Recognizing Ecological Behavior Patterns with Deep Learning

PhD-candidate: Håkon Måløy

Professor Keith L. Downing (NTNU)
Associate Professor Kerstin Bach (NTNU)
Senior Research Scientist Ekrem Misimi (SINTEF Ocean)

Duration: Q2 2018 – Q2 2022

Fish biology in exposed aquaculture is a four year project involving IMR, NTNU, SINTEF Ocean, Cermaq, Salmar, Marine Harvest and Kongsberg Maritim. The project aims to evaluate fundamental welfare indicators in Atlantic salmon both at the individual and school levels.

Fish biology in exposed aquaculture aims to uncover and validate operational welfare indicators for use within breeding cages. The project is expected to result in increased insight into how production affects salmon welfare as well as improved observational methods and tools.

Through the use of datadriven methods and machine learning, sensor data from within breeding cages will be analyzed to uncover co-relations between external environmental factors and the behavioral patterns in salmon.

Previous experiments using similar methods have resulted in promising results [1] and access to more and better sensor data makes it possible to test and evaluate candidate approaches in lab. The most promising approaches will then be tested in large scale, operational environments.

Results so far:

  • The PhD project started in April 2018. The first publication came in 2019 with results from the use of video of deep neural networks to understand feeding patterns for salmon [1].

Status and future work:

  • A new publications is written now with a focus on the use of echo data to detect salmon illnesses.
  • A fusion of echo and video data is a possibility to further understand the needs the salmon has.
  • The most promising approaches will be tested at full scale production environments in cages towards the end of the project.


[1] Måløy, Håkon; Aamodt, Agnar; Misimi, Ekrem. (2019) A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture. Computers and Electronics in Agriculture. vol. 167.