ArticleEcology and evolution2024
Species identification through deep learning and geometrical morphology in oaks (
Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Fine-Scale Spatial Genetic Structure and Leaf Shape Variation in Five Fagaceae Species: Insights Into Conservation and Adaptation.Ecology and evolution · 2026Article
- Clustering and classification of soybean leaves based on angle features.Frontiers in plant science · 2026Article
- Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers.Heliyon · 2025Article
- Species identification through deep learning and geometrical morphology in oaks (Ecology and evolution · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant phenotypic characteristics, especially leaf morphology of leaves, are an important indicator for species identification. However, leaf shape can be extraordinarily complex in some species, such as oaks. The great variation in leaf morphology and difficulty of species identification in oaks have attracted the attention of scientists since Charles Darwin. Recent advances in discrimination technology have provided opportunities to understand leaf morphology variation in oaks. Here, we aimed to compare the accuracy and efficiency of species identification in two closely related deciduous oaks by geometric morphometric method (GMM) and deep learning using preliminary identification of simple sequence repeats (nSSRs) as
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