Potential Applications of Data Mining in Aquaculture

Over 70 % of the earth’s surface is covered by oceans and inland water bodies, and they serve as an enormous source of valuable natural resources, livelihood and food security. Globally, as the demand for high quality nutrient rich seafood is increasing, the contribution of aquaculture has increased manifold and it is presently considered as one of the fastest growing food production sectors which is economically feasible for farmers and entrepreneurs. The progressive adoption of innovative sensor (optical, acoustic and biological) networks and technologies that are capable of analysing and revealing useful information from the existing aquaculture data has enabled the development of intelligent decision support systems based on data mining and artificial intelligence. The techniques and algorithms of data mining provides deeper insights on aquaculture and fisheries operations and realities by analysing the huge volume of scientific data and information that are available. Considering the research and developments in recent years and growing interest of aquaculturists on data science, in this review, we have consolidated and updated information on the possible applications of data mining, artificial intelligence and machine learning in different domains of aquaculture, from various scientific publications. This includes 1) an overview of data mining and machine intelligence in aquaculture; applications in 2) environment analysis; 3) feed management; 4) disease management; and 5) fish stock assessment. Along with these broad spectrum of aquaculture applications, we indicate the potential areas for further research and also the challenges in the adoption of data mining tools.