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Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

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@article{IJASEIT2990,
   author = {Aimi Athirah Aznan and Rashidah Ruslan and Ibni Hajar Rukunudin and Fathin Ayuni Azizan and Ahmad Yusuf Hashim},
   title = {Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {6},
   year = {2017},
   pages = {2220--2225},
   keywords = {seed classification; machine vision; colour features; artificial neural network; rice varieties},
   abstract = {Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system.  This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2990},
   doi = {10.18517/ijaseit.7.6.2990}
}

EndNote

%A Aznan, Aimi Athirah
%A Ruslan, Rashidah
%A Rukunudin, Ibni Hajar
%A Azizan, Fathin Ayuni
%A Hashim, Ahmad Yusuf
%D 2017
%T Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)
%B 2017
%9 seed classification; machine vision; colour features; artificial neural network; rice varieties
%! Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)
%K seed classification; machine vision; colour features; artificial neural network; rice varieties
%X Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system.  This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2990
%R doi:10.18517/ijaseit.7.6.2990
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 6
%@ 2088-5334

IEEE

Aimi Athirah Aznan,Rashidah Ruslan,Ibni Hajar Rukunudin,Fathin Ayuni Azizan and Ahmad Yusuf Hashim,"Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, pp. 2220-2225, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.6.2990.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Aznan, Aimi Athirah
AU  - Ruslan, Rashidah
AU  - Rukunudin, Ibni Hajar
AU  - Azizan, Fathin Ayuni
AU  - Hashim, Ahmad Yusuf
PY  - 2017
TI  - Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 6
Y2  - 2017
SP  - 2220
EP  - 2225
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - seed classification; machine vision; colour features; artificial neural network; rice varieties
N2  - Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system.  This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2990
DO  - 10.18517/ijaseit.7.6.2990

RefWorks

RT Journal Article
ID 2990
A1 Aznan, Aimi Athirah
A1 Ruslan, Rashidah
A1 Rukunudin, Ibni Hajar
A1 Azizan, Fathin Ayuni
A1 Hashim, Ahmad Yusuf
T1 Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 6
YR 2017
SP 2220
OP 2225
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 seed classification; machine vision; colour features; artificial neural network; rice varieties
AB Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system.  This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2990
DO  - 10.18517/ijaseit.7.6.2990