Smart Aquaculture

Example of using AI for epileptic diagnosis.

In our research, We propose to combine these advanced techniques for fish activity recognition taking into consideration the limitations related to water clarity, turbulences … etc. From the control point of view, we will track the fish using motion modeling-based techniques and classify the fish activity based on its trajectory and its spatiotemporal speed variation. Overall, we aim to learn fish behavior, which will be used as an additional input to the control system design.

Types of aquaculture.

Our proposed smart aquaculture system focuses on the following points:

  1. Studying the effect of the the environmental conditions on fish health and growth
  2. Optimal feeding control using MPC controller
  3. Fish health assessment using computer vision.
  4. In our different studies, we targeted the following aspects:

This project will be conducted by a team of three professors. Prof. Laleg’s team has significant expertise in developing control, optimization and monitoring methods. She has been working on simulations and lab experimental testing of her algorithms. Prof. Ghanem’s team is particularly well suited to performing the computer vision tasks. Prof. Berumen’s team will provide their expertise in fish behavior and will help in collecting data. In particular, they will work on labelling the fish behavior and will support the experimental setup with their ongoing aquaria work in KAUST’s marine core lab (CMRCL).

Abderrazak Chahid
Abderrazak Chahid
PhD candidiate

My research interests include feature extraction, real-time implementation of smart decision making systems.

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