

[26]  Victor Alchanatis,et al.  Real-time underwater sorting of edible fish species , 2007 .

[27]  Jens G. Balchen.  Bridging the gap between aquaculture and the information sciences , 1986 .

[28]  Boaz Zion,et al.  In-vivo fish sorting by computer vision , 2000 .

[29]  F. Amezcua,et al.  Length–weight relationship for 21 fish species from a coastal lagoon in the southwestern Gulf of California , 2008 .

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

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

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

[33]  Rong Zhang,et al.  A Method for Real-Time Medaka (Oryzias latipes) Detection Base on Machine Vision Technology , 2013 .

[34]  Federico Pallottino,et al.  An Advanced Colour Calibration Method for Fish Freshness Assessment: a Comparison Between Standard and Passive Refrigeration Modalities , 2012, Food and Bioprocess Technology.

[35]  Murat O. Balaban,et al.  Objective Quality Assessment of Raw Tilapia (Oreochromis niloticus) Fillets Using Electronic Nose and Machine Vision , 2001 .

[36]  Kresimir Williams,et al.  Use of stereo camera systems for assessment of rockfish abundance in untrawlable areas and for recording pollock behavior during midwater trawls , 2010 .

[37]  Stuart Robson,et al.  The accuracy and precision of underwater measurements of length and maximum body depth of southern bluefin tuna (Thunnus maccoyii) with a stereo-video camera system , 2003 .

[38]  Soleiman Hosseinpour,et al.  Application of computer vision technique for on-line monitoring of shrimp color changes during drying , 2013 .

[39]  M. Balaban,et al.  Using image analysis to predict the weight of Alaskan salmon of different species. , 2010, Journal of food science.

[40]  Alberto García,et al.  Quality analysis of tuna meat using an automated color inspection system , 2006 .

[41]  Apinai Rerkratn,et al.  System Development for Quality Evaluation of Fish Fillet Using Image Processing , 2014, INFOCOM 2014.

[42]  Niu Zhiyou,et al.  Weight grading of freshwater fish based on computer vision , 2011 .

[43]  Murat O. Balaban,et al.  Machine Vision Applications to Aquatic Foods: A Review , 2011 .

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

[45]  Murat O. Balaban,et al.  Analysis of visual quality attributes of white shrimp by machine vision , 1997 .

[46]  Murat O. Balaban,et al.  Prediction of the Weight of Aquacultured Rainbow Trout (Oncorhynchus mykiss) by Image Analysis , 2010 .

[47]  Mahmoud Omid,et al.  Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes , 2013 .

[48]  Alf Harbitz.  Estimation of shrimp (Pandalus borealis) carapace length by image analysis , 2007 .

[49]  Murat O. Balaban,et al.  Analysis of Skin Color Development in Live Goldfish Using a Color Machine Vision System , 2002 .

[50]  R. Froese.  Length‐weight relationships for 18 less‐studied fish species , 1998 .

[51]  Hui Yang,et al.  Prediction of shelled shrimp weight by machine vision , 2008, Journal of Zhejiang University SCIENCE B.

[52]  Tsutomu Takagi,et al.  A digital stereo-video camera system for three-dimensional monitoring of free-swimming Pacific bluefin tuna, Thunnus orientalis, cultured in a net cage , 2011 .

[53]  Franco Pedreschi,et al.  Color of Salmon Fillets By Computer Vision and Sensory Panel , 2010 .

[54]  John Reidar Mathiassen,et al.  AUTOMATIC WEIGHT AND QUALITY GRADING OF WHOLE PELAGIC FISH , 2006 .

[55]  Mark R. Shortis,et al.  Using stereo-video for deep water benthic habitat surveys , 2008 .

[56]  Seyed Saeid Mohtasebi,et al.  Application of machine-vision techniques to fish-quality assessment , 2012 .

[57]  Nigel J. B. McFarlane,et al.  Estimating Dimensions of Free-Swimming Fish Using 3D Point Distribution Models , 2000, Comput. Vis. Image Underst..

[58]  T. R. Gormley,et al.  A Note on Consumer Preference of Smoked Salmon Colour , 1992 .

[59]  Murat O. Balaban,et al.  Quality Evaluation of Raw and Cooked Catfish (Ictalurus punctatus) Using Electronic Nose and Machine Vision , 2001 .

[60]  M. O. Balaban,et al.  COMPARISON OF A COLORIMETER WITH A MACHINE VISION SYSTEM IN MEASURING COLOR OF GULF OF MEXICO STURGEON FILLETS , 2006 .

[61]  J. A. Marchant,et al.  Fish sizing and monitoring using a stereo image analysis system applied to fish farming , 1995 .

[62]  Michele Scardi,et al.  Extracting fish size using dual underwater cameras , 2006 .

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

[64]  Dah-Jye Lee,et al.  Automatic shrimp shape grading using evolution constructed features , 2014 .

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

[66]  Barbara Rasco,et al.  Quality changes of salmon (Oncorhynchus gorbuscha) muscle during thermal processing. , 2007, Journal of food science.

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

[68]  Royann J. Petrell,et al.  Measurement of fish size in atlantic salmon (salmo salar l.) cages using stereographic video techniques , 1998 .

[69]  Michele Scardi,et al.  A dual camera system for counting and sizing northern bluefin tuna (Thunnus thynnus; Linnaeus, 1758) stock, during transfer to aquaculture cages, with a semi automatic Artificial Neural Network tool. , 2009 .

[70]  S. W. Searcy,et al.  FEATURE EXTRACTION FOR A MACHINE-VISION-BASED SHRIMP DEHEADER , 1991 .

[71]  Francisco Arreguín Sánchez,et al.  Length-weight relationship of demersal fish from the eastern coast of the mouth of the Gulf of California , 2010 .

[72]  Boaz Zion,et al.  Review: The use of computer vision technologies in aquaculture - A review , 2012 .