Feeding control and water quality monitoring in aquaculture systems: Opportunities and challenges

TL;DR

This paper reviews advanced control strategies, including model-based and reinforcement learning methods, to optimize feeding and water quality in aquaculture, aiming to improve fish growth, welfare, and reduce costs.

Contribution

It provides a comprehensive review of control techniques for aquaculture, highlighting the integration of model-based and model-free reinforcement learning approaches.

Findings

Model-based control supports reinforcement learning for better constraint handling.

Reinforcement learning can optimize fish growth trajectories.

Control strategies can improve water quality and reduce operational costs.

Abstract

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.