Pest Detection for Precision Agriculture Based on IoT Machine Learning

  • Andrea Albanese
  • Donato d’Acunto
  • Davide BrunelliEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 627)


Apple orchards are widely expanding in many countries of the world, and one of the major threats of these fruit crops is the attack of dangerous parasites such as the Codling Moth. IoT devices capable of executing machine learning applications in-situ offer nowadays the possibility of featuring immediate data analysis and anomaly detection in the orchard. In this paper, we present an embedded electronic system that automatically detects the Codling Moths from pictures taken by a camera on top of the insects-trap. Image pre-processing, cropping, and classification are done on a low-power platform that can be easily powered by a solar panel energy harvester.


Internet of Things Machine learning Precision agriculture 



This research was supported by the IoT Rapid-Proto Labs projects, funded by Erasmus+ Knowledge Alliances program of the European Union (588386-EPP-1-2017-FI-EPPKA2-KA).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Andrea Albanese
    • 1
  • Donato d’Acunto
    • 1
  • Davide Brunelli
    • 1
    Email author
  1. 1.Department of Industrial EngineeringUniversity of TrentoPovoItaly

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