Publisher's Synopsis
Fishery products are crucial in ensuring food security and nutrition strategies at all levels. Fish possess significant potential to serve as a valuable alternative source of protein in lieu of livestock and poultry. The global fishing industry has facing challenges such as scarcity of labor, the deterioration of aquaculture water quality, outdated aquaculture methods, human activities, industrial pollution, and an increased occurrence of diseases in aquatic products. Concurrently, the appropriate areas for aquaculture will gradually decrease, leading to a decline in the profitability per unit of land. Insufficient scientific and technological innovation and engineering technical support exist throughout the entire fishery industry chain. Insufficient fundamental research and theoretical understanding exist regarding the interaction between biological environments and the integration of engineering processes. The progress in aquaculture engineering, mechanization, information technology, and equipment construction is significantly inadequate. The solution to these problems is Smart aquaculture involving intelligence and automation. A smart solution to address these gaps in the aquaculture ecosystem is a blended approach - a combination of human and technological intervention to ensure that farmers are aware of the best practices prevalent in aquaculture. Smart technology is the key to better productivity and disease management. From the internet of things (IoT) and artificial intelligence (AI) to remotely operated vehicles (ROVs), Intelligent fish farms, Climate Smart aquaculture, Nanotechnology applications in aquauclture systems to effective drug delivery for fish health management and in seafood processing, remote satellite imagery, these new technologies are being leveraged by several emerging players to combat the existing challenges in aqua farming and enhance overall production. Modern intelligent technologies hold prospects in aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. Moreover, there are numerous components of smart aquaculture, such as the collection of information through a variety of temperature, dissolved oxygen, humidity, light, and pH sensors to manage the water quality parameters in the aquaculture system; the transmission of the collected data to the control center via communication nodes; the analysis of data and decision-making stored in cloud platforms; the feedback of decision-making to each execution equipment; and the intelligence to operate a system automatically in order to develop aquaculture in a sustainable and efficient manner that is environmentally friendly.
