Insects serve vital ecological functions as the most prevalent animal group on Earth. Nonetheless, recent studies have revealed a decline in insect populations across various species and geographical regions. Challenges associated with studying insect populations arise from the labor-intensive and inefficient nature of traditional monitoring techniques. Incorporation of recent computer-based technology and artificial intelligence provides an efficient solution for addressing global issues more effectively. Various computer-based and artificial intelligence-based technologies, such as DNA barcoding, image processing systems or computer vision, utilization of citizen science data sources, acoustic analysis and monitoring, multi-sensor fusion and Internet of Things, remote sensing, light detection and ranging technologies, have been incorporated in the entomological research field for the identification of species as well as monitoring of insect populations and abundance in particular regions. Furthermore, these technologies have been adopted for the assessment of pest species and their management in agro ecosystems. AI is also used for the identification of species and their sex, pest population and monitoring, study of behavior and ecology, as well as in the digitalization of insect species for the construction of digital museums. Integration of AI with Internet of Things (IoT) devices and remote sensing technologies facilitates real-time monitoring of insects across diverse habitats. For example, the installation of camera traps with sensors can continuously collect data, which are subsequently processed by machine-learning algorithms to automatically detect and identify insect species. These technological advancements are enhancing the efficiency and effectiveness with which researchers study insect communities and their behavior in both natural and controlled environments.
Vishnoi et al. (Fri,) studied this question.