Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (Vespa velutina) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects V. velutina individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as V. velutina can be found in various areas of apiary sites. To detect V. velutina, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution ($3840\times 2160$) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect V. velutina individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of V. velutina during their hunting activities and send alarms to registered mobile application users.

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.