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Abstract Detail

The Potential of Machine Learning for Plant Biology

White, Alex [1], Trizna, Micheal [2], Frandsen, Paul [3], Dorr, Laurence [4], Dikow, Rebecca [2], Schuettpelz, Eric [1].

Evaluating geographic patterns of morphological disparity in ferns and lycophytes using deep neural networks .

With digitized herbarium specimens and associated metadata accumulating rapidly in open access repositories, we are now able to exploit data-hungry computer vision techniques in order to evaluate fundamental questions in plant evolution. High among the list of unknowns is the role that ecological factors and morphological similarity play in mediating biogeographic patterns of taxonomic and phylogenetic diversity. Here, we integrate deep convolutional neural networks (CNNs) into a biogeographic study of morphological, taxonomic, and phylogenetic diversity in ferns and lycophytes. We show how CNNs and digitized specimens can be used to extract quantitative estimates of morphospace occupation, and we use these techniques to evaluate diversity-disparity relationships within ferns across latitudes. We also discuss how CNNs can be used to overcome logistical obstacles arising from modern workflows involving millions of images.

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1 - Smithsonian Institution, Department of Botany , 1000 Constitution Ave NW, Washington, DC, 20560, USA
2 - Smithsonian Institution, Data Science Lab, 1000 Constitution Ave NW, Washington, DC, 20560, United States
3 - Brigham Young University, Plant & Wildlife Sciences, 5115 LSB, Provo, UT, 84602, USA
4 - Smithsonian Institution, Department of Botany, 1000 Constitution Ave NW, Washington, DC, 20560, United States

machine learning
morphological disparity
latitudinal diversity gradient.

Presentation Type: Symposium Presentation
Session: SYM1, The Potential of Machine Learning for Plant Biology
Location: Tucson F/Starr Pass
Date: Monday, July 29th, 2019
Time: 10:15 AM
Number: SYM1005
Abstract ID:684
Candidate for Awards:Margaret Menzel Award,Edgar T. Wherry award

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