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AEFishBIT: smart device for tracking fish behaviour and activity
- Publication Year :
- 2019
-
Abstract
- The use of new technologies for individual and non-invasive monitoring of farmed fish is becoming an urgent demand of the aquaculture industry (Føre et al., 2018). This offers the possibility of the measurement of a range of variables that are directly or indirectly related with metabolic condition, health and welfare status (Metcalfe et al., 2016; Neethirajan et al., 2017). The solution proposed within the AQUAEXCEL2020 EU project is a smart device named AEFishBIT, developed and validated to provide reliable and simultaneous measurements of physical activity and respiratory frequency. The device is attached in the operculum using a fairly invasive procedure, and initial testing has been conducted in gilthead sea bream and European sea bass, the two most important farmed fish of Mediterranean aquaculture. The AEFishBIT is a tri-axial accelerometer for recording and processing acceleration data from x-, y- and z-axes. Records of operculum breathing (z-axis acceleration) served as a direct measurement of respiratory frequency, whereas estimation of fishactivity was derived from the x- and y-axis signals. The device also includes a passive RFID tagging device for rapid identification.The finalweight of the full packaged device is less than 1 g in air. The autonomy of the system in stand-alone mode is 6 h of continuous data recording with different programmable time schedules. Algorithms to convert accelerometer measurements in physical activity and respiratory frequency (breaths/s) have been validated with exercised juveniles in a 10-L swim tunnel respirometer (Loligo® Systems). Fish were submitted to controlled speeds from 1 body-length per second (BL/s) until exhaustion by 0.5 BL/s increase steps, with measurements of O2 consumption rates (MO2). AEFishBIT was programmed for 2-min time window at different intervals. The close parallelism between the inferred respiratory frequency and MO2 highly validates the use of the proposed algorithm to infer respiratory frequency
Details
- Database :
- OAIster
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1286539179
- Document Type :
- Electronic Resource