Quality prediction of seabream Sparus aurata by deep learning algorithms and explainable artificial intelligence

In this study, Convolutional Neural Network (CNN), DenseNet121, Inception V3 and ResNet50 machine learning algorithms were used to determine the quality changes in sea bream stored in refrigerator conditions using eye and gill images. The sea bream were categorized into 3 different freshness categories as fresh, moderate and spoiled and analysed with machine learning algorithms. According to the Confusion matrix values, it was determined that the prediction performance of the model was 100 % and the lowest value was calculated to be 98.42 % in the spoiled class in the eye parameter. The values obtained from machine learning algorithms were analysed with Explainable Artificial Intelligence (XAI) algorithms (Grad-CAM and LIME). The study was concluded that the CNN and DenseNet 121 developed along with Grad-CAM and LIME is a non-destructive method that can be used in determining the freshness of sea bream under refrigerator conditions.