Identifying of Quercus vulcanica and Q. frainetto growing in different environments through deep learning analysis

Işık, Ali Hakan; Yücedağ, Cengiz; ESKİCİOĞLU, Ömer Can; Gailing, Oliver

doi:10.1007/s10661-021-09565-2

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…Deep learning has been successfully used to identify related species, providing valuable insights for the identification of other species (Işık et al., 2021 ). Deep learning eliminates the manual search for suitable characteristics by automatically learning relevant characteristics, shortening the classification time, and improving the discrimination accuracy for this specific application.…”

Section: Discussionmentioning

confidence: 99%

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Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons

Qi,

Du,

Guo

et al. 2024

Ecology and Evolution

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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 a prior. A total of 538 Asian deciduous oak trees, 16 Q. aliena and 23 Q. dentata populations, were firstly assigned by nSSRs Bayesian clustering analysis to one of the two species or admixture and this grouping served as a priori identification of these trees. Then we analyzed the shapes of 2328 leaves from the 538 trees in terms of 13 characters (landmarks) by GMM. Finally, we trained and classified 2221 leaf‐scanned images with Xception architecture using deep learning. The two species can be identified by GMM and deep learning using genetic analysis as a priori. Deep learning is the most cost‐efficient method in terms of time‐consuming, while GMM can confirm the admixture individuals' leaf shape. These various methods provide high classification accuracy, highlight the application in plant classification research, and are ready to be applied to other morphology analysis.

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Section: Discussionmentioning

confidence: 99%

“…Deep learning has been successfully used to identify related species, providing valuable insights for the identification of other species (Işık et al, 2021) (Grinblat et al, 2016).…”

Section: Comprehensive Comparison Of Different Identification Methodsmentioning

confidence: 99%

Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons

Qi,

Du,

Guo

et al. 2024

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

Identifying of Quercus vulcanica and Q. frainetto growing in different environments through deep learning analysis

Cited by 1 publication

References 17 publications

Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons

Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons

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