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Inferring tephritid fly pupal development stage using non-invasive near infrared imaging and machine learning

Guadalupe Córdova-García1, Horacio Tapia-McClung1, Dinesh Rao2

  • 1Instituto de Investigaciones en Inteligencia Artificial, Universidad Veracruzana, Xalapa, Veracruz, México.

Bulletin of Entomological Research
|September 8, 2025
PubMed
Summary

Determining the age of Mexican fruit fly pupae is crucial for pest control. Near-infrared imaging and machine learning offer a non-invasive method to assess pupal age and viability, aiding agricultural management.

Keywords:
Anastrepha ludensDipteraTephritidaemachine-learningpupae

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Area of Science:

  • Entomology
  • Agricultural Science
  • Computer Science

Background:

  • Accurate determination of insect pupal age is vital for managing agriculturally significant pests like Tephritid flies.
  • Current methods for age determination are invasive, requiring specimen dissection and hindering longitudinal studies.
  • Non-invasive techniques are needed to assess pupal physiological age ethically and efficiently.

Purpose of the Study:

  • To develop and validate a non-invasive method for determining the physiological age of Mexican fruit fly (Anastrepha ludens) pupae.
  • To apply machine learning algorithms to near-infrared (NIR) imaging data for pupal age estimation.
  • To assess the potential of this method for estimating pupal viability without awaiting adult emergence.

Main Methods:

  • Utilized non-invasive near-infrared (NIR) imaging (850-1100 nm) to capture images of Anastrepha ludens pupae throughout their development.
  • Photographed pupae at regular intervals under controlled conditions (26°C, 75-80% RH).
  • Analyzed NIR images using a convolutional neural network (CNN) to classify pupal developmental stages and estimate physiological age.

Main Results:

  • The intrapuparial period for Anastrepha ludens ranged from 17 to 19 days.
  • Key morphological changes, including eye darkening (day 12) and wing/leg/setae melanization (days 13-15), were observed.
  • The developed CNN model achieved an average accuracy of 71.77% in identifying physiological age ranges.

Conclusions:

  • Near-infrared imaging combined with machine learning provides a non-invasive method for determining the physiological age of Anastrepha ludens pupae.
  • This approach allows for developmental stage assessment without harming the pupae.
  • The method shows promise for estimating pupal viability, improving pest management strategies.