2016
DOI: 10.1073/pnas.1524473113
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Computer vision cracks the leaf code

Abstract: Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. 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. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learni… Show more

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Cited by 126 publications
(132 citation statements)
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“…In plants, computer vision has been applied to images of 1907 fresh leaves belonging to 32 different plant species [6], images of fresh leaves of a few tree species [7], and images of fresh leaves of three legume species [8]. One study has applied machine learning to images of dead leaves, using (digitized images of) 7597 leaf clearings from 2001 genera of flowering plants to categorize leaf vein patterns [9]. Leaf clearings are leaves that have been chemically treated and preserved to show the veins.…”
Section: Introductionmentioning
confidence: 99%
“…In plants, computer vision has been applied to images of 1907 fresh leaves belonging to 32 different plant species [6], images of fresh leaves of a few tree species [7], and images of fresh leaves of three legume species [8]. One study has applied machine learning to images of dead leaves, using (digitized images of) 7597 leaf clearings from 2001 genera of flowering plants to categorize leaf vein patterns [9]. Leaf clearings are leaves that have been chemically treated and preserved to show the veins.…”
Section: Introductionmentioning
confidence: 99%
“…Some cereal-specific examples include using expectation maximization to identify wheat streak mosaic virus [6]; Simplex Volume Maximisation to discover characteristic spectra in hyperspectral data of barley diseases [25]; and a support vector machine method to detect flowering rice in RGB images [11]. A support vector machine approach has also been used [27] to learn from SIFT features and a codebook generated over 7,500 images to classify higher plant taxa from images of leaves, achieving an accuracy of 72%.…”
Section: Related Workmentioning
confidence: 99%
“…However, the small computers now available are powerful, use very little power and the software needed is simple and versatile. The use of these little devices is economical and they can easily be used for such applications as goods inventory [15,16].…”
Section: Mechanical Design Of the Grippermentioning
confidence: 99%