## 4 Discussion
### Carp
In a future study, we intend to improve this approach for carp as it is obvious from the data set that carp scales have unique patterns. Another approach used to identify fish species is deep learning. There is a first attempt for this task by [PERSON] [24]. Brown trout (a commercially important fish species) was used in this study. Their method was able to identify individual fish with 94.6% precision and 7.6% recall. However, the limitation of the study is the low number of individuals (39 individuals). Brown trout is the species with a clearly visible dot pattern on the body. Furthermore, the identification was conducted only for short term data. No experiments with the long-term data were performed. The deep learning based methods are promising, especially in the task of fish detection combined with identification. [PERSON] et al. [18] explored the fish identification of coral reef fish species using HOG features combined with support vector machine (SVM) and deep learning. The results for deep learning (T = 98%) were more robust than those for the HOG and SVM approaches (below 49%). In 2018, [PERSON] et al. [17] used the deep learning method to achieve accurate and fast identification of coral reef fish (20 fish sp.) in underwater images. The rate of correct identification was 94.9%, which was greater than the rate of correct human-based identification (89.3%). [PERSON] et al. [44] used the deep learning method for real-time reef-fish identification (24 species of reef fish), with the highest percentage of detection accuracy observed for Holacanthis tricolor (90.70%). However, all present studies focused on species identification did not include long-term identification. No study which deals with the identification of fish without an obvious pattern on the fish body was published and there is no study presenting real-time identification of the fish without an obvious pattern on the body. Our study used 300 individual fish for identification in the short-term experiment. The number of fish in the cultivation tank varied from tens to thousands of individuals. The accuracy of 100%, which was achieved for both species, shows that the studied patterns are unique and could be used for a larger number of individual fish. However, the exact limits of the patterns among seabass and carp must be studied in a larger sample to confirm this observation. The long-term experiment covered a four-month cultivation period for carp and a two-month period for seabass. The standard cultivation periods for both species, however, are longer. No significant distortions in the lateral lines or scale patterns were observed for seabass; we expect that only scale loss or bodily injury would affect long-term identification accuracy. For the long-term identification of carp, the main issue was the data collection procedure and the localization of the ROI. Carp have larger scales, meaning that scale loss can more strongly affect identification accuracy for carp. Larger (mature) fish were used in our experiment when a stable scale pattern was expected. Continuous identification from larva to maturity could be problematic due to development of the fish and changes in the scale patterns. To solve this problem, the identification process could be divided into the periods of fish cultivation with stable short-term patterns. The main limitation of the present approach is that each fish was photographed out of water under anesthesia. As we demonstrated in a previous paper [27], underwater imaging of non-anaesthetized fish is also possible for image-based identification. Studying the application of this method to identification under real conditions is our future research goal. In the future, individual identification based on fish scale patterns could be used for individualized fish treatment. Such a system could continuously identify individuals in the tank/sea cage swimming around the camera and provide information for other systems to ensure fish welfare, growth, and disease analysis.