Real-time nondestructive monitoring of Common Carp Fish freshness using robust vision-based intelligent modeling approaches

In the current research, the potential of a novel method based on the artificial neural network was investigated to diagnose the freshness of common carp (Cyprinus carpio) during ice storage. Fish as an aquaculture product has high nutrients and low-fat content. So, people have consumed it as a safe and high-value foodstuff in their daily diet. Investigation of fish freshness is proposed as a significant issue in the aquaculture industry since fish spoils rapidly. The applied system of this study is comprised of the following steps: First, images of samples were captured and the pre-processing operation was done on the images. Then, particular channels including R, G, B, H, S, I, L*, a*, and b* were computed. Next, feature extraction was performed to obtain 6 types of texture features from each channel. Afterward, the hybrid Artificial Bee Colony-Artificial Neural Network (ABC-ANN) algorithm was applied to select the best features. Finally, the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Artificial Neural Network (ANN) algorithms as the most common methods were used to classify fish images. The best performance of the K-NN classifier was calculated in the k = 8 neighborhood size with the accuracy of 90.48. The best kernel function for the SVM algorithm was polynomial with C, sigma, and accuracy of 1, 2 and 91.52 percent, respectively. In this system, the input layer has consisted of 22 neurons based on the feature selection operation and 4 classes including most fresh, fresh, fairly fresh and spoiled have been used as the number of output layer. At the end, the best results of the MLP networks were achieved by LM learning algorithm and 6 neurons in the hidden layer with the 22-10-4 topology and accuracy of 93.01 percent. The achieved results demonstrate the high performance of the ANN classifier for evaluation of common carp freshness during ice storage as a rapid, accurate, non-destructive, real-time and automated method. It shows the potential of computer vision method in combination with artificial neural networks as an intelligent technique for evaluation of fish freshness.