Seafood demands are rising and show no signs of slowing down. Because of population growth, global fish consumption is forecasted to increase by 20% by 2030. The aquaculture industry must keep up.
Maintaining good water quality ensures that the fish farming industry consistently delivers quality seafood to consumers worldwide. Unfortunately, capturing water quality measurements is time-consuming and often prone to human errors. Not only will this compromise fish quality and health, but it can also significantly decrease reproduction rates and produce more waste, compromising sustainability efforts.
As such, aquaculture farms must implement better quality control processes to preserve superior water quality. Machine learning technology delivers real-time insights, allowing fish farmers to glean a better understanding of environmental conditions and significantly improve fish health.
Superior Water Quality Is Essential to Optimal Fish Production
Fish farms must carefully monitor and measure two main water quality parameters: pH and dissolved oxygen.
pH levels are intrinsically linked to fish growth, health, and reproduction. If pH levels are too extreme, it can lead to mass fatalities. Microorganisms affected by pH levels will also impact water quality. Some types of microorganisms, such as phytoplankton, perform photosynthesis. This biochemical function helps to reduce the amount of ammonia in the tank, protecting fish from ammonia poisoning. Proper pH levels also allow beneficial microorganisms to outdo hazardous ones, drastically improving ecosystem conditions.
Dissolved oxygen is equally as important to optimal water quality. Low levels can lead to substandard growth and reproduction rates.
Fish farmers must vigilantly track pH and dissolved oxygen to maintain and improve water quality. If they fail to do so, fish survival and reproduction rates can drop significantly. Substandard environmental conditions can also promote the spread of infectious diseases, further hindering fish health.
However, traditional water quality monitoring methodologies are tedious, labor-intensive, and susceptible to mistakes. This hinders a fish farm’s efficiency and effectiveness. Manual processes will also disrupt fish, causing unnecessary stress and possible harm. Traditional processes also offer limited insights into overall environmental conditions.
Fortunately, AI-enabled analytics can help aquaculture farms continuously maintain excellent water quality. This advanced software captures better water quality information, streamlining fish farms’ quality control and biomass monitoring processes.
Top Benefits of AI in Aquaculture
Underwater IoT, machine learning, and artificial intelligence doggishly capture water quality details and other vital information 24/7. This alleviates the burden of tedious QA processes and ensures aquaculture farms better understand environmental conditions.
AI does this by providing relevant, accurate, real-time data analytics. This technology works with countless hardware sensors and software to generate live data collection, holistic visualization, and automated reporting on water quality parameters and environmental conditions.
Artificial intelligence also delivers predictive forecasts, allowing aquaculture farms to identify potential inefficiencies and problems before they even happen, including algae bloom conditions, temperature fluctuations, and drops in dissolved oxygen and pH levels.
With AI-enabled analytics, fish farms can:
- Maximize efficiency
- Automate time-consuming quality control processes
- Maintain superior water quality and environmental conditions
- Identify issues before they can happen
- Prevent the spread of infectious disease
- Efficiently monitor tank conditions without disrupting or harming fish
- Improve profitability and productivity
By leveraging AI and machine learning, fish farms can do away with time-consuming manual processes and make data-driven decisions to optimize fish growth, health, quality, and reproduction.
The Future of Quality Assurance in Fish Production
The future of QA aquaculture processes will be data-driven. As global demands for seafood continue to rise, more fish farms will turn to machine learning and AI to optimize fish production to keep pace.
As AI-based monitoring system penetration increases, more and more aquaculture organizations will do away with traditional error-prone manual processes and adopt data analytics into their quality control measures. This boosts accuracy and efficiency and allows aquaculture operators to make confident decisions about potential future problems before they arise. It also helps fish farms to support sustainability by reducing waste and overfeeding while remaining compliant with increasingly strict rules and regulations.
AI-Enabled Analytics Fishes Out Insightful Data
Global seafood demands will only continue to accelerate. Aquaculture farms must embrace AI, machine learning, and data analytics to deliver high-quality products continuously.
Fish farms can improve efficiency, maintain good water quality, mitigate potential issues, and maximize fish health, growth, and reproduction by taking a data-centric approach to fish out crucial insights in real-time.
Sightline EDM is the premier choice for aquaculture AI and data analytics. Our software allows fish farms to monitor data across all facilities, maximize yields and up-time, monitor water quality parameters 24/7, get customized alerts for potential irregularities, and drive ROI.
Are you ready to embrace the future of fish farms? Contact us today.
