Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging
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Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging
Aldabashi, Nawaf
;
Williams, Sam
;
Eltokhy, Amira
;
Palmer, Edward
;
Cross, Paul
;
Palego, Cristiano
Full Text link:
https://bit.ly/3oj3kre
Date issued:
25/06/2021
Scientific Journal Rankings:
Click Here
Doi:
https://doi.org/10.1109/IMS19712.2021.9574826
Publisher:
IEEE
Series Info:
IEEE MTT-S International Microwave Symposium Digest.;Volume 2021-June, Pages 625 - 6287 June 2021 2021 IEEE MTT-S International Microwave Symposium, IMS 2021Virtual, Atlanta7 June 2021 through 25 June 2021Code 173191
Type:
Article
Keywords:
Bistatic radar , Doppler radar , Machine learning , Radar cross-sections , Simulation
Abstract:
A 5.8GHz Doppler radar was used to monitor free flying honeybees entering and leaving their hive at a 2m distance. Free falling metal spheres of different size and materials were first used, along with radar cross section (RCS) simulations, for calibration of an in house continuous-wave (CW) radar system. The system was then applied to extract the RCS of free flying honeybees (n=164) at 5.8GHz, which fills a gap in the literature and was found to be in the range of-55 to-60dBsm ± 3dBsm. The Doppler radar was hence integrated with machine learning (ML) techniques to autonomously discriminate the incoming and outgoing flights of honeybees. A neural network built through a random forest algorithm and processing of the data as Line Spectral Pairs (LSPs) achieved a maximum accuracy of 87.83% with a Binary Cross Entropy loss of 0.4274 when interpreting hive departure/entrance events. © 2021 IEEE.
Description:
Scopus
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