2022-05-12 ペンシルベニア州立大学(PennState)
研究チームは、機械学習プログラムが作成したヒートマップ(葉の画像を小さな赤いボックスで覆い、コンピューターが識別に重要だと判断した領域を強調したもの)を用いて、異なる植物科間でこれらの領域の領域を分析する手動スコアリングシステムを開発した。
<関連情報>
- https://www.psu.edu/news/research/story/decoding-leaf-scientists-search-features-id-modern-fossil-leaves/
- https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/ajb2.1842
コンピュータビジョンのヒートマップから、現代と化石の葉の家系レベルの特徴を読み解く Decoding family-level features for modern and fossil leaves from computer-vision heat maps
Edward J. Spagnuolo,Peter Wilf,Thomas Serre
American Journal of Botany First published: 23 March 2022
DOI:https://doi.org/10.1002/ajb2.1842
Abstract
Premise
Angiosperm leaves present a classic identification problem due to their morphological complexity. Computer-vision algorithms can identify diagnostic regions in images, and heat map outputs illustrate those regions for identification, providing novel insights through visual feedback. We investigate the potential of analyzing leaf heat maps to reveal novel, human-friendly botanical information with applications for extant- and fossil-leaf identification.
Methods
We developed a manual scoring system for hotspot locations on published computer-vision heat maps of cleared leaves that showed diagnostic regions for family identification. Heat maps of 3114 cleared leaves of 930 genera in 14 angiosperm families were analyzed. The top-5 and top-1 hotspot regions of highest diagnostic value were scored for 21 leaf locations. The resulting data were viewed using box plots and analyzed using cluster and principal component analyses. We manually identified similar features in fossil leaves to informally demonstrate potential fossil applications.
Results
The method successfully mapped machine strategy using standard botanical language, and distinctive patterns emerged for each family. Hotspots were concentrated on secondary veins (Salicaceae, Myrtaceae, Anacardiaceae), tooth apices (Betulaceae, Rosaceae), and on the little-studied margins of untoothed leaves (Rubiaceae, Annonaceae, Ericaceae). Similar features drove the results from multivariate analyses. The results echo many traditional observations, while also showing that most diagnostic leaf features remain undescribed.
Conclusions
Machine-derived heat maps that initially appear to be dominated by noise can be translated into human-interpretable knowledge, highlighting paths forward for botanists and paleobotanists to discover new diagnostic botanical characters.
