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Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching.

Igor S Santos1, Caio P Tavares2, Guilherme M Klafke3

  • 1Applied Computing Core, Federal University of Maranhão - UFMA, São Luís, Brazil.

Medical and Veterinary Entomology
|May 15, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method to predict cattle tick (Rhipicephalus microplus) larval hatching from egg morphology, significantly reducing analysis time. The validated AI tool accurately predicts hatching rates, aiding in faster acaricide resistance screening.

Keywords:
controlegglarval hatchingresistanceticks

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Area of Science:

  • Veterinary Entomology
  • Applied Artificial Intelligence
  • Parasitology

Background:

  • Increasing resistance of Rhipicephalus microplus to acaricides poses a global challenge for cattle health.
  • Conventional methods for assessing acaricide resistance, like the adult immersion test, are time-consuming (40+ days).
  • There is a need for rapid, accurate methods to screen for tick resistance.

Purpose of the Study:

  • To develop and validate an automated deep learning-based method for predicting Rhipicephalus microplus larval hatching from egg morphology.
  • To significantly reduce the time required for assessing tick viability and potential acaricide resistance.

Main Methods:

  • Embryonic development time course analysis to differentiate viable from non-viable tick eggs.
  • Application of deep learning techniques for automated classification and counting of Rhipicephalus microplus eggs.
  • Validation using three Rhipicephalus microplus populations, comparing AI predictions with manual assessments and biological assays.

Main Results:

  • The automated method accurately predicted larval hatching percentages, with no statistical difference compared to manual specialist predictions and biological assays.
  • Mean predicted hatching rates for viable eggs were consistently high (e.g., 96.35% ± 3.33 for Piracanjuba population with 3 images).
  • The method demonstrated effectiveness even with limited image data (3-6 images per egg group).

Conclusions:

  • The developed deep learning method is a validated and effective tool for automatically predicting Rhipicephalus microplus larval hatching.
  • This automated approach offers a substantial reduction in the time needed to obtain results for acaricide resistance screening.
  • The AI-driven prediction based on egg morphology shows promise for rapid diagnostics in veterinary entomology.