## 7 Discussion  
In this study, a method for fish freshness classification was developed by combining laser data and image features. The results of the experiments provide a detailed description of the performance of the different types of data and machine learning algorithms used. The results obtained with only laser data show that this type of feature has limited predictive capacity. Although the RF algorithm performs the best, with accuracy of 69.22%, the overall accuracy is low, indicating that laser reflectance data alone are not sufficient. In the experiments using only images, the combination of ConvNeXt features and SVM provided the best results, with average accuracy of 85.97%, while the combination of ResNet50 features and the MLP performed similarly, with average accuracy of 84.29%. These findings suggest that visual features provide stronger discriminative information for freshness prediction. In the experiments where laser reflectance data and visual data were combined, the performance improved significantly, with the MLP classifier achieving the highest performance, with average accuracy of 88.44%. This shows that data from laser sensors can be effective in providing complementary information for freshness prediction, but other types of data are needed for robust predictions. In particular, the visual features extracted by CNN models, such as edges, textures, and colors, are complemented by the reflectance data from laser sensors, demonstrating that combining these two data sources strengthens class separation and improves the overall model performance. It is crucial to evaluate the success metrics used in classification problems not only in statistical terms but also in the context of applications. As in this study, misclassifications in classification problems related to food safety can lead to different outcomes; for example, misclassifications can increase public health risks, cause economic losses, or negatively affect the supply chain management efficiency. Therefore, a high recall value in the Day 3 class, i.e., the rate at which examples belonging to this class are correctly recognized, is critical for the reliability of the system. On the other hand, a high precision value in the Day 1 class is important to prevent examples belonging to this class from being incorrectly eliminated. The F1-score, which optimizes both metrics together, should be preferred as a balanced and comprehensive success indicator across all classes. The results presented in Figure 5 demonstrate that the classification performance should be evaluated not only in statistical terms but also in the context of applications. In  
Figure 5: Comparison of performance metrics by day for classifiers that give the best results among all models combining image and laser features. particular, in the Day 1 class, the ResNet50 + Laser + MLP model shows that examples belonging to this class are recognized with high accuracy and that the misclassification rate can be kept low, with a precision value of 93.4% and an F1-score value of 91.9%. The ConvNeXt + Laser + SVM model, on the other hand, was the most successful combination in accurately capturing Day 1 samples, with a recall value of 93.8%. This is important in terms of maintaining product quality in the supply chain and presentation to the consumer. However, a decrease in the precision and recall values was observed in all models in the Day 2 class. The intermediate transition feature of this class and its similarity to other classes in terms of both visual and optical characteristics resulted in limited discriminative power. In the Day 3 class, the recall values of some models being above 90% indicates that the samples belonging to this class are largely correctly identified and supports the functionality of the system in terms of food safety. All these results demonstrate that the F1-score is a suitable performance metric for model comparisons and reliability assessments by balancing precision and recall.