Deep Learning for Vespa Velutina Detection

Vespa velutina, an invasive insect introduced to Europe from Asia, is the primary predator of honeybees, significantly contributing to the decline of their populations. Additionally, Vespa velutina has become a considerable threat to human health, as its sting can be lethal to individuals with allergies. The invasion of Vespa velutina disrupts ecosystems by threatening biodiversity and preventing pollination processes, and it also incurs socioeconomic costs, including negative impacts on apiculture and associated management expenses. To address these challenges, it is essential to develop fast and user-friendly automatic identification tools for Vespa velutina. This study proposes to design an artificial intelligence model capable of recognizing and identifying Vespa velutina among various insects. Such a model would enable the creation of devices that can automatically transmit images and geolocations in real-time, thereby enhancing the response efficiency of relevant authorities. The results of this work demonstrate the feasibility of accurately recognizing Vespa velutina using artificial intelligence technology, which supports the implementation of automated systems that slow the spread of this invasive species and protect the beekeeping ecosystem.