Image-based Shrimp Monitoring System

November 20, 2024

Shrimp farming is a growing industry, and automating certain processes within aquaculture tanks is becoming increasingly important to improve efficiency. This thesis proposes an image-based system designed to address four key tasks in a aquaculture tank with Penaeus Vannamei: estimating shrimp length and weight, counting shrimps, and evaluating pellet food attractiveness. A setup was designed, including a camera connected to a Raspberry Pi computer, to capture high-quality images around a feeding plate during feeding moments. A dataset with more than 1000 images was captured over multiple days and different times of the day, under varying lightning conditions. This dataset has been used to train a segmentation model, which was employed to detect and filter shrimps in optimal positions for dimensions estimation. Promising results were achieved. For length estimation, the Minimum Bounding Box method achieved a MAPE of 1,56% , and width estimation resulted in a MAPE of 0,15%. These dimensions were then used to estimate the shrimp’s weight. Shrimp counting also yielded results with an average MAPE of 7.17%, ensuring a satisfactory estimation of the population in the tank. Additionally, two different approaches were explored to analyse pellet attractiveness. The prototype developed in this thesis provides a foundation for an adaptable system that can be scaled across multiple tanks, enabling efficient, automated monitoring. Additionally, it could also be adapted to monitor other species raised in similar aquaculture environments.

Authors in the community:

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  B

  Beatriz Pêga Correia

  ist196165

Supervisors of this institution:

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  P

  Paulo Luís Serras Lobato Correia

  ist13111

electrical-engineering-electronic-engineering-information-engineering - Electrical engineering, electronic engineering, information engineering

por - Portuguese

Instituto Superior Técnico