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.

Ultra-low energy pest detection for smart agriculture / Brunelli, D.; Polonelli, T.; Benini, L.. - ELETTRONICO. - (2020), pp. 1-4. (Intervento presentato al convegno 2020 IEEE Sensors tenutosi a Rotterdam, Netherlands nel 25th-28th October 2020) [10.1109/SENSORS47125.2020.9278587].

Ultra-low energy pest detection for smart agriculture

Brunelli D.;
2020-01-01

Abstract

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.
2020
IEEE Sensors 2020 Conference Proceedings
Piscataway, NJ
Institute of Electrical and Electronics Engineers Inc.
978-1-7281-6801-2
Brunelli, D.; Polonelli, T.; Benini, L.
Ultra-low energy pest detection for smart agriculture / Brunelli, D.; Polonelli, T.; Benini, L.. - ELETTRONICO. - (2020), pp. 1-4. (Intervento presentato al convegno 2020 IEEE Sensors tenutosi a Rotterdam, Netherlands nel 25th-28th October 2020) [10.1109/SENSORS47125.2020.9278587].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/287333
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