ISSN: 2689-7822
Authors: Jones CL*
This contribution demonstrates an example of using a social network to create a visual search database to support human inspection of microscope imagery data of fungal spores. Current monitoring of airborne spore counts follows the standard test method for inertial impaction sampling (D7391-17e1) including spore identification, counting and subsequent statistical treatment. One bottleneck with the traditional aerobiology approach is the skill and time required to learn to identify unknown spores. Digital microscopy combined with the rapid proliferation of image sharing platforms offers a unique opportunity to harness this shared visual content for expanding education about key fungal spore type morphologies of interest to the environmental mycology community. A Pinterest board was developed that correlated with the D7391-17e1 Standard as a form of public curation. Interacting with the board exploits the machine-learning embedded into the Pinterest platform and allows content to be aggregated from any URL and then suggests similar images based on existing content. In this way, the board can expand over time and image copyright is maintained despite becoming a shared resource. The paper concludes with a review of the statistical treatment of spore counts in order to maximise the value of the identifications.
Keywords: Spore Trap; Mould; Spore Identification; Indoor Air Quality; Pinterest; Crowdsourcing; Citizen Science; Non-Viable Methodology; Social networks
