Cited by Integrating AIoT Technologies in Aquaculture: A Systematic Review

Integrating AIoT Technologies in Aquaculture: A Systematic Review DOI

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(5), P. 199 - 199

Published: April 30, 2025

The increasing global demand for seafood underscores the necessity sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and threat of disease outbreaks, impede progress in this field. This review explores transformative role Artificial Intelligence Things (AIoT) mitigating these challenges. We analyse current research on AIoT applications aquaculture, with a strong emphasis use IoT sensors real-time data collection AI algorithms effective analysis. Our focus areas include monitoring water quality, implementing smart feeding strategies, detecting diseases, analysing fish behaviour, employing automated counting techniques. Nevertheless, gaps remain, particularly regarding integration broodstock management, development multimodal systems, challenges model generalization. Future advancements should prioritise adaptability, cost-effectiveness, sustainability while emphasizing importance advanced biosensing capabilities, digital twin technologies. In conclusion, presents substantial opportunities enhancing practices, successful implementation will depend overcoming related to scalability, cost, technical expertise, improving models’ ensuring sustainability.

Language: Английский

A Comprehensive Review of Quality of Aquaculture Services in Integrated Multi-Trophic Systems DOI

Jorge A. Ruíz-Vanoye, Ocotlán Díaz-Parra, Marco Antonio Márquez-Vera

et al.

Fishes, Journal Year: 2025, Volume and Issue: 10(2), P. 54 - 54

Published: Jan. 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.

Language: Английский

Citations

EcoGuard: Advancing IoT-based Aquaculture with Machine Learning for Enhanced Productivity and Automation DOI

Jarin Nooder Esty,

Abu Salyh Muhammad Mussa,

Md. Fazle Rabbi

et al.

Journal of ISMAC, Journal Year: 2025, Volume and Issue: 7(1), P. 18 - 41

Published: Feb. 27, 2025

The increasing demand for sustainable aquaculture necessitates efficient water quality management to enhance fish health, reduce mortality rates, and improve overall productivity. However, conventional monitoring relies on manual testing, which is labour-intensive, time-consuming, ineffective in detecting rapid environmental fluctuations. To address these limitations, this study presents EcoGuard, an IoT-enabled smart system that integrates edge computing federated learning-based predictive analytics real-time assessment management. EcoGuard continuously monitors the important parameters, including pH, dissolved oxygen (DO), temperature, turbidity, ammonia levels, through a wireless sensor network. module, employing Random Forest Long Short-Term Memory (LSTM) models, forecasts trends, enabling early intervention risk mitigation. A key feature of its learning framework, facilitates collaborative model training across multiple farms while ensuring data privacy security. utilizes MQTT protocol low-latency transmission, integrated mobile application provides alerts decision support optimized resource Experimental validation demonstrates effectively reduces mortality, enhances operational efficiency, supports practices. By utilizing IoT, AI, learning, proposed offers scalable, cost-effective, intelligent solution modernizing aquaculture, contributing food security, conservation, resilient fisheries

Language: Английский

Citations

Reliable Water Quality Prediction Using Bayesian Multi-Scale Convolutional Attention Network DOI