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Shrimp classification for white spot syndrome detection through enhanced gated recurrent unit-based wild geese

L Ramachandran1, S P Mangaiyarkarasi2, A Subramanian3

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White Spot Syndrome Virus (WSSV) causes devastating shrimp losses. A new deep learning method accurately classifies WSSV-infected shrimp, aiding disease management.

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Area of Science:

  • Aquaculture
  • Veterinary Virology
  • Machine Learning in Biology

Background:

  • White Spot Syndrome Virus (WSSV) is a major viral pathogen in cultivated shrimp, causing up to 100% mortality within days.
  • WSSV affects various crustacean hosts, predominantly commercially farmed marine shrimp across all age groups.
  • Understanding WSSV pathogenicity is complex, with ongoing research into genomic variations and protein factors.

Purpose of the Study:

  • To develop and evaluate a novel deep learning methodology for classifying WSSV-infected shrimp.
  • To accurately distinguish between healthy and WSSV-affected shrimp using advanced image analysis techniques.
  • To improve early detection and management of WSSV outbreaks in aquaculture.

Main Methods:

  • Data collection from shrimp farms and online sources, followed by image pre-processing using the Local Binary Pattern (LBP) technique.
  • Image segmentation via the TGVFCMS approach and feature extraction using the Probabilistic Linear Discriminant Analysis (PLDA) technique.
  • Classification of shrimp using an Enhanced Gated Recurrent Unit (EGRU) model, with parameter tuning by a wild Grey Wolf Optimizer (GWO) algorithm for accuracy maximization.

Main Results:

  • The proposed deep learning methodology demonstrated high accuracy in classifying shrimp as either healthy or WSSV-affected.
  • Performance indicators, particularly accuracy, were compared favorably against various conventional methods.
  • The study successfully identified WSSV illness in shrimp using the developed computational approach.

Conclusions:

  • The novel deep learning approach offers a robust and accurate solution for WSSV detection in shrimp.
  • This methodology can significantly aid in the early identification of WSSV, mitigating economic losses in shrimp farming.
  • Further research can explore broader applications of this technique for other aquaculture diseases.