Datum
2022-10-18Autor
Adenaike, Adeyemi SundayOloye, Olanrewaju SimiloluwaEmmanuel, Happiness OshioghiemeBello, Kazeem OlajideIkeobi, Christian Ndubuisi ObioraSchlagwort
570 Biowissenschaften, Biologie 590 Tiere (Zoologie) 630 Landwirtschaft, Veterinärmedizin NigeriaSupport-Vektor-MaschineTruthahnGefiederKlassifikationBiometrieMethodenmixNeuronales NetzMorphologie <Biologie>Maschinelles LernenMetadata
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Aufsatz
Comparison of linear discriminant analysis, support vector machine and artificial neural network in classifying Nigerian local turkeys based on plumage colours using biometric traits
Zusammenfassung
The ability of linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN) models to differentiate biometric traits of Nigerian local turkeys was investigated in this study. The biometric traits (bodyweight, body length, breast girth, thigh length, shank length, keel length, wing length, and wingspan) in 200 (20-week-old) turkeys were measured. Seventy percent of the datasets were used to train the three models, with the remaining 30% being used to test their performance. All biometric traits were positively associated, with strong correlation values for several pairs of traits. In the testing dataset (Lavender = 30.0%, Black = 51.9% and White = 65.5%), the LDA had lower classification efficiency than in the training dataset (Lavender = 55.2%, Black = 43.4%, and White = 65.5%), indicating that the training model was not efficient in classification at the testing stage. In comparison to the training dataset (Lavender = 100.0%, Black = 87.3% and White = 98.2%), the SVM showed low classification efficiency for the testing dataset (Lavender = 70.0%, Black = 76.0% and White = 64.0%). However, in ANN, there was no variation in classification efficiency between the testing and training datasets (Lavender = 100.0%, Black = 100.0% and White =100.0%). In categorizing turkey plumage colours, the ANN model is the most powerful, followed by SVM. When the dataset's normality or multi-colinearity is broken, we propose using an ANN model rather than a standard model like the LDA for classification of biometric traits of Nigerian local turkeys.
Zitierform
In: Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS) Vol. 123 / No. 2 (2022-10-18) , S. 197-204 ; eissn:2363-6033Sammlung(en)
Vol 123, No 2 (2022) (Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS))Zitieren
@article{doi:10.17170/kobra-202210116964,
author={Adenaike, Adeyemi Sunday and Oloye, Olanrewaju Similoluwa and Emmanuel, Happiness Oshioghieme and Bello, Kazeem Olajide and Ikeobi, Christian Ndubuisi Obiora},
title={Comparison of linear discriminant analysis, support vector machine and artificial neural network in classifying Nigerian local turkeys based on plumage colours using biometric traits},
journal={Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS)},
year={2022}
}
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2022-10-21T11:45:22Z 2022-10-21T11:45:22Z 2022-10-18 doi:10.17170/kobra-202210116964 http://hdl.handle.net/123456789/14208 eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ classification lavender testing training mixed methods 570 590 630 Comparison of linear discriminant analysis, support vector machine and artificial neural network in classifying Nigerian local turkeys based on plumage colours using biometric traits Aufsatz The ability of linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN) models to differentiate biometric traits of Nigerian local turkeys was investigated in this study. The biometric traits (bodyweight, body length, breast girth, thigh length, shank length, keel length, wing length, and wingspan) in 200 (20-week-old) turkeys were measured. Seventy percent of the datasets were used to train the three models, with the remaining 30% being used to test their performance. All biometric traits were positively associated, with strong correlation values for several pairs of traits. In the testing dataset (Lavender = 30.0%, Black = 51.9% and White = 65.5%), the LDA had lower classification efficiency than in the training dataset (Lavender = 55.2%, Black = 43.4%, and White = 65.5%), indicating that the training model was not efficient in classification at the testing stage. In comparison to the training dataset (Lavender = 100.0%, Black = 87.3% and White = 98.2%), the SVM showed low classification efficiency for the testing dataset (Lavender = 70.0%, Black = 76.0% and White = 64.0%). However, in ANN, there was no variation in classification efficiency between the testing and training datasets (Lavender = 100.0%, Black = 100.0% and White =100.0%). In categorizing turkey plumage colours, the ANN model is the most powerful, followed by SVM. When the dataset's normality or multi-colinearity is broken, we propose using an ANN model rather than a standard model like the LDA for classification of biometric traits of Nigerian local turkeys. open access Adenaike, Adeyemi Sunday Oloye, Olanrewaju Similoluwa Emmanuel, Happiness Oshioghieme Bello, Kazeem Olajide Ikeobi, Christian Ndubuisi Obiora Nigeria Support-Vektor-Maschine Truthahn Gefieder Klassifikation Biometrie Methodenmix Neuronales Netz Morphologie <Biologie> Maschinelles Lernen publishedVersion eissn:2363-6033 No. 2 Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS) 197-204 Vol. 123 false
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