| Summary: | Various approaches have been applied to transform aquaculture from a manual,
labour-intensive industry to one dependent on automation technologies in the era
of the fourth industrial revolution. Technologies associated with the monitoring
of physical condition have successfully been applied in most aquafarm
facilities; however, real-time biological monitoring systems that can observe
fish condition and behaviour are still required. In this study, we used a video
recorder placed on top of a fish tank to observe the swimming patterns of rock
bream (Oplegnathus fasciatus), first one fish alone and then a
group of five fish. Rock bream in the video samples were successfully identified
using the you-only-look-once v3 algorithm, which is based on the Darknet-53
convolutional neural network. In addition to recordings of swimming behaviour
under normal conditions, the swimming patterns of fish under abnormal conditions
were recorded on adding an anaesthetic or lowering the salinity. The abnormal
conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s)
involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon
2-phenoxyethanol treatment) before the fish stopped moving, as well as changing
from swimming upright to dying lying on their sides. Machine learning was
applied to datasets consisting of normal or abnormal behaviour patterns, to
evaluate the fish behaviour. The proposed algorithm showed a high accuracy
(98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude
that artificial intelligence-based detection of abnormal behaviour can be
applied to develop an automatic bio-management system for use in the aquaculture
industry.
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