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Image-Based Shrimp Aquaculture Monitoring.

Beatriz Correia1, Osvaldo Pacheco2, Rui J M Rocha3,4

  • 1Instituto de Telecomunicações (IT), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.

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|January 11, 2025
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Summary
This summary is machine-generated.

This study introduces an automated image-based system for shrimp farming, accurately estimating shrimp size, weight, and count. The system also evaluates feed attractiveness, paving the way for efficient aquaculture monitoring.

Keywords:
Raspberry Piaquaculture systemfeed pellet attractivenessimage-based shrimp monitoring systemobject detection and segmentationshrimp length estimationshrimp weight estimationshrimp width estimation

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Area of Science:

  • Aquaculture technology
  • Computer vision in agriculture
  • Biometric analysis of aquatic species

Background:

  • Shrimp farming requires efficient monitoring for optimal production.
  • Manual monitoring in aquaculture is labor-intensive and prone to errors.
  • Automation is crucial for improving efficiency and sustainability in aquaculture operations.

Purpose of the Study:

  • To develop and validate an image-based automated system for key tasks in shrimp aquaculture.
  • To estimate shrimp length, weight, and count using computer vision.
  • To assess feed pellet attractiveness and explore adaptable monitoring solutions for aquaculture.

Main Methods:

  • Designed an image capture system using a camera and Raspberry Pi.
  • Collected a dataset of 1140 images under various conditions.
  • Trained a segmentation model for shrimp detection and dimension estimation.
  • Utilized estimated dimensions for weight calculation and population counting.

Main Results:

  • Achieved high accuracy in length (1.56% MAPE) and width (0.15% MAPE) estimation.
  • Obtained satisfactory shrimp counting with 7.17% MAPE.
  • Developed qualitative approaches for feed pellet attractiveness evaluation.
  • Demonstrated the system's potential for scalable, automated aquaculture monitoring.

Conclusions:

  • The proposed image-based system offers a foundation for automated, efficient shrimp farm monitoring.
  • The system shows promise for adaptation to other aquaculture species and environments.
  • Further research is needed to address environmental influences on feed attractiveness evaluation.