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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

There are about 90 different varieties of chickpeas around the world. In Iran, where this study takes place, there are five species that are the most popular (Adel, Arman, Azad, Bevanij and Hashem), with different properties and prices. However, distinguishing them manually is difficult because they have very similar morphological characteristics. In this research, two different computer vision methods for the classification of the variety of chickpeas are proposed and compared. The images were captured with an industrial camera in Kermanshah, Iran. The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial neural networks and particle swarm optimization (ANN-PSO). The second method is not based on an explicit extraction of features; instead, image patches (RGB pixel values) are directly used as input for a three-layered backpropagation ANN. The first method achieved a correct classification rate (CCR) of 97.0%, while the second approach achieved a CCR of 99.3%. These results prove that visual classification of fruit varieties in agriculture can be done in a very precise way using a suitable method. Although both techniques are feasible, the second method is generic and more easily applicable to other types of crops, since it is not based on a set of given features.

Details

Title
Automatic Classification of Chickpea Varieties Using Computer Vision Techniques
Author
Pourdarbani, Razieh 1 ; Sabzi, Sajad 1   VIAFID ORCID Logo  ; García-Amicis, Víctor Manuel 2 ; García-Mateos, Ginés 2   VIAFID ORCID Logo  ; José Miguel Molina-Martínez 3 ; Ruiz-Canales, Antonio 4 

 Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; [email protected] 
 Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain; [email protected] (V.M.G.-A.); [email protected] (G.G.-M.) 
 Agromotic and Marine Engineering Research Group, Technical University of Cartagena, 30203 Cartagena, Spain; [email protected] 
 Engineering Department, Miguel Hernandez University of Elche, 03312 Orihuela, Spain; [email protected] 
First page
672
Publication year
2019
Publication date
2019
Publisher
MDPI AG
e-ISSN
20734395
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2545585830
Copyright
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.