AI Applications in Aquaculture
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Identification of fish diseases. Traditional methods of disease diagnosis frequently rely on manual examination and subjective decision-making, which may result in errors and delays in treatment. An approach that has gained traction in recent years for automated and impartial fish disease identification. The principal objective of this study is to create a dependable and efficient framework that can accurately identify and classify various ailments that afflict fish by leveraging advanced AI algorithms and intricate image analysis methodologies to extract visual cues from images. By enabling e...
Identification of fish diseases. Traditional methods of disease diagnosis frequently rely on manual examination and subjective decision-making, which may result in errors and delays in treatment. An approach that has gained traction in recent years for automated and impartial fish disease identification. The principal objective of this study is to create a dependable and efficient framework that can accurately identify and classify various ailments that afflict fish by leveraging advanced AI algorithms and intricate image analysis methodologies to extract visual cues from images. By enabling early detection, prompt intervention and productivity of fish populations, the implementation of such a system has the potential to revolutionise disease management procedures in aquaculture. Effective disease management in aquaculture depends on the prompt and accurate diagnosis of fish diseases. This abstract combines findings from three studies that suggest novel methods for identifying fish diseases.In the first paper, a novel method for diagnosing fish diseases using the Unscented Kalman Filter (UKF) and Elman Neural Networks (ENN) is presented.
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