In recent years, insect husbandry has seen an increased in- terest in order to supply in the production of raw materials, food or as biological/environmental control. Unfortunately, large insect rearings are susceptible to pathogens, pests and parasitoids which can spread rapidly due to the confined nature of a rearing system. Thus, it is of interest to quickly and efficiently monitor the spread of such manifesta- tions and the overall population size. Medical imaging techniques could be used for this purpose, as large volumes can be scanned non-invasively. Due to its 3D acquisition nature, computed tomography seems to be the most suitable for this task. This study presents an automated, computed tomography-based, counting method for bee rearings that performs com- parable/similar to identifying all Osmia cornuta cocoons manually. The proposed methodology achieves this in an average of 7 minutes per sam- ple, compared to 90 minutes per sample for the manual count over a total of 12 samples collected around lake Zurich in 2020. Such an automated bee population evaluation tool is a valuable in combating environmental influences on bee, and potentially other insect, rearings.