Adoption of AI/ML in Aquaculture: a study on Pisciculture

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Dr. Rajesh Kumar Panda, Prof. Dipak Baral


This article focuses on the adoption of artificial intelligence (AI) and machine learning (ML) in Pisciculture, which refers to the rearing of fish in controlled environments. The study explores the potential benefits of using AI and ML in Pisciculture, such as improved feed management, disease diagnosis, and water quality monitoring. The research also investigates the challenges associated with implementing these technologies, including the need for high-quality data and the complexity of integrating AI and ML into existing Pisciculture systems. The study concludes by highlighting the importance of collaboration between academia, industry, and government to develop AI and ML tools that are tailored to the specific needs of Pisciculture producers, and that take into account ethical, legal, and environmental considerations. Overall, this study provides valuable insights into the potential of AI and ML to improve the efficiency and sustainability of Pisciculture, and identifies areas for future research in this field.

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