Feeding control and water quality monitoring in aquaculture systems:
  Opportunities and challenges
Fahad Aljehani Ibrahima N'Doye Taous-Meriem Laleg-Kirati
Aquaculture systems can benefit from the recent development of advanced
control strategies to reduce operating costs and fish loss and increase growth
production efficiency, resulting in fish welfare and health. Monitoring the
water quality and controlling feeding are fundamental elements of balancing
fish productivity and shaping the fish growth process. Currently, most
fish-feeding processes are conducted manually in different phases and rely on
time-consuming and challenging artificial discrimination. The feeding control
approach influences fish growth and breeding through the feed conversion rate;
hence, controlling these feeding parameters is crucial for enhancing fish
welfare and minimizing general fishery costs. The high concentration of
environmental factors, such as a high ammonia concentration and pH, affect the
water quality and fish survival. Therefore, there is a critical need to develop
control strategies to determine optimal, efficient, and reliable feeding
processes and monitor water quality. This paper reviews the main control design
techniques for fish growth in aquaculture systems, namely algorithms that
optimize the feeding and water quality of a dynamic fish growth process.
Specifically, we review model-based control approaches and model-free
reinforcement learning strategies to optimize the growth and survival of the
fish or track a desired reference live-weight growth trajectory. The model-free
framework uses an approximate fish growth dynamic model and does not satisfy
constraints. We discuss how model-based approaches can support a reinforcement
learning framework to efficiently handle constraint satisfaction and find
better trajectories and policies from value-based reinforcement learning.