A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves

Societal Impact Statement Grapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this is used to distinguish grape varieties. In an era of computationally enabled machine learning‐derived represent...

Full description

Saved in:

Bibliographic Details
Main Authors:	Daniel H. Chitwood, Efrain Torres‐Lomas, Ebi S. Hadi, Wolfgang L. G. Peterson, Mirjam F. Fischer, Sydney E. Rogers, Chuan He, Michael G. F. Acierno, Shintaro Azumaya, Seth Wayne Benjamin, Devendra Prasad Chalise, Ellice E. Chess, Alex J. Engelsma, Qiuyi Fu, Jirapa Jaikham, Bridget M. Knight, Nikita S. Kodjak, Adazsofia Lengyel, Brenda L. Muñoz, Justin T. Patterson, Sundara I. Rincon, Francis L. Schumann, Yujie Shi, Charlie C. Smith, Mallory K. St. Clair, Carly S. Sweeney, Patrick Whitaker, James Wu, Luis Diaz‐Garcia
Format:	Article
Language:	English
Published:	Wiley 2025-01-01
Series:	Plants, People, Planet
Subjects:	ampelography grapevine leaf development leaf shape morphospace Vitis
Online Access:	https://doi.org/10.1002/ppp3.10561
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves

Similar Items