PANDI: a hybrid open source edge-based system for environmental and real-time passive acoustic monitoring - prototype design and development

Sean Sturley*, Santiago Matalonga

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Citations (Scopus)
    147 Downloads (Pure)

    Abstract

    Background
    Improving the accuracy of bird population estimation is crucial for determining their species concern. In Scotland, several species, including the Eurasian Bittern and the Corn Crake, pose challenges as their choice of habitat and behaviour makes it extremely difficult for researchers to obtain accurate population numbers.

    Aims
    To identify the targeted species individuals and aid researchers in pinpointing their location.

    Method
    A low-cost open-source Edge-based passive acoustic monitoring system was designed (PANDI).

    Results
    The PANDI system design and initial validation are presented in this paper.

    Future work
    Include increasing classification accuracy and scope and commencement of long-term field-trials.
    Original languageEnglish
    Title of host publication2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2020
    Subtitle of host publicationMarch 19-20, 2020, FS Meknes, Morocco
    PublisherIEEE
    ISBN (Electronic)9781728149790
    DOIs
    Publication statusPublished - 14 May 2021
    EventThe 1st International Conference on Innovative Research in Applied Science, Engineering and Technology - Moulay Ismail University, Meknes, Morocco
    Duration: 19 Mar 202020 Mar 2020
    http://www.fs-umi.ac.ma/IRASET2020/

    Conference

    ConferenceThe 1st International Conference on Innovative Research in Applied Science, Engineering and Technology
    Abbreviated titleIRASET’2020
    Country/TerritoryMorocco
    CityMeknes
    Period19/03/2020/03/20
    Internet address

    Keywords

    • passive acoustic monitoring
    • habitat monitoring
    • ESP32
    • Raspberry Pi Zero
    • sensors
    • machine learning
    • LoRaWAN

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