Composite modeling of leaf shape across shoots discriminatesVitisspecies better than individual leaves

Abstract

Premise of studyLeaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. We measured leaf morphology from over 200 vines over four years, and modeled changes in leaf shape along the shoot to determine if a composite “shape of shapes” can better capture variation and predict species identity compared to individual leaves.MethodsUsing homologous universal landmarks found in grapevine leaves, we modeled various morphological features as a polynomial function of leaf node. The resulting functions are used to reconstruct modeled leaf shapes across shoots, generating composite leaves that comprehensively capture the spectrum of possible leaf morphologies.ResultsWe found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict species identity of previously unassigned vines, which were verified with genotyping.DiscussionObservations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape—an assemblage of modeled leaf snapshots across the shoot—is a better representation of the dynamic and essential shapes of leaves, as well as serving as a better predictor of species identity than individual leaves.

The shapes of wine and table grape leaves: An ampelometric study inspired by the methods of Pierre Galet

“…In these cases, subtle phenotypic features of clones are used for identification, the shape of leaves among the principal ampelographic tools available (Martínez, Boursiquot, et al, 1997; Martínez, Grenan, et al, 1997). The ability to recognize by eye or quantify genetic variation in leaf shape can be extended to development (Bryson et al., 2020; Chitwood, Klein, et al, 2016), disease (Klein et al., 2017), herbicide damage (Morton, 2019), and responses to climate change (Baumgartner, Donahoo, Chitwood, & Peppe, 2020; Chitwood et al., 2020; Chitwood, Rundell, et al, 2016). The shape of a leaf is a narrative of its history.…”

Decoding family‐level features for modern and fossil leaves from computer‐vision heat maps

“…Machine learning identification of extant and fossil pollen at the species level has advanced significantly (Punyasena et al, 2012; Tcheng et al, 2016; Romero et al, 2020; White, 2020). Automated species identification of leaf images, in particular, is a well‐studied problem in computer vision (Im et al, 1998; Wu et al, 2007; Nam et al, 2008; Park et al, 2008; Caballero and Aranda, 2010; Bama et al, 2011; Hu et al, 2012; Laga et al, 2012; Larese et al, 2012; Mouine et al, 2012; Priya et al, 2012; Charters et al, 2014; Larese et al, 2014a, b; Jamil et al, 2015; Mata‐Montero and Carranza‐Rojas, 2015, 2016; Zhao et al, 2015; Grinblat et al, 2016; Larese and Granitto, 2016; Carranza‐Rojas, Mata‐Montero et al, 2018; Wäldchen and Mäder, 2018; Wäldchen et al, 2018; Almeida et al, 2020; Banerjee and Pamula, 2020; Bryson et al, 2020; Pryer et al, 2020; Soltis et al, 2020; Mukherjee et al, 2021; Zhou et al, 2021). However, there have been few efforts to unpack the diagnostic features revealed from AI for the benefit of botanists.…”