Collembola are very abundant organisms in soils (several thousand individuals per square meter) and are considered to be good indicators of soil quality. These indicators are mainly based on the number of individuals observed (abundance per square meter of soil), but also the singularity and number of species present (species richness). A limitation that comes with the usage of collembola as an indicator is the complexity of the identification of the species under a microscope, how time-consuming it is, and the morphological similarity between some species. Deep learning approaches have been very successful in the resolution of image-based problems. Still, no work yet exists that uses deep learning in the recognition of collembola on a microscope slide. This could be a valuable tool for experts seeking to use Collembola as a metric on a larger scale. In this work, we explore and evaluate the performance of state-of-the-art deep learning techniques over the identification of Collembola on a new manually annotated dataset.