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An objective method of shape descriptor state establishment using elliptic Fourier analysis (EFA)

Published online by Cambridge University Press:  02 October 2019

Renerio P. Gentallan Jr.*
Affiliation:
Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines
Nestor C. Altoveros
Affiliation:
Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines
Teresita H. Borromeo
Affiliation:
Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines
Leah E. Endonela
Affiliation:
Institute of Plant Breeding, College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines
Fiona R. Hay
Affiliation:
Department of Agroecology, Aarhus Universit, Forsøgsvej 1, 4200Slagelse, Denmark
Antonio G. Lalusin
Affiliation:
Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines
Consorcia E. Reaño
Affiliation:
Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines
Yosuke Yoshioka
Affiliation:
Faculty of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan
*
*Corresponding author. E-mail: rpgentallan@up.edu.ph

Abstract

This research attempts to systematically establish shape descriptor states through elliptic Fourier analysis (EFA) using pili (Canarium ovatum Engl.) kernel as a model. Kernel images of 53 pili accessions from the National Plant Genetic Resources Laboratory (NPGRL), University of the Philippines Los Baños were acquired using VideometerLab 3. Shape features, such as roundness, compactness and elongation, were extracted from the images. Shapes outlines were characterized using elliptic Fourier coefficients calculated from SHAPE version 1.3 software. Principal component analysis and cluster analysis were used to elucidate clusters representing the shape descriptor states. The first principal component accounts for the variation in length to width ratio; whereas, the second and third principal components explain the variation in the location of the widest portion and the truncation of the apex and base of the kernel, respectively. Cluster analysis separated the different accessions into six distinct clusters at 0.04 Euclidian distance. Six descriptor states, narrowly elliptic, elliptic, widely elliptic, ovate, obovate and lance-ovate, were characterized from the shape outlines and visualized through R's shape on r package. The discrimination between clusters was validated through MANOVA and LDA with 95% correct classification. The Fourier coefficients were also able to represent the variation observed from the physical properties of shape. The method may be used in establishing shape descriptors of all plant parts of all crop species.

Type
Research Article
Copyright
Copyright © NIAB 2019

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