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A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis
Year:
2022
Source of publication :
Journal of Pest Science
Authors :
Alchanatis, Victor
;
.
Altman, Yam
;
.
Nestel, David
;
.
Shaked, Ben
;
.
Volume :
Co-Authors:
  • Yoshua Diller, 
  • Aviv Shamsian, 
  • Ben Shaked, 
  • Yam Altman, 
  • Bat-Chen Danziger, 
  • Aruna Manrakhan, 
  • Leani Serfontein, 
  • Elma Bali, 
  • Matthias Wernicke, 
  • Alois Egartner, 
  • Marco Colacci, 
  • Andrea Sciarretta, 
  • Gal Chechik, 
  • Victor Alchanatis, 
  • Nikos T. Papadopoulos 
  • David Nestel 
Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF (Ceratitis capitataBactrocera dorsalis and B. zonata). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species (C. capitata and B. zonata). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap.

Note:
Related Files :
Bactrocera dorsalis
Bactrocera zonata
Ceratitis capitata
Deep learning
Detection
Smart-trap
Show More
Related Content
More details
DOI :
10.1007/s10340-022-01528-x
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
61609
Last updated date:
24/08/2022 17:10
Creation date:
24/08/2022 17:09
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Scientific Publication
A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis
  • Yoshua Diller, 
  • Aviv Shamsian, 
  • Ben Shaked, 
  • Yam Altman, 
  • Bat-Chen Danziger, 
  • Aruna Manrakhan, 
  • Leani Serfontein, 
  • Elma Bali, 
  • Matthias Wernicke, 
  • Alois Egartner, 
  • Marco Colacci, 
  • Andrea Sciarretta, 
  • Gal Chechik, 
  • Victor Alchanatis, 
  • Nikos T. Papadopoulos 
  • David Nestel 
A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis

Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF (Ceratitis capitataBactrocera dorsalis and B. zonata). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species (C. capitata and B. zonata). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap.

Scientific Publication
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