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Combining image processing technique and three artificial intelligence methods to recognize the freshness of freshwater shrimp | ||
| Journal of Food and Bioprocess Engineering | ||
| مقاله 11، دوره 2، شماره 2، اسفند 2019، صفحه 155-162 اصل مقاله (788.06 K) | ||
| نوع مقاله: Original research | ||
| نویسندگان | ||
| Hassan Zaki Dizaji* 1؛ Hossein Javadikia* 2؛ Samira Azizi3؛ Leila Naderloo4 | ||
| 1Assistant Professor, Biosystems Engineering Dept., Agricultural faculty, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 2Assistant 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 | ||
| 3Graduated M.Sc. of Mechanical Engineering of Agricultural Machinery, Razi University, Kermanshah, Iran | ||
| 4Assistant 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 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Recognizing algorithms؛ Machine vision؛ Modeling؛ Artificial intelligence؛ Controlled storage | ||
| مراجع | ||
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