## 6 Experiments
### Experimental Results  
In this study, fish freshness classification was performed on a daily basis with three different classifiers using visual and laser data. The results obtained from the experimental studies are shown in the tables below. The most successful results are shown in bold. Table 4 shows the classification results with the SVM, RF, and MLP algorithms using only data from laser sensors. The results show that the accuracy of the laser data is relatively low. RF was the most successful algorithm and outperformed the other algorithms, with average accuracy of 69.22%. This indicates that laser data alone have limited predictive capacity. Features were extracted from the visual data using four pretrained CNN (ResNet, EfficientNet, MobileNet, and ConvNeXt) models, and classification was performed with the SVM, RF, and MLP machine learning algorithms. The results obtained are shown in Table 5. When the results are analyzed, it is seen that the combination of features obtained with ConvNeXt and the SVM classifier performs best, with average accuracy of 85.97%. These results show that ConvNeXt provides a stronger representation than the others in the extraction of visual features, and SVM is effective in classification. The second-best performance was obtained by classifying the features extracted by the ResNet50 model using the MLP, achieving average accuracy of 84.29%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Acc \(\pm\) Std (\%)** & **F1-Score \(\pm\) Std (\%)** \\ \hline SVM & 67.66 \(\pm\) 3.84 & 67.24 \(\pm\) 3.47 \\ RF & **69.22 \(\pm\) 3.25** & **68.35 \(\pm\) 3.10** \\ MLP & 67.79 \(\pm\) 2.89 & 67.52 \(\pm\) 2.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of classifiers using only laser reflectance data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Configuration Parameters** \\ \hline SVM & Kernel: RBF \\ RF & Number of Trees: 100 \\ MLP & Hidden Layers: (100, 50) \\ Max Iterations: 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter settings used for classifiers. The experimental results obtained by using laser data and visual features together are given in Table 6. The combination of visual features and laser data provided higher accuracy rates in the experiments. The most successful results were obtained with an average accuracy rate of 88.44% when the features extracted from the ResNet50 model and the data obtained from the laser sensors were used together and classified with the MLP. This shows that laser and visual data provide complementary information and improve the prediction performance when used together. The second most successful result was obtained with an average accuracy rate of 88.31% in the SVM classifier with the data obtained from the laser sensors and the features extracted from the ConvNeXt model. Figure 5 presents the precision, recall, and F1-score values for four different model-feature combinations across three days. The best overall performance was achieved by the ResNet50 + Laser + MLP model on Day 1, with precision of 93.41% and an F1-score of 91.96%, indicating highly accurate and balanced predictions. The ConvNeXt + Laser + SVM combination reached the highest recall value of 93.79% on the same day, highlighting its ability to detect fresh samples without missing relevant instances. While the MobileNet and EfficientNet models also demonstrated relatively strong performance on Day 1, a decline was observed on Days 2 and 3, particularly in the F1-score. Although the recall values were relatively preserved on Day 2, both the precision and F1-score showed a general decrease. By Day 3, all models exhibited more pronounced drops, especially in the recall