## 9 Monitoring Applied to Aquaculture and Fishery Productions  
Various technological applications of artificial intelligence (AI) were set for improving the sustainability of monitoring in coastal waters and oceans, as well as in aquaculture and smart fishery plants, and they are widespread nowadays [138]. In particular, the attention of scientists in AI-inspired fisheries focused mostly on monitoring the automation of fishery resources (mainly detection, identification, and classification). However, it is still unclear how fishers perceive AI needs and how governments exhibit a tangible strategy on the regulation of AI concerning smart fishery systems to promote the value and potential of the techniques of AI-inspired fisheries. AI has great influence on catch monitoring across fishery systems at sea [139], and several AI applications improved fishing activities, helping the economic evaluation of commercial fleets [140]. In addition, a fishery may be helped by electronic monitoring of the catch and bycatch [141], as well as the detection and forecasting of fishing grounds [142], eventually applying mathematical models to simulate fishing vessel behavior [143]. This also helps to reduce fishery wastes [144] by optimizing the sorting operations. Finally, automation of the monitoring of illegal fishery methods [145] is also possible for reducing the negative impacts of fisheries on coastal areas. The AI technologies of fish farming mainly focus on the means for optimizing the efficient use of resources in ecosystem management [146]. In several instances, fishery and ecological monitoring have been strictly interconnected. In fact, sustainable fisheries are related to environmental monitoring [147]. Various authors stressed the scope of smart fisheries because of the \"epidemic of plastics entering the sea\". This warrants urgent action if humanity is to stave off a collapse in fish stocks. Additionally, oil spills [148] and global changes [149], as mentioned above, are topics of great concern [150], prompting not only issues of environmental conservation but also large impacts on fisheries [151], requiring accurate and modern monitoring activities. The employment of smart systems able to autonomously tune their activities according to local perturbations and able to be trained for the detection of various compounds (both in the water and over the water, evolving into e-noses) is boosting improvements in various fields of environmental monitoring. Figure 6: Physical architecture of smart monitoring systems with data collection networks. A special case of monitoring marine environments is the one applied to aquaculture activities [152; 153], primarily because these activities may impact various coastal areas when practiced in cages, pens, floating tanks, and raceways deployed in open waters [154]. There is great concern about the potential environmental effects of marine finfish cages on the water quality [155] and a large interest in developing an ecologically responsible industry [156; 157]. Several reviews [158] have broadly addressed this topic [159; 160; 161]. In addition, aquaculture ponds may be considered very special marine environments, and they need continuous monitoring and real-time reactions to negative changes impacting the organisms contained therein [162]. In addition, in this case, artificial intelligence and IoT devices may be applied to improve production efficiency and reduce impacts and risks. In fact, the connection between good environmental conditions and seafood health in aquaculture has been documented [163; 164; 165]. Sea cages can be more than 45 m in diameter and 30 m deep, and they need frequent inspections. Although a single cage can contain high value production [166; 167], the level of surveillance of the product and of the closer environments is often low [168].