AIAQUAMI

About the project

Biodiversity loss and degradation of aquatic ecosystems have been accelerating worldwide over the last decades. To halt or even revert this process is of pivotal importance for sustainable development. A cost-effective biomonitoring system is a basis for any conservation management, through which comprehensive taxa lists, ideally at high taxonomic resolution (i.e., species level), are provided. The identification of biotic material as the first step in this process is hindered when the traditional morphology-based approach is applied. This is especially true for „dark taxa“, such as non-biting midges (Chironomidae, Diptera), a group of species which is ecologically abundant and important, yet their identification is difficult, time-consuming, and requires high expertise. Having all this in mind, this project is combining morphology-, DNA- and deep learning-based approaches simultaneously on aquatic macroinvertebrate samples in order to build a new application for artificial intelligence-based identification of species. This will be one of the first implementations of artificial intelligence and deep learning method in the biomonitoring world. To realize the main objective of the project, two hundred macroinvertebrate species, encompassing chironomids and EPT group, from the South Morava river basin and Danube, morphologically identified and validated by DNA taxonomy, will be used for the construction of the deep learning model. The main project deliverable will be the web-based and standalone applications suitable to end-users that employ the obtained deep learning model. The results of the proposed project will facilitate the identification process of macroinvertebrates, as a key group for aquatic ecosystem monitoring, via machine learning and enable their cost-effective implementation in routine bioassessment programs.