Automated pollen identification system for forensic geo-historical location applications

Grace M. Hwang, Kim C. Riley, Carol T. Christou, Garry M. Jacyna, Jeffrey P. Woodard, Regina M. Ryan, Mark B. Bush, Bryan G. Valencia, Crystal N.H. McMichael, Surangi W. Punyasena, David L. Masters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The use of pollen grain analysis for forensic geo-historical location has been explored for several decades, yet it is not widely adopted in the United States. We confirmed significant improvement in geographic precision, i.e., from 2.5×107 to 1.2×105 km2, by simultaneously applying flowering plant data from four different taxa at the genus and species levels. Moreover, when we calculated precision using collected pollen data, we found that co-occurring, pairwise genus-level distinctions based on expert-provided indicator taxa resulted in average precision values of 4° and 4.5° in latitude and longitude, respectively - corresponding to roughly 1.8×105 km2. We also applied computer vision techniques to identify morphologically similar pollen grains, which resulted in grain-identification error rates of 2.18% and 6.24% at the genus and species levels, respectively, surpassing previously published records. Collectively, our results demonstrate that algorithmic identification of species-specific pollen morphology, founded on established computer vision techniques, when combined with species-level pollen distribution, has the potential to revolutionize the scope, accuracy, and precision of forensic geographic attribution.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Technologies for Homeland Security, HST 2013
Pages297-303
Number of pages7
DOIs
StatePublished - 2013
Event2013 13th IEEE International Conference on Technologies for Homeland Security, HST 2013 - Waltham, MA, United States
Duration: Nov 12 2013Nov 14 2013

Publication series

Name2013 IEEE International Conference on Technologies for Homeland Security, HST 2013

Other

Other2013 13th IEEE International Conference on Technologies for Homeland Security, HST 2013
CountryUnited States
CityWaltham, MA
Period11/12/1311/14/13

Keywords

  • Bayesian methods
  • GBIF
  • computer vision
  • geo-historical location
  • geographic attribution
  • machine learning
  • plant taxa
  • pollen forensics

ASJC Scopus subject areas

  • Public Administration

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