Abstract
AI-driven surveillance has emerged as a transformative approach for monitoring the health and disease status of ocean organisms. With the increasing availability of data and advancements in artificial intelligence algorithms, researchers and conservationists are leveraging AI to gain insights into the well-being of marine ecosystems. This abstract highlights the key aspects and benefits of AI-driven surveillance in the context of ocean organism health. By utilizing various data collection technologies such as sensors, satellites, underwater cameras, and acoustic devices, a vast amount of data on environmental parameters, species presence, and behavior can be acquired. AI algorithms play a crucial role in integrating and processing these diverse data sources, enabling a comprehensive view of the monitored ecosystem. Through advanced machine learning, deep learning, and computer vision techniques, AI algorithms can automatically identify and monitor marine species. This facilitates efficient species monitoring and enables the detection of changes in population dynamics, distribution, and behavior. Furthermore, AI-based systems play a vital role in monitoring environmental parameters such as water quality, temperature, and nutrient levels. By continuously monitoring these factors, AI algorithms provide valuable insights into the overall health and conditions of the monitored ecosystem, aiding in the assessment of ecosystem resilience and potential risks. By harnessing the power of AI algorithms, researchers and conservationists can gain a deeper understanding of marine ecosystems, enabling effective management and conservation efforts for the long-term well-being of ocean organisms and the preservation of our oceans.
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Mandal, A., Ghosh, A.R. AI-driven surveillance of the health and disease status of ocean organisms: a review. Aquacult Int 32, 887–898 (2024). https://doi.org/10.1007/s10499-023-01192-7
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DOI: https://doi.org/10.1007/s10499-023-01192-7