AgriAdapt: Towards Resource-Efficient UAV Weed Detection using Adaptable Deep Learning-Reference-Cited by-同舟云学术

AgriAdapt: Towards Resource-Efficient UAV Weed Detection using Adaptable Deep Learning

Published:2023-10-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd Workshop on Networked Sensing Systems for a Sustainable Society
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Author:

Machidon Octavian M.¹^ORCID,Krašovec Andraž¹^ORCID,Machidon Alina L.¹^ORCID,Pejović Veljko¹^ORCID,Latini Daniele²^ORCID,Sasidharan Sarathchandrakumar T.³^ORCID,Del Frate Fabio³^ORCID

Affiliation:

1. University of Ljubljana, Ljubljana, Slovenia

2. GEO-K s.r.l., Rome, Italy

3. "Tor Vergata" University of Rome, Rome, Italy

Funder

Javna Agencija za Raziskovalno Dejavnost RS

Horizon 2020 Framework Programme

Publisher

ACM

Reference27 articles.

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3. Nazanin Beheshti and Lennart Johnsson . 2020 . Squeeze u-net: A memory and energy efficient image segmentation network . In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 364--365 . Nazanin Beheshti and Lennart Johnsson. 2020. Squeeze u-net: A memory and energy efficient image segmentation network. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 364--365.

4. A system for weeds and crops identification---reaching over 10 fps on raspberry pi with the usage of mobilenets, densenet and custom modifications;Chechliński Łukasz;Sensors,2019

5. NVIDIA Developer. 2022. Jetson Nano Developer Kit. https://developer.nvidia.com/embedded/jetson-nano-developer-kit visited on 2023-06-14. NVIDIA Developer. 2022. Jetson Nano Developer Kit. https://developer.nvidia.com/embedded/jetson-nano-developer-kit visited on 2023-06-14.