In this post, I would like to share with you some of the most recent and promising developments in the field of aquaculture, especially with regard to fish health and welfare. As you know, aquaculture is an economically and environmentally important activity that contributes to food security, employment generation and conservation of natural resources. However, it also faces many challenges, such as increasing demand, competition for space and water, and the threat of infectious diseases.

Diseases are one of the main factors limiting aquaculture production and profitability. They not only cause direct economic losses through mortality and reduced growth, but can also negatively affect product quality, trade, biodiversity and public health. Effective and sustainable strategies to prevent, diagnose and treat diseases in aquaculture are therefore essential.

Fortunately, science and technology offer us innovative and powerful tools to improve our ability to manage fish health and reduce the impact of disease. In this post, I am going to tell you about some of these technologies and approaches that are currently being applied or developed, and that have great potential to transform the aquaculture sector in the future.

One of these technologies is the rapid and accurate detection of diseases. This is essential in order to be able to intervene in time and prevent them from spreading or worsening. There are several methods for detecting diseases in fish, ranging from traditional methods based on clinical observation and microbiological analysis to more modern methods based on molecular biology and genomics. The latter make it possible to identify pathogens with greater sensitivity and specificity, as well as to determine their genetic diversity, geographical origin and antimicrobial resistance.

Antimicrobials are substances used to treat bacterial infections in fish. However, their excessive or inappropriate use can lead to the emergence of resistant bacteria that can be transmitted to other animals or to the environment. This represents a serious risk to animal and human health, as well as to the efficacy of available treatments. It is therefore necessary to promote responsible and prudent use of antimicrobials in aquaculture and to develop safer and more efficient alternatives such as vaccines, probiotics, phytotherapeutics or immunomodulators.

Some of the advanced technologies used in aquaculture for fish disease detection and control include the use of geographic information systems (GIS) for mapping, biosensors, biomimetics, image-based machine learning, and rapid genomic techniques for fish disease detection.

All this through machine learning, a branch of artificial intelligence that allows the creation of systems capable of learning from data and improving their performance without the need for explicit programming. Machine learning can be applied to various aspects of aquaculture, such as the design of production systems, environmental monitoring, quality control, nutritional management or health management.

Machine learning and artificial intelligence can contribute to the early detection and prevention of diseases in aquaculture in several ways. For example, machine learning can detect disease risks on aquaculture farms with an accuracy of more than 93%, allowing early detection of potential disease outbreaks before they occur. In addition, artificial intelligence can compare programmed data with data collected at the farm site, allowing AI algorithms to identify disease epidemics before they occur, proposing appropriate preventive actions. These technologies can also provide specific recommendations in an action plan, such as optimising feeding, predicting and managing diseases, and receiving advisory services. In addition, artificial intelligence can help reduce reliance on technicians to perform routine tasks on aquaculture farms, benefiting coastal communities and rural farmers in remote locations.

In addition to machine learning, there are other advanced technologies that can be used to improve disease management in aquaculture. For example:

• Image processing makes it possible to analyse images captured by cameras or drones to assess the health status, behaviour or growth of fish.
• Genomics allows the study of fish DNA and pathogens to identify genes or markers associated with disease resistance, susceptibility or virulence.
• Sensors allow real-time measurement of various physical, chemical or biological parameters that can affect or reflect fish health, such as temperature, oxygen, pH or stress.
• GIS (geographic information system) mapping allows visualisation and analysis of the spatial and temporal distribution of fish, pathogens and environmental factors that may influence disease transmission or prevention.

These technologies can be integrated into what are known as smart aquaculture models, which combine data collection, processing and interpretation with automation, optimisation and decision-making. These models can improve the efficiency, productivity and sustainability of aquaculture, as well as reduce costs, risks and environmental impact.

Some of the challenges of using smart aquaculture models and big data analytics for fish health management include the significant complexity of aquaculture data, such as fish, water, feed and productivity data, as well as the increasing amount of structured and unstructured data. In addition, the management, processing and security of big data can pose significant technical and privacy challenges.

On the other hand, potential benefits include the ability to identify and treat harmful fish infections early, prevent future losses and improve the sustainability of aquaculture. In addition, the use of smart aquaculture models and big data analytics can provide valuable information, patterns and trends that enable informed decisions and improved operations.