Revolutionising Fish Health with AI-driven Disease Detection in Aquaculture

By : Justin Gladhys Jerlin Mol , Arumugam Uma

Page No: 7-12

Abstract
Aquaculture plays a crucial role in global food security, yet disease outbreaks remain one of the most significant challenges affecting fish health and farm productivity. Traditional disease monitoring methods rely heavily on manual observation, which is often time-consuming, error-prone, and ineffective for early detection. The integration of Artificial Intelligence (AI) into aquaculture health management offers transformative potential to overcome these limitations. AI-driven systems can analyse behavioural, visual, and environmental data in real time to identify subtle signs of disease or stress, enabling rapid and precise responses. Techniques such as image recognition, behavioural tracking through camera systems, and machine learning-based water quality monitoring have demonstrated promising results in predicting and mitigating disease outbreaks. Despite these advances, several barriers including the lack of standardised, high-quality datasets, data-sharing protocols, and concerns about data privacy and implementation costs continue to hinder large-scale adoption. Addressing these challenges through improved infrastructure, collaborative data frameworks, and ongoing algorithmic refinement will be key to unlocking the full potential of AI in aquaculture. Ultimately, the application of AI in disease detection can enhance fish welfare, reduce antibiotic usage, and ensure sustainable and profitable aquaculture practices.

Authors:
Justin Gladhys Jerlin Mol : Department of Aquatic Animal Health Management, Tamil Nadu Dr. J. Jayalalithaa Fisheries University, Dr. M.G.R. Fisheries College and Research Institute, Ponneri, Tamil Nadu,
Arumugam Uma : Directorate of Incubation and Vocational Training in Aquaculture, Tamil Nadu Dr. J. Jayalalithaa Fisheries University, Muttukadu, Kancheepuram, Tamil Nadu,
 

DOI: http://doi.org/10.32381/JAT.2026.41.1-4.2