Document Type : Original research


1 Assistant Professor, Biosystems Engineering Dept., Agricultural faculty, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Assistant Professor of Mechanical Engineering of Agricultural Machinery, Department of Mechanical Biosystems Engineering, Faculty of Agriculture, College of Agriculture and Natural Science, Razi University, Kermanshah, Iran

3 Graduated M.Sc. of Mechanical Engineering of Agricultural Machinery, Razi University, Kermanshah, Iran

4 Assistant Professor of Mechanization Engineering of Agricultural Machinery, Department of Mechanical Biosystems Engineering, Faculty of Agriculture, College of Agriculture and Natural Science, Razi University, Kermanshah, Iran


Since seafood is highly susceptible to corruption, it is important to check their storage and shelf-life time. In this research, image processing technology was used to recognize the freshness (time lasted of catching) of shrimps. Shrimp samples were randomly selected from shrimp farming pools and stored in three storage conditions: freezer, refrigerator, and cool environments. Images were taken from the samples at intervals of two hours in a controlled environment for more than a month. Finally, 482 properties were extracted from each image. Three effective parameters for modeling were selected by sensitivity analysis. The time that lasted from catching was the output of the models. Modeling was performed using ANFIS, ANN, and RSM algorithms. In the modeling, the R2 values of the ANN algorithm with 0.987006, 0.987009, 0.984484, and 0.976001 were the best model for storing conditions: freezer, refrigerator, cooler environments and the total of storage conditions, respectively. All three modeling methods can estimate the catching time with high accuracy. But the ANN model was recognized as the best one according to the remaining diagram and the values of R2 and MSE.


Abdullah, M., Guan, L., Lim, K., & Karim, A. (2004). The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. Journal of food engineering, 61(1), 125-135.
Alimelli, A., Pennazza, G., Santonico, M., Paolesse, R., Filippini, D., D’Amico, A., & Di Natale, C. (2007). Fish freshness detection by a computer screen photo assisted based gas sensor array. Analytica Chimica Acta, 582(2), 320-328.
Cheng, J. H., Qu, J. H., Sun, D. W., & Zeng, X. A. (2014). Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon Idella) as affected by frozen storage. Food Research International, 56, 190–198.
Dowlati, M., Mohtasebi, S.S., Omid, M., Razavi, S.H., 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. Journal of Food Engineering, 119(2), 277-287.
Du, L., Chai, C., Guo, M., & Lu, X. (2015). A model for discrimination freshness of shrimp. Sensing and Bio-Sensing Research, 6, 28-32.
Ghasemi-Varnamkhasti, M., Goli, R., Forina, M., Mohtasebi, S. S., Shafiee, S., & Naderi-Boldaji, M. (2016). Application of image analysis combined with computational expert approaches for shrimp freshness evaluation. International Journal of Food Properties19(10), 2202-2222.
Goyache, F., Bahamonde, A., Alonso, J., López, S., Del Coz, J., Quevedo, J., Ranilla, J., Luaces, O., Alvarez, I., & Royo, L. (2001). The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science & Technology, 12(10), 370-381.
Gram, L., & Dalgaard, P. (2002). Fish spoilage bacteria–problems and solutions. Current opinion in biotechnology, 13(3), 262-266.
Hill, M. C., & Tiedeman, C. R. (2006). Effective groundwater model calibration: with analysis of data, sensitivities, predictions, and uncertainty. John Wiley & Sons.
Hosseinpour, S., Rafiee, S., & Mohtasebi, S. S. (2011). Application of image processing to analyze shrinkage and shape changes of shrimp batch during drying. Drying Technology, 29(12), 1416-1438.
Hosseinpour, S., Rafiee, S., & Mohtasebi, S.S. (2013). Application of computer vision technique for on-line monitoring of shrimp color changes during drying. Journal of Food Engineering, 115(1), 99-114.
Mohebbi, M., Akbarzadeh, M. R., Shahidi, F., Moussavi, M., & Ghoddusi, H. B. (2009). Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp. Computers and electronics in agriculture, 69(2), 128-134.
Moini, S., & Pazira, A. (2004). The effect of cold storage on the quality of cultured P. indicus and sea P. Semisulcatus. Iranian Journal of Natural Resource, 57(3), 469-478.
Mollazade, K., Omid, M., Akhlaghian Tab, F., & Mohtasebi S. S. (2012). Principles and applications of light backscattering imaging in quality evaluation of agro-food products: a review. Food and Bioprocess Technology, 5(5), 1465-1485.
Nollet, L. M., & Toldrá, F. (2010). Handbook of Seafood and Seafood Products Analysis; CRC Press: Boca Raton, New York.
Okpala, C. O. R., Choo, W. S., & Dykes, G. A. (2014). Quality and shelf life assessment of Pacific white shrimp (Litopenaeus vannamei) freshly harvested and stored on ice. LWT-Food Science and Technology, 55(1), 110-116.
Omid, M., Khojastehnazhand, M., & Tabatabaeefar, A. (2010). Estimating volume and mass of citrus fruits by image processing technique. Journal of food Engineering, 100(2), 315-321.
Omid, M., Mahmoudi, A., & Omid M. H. (2009). An intelligent system for sorting pistachio nut varieties. Expert systems with applications, 36(9), 11528-11535.
Paulus, I., De Busscher, R., & Schrevens, E. (1997). Use of image analysis to investigate human quality classification of apples. Journal of Agricultural Engineering Research, 68(4), 341-353.
Rakesh, R. R., Chaudhuri, P., & Murthy, C. (2004). Thresholding in edge detection: a statistical approach. IEEE Transactions on Image Processing, 13(7), 927-936.
Sun, D.-W., & Brosnan, T. (2003). Pizza quality evaluation using computer vision––Part 2: Pizza topping analysis. Journal of Food Engineering, 57(1), 91-95.
Zhaohe,Y., Yanlei,Y., Jianlu, Q., Liqin, Z., & Yun, L. (2007). Population genetic diversity in Chinese pomegranate (Punica granatum L.) cultivars revealed by fluorescent-AFLP markers. Journal of Genetics and Genomics, 34(12), 1061-1071.