Apple is one of the most produced fruit crops in the world. Recent advances in Artificial Intelligence and the Internet of Things can reduce production costs and improve crop quality by providing prompt detection of dangerous parasites. This paper presents an effective solution to automate the detection of the Codling Moths. The system takes pictures of trapped insects in the orchard, analyzes them through a DNN algorithm, and sends alarms to the farmer in case of a positive detection. The system is fully autonomous and can operate unattended for the entire crop season. Detection reports are used for optimizing the treatment with chemicals only when threats are identified. The prototype is designed with an embedded platform powered by a small solar panel to achieve an energy-neutral balance.

Neural networks for Pest Detection in Precision Agriculture / Segalla, A.; Fiacco, G.; Tramarin, L.; Nardello, M.; Brunelli, D.. - ELETTRONICO. - (2020), pp. 7-12. (Intervento presentato al convegno 3rd IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2020 tenutosi a Trento nel 4th-6th November 2020) [10.1109/MetroAgriFor50201.2020.9277657].

Neural networks for Pest Detection in Precision Agriculture

Nardello M.;Brunelli D.
2020-01-01

Abstract

Apple is one of the most produced fruit crops in the world. Recent advances in Artificial Intelligence and the Internet of Things can reduce production costs and improve crop quality by providing prompt detection of dangerous parasites. This paper presents an effective solution to automate the detection of the Codling Moths. The system takes pictures of trapped insects in the orchard, analyzes them through a DNN algorithm, and sends alarms to the farmer in case of a positive detection. The system is fully autonomous and can operate unattended for the entire crop season. Detection reports are used for optimizing the treatment with chemicals only when threats are identified. The prototype is designed with an embedded platform powered by a small solar panel to achieve an energy-neutral balance.
2020
2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)
Piscataway, NJ
Institute of Electrical and Electronics Engineers Inc.
978-1-7281-8783-9
Segalla, A.; Fiacco, G.; Tramarin, L.; Nardello, M.; Brunelli, D.
Neural networks for Pest Detection in Precision Agriculture / Segalla, A.; Fiacco, G.; Tramarin, L.; Nardello, M.; Brunelli, D.. - ELETTRONICO. - (2020), pp. 7-12. (Intervento presentato al convegno 3rd IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2020 tenutosi a Trento nel 4th-6th November 2020) [10.1109/MetroAgriFor50201.2020.9277657].
File in questo prodotto:
File Dimensione Formato  
IEEE 09277657.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 345.19 kB
Formato Adobe PDF
345.19 kB Adobe PDF   Visualizza/Apri
Post Referred.pdf

Open Access dal 01/01/2023

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 748.72 kB
Formato Adobe PDF
748.72 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/287335
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 17
  • ???jsp.display-item.citation.isi??? 13
  • OpenAlex ND
social impact