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Akgül İ, Kaya V, Zencir Tanır Ö. A novel hybrid system for automatic detection of fish quality from eye and gill color characteristics using transfer learning technique. PLoS One 2023; 18:e0284804. [PMID: 37098040 PMCID: PMC10128947 DOI: 10.1371/journal.pone.0284804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 04/08/2023] [Indexed: 04/26/2023] Open
Abstract
Fish remains popular among the body's most essential nutrients, as it contains protein and polyunsaturated fatty acids. It is extremely important to choose the fish consumption according to the season and the freshness of the fish to be purchased. It is very difficult to distinguish between non-fresh fish and fresh fish mixed in the fish stalls. In addition to traditional methods used to determine meat freshness, significant success has been achieved in studies on fresh fish detection with artificial intelligence techniques. In this study, two different types of fish (anchovy and horse mackerel) used to determine fish freshness with convolutional neural networks, one of the artificial intelligence techniques. The images of fresh fish were taken, images of non-fresh fish were taken and two new datasets (Dataset1: Anchovy, Dataset2: Horse mackerel) were created. A novel hybrid model structure has been proposed to determine fish freshness using fish eye and gill regions on these two datasets. In the proposed model, Yolo-v5 and Inception-ResNet-v2 and Xception model structures are used through transfer learning. Whether the fish is fresh in both of the Yolo-v5 + Inception-ResNet-v2 (Dataset1: 97.67%, Dataset2: 96.0%) and Yolo-v5 + Xception (Dataset1: 88.00%, Dataset2: 94.67%) hybrid models created using these model structures has been successfully detected. Thanks to the model we have proposed, it will make an important contribution to the studies that will be conducted in the freshness studies of fish using different storage days and the estimation of fish size.
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Affiliation(s)
- İsmail Akgül
- Department of Computer Engineering, Faculty of Engineering and Architecture, Erzincan Binali Yıldırım University, Erzincan, Türkiye
| | - Volkan Kaya
- Department of Computer Engineering, Faculty of Engineering and Architecture, Erzincan Binali Yıldırım University, Erzincan, Türkiye
| | - Özge Zencir Tanır
- Department of Biology, Faculty of Arts and Science, Erzincan Binali Yıldırım University, Erzincan, Türkiye
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Chen R, Zhao Y, Yang Y, Wang S, Li L, Sha X, Liu L, Zhang G, Li WJ. Online estimating weight of white Pekin duck carcass by computer vision. Poult Sci 2022; 102:102348. [PMID: 36521297 PMCID: PMC9768378 DOI: 10.1016/j.psj.2022.102348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 11/21/2022] Open
Abstract
The increasing consumption of ducks and chickens in China demands characterizing carcasses of domestic birds efficiently. Most existing methods, however, were developed for characterizing carcasses of pigs or cattle. Here, we developed a noncontact and automated weighing method for duck carcasses hanging on a production line. A 2D camera with its facilitating parts recorded the moving duck carcasses on the production line. To estimate the weight of carcasses, the images in the acquired dataset were modeled by a convolution neuron network (CNN). This model was trained and evaluated using 10-fold cross-validation. The model estimated the weight of duck carcasses precisely with a mean abstract deviation (MAD) of 58.8 grams and a mean relative error (MRE) of 2.15% in the testing dataset. Compared with 2 widely used methods, pixel area linear regression and the artificial neural network (ANN) model, our model decreases the estimation error MAD by 64.7 grams (52.4%) and 48.2 grams (45.0%). We release the dataset and code at https://github.com/RuoyuChen10/Image_weighing.
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Affiliation(s)
- Ruoyu Chen
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
| | - Yuliang Zhao
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China,Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China,Corresponding author: Yuliang Zhao
| | - Yongliang Yang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Shuyu Wang
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China,Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Lianjiang Li
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China,Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Xiaopeng Sha
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China,Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Lianqing Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China,Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Guanglie Zhang
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077, China
| | - Wen Jung Li
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077, China
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Chen HC, Xu SY, Deng KH. Water Color Identification System for Monitoring Aquaculture Farms. SENSORS (BASEL, SWITZERLAND) 2022; 22:7131. [PMID: 36236230 PMCID: PMC9571723 DOI: 10.3390/s22197131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
This study presents a vision-based water color identification system designed for monitoring aquaculture ponds. The algorithm proposed in this system can identify water color, which is an important factor in aquaculture farming management. To address the effect of outdoor lighting conditions on the proposed system, a color correction method using a color checkerboard was introduced. Several candidates for water-only image patches were extracted by performing image segmentation and fuzzy inferencing. Finally, a deep learning-based model was employed to identify the color of these patches and then find the representative color of the water. Experiments at different aquaculture sites verified the effectiveness of the proposed system and its algorithm. The color identification accuracy exceeded 96% for the test data.
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Gümüş B. Image Analysis to Quantify Weight-length, Weight-area, and Change of Color of Three Commercial Mullidae Species during Cold Storage. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2021. [DOI: 10.1080/10498850.2020.1869877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Bahar Gümüş
- Department of Gastronomy and Culinary Arts, Faculty of Tourism, Akdeniz University, Antalya, Turkey
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Martínez-Vázquez JM, Valcarce DG, Riesco MF, Marco VS, Matsuoka M, Robles V. Artificial Neural Network (ANN) as a Tool to Reduce Human-Animal Interaction Improves Senegalese Sole Production. Biomolecules 2019; 9:biom9120778. [PMID: 31775393 PMCID: PMC6995621 DOI: 10.3390/biom9120778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/21/2019] [Accepted: 11/21/2019] [Indexed: 12/17/2022] Open
Abstract
Manipulation is usually required for biomass calculation and food estimation for optimal fish growth in production facilities. However, the advances in computer-based systems have opened a new range of applied possibilities. In this study we used image analysis and a neural network algorithm that allowed us to successfully provide highly accurate biomass data. This developed system allowed us to compare the effects of reduced levels of human-animal interaction on the culture of adult Senegalese sole (Solea senegalensis) in terms of body weight gain. For this purpose, 30 adult fish were split into two homogeneous groups formed by three replicates (n=5) each: a control group (CTRL), which was standard manipulated and an experimental group (EXP), which was maintained under a lower human-animal interaction culture using our system for biomass calculation. Visible implant elastomer was, for the first time, applied as tagging technology for tracking soles during the experiment (four months). The experimental group achieved a statistically significant weight gain (p<0.0100) while CTRL animals did not report a statistical before-after weight increase. Individual body weight increment was lower (p<0.0100) in standard-handled animals. In conclusion, our experimental approach provides evidence that our developed system for biomass calculation, which implies lower human-animal interaction, improves biomass gain in Senegalese sole individuals in a short period of time.
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Affiliation(s)
- Juan M. Martínez-Vázquez
- IEO, Spanish Institute of Oceanography, Planta de Cultivos El Bocal, Barrio Corbanera, 39012 Monte, Santander, Spain; (J.M.M.-V.); (D.G.V.); (M.F.R.)
| | - David G. Valcarce
- IEO, Spanish Institute of Oceanography, Planta de Cultivos El Bocal, Barrio Corbanera, 39012 Monte, Santander, Spain; (J.M.M.-V.); (D.G.V.); (M.F.R.)
| | - Marta F. Riesco
- IEO, Spanish Institute of Oceanography, Planta de Cultivos El Bocal, Barrio Corbanera, 39012 Monte, Santander, Spain; (J.M.M.-V.); (D.G.V.); (M.F.R.)
| | - Vicent Sanz Marco
- Cybermedia Center, Osaka University 1-32 Machikaneyama, Toyonaka, Osaka 560-0043, Japan; (V.S.M.); (M.M.)
| | - Morito Matsuoka
- Cybermedia Center, Osaka University 1-32 Machikaneyama, Toyonaka, Osaka 560-0043, Japan; (V.S.M.); (M.M.)
| | - Vanesa Robles
- IEO, Spanish Institute of Oceanography, Planta de Cultivos El Bocal, Barrio Corbanera, 39012 Monte, Santander, Spain; (J.M.M.-V.); (D.G.V.); (M.F.R.)
- Department of Molecular Biology, Universidad de León, 24071 León, Spain
- Correspondence: ; Tel.: +34-987-291-487
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Abstract
Successful manipulation of unknown objects requires an understanding of their physical properties. Infrared thermography has the potential to provide real-time, contactless material characterization for unknown objects. In this paper, we propose an approach that utilizes active thermography and custom multi-channel neural networks to perform classification between samples and regression towards the density property. With the help of an off-the-shelf technology to estimate the volume of the object, the proposed approach is capable of estimating the weight of the unknown object. We show the efficacy of the infrared thermography approach to a set of ten commonly used materials to achieve a 99.1% R 2 -fit for predicted versus actual density values. The system can be used with tele-operated or autonomous robots to optimize grasping techniques for unknown objects without touching them.
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Lee D, Kim S, Kim P, Yang Y. Automatic sea squirt sorting algorithm based on the HSV color model and weight estimation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Donggil Lee
- Fisheries Engineering Research Division, National Institute of Fisheries Science (NIFS), Gijang-Eup, Gijang-gun, Busan, South Korea
| | - Seonghun Kim
- Fisheries Engineering Research Division, National Institute of Fisheries Science (NIFS), Gijang-Eup, Gijang-gun, Busan, South Korea
| | - Pyungkwan Kim
- Fisheries Engineering Research Division, National Institute of Fisheries Science (NIFS), Gijang-Eup, Gijang-gun, Busan, South Korea
| | - Yongsu Yang
- Fisheries Engineering Research Division, National Institute of Fisheries Science (NIFS), Gijang-Eup, Gijang-gun, Busan, South Korea
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Ayvaz Z, Balaban MO, Kong KJW. Effects of Different Brining Methods on Some Physical Properties of Liquid Smoked King Salmon. J FOOD PROCESS PRES 2016. [DOI: 10.1111/jfpp.12791] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zayde Ayvaz
- Canakkale Onsekiz Mart University, Terzioglu Campus, Marine Science and Technology Faculty, Department of Marine Technology Engineering 17100; Canakkale Turkey
| | - Murat O. Balaban
- Department of Chemical and Materials Engineering; the University of Auckland; Auckland New Zealand
| | - Kelvin Jia Wey Kong
- Department of Chemical and Materials Engineering; the University of Auckland; Auckland New Zealand
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Cheng JH, Sun DW, Zeng XA, Liu D. Recent advances in methods and techniques for freshness quality determination and evaluation of fish and fish fillets: a review. Crit Rev Food Sci Nutr 2016; 55:1012-225. [PMID: 24915394 DOI: 10.1080/10408398.2013.769934] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The freshness quality of fish plays an important role in human health and the acceptance of consumers as well as in international fishery trade. Recently, with food safety becoming a critical issue of great concern in the world, determination and evaluation of fish freshness is much more significant in research and development. This review renovates and concentrates recent advances of evaluating methods for fish freshness as affected by preharvest and postharvest factors and highlights the determination methods for fish freshness including sensory evaluation, microbial inspection, chemical measurements of moisture content, volatile compounds, protein changes, lipid oxidation, and adenosine triphosphate (ATP) decomposition (K value), physical measurements, and foreign material contamination detection. Moreover, the advantages and disadvantages of these methods and techniques are compared and discussed and some viewpoints about the current work and future trends are also presented.
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Affiliation(s)
- Jun-Hu Cheng
- a College of Light Industry and Food Sciences, South China University of Technology , Guangzhou , China
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Lee D, Lee K, Kim S, Yang Y. Design of an optimum computer vision-based automatic abalone (Haliotis discus hannai) grading algorithm. J Food Sci 2015; 80:E729-33. [PMID: 25874500 DOI: 10.1111/1750-3841.12799] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2014] [Accepted: 12/04/2014] [Indexed: 11/30/2022]
Abstract
An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively.
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Affiliation(s)
- Donggil Lee
- Fisheries System Engineering Div, Natl. Fisheries Research & Development Inst, Korea
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Alçiçek Z, Balaban MÖ. Characterization of Green Shelled Mussel Meat. Part I: Quantification of Color Changes During Brining and Liquid Smoke Application Using Image Analysis. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2015. [DOI: 10.1080/10498850.2012.751566] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Alçiçek Z, Balaban MÖ. Characterization of Green Lipped Mussel Meat. Part II: Changes in Physical Characteristics as a Result of Brining and Liquid Smoke Application. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2015. [DOI: 10.1080/10498850.2012.760188] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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He HJ, Wu D, Sun DW. Nondestructive Spectroscopic and Imaging Techniques for Quality Evaluation and Assessment of Fish and Fish Products. Crit Rev Food Sci Nutr 2014; 55:864-86. [DOI: 10.1080/10408398.2012.746638] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Cheng JH, Sun DW. Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: Current research and potential applications. Trends Food Sci Technol 2014. [DOI: 10.1016/j.tifs.2014.03.006] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Dowlati M, de la Guardia M, Dowlati M, Mohtasebi SS. Application of machine-vision techniques to fish-quality assessment. Trends Analyt Chem 2012. [DOI: 10.1016/j.trac.2012.07.011] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Balaban MO, Chombeau M, Gümüş B, Cırban D. Quality Evaluation of Alaska Pollock (Theragra chalcogramma) Roe by Image Analysis. Part I: Weight Prediction. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2012. [DOI: 10.1080/10498850.2011.583377] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Mathiassen JR, Misimi E, Toldnes B, Bondø M, Østvik SO. High-speed weight estimation of whole herring (Clupea harengus) using 3D machine vision. J Food Sci 2011; 76:E458-64. [PMID: 22417497 DOI: 10.1111/j.1750-3841.2011.02226.x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
UNLABELLED Weight is an important parameter by which the price of whole herring (Clupea harengus) is determined. Current mechanical weight graders are capable of a high throughput but have a relatively low accuracy. For this reason, there is a need for a more accurate high-speed weight estimation of whole herring. A 3-dimensional (3D) machine vision system was developed for high-speed weight estimation of whole herring. The system uses a 3D laser triangulation system above a conveyor belt moving at a speed of 1000 mm/s. Weight prediction models were developed for several feature sets, and a linear regression model using several 2-dimensional (2D) and 3D features enabled more accurate weight estimation than using 3D volume only. Using the combined 2D and 3D features, the root mean square error of cross-validation was 5.6 g, and the worst-case prediction error, evaluated by cross-validation, was ±14 g, for a sample (n = 179) of fresh whole herring. The proposed system has the potential to enable high-speed and accurate weight estimation of whole herring in the processing plants. PRACTICAL APPLICATION The 3D machine vision system presented in this article enables high-speed and accurate weight estimation of whole herring, thus enabling an increase in profitability for the pelagic primary processors through a more accurate weight grading.
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Mathiassen JR, Misimi E, Bondø M, Veliyulin E, Østvik SO. Trends in application of imaging technologies to inspection of fish and fish products. Trends Food Sci Technol 2011. [DOI: 10.1016/j.tifs.2011.03.006] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Veliyulin E, Misimi E, Bondø M, Vebenstad PA, Østvik SO. A Simple Method for Weight Estimation of Whole Herring (Clupea harengus) Using Planar X-Ray Imaging. J Food Sci 2011; 76:E328-31. [PMID: 21535833 DOI: 10.1111/j.1750-3841.2011.02093.x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Emil Veliyulin
- SINTEF Fisheries and Aquaculture, N-7465 Trondheim, Norway
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Balaban MO, Ünal Şengör GF, Soriano MG, Ruiz EG. Quantification of Gaping, Bruising, and Blood Spots in Salmon Fillets Using Image Analysis. J Food Sci 2011; 76:E291-7. [DOI: 10.1111/j.1750-3841.2011.02060.x] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Balaban MO, Chombeau M, Gümüş B, Cirban D. Determination of Volume of Alaska Pollock (Theragra chalcogramma) by Image Analysis. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2011. [DOI: 10.1080/10498850.2010.531996] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Gümüş B, Balaban MO. Prediction of the Weight of Aquacultured Rainbow Trout(Oncorhynchus mykiss)by Image Analysis. JOURNAL OF AQUATIC FOOD PRODUCT TECHNOLOGY 2010. [DOI: 10.1080/10498850.2010.508869] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Balaban MO, Chombeau M, Cırban D, Gümüş B. Prediction of the Weight of Alaskan Pollock Using Image Analysis. J Food Sci 2010; 75:E552-6. [DOI: 10.1111/j.1750-3841.2010.01813.x] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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