| Abstract Detail
Biodiversity Informatics & Herbarium Digitization Oso, Oluwatobi [1], Jayeola, Adeniyi [1]. Botanical Digitization: Application of MorphoLeaf in 2D Shape Visualization, Digital Morphometrics, and Species Delimitation, using Homologous Landmarks of Cucurbitaceae Leaves as a Model. Plant leaves are one of the most important organs in plant identification due to their variability across different plant groups. While traditional morphometrics has contributed tremendously to reducing the problems accompanying plant identification and morphology-based species delimitation, deep learning digital solutions have made it easy to detect more characters to complement already existing leaf datasets. Here, we apply MorphoLeaf in generating a morphometric dataset from 140 leaf specimens of seven Cucurbitaceae species via landmark extraction, reparametrization of leaf contours, data quantification, and analysis. PCA analysis revealed that blade area, blade perimeter, tooth area, tooth perimeter, the height of tooth from the tip, and the height of each tooth from the base are important and informative landmarks that contribute to the variation within the species studied. Our results demonstrate that MorphoLeaf can be applied to quantitatively track leaf diversity, and functionally integrate morphometrics and shape visualization in the digital identification of plants. The success of digital morphometrics in leaf outline analysis presents researchers with opportunities to carry out more accurate image-based researches in areas including, but not limited to, plant development, evolution, and phenotyping.
Log in to add this item to your schedule
Related Links:
Preprint available on bioRxiv
1 - University of Ibadan, Department of Botany, Ibadan, Oyo, 200284, Nigeria
Keywords:
Morpholeaf
Geomorphometrics
Landmarks
Leaf Outline Analysis
Shape Visualization
Botanical Digitization.
Presentation Type: Oral Paper
Session: BIHDI, Biodiversity Informatics & Herbarium Digitization I
Location: Virtual/Virtual
Date: Monday, July 19th, 2021
Time: 12:30 PM(EDT)
Number: BIHDI001
Abstract ID:49
Candidate for Awards:None |
