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Open AccessArticle

A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars

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Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Health, Queensland University of Technology, 130 Victoria Park Road, Kelvin Grove, QLD 4059, Australia
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Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany
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Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
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Authors to whom correspondence should be addressed.
Agronomy 2020, 10(1), 117; https://doi.org/10.3390/agronomy10010117
Received: 15 November 2019 / Revised: 19 December 2019 / Accepted: 23 December 2019 / Published: 14 January 2020
Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars. View Full-Text
Keywords: rice; crop; image processing; cultivar; artificial intelligence; artificial neural networks; big data; food informatics; machine learning rice; crop; image processing; cultivar; artificial intelligence; artificial neural networks; big data; food informatics; machine learning
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Abbaspour-Gilandeh, Y.; Molaee, A.; Sabzi, S.; Nabipur, N.; Shamshirband, S.; Mosavi, A. A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars. Agronomy 2020, 10, 117.

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