Abstract
The global aquaculture industry is experiencing a significant expansion, and it currently accounts for approximately 44% of the overall fish production worldwide. Despite encountering various obstacles in the aquaculture ecosystem, this surge in production has been achieved. In order to mitigate the negative effects of fish diseases, it is crucial to adopt scientifically proven and recommended methods for addressing health limitations. This paper aims to highlight some of the most effective techniques for identifying fish in an image using the ResNet50 model. Additionally, it intends to predict whether a fish is normal or abnormal based on its physical characteristics in aquaculture. To avoid spreading viral infections, fishermen must discard damaged or dead fish. Even for skilled fishermen, it might be challenging to spot odd fish since infected fish can be harder to identify than dead fish. As a result, it is desirable for anomalous fish to be detected automatically. Deep learning needs a lot of visual data, including both healthy and unhealthy fish, to detect abnormal fish from the image dataset. Aqua farmers are facing problems in assessing the health of the fish. Periodical observation of length is one of the parameters for assessing the health of the fish. But, farmers are failing to find the precise length of the fish. The length of the male sea bass fish is measured, and then length–weight relationship is applied for the derived length to achieve weight. Finally, we predicted whether the male sea bass fish is normal or abnormal with an accuracy of 92%.
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Sreeja, V.S., Kumari, K.S., Reddy, D.B., Ujjwala, P. (2023). A Deep Learning-Based Prediction Model for Wellness of Male Sea Bass Fish. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_19
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DOI: https://doi.org/10.1007/978-981-99-6706-3_19
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