Affiliation:
1. The International Graduate Program of Veterinary Science and Technology (VST), Department of Veterinary Pathology, Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand
2. Department of Fisheries and Marine Bioscience, Faculty of Biological Sciences Jashore University of Science and Technology Jashore Bangladesh
3. Division of Biological and Biomedical Sciences College of Health and Life Sciences, Hamad Bin Khalifa University Doha Qatar
4. Department of Rangeland, Wildlife and Fisheries Management Texas A&M University College Station Texas USA
Abstract
AbstractAquaculture is now the main source of seafood in human diets and is one of its fastest‐growing industries worldwide. However, the industry is facing several difficulties, including infectious diseases, the most significant limiting factor for aquaculture expansion. The impact of diseases on aquaculture growth, fecundity, mortality rates, and marketability is profound. Hence, the ability to predict disease outbreaks is crucial to overcoming these challenges. Various infectious agents such as bacteria, viruses, fungi, and parasites can cause significant losses of fish in intensive aquaculture practices. In an aquaculture environment, the high host density coupled with restricted water flow promotes pathogen spread. Early detection of disease is crucial for farmers as mortality rates can reach as high as 100% if left untreated. Therefore, new techniques and technical solutions for disease management in aquaculture are required. In this context, data analytics technologies, such as internet of things (IoT) sensors, artificial intelligence, and machine learning, allow farmers to proactively monitor their farms and detect potential disease outbreaks before they strike. Here, we highlighted the potential of machine learning algorithms in early pathogen detection and the possibilities of intelligent aquaculture in controlling disease outbreaks at the farm level. IoT is currently a popular study topic for smarter and sustainable aquaculture, as seen by the growing interest and broad overall assumptions. Therefore, this review aims to provide comprehensive information on the various aspects and challenges associated with modern technologies for controlling pathogenic microorganisms, as well as the potential benefits of using the IoT to improve fish health and welfare in aquaculture.
Subject
Agronomy and Crop Science,Aquatic Science
Reference88 articles.
1. Use of artificial intelligence in infectious diseases
2. Fish disease detection using image based machine learning technique in aquaculture;Ahmed M. S.;Journal of King Saud University – Computer and Information Sciences,2021
3. Surface Plasmon Resonance Aptamer Biosensor for Discriminating Pathogenic Bacteria Vibrio parahaemolyticus
4. Newly emerging MDR B. cereus in Mugil seheli as the first report commonly harbor nhe, hbl, cytK, and pc‐plc virulence genes and bla1, bla2, tetA, and ermA resistance genes;Algammal A. M.;Infection and Drug Resistance.,2022
5. Prevalence, antimicrobial resistance (AMR) pattern, virulence determinant and AMR genes of emerging multi‐drug resistant Edwardsiella tarda in Nile tilapia and African catfish;Algammal A. M.;Aquaculture,2022