Traditional leaf architectural methods ( 11, 12) are extremely labor-intensive, inhibiting detailed analyses of leaf variation across angiosperms accordingly, recent studies have been limited in taxonomic and character coverage ( 3, 13, 14). The only major survey of leaf architectural variation is more than 40 y old ( 1), long preceding the reorganization of angiosperm phylogeny based on molecular data ( 9, 10). With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters.

The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Previous computer vision approaches have primarily focused on leaf identification at the species level. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns.

Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany.

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