TY  - JOUR
A1  - Abbaspour-Gilandeh, Yousef
A1  - Molaee, Amir
A1  - Sabzi, Sajad
A1  - Nabipour, Narjes
A1  - Shamshirband, Shahaboddin
A1  - Mosavi, Amir
T1  - A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars
JF  - agronomy
N2  - 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.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - food informatics
KW  - big data
KW  - artificial neural networks
KW  - artificial intelligence
KW  - image processing
KW  - rice
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200123-40695
UR  - https://www.mdpi.com/2073-4395/10/1/117
VL  - 2020
IS  - Volume 10, Issue 1, 117
PB  - MDPI
ER  -