*Result*: Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.
Title:
Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.
Authors:
Hodač L; Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany., Karbstein K; Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany., Kösters L; Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany., Rzanny M; Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany., Wittich HC; Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Ilmenau, Germany., Boho D; Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Ilmenau, Germany., Šubrt D; Faculty of Science, Jan Evangelista Purkyně University in Ústí nad Labem, Ústí nad Labem, Czech Republic., Mäder P; Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Ilmenau, Germany.; German Centre for Integrative Biodiversity Research - iDiv (Halle-Jena-Leipzig), Leipzig, Germany.; Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany., Wäldchen J; Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.; German Centre for Integrative Biodiversity Research - iDiv (Halle-Jena-Leipzig), Leipzig, Germany.
Source:
The Plant journal : for cell and molecular biology [Plant J] 2024 Nov; Vol. 120 (4), pp. 1343-1357. Date of Electronic Publication: 2024 Oct 09.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Blackwell Scientific Publishers and BIOS Scientific Publishers in association with the Society for Experimental Biology Country of Publication: England NLM ID: 9207397 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-313X (Electronic) Linking ISSN: 09607412 NLM ISO Abbreviation: Plant J Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Blackwell Scientific Publishers and BIOS Scientific Publishers in association with the Society for Experimental Biology, c1991-
MeSH Terms:
References:
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Grant Information:
01IS20062 German Federal Ministry of Education and Research (BMBF); 3519685A08 German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV); 3519685B08 German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV); 67KI2086 German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV); 0901-44-8652 Thuringian Ministry for Environment, Energy and Nature Conservation
Contributed Indexing:
Keywords: Ranunculus auricomus; deep learning; eXplainable AI; geometric morphometrics; leaf images; phenotypic variation; smartphone imaging in situ
Entry Date(s):
Date Created: 20241009 Date Completed: 20241118 Latest Revision: 20241118
Update Code:
20260130
DOI:
10.1111/tpj.17053
PMID:
39383323
Database:
MEDLINE
*Further Information*
*Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effectiveness of DL in capturing leaf shape, we used geometric morphometrics (GM), an emerging component of eXplainable Artificial Intelligence (XAI) toolkits. We photographed Ranunculus auricomus leaves directly in situ and after herbarization. From these corresponding leaf images, we automatically extracted DL features using a neural network and digitized leaf shapes using GM. The association between the extracted DL features and GM shapes was then evaluated using dimension reduction and covariation models. DL features facilitated the clustering of leaf images by source populations in both in situ and herbarized leaf image datasets, and certain DL features were significantly associated with biological leaf shape variation as inferred by GM. DL features also enabled leaf classification into morpho-phylogenomic groups within the intricate R. auricomus species complex. We demonstrated that simple in situ leaf imaging and DL reproducibly captured leaf shape variation at the population level, while combining this approach with GM provided key insights into the shape information extracted from images by computer vision, a necessary prerequisite for reliable automated plant phenotyping.
(© 2024 The Author(s). The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)*