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Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities.

Ioannis Saradopoulos1, Ilyas Potamitis2, Stavros Ntalampiras3

  • 1Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece.

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|March 10, 2022
PubMed
Summary

This study introduces low-cost, autonomous electronic insect traps using edge computing and deep learning for pest counting. The ESP32 device offers the best balance of performance and power efficiency for this application.

Keywords:
e-trapsedge computingimage sensorspest detection

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Area of Science:

  • Agricultural Technology
  • Computer Science
  • Entomology

Background:

  • Traditional pest monitoring is labor-intensive and lacks real-time data.
  • Electronic insect traps offer automated pest detection but often face power and processing limitations.

Purpose of the Study:

  • To evaluate edge-computing solutions for camera-based electronic insect traps.
  • To develop a low-cost, high-power-autonomy device for automated pest counting.
  • To compare the performance of different edge devices for deep learning-based insect identification.

Main Methods:

  • Implementation of quantized and embedded deep learning models (TensorFlow Lite) on edge devices.
  • Testing and comparison of ESP32, Raspberry Pi Model 4 (RPi), and Google Coral for image processing and insect counting.
  • Evaluation of device performance based on accuracy, processing speed, and power consumption.

Main Results:

  • All tested edge devices achieved over 95% accuracy in insect counting.
  • Significant variations in processing rates and power consumption were observed among the devices.
  • The ESP32 demonstrated a favorable performance-to-power ratio for this application.

Conclusions:

  • Edge computing is a viable solution for enhancing electronic insect trap functionality.
  • The ESP32 is recommended as the optimal edge device for low-cost, power-efficient, automated pest monitoring systems.