Applications of data mining and machine learning framework in aquaculture and fisheries: A review

Aquaculture and fisheries sectors are finding ingenious ways to grow and meet the soaring human demand for nutrient-rich fish and seafood by efficiently utilizing the vast water resources and biodiversity of aquatic life on earth. This includes the progressive integration of information technology, data science and artificial intel-ligence with fishing and fish farming methods to enable intensification of aquaculture production, sustainable exploitation of natural fishery resources and mechanization-automation of allied activities. Exclusive data min-ing and machine learning systems are being developed to process complex datasets and perform intelligent tasks like analysing cause-effect associations, forecasting problems and providing smart-precision solutions for farming and catching fish. Considering the intensifying research and growing interest of stakeholders, in this review, we have consolidated basic information on the various practical applications of data mining and machine learning in aquaculture and fisheries domains from representative selection of scientific literature. This includes an overview of research and applications in (1) aquaculture activities such as monitoring and control of the production en-vironment, optimization of feed use, fish biomass monitoring and disease prevention; (2) fisheries management aspects such as resource assessment, fishing, catch monitoring and regulation; (3) environment monitoring related to hydrology, primary production and aquatic pollution; (4) automation of fish processing and quality assurance systems; and (5) fish market intelligence, price forecasting and socioeconomics. While aquaculture has been rel-atively faster in integrating data mining and machine learning tools with advanced farming systems, capture fisheries is finding reliable methods to sort the complexities in data collection and processing. Finally, we have pointed out some of the challenges and future perspectives related to large-scale adoption.