Research Square (Research Square), Год журнала: 2025, Номер unknown
Опубликована: Май 6, 2025
Язык: Английский
Research Square (Research Square), Год журнала: 2025, Номер unknown
Опубликована: Май 6, 2025
Язык: Английский
Fishes, Год журнала: 2025, Номер 10(2), С. 54 - 54
Опубликована: Янв. 29, 2025
The concept of Quality Aquaculture Services (QoAS) is inspired by the Service (QoS) principle, originally developed in field networks and telecommunications, where it refers to ability guarantee quality, availability, priority service a communications system. Adapted aquaculture context, QoAS fundamental maximising benefits Integrated Multi-Trophic (IMTA). IMTA has emerged as sustainable approach meet growing global demand for aquatic food products combining species from different trophic levels single system, optimising resource use, improving environmental performance, diversifying production. However, ensuring these complex systems requires implementation advanced technologies monitor, manage, optimise every aspect process. This article presents comprehensive review applied at IMTA, focusing on IoT-based monitoring systems, management algorithms, water recirculation technologies, intelligent automation, biosecurity, data platforms. Our finds that IoT automation-based solutions significantly enhance real-time monitoring, increasing operational efficiency sustainability. Key challenges identified include integration complexity, high costs, technical expertise requirements, but ongoing development modular, user-friendly indicates promising trajectory. highlights transformative role technological innovation providing foundation future research advancements aquaculture.
Язык: Английский
Процитировано
0Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 18, 2025
Abstract Measuring and monitoring fish welfare in aquaculture research relies on the use of outcome- (biotic) input-based (e.g., abiotic) indicators (WIs). Incorporating behavioural auditing into this toolbox can sometimes be challenging because sourcing quantitative data is often labour intensive it a time-consuming process. Digitalization process via computer vision artificial intelligence help automate streamline procedure, gather continuous optimisation assist decision-making. The tool introduced study (1) adapts DeepLabCut framework, based machine learning, to obtain pose estimation Atlantic salmon parr under replicated experimental conditions, (2) quantifies spatial distribution through metrics inspired by ecological concepts home range core area, (3) applies inspect variability around feeding. This proof concept demonstrates potential our methodology for automating analysis behaviour relation including detection, variations within between tanks. impact feeding these patterns also briefly outlined, using 5 days as demonstrative case study. approach provide stakeholders with valuable information how their rearing environment small-scale settings used further development technologies measuring future studies.
Язык: Английский
Процитировано
0Aquaculture International, Год журнала: 2025, Номер 33(4)
Опубликована: Март 26, 2025
Язык: Английский
Процитировано
0Research Square (Research Square), Год журнала: 2025, Номер unknown
Опубликована: Май 6, 2025
Язык: Английский
Процитировано
0