Kim Bjerge

Kim Bjerge serves as Associate Professor and Group Leader in Aarhus University's Department of Electrical and Computer Engineering, specializing in computer vision and machine learning applications for ecological monitoring. His research bridges engineering and environmental science to develop innovative solutions for insect biodiversity assessment and sustainable agriculture.

His core research interests include computer vision, deep learning, and edge computing systems for real-world ecological monitoring. Dr. Bjerge develops time-lapse camera pipelines and deep learning models specifically for insect population tracking in natural environments, with emphasis on agricultural applications like black soldier fly farming and biodiversity conservation. His work integrates signal processing techniques with biological data to create field-deployable monitoring systems.

Recent publications reveal a strong trend toward practical implementations of computer vision in entomology, particularly focusing on edge processing for camera traps, automated trait prediction in insect farming, and biodiversity monitoring systems. Key research areas include nocturnal insect monitoring, floral environment analysis, and developing specialized datasets like AMI for insect identification in wild settings.

He leads multiple significant research projects funded through competitive grants:

• MAMBO: Modern Approaches to Monitoring Biodiversity (2022-2026)
• FLYgene: Sustainable Insect Production for Livestock Feed (2022-2026)
• Automatisk monitering af nataktive insekter: Automatic nocturnal insect monitoring (2024-2029)
• Pilotprojekt for automatisk registrering af invasive plantearter: Invasive species monitoring (2020-2021)

As head of the Signal Processing and Machine Learning research group, Dr. Bjerge directs interdisciplinary teams developing computer vision solutions for biological monitoring systems. His laboratory focuses on creating robust field-deployable technologies including scanner-based arthropod imaging systems, time-lapse camera networks for floral environments, and edge AI processors for real-time insect monitoring in agricultural settings.