Automated pollen identification system for forensic geo-historical location applications

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. © 2013 IEEE.

Año de publicación:

2013

Keywords:

• geographic attribution
• Machine learning
• Bayesian methods
• Computer Vision
• geo-historical location
• pollen forensics
• plant taxa
• GBIF
• Machine Learning

Fuente:

scopus

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