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Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Standard Standard

Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging. / Aldabashi, Nawaf; Williams, Sam; Eltokhy, Amira et al.
2021 IEEE MTT-S International Microwave Symposium (IMS). IEEE, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

HarvardHarvard

Aldabashi, N, Williams, S, Eltokhy, A, Palmer, E, Cross, P & Palego, C 2021, Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging. in 2021 IEEE MTT-S International Microwave Symposium (IMS). IEEE. https://doi.org/10.1109/IMS19712.2021.9574826

APA

Aldabashi, N., Williams, S., Eltokhy, A., Palmer, E., Cross, P., & Palego, C. (2021). Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging. In 2021 IEEE MTT-S International Microwave Symposium (IMS) IEEE. https://doi.org/10.1109/IMS19712.2021.9574826

CBE

Aldabashi N, Williams S, Eltokhy A, Palmer E, Cross P, Palego C. 2021. Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging. In 2021 IEEE MTT-S International Microwave Symposium (IMS). IEEE. https://doi.org/10.1109/IMS19712.2021.9574826

MLA

Aldabashi, Nawaf et al. "Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging". 2021 IEEE MTT-S International Microwave Symposium (IMS). IEEE. 2021. https://doi.org/10.1109/IMS19712.2021.9574826

VancouverVancouver

Aldabashi N, Williams S, Eltokhy A, Palmer E, Cross P, Palego C. Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging. In 2021 IEEE MTT-S International Microwave Symposium (IMS). IEEE. 2021 doi: 10.1109/IMS19712.2021.9574826

Author

Aldabashi, Nawaf ; Williams, Sam ; Eltokhy, Amira et al. / Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging. 2021 IEEE MTT-S International Microwave Symposium (IMS). IEEE, 2021.

RIS

TY - GEN

T1 - Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging

AU - Aldabashi, Nawaf

AU - Williams, Sam

AU - Eltokhy, Amira

AU - Palmer, Edward

AU - Cross, Paul

AU - Palego, Cristiano

PY - 2021/10/27

Y1 - 2021/10/27

N2 - 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.

AB - 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.

U2 - 10.1109/IMS19712.2021.9574826

DO - 10.1109/IMS19712.2021.9574826

M3 - Conference contribution

BT - 2021 IEEE MTT-S International Microwave Symposium (IMS)

PB - IEEE

ER -