## 3 Results
### Prediction, Training and Data Preparation Time  
Not only performance but time cost of the models should be accounted since economic and technical restrictions have to be considered. Prediction, training and data preparation times were analysed in order to evaluate the feasibility of the methods and to underscore the differences between them. The prediction time of the model for image segmentation is crucial to determine the feasibility of its implementation in a future field application. Additionally, it must be noted that the computing power in this case would be significantly lower if it were not performed remotely. The average times for the segmentation of the test pictures executed by SVM models are presented in Table 5. According to results, SVM prediction time increases with the number of training images. The larger the dataset, the more complex and heavier the model is, so the prediction requires a higher computational cost. In the case of deep-learning models, prediction times are affected by the encoder depth parameter, which defines the number of network layers. Table 5 summarizes the average prediction times for trained deep-learning models with the same encoder depth values. From the average prediction times of the test pictures in the best SVM and deep-learning models, which are shown in Table 6, it is appreciated that the SVM model takes approximately 42 times more prediction time. The SVM model is simpler, but requires individual prediction of each of the pixels that make up the picture. In contrast, the deep-learning model with the SegNet network architecture based on the encoder-decoder structure is more agile in prediction. The time needed for models training is not a determining parameter to consider in the comparison between models, since it is a machine processing time and it is performed only once. However, it is interesting to take this into account as excessively high times could be a problem for future training with a greater number of pictures. Table 7 shows how the training time of SVM models rises as the dataset increases, as expected. In the case of the deep-learning model, the training time depends on the number of training images that compose the dataset, added to the validation frequency and validation patience parameters that define the stop criteria, as well as the learning rate and the regularization value. Based on the results of the best SVM and deep-learning models, which are indicated in Table 8, the SVM model takes approximately 67 times less training time. The SVM model is simpler and its training does not require the computational capacity that the deep-learning model does. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **SVM 4** & **Deep Learning 13** \\ \hline Images & 251 & 121 \\ Prediction Time (s) & 31.78 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SVM and deep-learning prediction time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SVM** & Images & 50 & 122 & 190 & 251 \\ \cline{2-5}  & Prediction Time (s) & 3.90 & 11.69 & 23.35 & 31.78 \\ \hline \hline \multirow{2}{*}{**Deep Learning**} & Encoder Depth & 2 & 4 & 5 & 6 \\ \cline{2-5}  & Prediction Time (s) & 0.701 & 0.745 & 0.750 & 0.761 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SVM and deep-learning prediction time. The time it takes to prepare the data for training is a key factor in the process. This can be a bottleneck and the most determinant procedure, as the resulting model will be as good as the data we are training with. The data preparation times are then compared for both methods in Table 9. In the case of SVM, the computation time of the clustering and the time of manual classification of the clusters by means of the GUI are taken into account. Instead, for the deep-learning model it is necessary to add to the time for mask generation in the previous process, the manual editing time to adjust it perfectly. It is obtained that for each picture the preparation time of the SVM model is significantly lower (27 times) than that of the deep learning one.