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

  • 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,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.show moreshow less

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Metadaten
Document Type:Article
Author: Yousef Abbaspour-GilandehORCiD, Amir Molaee, Sajad SabziORCiD, Narjes NabipurORCiD, Shahaboddin ShamshirbandORCiD, Amir Mosavi
DOI (Cite-Link):https://doi.org/10.3390/agronomy10010117Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200123-40695Cite-Link
URL:https://www.mdpi.com/2073-4395/10/1/117
Parent Title (English):agronomy
Publisher:MDPI
Language:English
Date of Publication (online):2020/01/22
Date of first Publication:2020/01/14
Release Date:2020/01/23
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 10, Issue 1, 117
Pagenumber:21
Tag:Machine learning; artificial intelligence; artificial neural networks; big data; food informatics; image processing; rice
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:06 Information und Dokumentation
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)