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Related Experiment Video

Updated: Aug 1, 2025

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Automatic Detection of Moths (Lepidoptera) with a Funnel Trap Prototype.

Norbert Flórián1, Júlia Katalin Jósvai2, Zsolt Tóth1

  • 1Institute for Soil Sciences, Centre for Agricultural Research, ELKH, Herman Ottó út 15, H-1022 Budapest, Hungary.

Insects
|April 27, 2023
PubMed
Summary

A new opto-electronic insect trap prototype, ZooLog VARL, accurately monitors moth populations in real-time. This technology aids pest control by providing precise data for timing interventions and reducing insecticide use.

Keywords:
automatic counting systempest detectionpheromone trapreal-time monitoringremote sensing

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Area of Science:

  • Agricultural Entomology
  • Pest Management Technologies
  • Sensor Development

Background:

  • Accurate insect population monitoring is crucial for effective pest control and minimizing insecticide application.
  • Current automatic insect traps often lack validated accuracy under field conditions.
  • Real-time monitoring requires high species specificity to differentiate pest populations.

Purpose of the Study:

  • To present and evaluate a novel opto-electronic device prototype (ZooLog VARL) for insect population monitoring.
  • To assess the accuracy and precision of the device's data filtering using an artificial neural network (ANN).
  • To determine the detection accuracy of the new probes for specific moth species.

Main Methods:

  • Development of an opto-electronic device prototype (ZooLog VARL) integrating a funnel trap with a blow-off mechanism, sensor-ring, and data communication.
  • Pilot field study conducted in summer/autumn 2018 to test the prototype.
  • Application of an artificial neural network (ANN) for data filtering and species detection accuracy assessment.
  • Monitoring of six moth species: *Agrotis segetum*, *Autographa gamma*, *Helicoverpa armigera*, *Cameraria ohridella*, *Grapholita funebrana*, and *Grapholita molesta*.

Main Results:

  • The artificial neural network (ANN) achieved detection accuracy consistently above 60%, reaching up to 90% for larger moth species.
  • The overall detection accuracy of the probes ranged from 84% to 92%.
  • The ZooLog VARL prototype successfully provided real-time, time-series data on moth catches, enabling analysis of daily and monthly flight patterns.

Conclusions:

  • The ZooLog VARL prototype demonstrates high detection accuracy for target moth species and effectively prevents insect escape.
  • The device provides valuable real-time data for monitoring pest dynamics and potentially forecasting population outbreaks.
  • Further research is needed to evaluate the catching efficiency, but the prototype shows promise for optimized pest management strategies.