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Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis

Published online by Cambridge University Press:  27 September 2019

A. Bagheri Open the ORCID record for A. Bagheri [Opens in a new window] ,
L. Eghbali  and
R. Sadrabadi Haghighi
A. Bagheri*
Affiliation:
Department of Agronomy and Plant Breeding, Razi University, Kermanshah, Iran
L. Eghbali
Affiliation:
Department of Agronomy and Plant Breeding, Azad University, Mashhad, Iran
R. Sadrabadi Haghighi
Affiliation:
Department of Agronomy and Plant Breeding, Azad University, Mashhad, Iran
*
Author for correspondence: A. Bagheri, E-mail: alireza884@gmail.com
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Abstract

The current study was conducted in 2013 to identify the seeds of three species of Amaranthus, Amaranthus viridis L., Amaranthus retroflexus L. and Amaranthus albus L., by using the artificial neural network (ANN) and canonical discriminant analysis (CDA) methods. To begin with, photographs were taken of the seeds and 13 morphological characteristics of each seed extracted as predictor variables. Backward regression was used to find the most influential variables and seven variables were derived. Thus, predictor variables were divided into two sets of 13 and seven morphological characteristics. The results showed that the recognition accuracy of the ANN made using 13 and seven predictor variables was 81.1 and 80.3%, respectively. Meanwhile, recognition accuracy of the CDA using the seven and 13 predictor variables was 74.0 and 75.7%, respectively. Therefore, in comparison to CDA, ANN showed higher identification accuracy; however, the difference was not statistically significant. Identification accuracy for A. retroflexus was higher using the CDA method than ANN, while the ANN method had higher recognition accuracy for A. viridis than CDA. In addition, use of 13 predictor variables yielded a greater identification accuracy than seven. The results of the current study showed that using seed morphological characteristics extracted by computer vision could be effective for reliable identification of the similar seeds of Amaranthus species.

Keywords

Image processingpattern recognitionseed identificationseed morphologyweed species
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 157 , Issue 4 , May 2019 , pp. 333 - 341
Copyright
Copyright © Cambridge University Press 2019 

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