In recent years, the integration of automated monitoring, artificial intelligence (AI), and computer vision technologies has revolutionized insect science, offering novel approaches to studying insect behavior, ecology, and environmental interactions. Our collection aims to highlight the diverse range of innovative methodologies employed by researchers to advance our understanding of insect biology, ecology, and evolution.  

Submissions will cover a broad spectrum of research methods related to emerging tools and technologies in insect science, including but not limited to: 

• Design and deployment of automated and molecular-based insect monitoring systems in natural and agricultural ecosystems 
• Development of computer vision/AI algorithms for species identification, classification, and behavioral analysis 
• Implementation of image processing techniques for insect movement patterns and ecological interactions 
• Utilization of machine learning for predictive modeling of insect dynamics, distribution patterns, and population trends 
• Integration of robotics, AI, sensor networks, and other emerging technologies for disease or pest management, biodiversity conservation, and agricultural sustainability 

Recent technological developments in computer vision, acoustic monitoring, radar, and molecular methods offer unprecedented possibilities for insect ecology and monitoring. However, it is crucial to prioritize effective communication and collaboration to establish international standards. Through a concise and visually engaging video format, authors can demonstrate complex methodologies involving AI-based algorithms or camera-based monitoring systems to aid comprehension and replication of these techniques in research. 

Our collection aims to contribute to developing best practices, disseminating knowledge, fostering collaboration, and accelerating advancements in innovative technologies and computer vision for insect science.