@inproceedings{Buss2022Phyto-59708,
  year={2022},
  doi={10.1145/3524458.3547266},
  title={PhytoNodes for Environmental Monitoring : Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System},
  isbn={978-1-4503-9284-6},
  publisher={ACM},
  address={New York, NY},
  booktitle={GoodIT '22 : Proceedings of the 2022 ACM Conference on Information Technology for Social Good},
  pages={258--264},
  author={Buss, Eduard and Rabbel, Tim-Lucas and Horvat, Viktor and Krizmancic, Marko and Bogdan, Stjepan and Wahby, Mostafa and Hamann, Heiko}
}

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    <dc:contributor>Rabbel, Tim-Lucas</dc:contributor>
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    <dc:creator>Wahby, Mostafa</dc:creator>
    <dc:creator>Krizmancic, Marko</dc:creator>
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    <dc:creator>Rabbel, Tim-Lucas</dc:creator>
    <dc:creator>Bogdan, Stjepan</dc:creator>
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    <dc:contributor>Krizmancic, Marko</dc:contributor>
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    <dc:contributor>Horvat, Viktor</dc:contributor>
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    <dcterms:title>PhytoNodes for Environmental Monitoring : Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System</dcterms:title>
    <dc:contributor>Buss, Eduard</dc:contributor>
    <dc:contributor>Bogdan, Stjepan</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-13T11:38:51Z</dcterms:available>
    <dc:language>eng</dc:language>
    <dc:creator>Hamann, Heiko</dc:creator>
    <dc:contributor>Hamann, Heiko</dc:contributor>
    <dcterms:issued>2022</dcterms:issued>
    <dc:creator>Buss, Eduard</dc:creator>
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    <dcterms:abstract xml:lang="eng">Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments, we gather electrophysiological data with sensor nodes. We expose the plant Zamioculcas zamiifolia to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data, we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.</dcterms:abstract>
    <dc:contributor>Wahby, Mostafa</dc:contributor>
    <dc:creator>Horvat, Viktor</dc:creator>
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