In recent years, artificial intelligence (AI) has grown in importance in aquaculture research and production, with both startups and established corporations developing new AI-based applications for the industry.
The artificial intelligence of iFarm is used to visualize salmon. Aquaculture is gradually adopting artificial intelligence (AI) to help with husbandry and other production operations. © Cermaq.
Aquaculture?
Aquaculture is the commercial production of aquatic animals and plants in varied water conditions. Aquaculture can be done in two separate environments: marine and freshwater. The rearing, breeding, and harvesting of aquatic livestock in a natural water body such as the ocean is referred to as marine aquaculture. A variety of elements, including water temperature, food pattern, and oxygen level, are required to maintain the highest quality of aquaculture animals and a suitable habitat.
Current Data of AI in Aquaculture
The identification of advanced diseases based on fish behavior and external appearance has emerged as a promising field for AI application. Chen et al. (2022) proposed a two-phase image analysis system that classifies three categories of aberrant appearance in cage-cultured grouper using deep learning and a convolutional neural network. The best of the four categorization models generated in the study had an average accuracy of 98.94 percent. Darapaneni et al. (2022) have presented a technique for early detection of disease outbreaks, giving artisanal farmers more time to respond with appropriate management alternatives. The system uses underwater cameras or similar sensors to capture images, which are then sent to a partner for processing and scoring through the Cloud. Source: Salmon being evaluated by ReelData’s health monitoring AI The Canadian firm ReelData AI has developed two products for land-based aquaculture operations © Reel Data AI. Recent aquaculture AI research has also focused on more efficient feeding procedures. Chen et al. (2021) predicted feed requirements and optimal feeding levels for shrimp farmed in a RAS facility using a biomass prediction formula based on a support vector machine model and real-time water quality data with artificial neural networks. The results showed a mean percentage error of 3.7 percent, which is likely significantly better than manual feeding.
Applications of AI in Aquaculture
While some industry observers and stakeholders are skeptical about the future of AI in production aquaculture or are hesitant to embrace it entirely, others are going all in. Some of the applications of AI in Aquaculture are here in this segment.
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- Feeding Automation System
The automated feeding system was the first application of artificial intelligence in fisheries. Even while freshwater aquaculture allows the farmer to stay close to the growing site, feeding the fish can be time consuming. Farmers, too, can sometimes forget or become unavailable for various reasons. This is where AI can beimmensely useful, stepping into the shoes of the cultivators via robotic feeding systems.AI can assist with automatic meals for the fish by utilizing the technology of an automated feeding system. Artificial intelligence-enabled devices can do this by dispensing food at regular intervals. This is also a very useful approach to reducing food waste and optimizing food intake among livestock. - Growth Statistics
The third application of AI in aquaculture is that it allows you to monitor and interpret growth statistics for your livestock, allowing you to significantly evaluate the best techniques. This means that by utilizing the power of AI, farmers may not only discover flaws in their farming practices but also be attentive to the following steps and damage control to ensure that their livestock is well. The usage of growth statistics is also a significant contribution to the collection of big data and big data analytics, which will aid others in analyzing the proper methods and steps. - Remote Monitoring and Upkeep
Artificial intelligence in aquaculture enables the cultivator to remotely monitor and maintain the agricultural location. This entailed alerting the farmer of any blockages, food supply exhaustion, or other issue that needed to be addressed right away.
The goal of remote monitoring and maintenance is to allow farmers to wander freely while accurately monitoring their aquaculture locations. Umitron Corporation, based in Tokyo, has been working to adapt AI technology for aquaculture applications. Using Umitron’s AI technologies, the Kura Sushi restaurant chain has begun cultivating mackerel tuna at its Kura Osakana production location. Umitron’s system makes decisions about when and how much food to to add to each fish cage based on real-time observation of swimming behavior. This method dramatically improves feed conversion efficiency while reducing waste, and it significantly minimizes the transport/logistics requirements associated with standard daily feeding. The fish are now known as “AI Sumagatsuo.” - Temperature Optimization
Temperature is important in aquaculture because it helps to keep the thermal environment surrounding the fish ideal and balanced.
Temperature optimization can be accomplished with the use of AI & ML algorithms, which enable farmers to specify their preferences and extract tailored models for their farming sites. For example, AI can assist farmers in lowering the temperature during the daytime while raising it at night. - Human-less Filtration
Human-less water filtration powered by AI is simple to accomplish with the help of pre-installed machinery. As a result, the application of AI in aquaculture eliminates the need for farmers to execute time-consuming tasks manually. It is vital to remember that all of these tasks can occur concurrently in order to meet the needs of the animals and result in better outcomes for the practice as a whole. - Big Data Analytics
The final application of AI in aquaculture is that it uses big data analytics to collect information and assist others in taking the appropriate procedures.
Farmers can very well appreciate the risks and obstacles that they may face during the process with the help of big data and take suitable steps to ensure that they are on the right track.
As a result, big data analytics not only gives a summary of the activity but also captures the dos and don’ts in order to assist farmers.
- Feeding Automation System
FAQs
AI and automation can dramatically increase seafood production to feed the world’s rising population
while lowering the expense and environmental footprint of aquaculture operations.
AI and automation can dramatically increase seafood production to feed the world’s rising population
while lowering the expense and environmental footprint of aquaculture operations.
While having systems monitor various processes and conditions and share information with operators
saves time and money, programming systems to control these situations is far more important.
It may be remotely managed and automated by utilizing IoT, big data, artificial intelligence, 5G, cloud
computing, and robotics.
Aquaculture relies on fish, and when AI is utilized as a tool rather than to replace human talent, it has
the potential to increase both efficiency and revenue, while also improving fish welfare.
Deep learning AI necessitates a significant amount of training data, which is often scarce. This is made
worse for some uses by the turbidity and biofouling common to many culture systems. Finally, costbenefit analysis will heavily influence the extent and pace of AI adoption. However, long-term adoption
tendencies are becoming more obvious.
