## 4 Discussion
### Carp  
The short-term identification for carp was 100% accurate under all three ROIs. The accuracy of long-term identification varied from 100% to 29% depending on the ROI and combination of data sets. The best long-term identification accuracy was higher than 80% for all combinations of data sets and ROI2. The accuracy of the combinations of data sets 01 and 02 was lower (80.64%) than that for the combination of 01 and 03 (100%), and the accuracy for the combination of 01 and 04 was higher (90%) than that for 02 and 04 (70%) (for ROI1). We expected that the identification accuracy would decrease with an increase in time between data collections, corresponding to the changes in the scale patterns influencing identification accuracy. The results, however, corresponded more strongly to issues with the data collection itself and localization of the ROI on the fish's body. This method was able to 100% correctly identify fish between sessions with a delay of two months, but only 80% accurately for one-month delays. The identification accuracy of ROI3 (operculum) clearly decreased from approximately 80% for one-month-delayed data sets to approximately 30% for three-month-delayed data sets. These results correspond to the changes in the operculum area from the perspective of texture-feature extraction. The contrast in the edges of ROI3 was much lower than the contrast in the edges for the scales of ROI1 and ROI 2, which is likely the reason for the decrease in accuracy. Based on the results of the experiments with both seabass and carp, the accuracy of the method is strongly dependent on many factors, such as the type of the scale (ctenoid or cycloid), the scale from which the ROI is extracted, the localization of the ROI, the extracted and chosen features, the sizes of the ROIs, and the conditions of the experiment, which all impact the quality of the data. There are three main aspects influencing individual-fish identification: the scale pattern itself, the proper localization of the ROI, and the image quality. The scale pattern is affected by the geographic location of the fish [36], which means that the conditions of fish cultivation (abiotic factors such as temperature, light conditions, etc.) also affect scale appearance and skin development. The recovery of lost scales influences identification as the growth of a scale begins not in the scale's center, but closer to its edge [37]. The individual identification of fish based on scales has not been well studied. Scales of fish have been used to identify species since the 1900s [38]. In subsequent years, scale-based identification of fish has become popular not only for species identification but also for population identification [12; 39]. Furthermore, descriptors for the shape of the scale have been developed [40; 41]. Based on this concept, [PERSON] et al. [39] developed geometric morphometric methods (GMMs) to identify two Mugilidae species using their scales. Another study [42] tested the identification of larvae from two species based on melanophore patterns. The authors examined the distribution patterns of melanophores in various larvae of bullet tuna (_Auxis rochei_) and frigate tuna (_Auxis thazard_). [PERSON] [43] tested the landmark-based geometric morphometric method to identify two species from mixed samples. Only one study [22] explored the identification of individuals of one species without obvious patterns. The authors used five common carps for individual identification by human experts and obtained 100% accuracy for the identification of five individuals. Our approach shows that fully automated identification is possible for a high number of fish (300 seabass and 32 carp). For seabass, long-term image-based identification was found to be possible with 100% accuracy and could thus serve as a substitute for invasive fish tagging. For common carp, short-term identification is possible (100% accuracy), but long-term identification (approximately 80%) cannot provide accurate identification.