Lights, camera…citizen science: assessing the effectiveness of smartphone-based video training in invasive plant identification

PLoS One. 2014 Nov 5;9(11):e111433. doi: 10.1371/journal.pone.0111433. eCollection 2014.

Abstract

The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional "in-person" training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Phone*
  • Humans
  • Introduced Species*
  • Light*
  • Plants* / classification

Grant support

The Outsmart Invasive Species project is funded through a grant (11-DG-11420004-294) from the Northeastern Area State and Private Forestry program, USDA Forest Service. CS, JF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.