How can cutting-edge technologies help us detect and control fish diseases?

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By Milthon Lujan

Proposed framework for image-based machine learning for fish disease detection modified from Chakravorty et al. (2015). SVM, support vector machine. Source: Islam et al., (2024); Journal of the World Aquaculture Society.
Proposed framework for image-based machine learning for fish disease detection modified from Chakravorty et al. (2015). SVM, support vector machine. Source: Islam et al., (2024); Journal of the World Aquaculture Society.

The fish, representing 17% of global animal protein consumption, plays a vital role in meeting the dietary needs of an expanding human population. As aquaculture undergoes technological transformation to meet growing demand, the integration of modern technologies becomes paramount.

Moreover, as the world’s fastest-growing industry, the potential of aquaculture is enormous, but diseases severely impact its growth, causing production, fecundity, and marketability losses. Addressing these challenges requires innovative solutions.

An article published by scientists from Chulalongkorn University, Hamad Bin Khalifa University, and Texas A&M University explores how modern technologies, specifically the Internet of Things (IoT), artificial intelligence (AI), and machine learning, can revolutionize disease management in aquaculture.

Evolution of Aquaculture: From Labor-Intensive Systems to Intelligent Systems

The exponential growth of global aquaculture, responsible for 70% of global edible fish production, has witnessed a shift from traditional labor-intensive methods to mechanized and highly automated systems. Labor shortages and disease risks led the industry to adopt smart and modern technology-based approaches, leveraging the power of IoT, big data, AI, 5G networks, cloud computing, and robotics.

Addressing Challenges Through Technology

Fish diseases, a significant threat to aquaculture, cause billions of dollars in losses each year. The adoption of modern software and computing technologies emerges as a crucial strategy for disease surveillance and diagnosis. Machine learning techniques, integrated into research projects, contribute to the development of disease prediction systems. Early disease detection is enhanced through digital image analysis, optimizing disease management and minimizing losses.

Oxygen Levels and Aquatic Organisms

Low oxygen levels in bodies of water pose a threat to aquatic organisms and affect their physical and physiological growth. Acidosis and nephrocalcinosis, induced by decreased oxygen levels, lead to granuloma formation in internal organs. The article emphasizes the use of advanced technologies to monitor and address issues related to oxygen levels, ensuring optimal growth conditions for fish.

Common Fish Pathogens: A Technological Approach

Bacterial infections, including those caused by Bacillus cereus, Edwardsiella tarda, and Vibrio, pose significant challenges to fish health and economic sustainability. Machine learning techniques and artificial intelligence algorithms, driven by historical data, are successful in predicting disease outbreaks. The integration of IoT and big data facilitates decision-making, ensuring timely responses to deviations from desirable settings within the aquaculture system.

The Role of Data Analysis Technologies

The integration of data analysis technologies, specifically IoT sensors, artificial intelligence, and machine learning, offers a promising path for disease management in aquaculture. These technologies enable farmers to monitor their farms in real-time, allowing early detection of potential disease outbreaks before they escalate. This proactive approach not only minimizes losses but also improves the overall health and well-being of fish in aquaculture systems.

Machine Learning Algorithms for Early Detection

Machine learning algorithms emerge as a turning point in the quest for early detection of pathogens. By analyzing complex datasets generated by IoT sensors, these algorithms can identify patterns indicative of disease presence. The ability to predict outbreaks before they occur provides fish farmers with a valuable tool to implement preventive measures and ultimately mitigate the impact of diseases on aquaculture production.

Smart Aquaculture

The concept of smart aquaculture, driven by IoT, AI, and machine learning, signifies a paradigm shift in disease management. Through real-time monitoring, predictive analysis, and automated responses, smart aquaculture enables farmers to make informed decisions, ensuring the health and sustainability of their operations. This transformative approach not only protects against disease outbreaks but also contributes to the overall resilience of the industry.

The article highlights the importance of GIS mapping, biosensors, biomimicry, image-based machine learning, and rapid genomic techniques in disease detection, providing a comprehensive overview of the technological landscape in aquaculture.


In the pursuit of sustainable aquaculture, technology serves as a guiding beacon, leading the industry toward intelligent and efficient practices. From disease prediction to monitoring oxygen levels, modern technologies play a crucial role in ensuring the health of aquatic organisms and the economic viability of aquaculture operations.

In the face of growing disease challenges, the fusion of IoT, AI, and machine learning emerges as a ray of hope for the aquaculture industry. This review summarizes the multifaceted benefits of modern technologies in controlling pathogenic microorganisms, with special attention to the transformative potential of IoT.

Sk Injamamul Islam
The International Graduate Program of Veterinary Science and Technology (VST), Department of Veterinary Pathology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok 10330, Thailand.
Email: injamamulislam017@gmail.com

Haitham Mohammed
Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University,
College Station, TX 77843, USA.
Email: haitham.mohammed@ag.tamu.edu

Reference (open access)
Islam, S. I., Ahammad, F., & Mohammed, H. (2024). Cutting-edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges. Journal of the World Aquaculture Society, 1–25. https://doi.org/10.1111/jwas.13051