Automatic solutions in Salmon aquaculture: From automatic feeding to the autonomous feeding system

Author: Caijun Zhao

Recent advances in automated inspection, artificial intelligence, and robotics are transforming the aquaculture production industry. The fish industry has been labor-intensive. While the largest aquaculture sites are usually located in remote or scarcely populated areas. Besides, repetitive and difficult work is another reason recruiting people becomes difficult (Einarsdottir et al., 2022). Additionally, overfeeding poses significantly negative impacts on the environment, which in turn may cause huge economic losses for the industry. Given the reasons mentioned above, great focus has been placed on automatic solutions. Among all the different modern data processing techniques used in aquaculture, automatic feeding technologies have been one of the most critical technologies that contributed to the success of the industry and mitigation of the negative impacts on the environment to some extent.  

The concept of automatic feeding in aquaculture was developed in Norway in the early 1980s. 

Many pieces of research confirm that automation increases the fingerlings’ growth of fish and feed conversion efficiency. Also, automatic solutions make the size control of fish possible by monitoring the social hierarchy in a group and the daily energy consumption in varying conditions (Ruohonen, 1986). Size variations pose a significant challenge in fish processing. Today’s feeding system is much more advanced in distributing feeds and having greater control over feeding. This is realized by adopting the auger screw-based system in which the thrust of the auger is the key to achieving the precise amount.  The professional can access the feeding parameter and manage feeding plans tailored to the fish farmers. The software integrates the feeding task with mobile devices, which enables workers to feed fish remotely (Fish Farm Feeder, 2020).  

Picture: Fish Farm Feeder, 2020. 

In addition, based on the fieldwork I conducted in the salmon aquaculture sites of Lerøy Seafood Group, located in Frøya, Norway, the workers in Frøya do not need to feed salmon. Instead, the feeding task is completed by someone sitting in another city’s office. This is because all the fish cages are installed in the monitoring systems underwater, and this person can remotely monitor the size of the fish. The CageEye system combines biological can environmental data through hydroacoustic sensors. In this way, the system can gather data to analyze fish behavior and appetite, making continuous real-time decisions and adjustments based on the original feeding plan (The Fish Site, 2020). Combing with the feeding plans provided by professionals, it is possible to operate aquaculture farms with the lower environmental impacts caused by overfeeding.  

Picture: Bendik Søvegjarto, CEO of CageEye. 


K. Ruohonen,. (1986). Biological Models Related to Automatic Control in Aquaculture. A Case Study: Automatic Feeding Control. Automation and Data Processing in Aquaculture, Trondheim, Norway.  

Einarsdottir, H., Guðmundsson, B. and Ómarsson, V. (2022). Automation in the fish industry. Animal Frontiers, Volume 12, Issue 2, Page 32-39,

Fish Farm Feeder. (2020). Feeding Systems for Fish Farms and RAS Fish Farming. 

The Fish Site. (2020). Autonomous aquaculture feeding system unveiled.

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