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Ultra-low energy pest detection for smart agriculture

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  • معلومة اضافية
    • Contributors:
      Brunelli, D.; Polonelli, T.; Benini, L.
    • بيانات النشر:
      Institute of Electrical and Electronics Engineers Inc.
      USA
      Piscataway, NJ
    • الموضوع:
      2020
    • Collection:
      Università degli Studi di Trento: CINECA IRIS
    • نبذة مختصرة :
      Apple is one of the most produced fruits in the world because it is easy to grow, store, and transport. The most significant threat of this crop is the attack of the codling moth, a small insect capable of damaging whole orchards in a few days. To prevent this parasite and to plan effective countermeasures, we present an ultra low power smart camera capable of detecting and recognizing the pest in the field; therefore, a wireless alarm can be transmitted over a long distance. The system implements a machine learning approach based on neural networks on the camera board. The sensor is also provided with long-range radio capability and an energy harvester; it permits to operate indefinitely because of its positive energy balance when deployed in the field. Experimental tests on the proposed energy-neutral smart camera demonstrate a validation accuracy of 93% and only 3.5mJ required for image analysis and classification.
    • File Description:
      ELETTRONICO
    • Relation:
      info:eu-repo/semantics/altIdentifier/isbn/978-1-7281-6801-2; info:eu-repo/semantics/altIdentifier/wos/WOS:000646236300017; ispartofbook:IEEE Sensors 2020 Conference Proceedings; 2020 IEEE Sensors; firstpage:1; lastpage:4; numberofpages:4; serie:PROCEEDINGS OF IEEE SENSORS .; http://hdl.handle.net/11572/287333; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85098684310; https://ieeexplore.ieee.org/document/9278587
    • الرقم المعرف:
      10.1109/SENSORS47125.2020.9278587
    • الدخول الالكتروني :
      http://hdl.handle.net/11572/287333
      https://doi.org/10.1109/SENSORS47125.2020.9278587
      https://ieeexplore.ieee.org/document/9278587
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.41225A4C