Development of a Real-Time IVespa velutina/I Nest Detection and Notification System Using Artificial Intelligence in Drones

Vespa velutina is an ecosystem disruptor that causes annual damage worth KRW 170 billion (USD 137 million) to the South Korean beekeeping industry. Due to its strong fertility and high-lying habitat, it is difficult to control. This study aimed to develop a system for the control of V. velutina nest...

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Veröffentlicht in:Drones (Basel) 2023-10, Vol.7 (10)
Hauptverfasser: Jeong, Yuseok, Jeon, Moon-Seok, Lee, Jaesu, Yu, Seung-Hwa, Kim, Su-bae, Kim, Dongwon, Kim, Kyoung-Chul, Lee, Siyoung, Lee, Chang-Woo, Choi, Inchan
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container_issue 10
container_start_page
container_title Drones (Basel)
container_volume 7
creator Jeong, Yuseok
Jeon, Moon-Seok
Lee, Jaesu
Yu, Seung-Hwa
Kim, Su-bae
Kim, Dongwon
Kim, Kyoung-Chul
Lee, Siyoung
Lee, Chang-Woo
Choi, Inchan
description Vespa velutina is an ecosystem disruptor that causes annual damage worth KRW 170 billion (USD 137 million) to the South Korean beekeeping industry. Due to its strong fertility and high-lying habitat, it is difficult to control. This study aimed to develop a system for the control of V. velutina nests using drones for detection and tracking the real-time location of the nests. Vespa velutina nest image data were acquired in Buan-gun and Wanju-gun (Jeollabuk-do), and artificial intelligence learning was conducted using YOLO-v5. Drone image resolutions of 640, 1280, 1920, and 3840 pixels were compared and analyzed. The 3840-pixel resolution model was selected, as it had no false detections for the verification image and showed the best detection performance, with a precision of 100%, recall of 92.5%, accuracy of 99.7%, and an F1 score of 96.1%. A computer (Jetson Xavier), real-time kinematics module, long-term evolution modem, and camera were installed on the drone to acquire real-time location data and images. Vespa velutina nest detection and location data were delivered to the user via artificial intelligence analysis. Utilizing a drone flight speed of 1 m/s and maintaining an altitude of 25 m, flight experiments were conducted near Gyeongcheon-myeon, Wanju-gun, Jeollabuk-do. A total of four V. velutina nests were successfully located. Further research is needed on the detection accuracy of artificial intelligence in relation to objects that require altitude-dependent variations in drone-assisted exploration. Moreover, the potential applicability of these research findings to diverse domains is of interest.
doi_str_mv 10.3390/drones7100630
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subjects Artificial intelligence
title Development of a Real-Time IVespa velutina/I Nest Detection and Notification System Using Artificial Intelligence in Drones
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