Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning.
Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morpho...
Main Authors: | Wilfried Wöber, Manuel Curto, Papius Tibihika, Paul Meulenbroek, Esayas Alemayehu, Lars Mehnen, Harald Meimberg, Peter Sykacek |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0249593 |
Similar Items
-
Genetic diversity of Nile tilapia (Oreochromis niloticus) populations in Ethiopia: insights from nuclear DNA microsatellites and implications for conservation
by: Genanaw Tesfaye, et al.
Published: (2021-06-01) -
Molecular genetic diversity and differentiation of Nile tilapia (Oreochromis niloticus, L. 1758) in East African natural and stocked populations
by: Papius Dias Tibihika, et al.
Published: (2020-01-01) -
Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning
by: Wilfried Wöber, et al.
Published: (2022-03-01) -
Exploring the morphological dynamics of Nile tilapia (Oreochromis niloticus Linn. 1758) in Victoria Nile as depicted from geometric morphometrics
by: Papius Dias Tibihika, et al.
Published: (2023-11-01) -
Investigating Explanatory Factors of Machine Learning Models for Plant Classification
by: Wilfried Wöber, et al.
Published: (2021-12-01)