First use of unmanned aerial vehicles to monitor Halyomorpha halys and recognize it using Artificial Intelligence

Background: Halyomorpha halys is one of the most damaging invasive agricultural pests in north America and southern Europe. It is commonly monitored using pheromone traps, which are not very effective as few bugs are caught, some escape and/or remain outside the trap on surrounding plants where they feed, increasing the damage. Other monitoring techniques are based on visual sampling, sweep netting and tree-beating. However, all these methods require several hours of human labour and are difficult to apply to large areas. The aim of this work is to develop an automated monitoring system that integrates the acquisition of images through the use of drones and the detection of H. halys through the use of artificial intelligence. Results: The results allowed the development of an automated flight protocol using a mobile app to capture high-resolution images. The drone showed low disturbance in both adult and intermediate instars, inducing freezing behavior in adults. Each of the artificial intelligence models used achieved very good performance, with detection accuracy of up to 97% and recall of up to 87% for the X-TL model. Conclusion: The first application of this novel monitoring system has demonstrated the potential of drones and artificial intelligence to detect and quantify the presence of H. halys. The ability to capture high-altitude, high-resolution images makes this method potentially suitable for a range of crops and pests.