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http://dx.doi.org/10.5657/KFAS.2021.0965

Analysis and Prediction of Behavioral Changes in Angelfish Pterophyllum scalare Under Stress Conditions  

Kim, Yoon-Jae (Department of aquatic life medicine, Pukyong National University)
NO, Hea-Min (Department of Electrical and Computer Engineering, Pusan National University)
Kim, Do-Hyung (Department of aquatic life medicine, Pukyong National University)
Publication Information
Korean Journal of Fisheries and Aquatic Sciences / v.54, no.6, 2021 , pp. 965-973 More about this Journal
Abstract
The behavior of angelfish Pterophyllum scalare exposed to low and high temperatures was monitored by video tracking, and information such as the initial speed, changes in speed, and locations of the fish in the tank were analyzed. The water temperature was raised from 26℃ to 36℃ or lowered from 26℃ to 16℃ for 4 h. The control group was maintained at 26℃ for 8 h. The experiment was repeated five times for each group. Machine learning analysis comprising a long short-term memory model was used to train and test the behavioral data (80 s) after pre-processing. Results showed that when the water temperature changed to 36℃ or 16℃, the average speed, changes in speed and fractal dimension value were significantly lower than those in the control group. Machine learning analysis revealed that the accuracy of 80-s video footage data was 87.4%. The machine learning used in this study could distinguish between the optimal temperature group and changing temperature groups with specificity and sensitivity percentages of 86.9% and 87.4%, respectively. Therefore, video tracking technology can be used to effectively analyze fish behavior. In addition, it can be used as an early warning system for fish health in aquariums and fish farms.
Keywords
Fish behavior; Angelfish; Temperature change; Video tracking;
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