Traditionally moth monitoring is done with light traps, where the moths are attracted to a light source. These traditional methods are very time consuming and challenging to scale in time and space. However, there is a need to make more efforts in getting data more efficiently to evaluate spatial, temporal and taxonomic aspects of moth population trends. Automated monitoring using cameras is a new approach that enables study of insects in greater spatial and temporal dimension. In this work we have recorded images of insects and moths from three different locations in Denmark in the season 2022 with habitats of nature covering bog, heather and forest in a summer period of four months. Time-lapse and motion based images were recorded during night with four traps on each location with a total of 12 Automated Moth Traps (The Aarhus AMT). The images were analysed with a deep learning pipeline to identify the moths and count the number of observed species. We have collected experience with the new automated method in relation to effort and challenges of recording image data. Finally we have analysed the observed occurrence of moths in relation to habitats and weather conditions during the monitoring period.