A Novel Method for Fish Spoilage Detection based on Fish Eye Images using Deep Convolutional Inception-ResNet-v2 | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 1، فروردین 2024، صفحه 105-113 اصل مقاله (1.48 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.14091.2519 | ||
| نویسندگان | ||
| Sekine Asadi Amiri* 1؛ Mahda Nasrolahzadeh2؛ Zeynab Mohammadpoory3؛ AbdolAli Movahedinia4؛ Amirhossein Zare1 | ||
| 1Department of Computer Engineering, University of Mazandaran, Babolsar, Iran | ||
| 2Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran | ||
| 3Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| 4Department of Marine Biology, University of Mazandaran, Babolsar, Iran | ||
| چکیده | ||
| Improving the quality of food industries and the safety and health of the people’s nutrition system is one of the important goals of governments. Fish is an excellent source of protein. Freshness is one of the most important quality criteria for fish that should be selected for consumption. It has been shown that due to improper storage conditions of fish, bacteria, and toxins may cause diseases for human health. The conventional methods of detecting spoilage and disease in fish, i.e. analyzing fish samples in the laboratory, are laborious and time-consuming. In this paper, an automatic method for identifying spoiled fish from fresh fish is proposed. In the proposed method, images of fish eyes are used. Fresh fish are identified by shiny eyes, and poor and stale fish are identified by gray color changes in the eye. In the proposed method, Inception-ResNet-v2 convolutional neural network is used to extract features. To increase the accuracy of the model and prevent overfitting, only some useful features are selected using the mRMR feature selection method. The mRMR reduces the dimensionality of the data and improves the classification accuracy. Then, since the number of samples is low, the k-fold cross-validation method is used. Finally, for classifying the samples, Naïve bayes and Random forest classifiers are used. The proposed method has reached an accuracy of 97% on the fish eye dataset, which is better than previous references. | ||
| کلیدواژهها | ||
| Fish eye؛ spoilage detection؛ Inception-ResNet-v2؛ mRMR | ||
| مراجع | ||
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