Automatic Classification of Chickpea Varieties Using Computer Vision Techniques
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AuthorPourdarbani, Razieh; Sabzi, Sajad; García Amicis, Víctor Manuel; García Mateos, Ginés; Molina Martínez, José Miguel; [et al.]
Knowledge AreaBotánicaIngeniería de Sistemas y Automática
SponsorsThis research was funded by the Spanish MICINN, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53. It has also been supported by the European Union (EU) under Erasmus + project entitled "Fostering Internationalization in Agricultural Engineering in Iran and Russia [FARmER]" with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP.
Realizado en/conUniversidad Politécnica de Cartagena; Universidad de Murcia; Universidad Miguel Hernández de Elche; University of Mohaghegh Ardabili
Bibliographic CitationPourdarbani R, Sabzi S, García-Amicis VM, García-Mateos G, Molina-Martínez JM, Ruiz-Canales A. Automatic Classification of Chickpea Varieties Using Computer Vision Techniques. Agronomy. 2019; 9(11):672. https://doi.org/10.3390/agronomy9110672
Computer vision in agriculture
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 ...
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