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The current reliability of species identifications by the Nature Identification API (NIA) of the app  ObsIdentify  is tested with a Coleoptera (Insecta) sample set from Germany. Seventy-five photographic beetle records taken with a smartphone camera under “average user” conditions are analysed in terms of correctness of the app’s identification result on various taxonomic levels, the displayed confidence level of the identification and the time until validation of the results. More than 60% of samples were identified correctly at the species level, but only 53% were validated within a month. The mechanisms by which users can upload pictures of their observations to be identified by the artificial intelligence and the validation process by experts are briefly explained. Regional specifics and further opportunities for data usage as well as currently existing problems are discussed and improvements are suggested. The expert validation of records is identified as a huge quality advantage of the Obs-Services. They are generally found to be a promising tool for lay people and professional institutions, despite still existing deficiencies such as identification failure in mutilated specimens, cryptic and rare species, doubtful species rarity ratings as well as the still insufficient capacity of validation. Experts and institutions are encouraged to volunteer as validators and collaborators.

tijdschrift voor entomologie

The Obs-Services and their potentials for biodiversity data assessments with a test of the current reliability of photo-identification of Coleoptera in the field