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Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances

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@article{IJASEIT542,
   author = {Dinah Cherie and Sam Herodian and Tineke Mandang and Usman Ahmad},
   title = {Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {5},
   number = {4},
   year = {2015},
   pages = {314--322},
   keywords = {FFB, Oil Content Prediction, Recording Distance, Machine Vision, MLP-ANN Methods},
   abstract = {In this study, the correlation between oil palm fresh fruits bunch (FFB) appearance and its oil content (OC) was explored. FFB samples were recorded from various distance (2, 7, and 10 m) with different lighting spectrums and configurations (Ultraviolet: 280-380nm, Visible: 400-700nm, and Infrared: 720-1100nm) and intensities (600watt and 1000watt lamps) to explore the correlations. The recorded FFB images were segmented and its color features were subsequently extracted to be used as input variables for modeling the OC of the FFB. In this study, four developed models were selected to perform oil content prediction (OCP) for intact FFBs. These models were selected based on their validity and accuracy upon performing the OCP. Models were developed using Multi-Linear-Perceptron-Artificial-Neural-Network (MLP-ANN) methods, employing 10 hidden layers and 15 images features as input variables. Statistical engineering software was used to create the models. Although the number of FFB samples in this study was limited, four models were successfully developed to predict intact FFB’s OC, based on its images’ color features. Three OCP models developed for image recording from 10 m under UV, Vis2, and IR2 lighting configurations. Another model was successfully developed for short range imaging (2m) under IR2 light. The coefficient of correlation for each model when validated was 0.816, 0.902, 0.919, and 0.886, respectively. For bias and error, these selected models obtained root-mean-square error (RMSE) of 1.803, 0.753, 0.607, and 1.104, respectively.},
   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=542},
   doi = {10.18517/ijaseit.5.4.542}
}

EndNote

%A Cherie, Dinah
%A Herodian, Sam
%A Mandang, Tineke
%A Ahmad, Usman
%D 2015
%T Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances
%B 2015
%9 FFB, Oil Content Prediction, Recording Distance, Machine Vision, MLP-ANN Methods
%! Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances
%K FFB, Oil Content Prediction, Recording Distance, Machine Vision, MLP-ANN Methods
%X In this study, the correlation between oil palm fresh fruits bunch (FFB) appearance and its oil content (OC) was explored. FFB samples were recorded from various distance (2, 7, and 10 m) with different lighting spectrums and configurations (Ultraviolet: 280-380nm, Visible: 400-700nm, and Infrared: 720-1100nm) and intensities (600watt and 1000watt lamps) to explore the correlations. The recorded FFB images were segmented and its color features were subsequently extracted to be used as input variables for modeling the OC of the FFB. In this study, four developed models were selected to perform oil content prediction (OCP) for intact FFBs. These models were selected based on their validity and accuracy upon performing the OCP. Models were developed using Multi-Linear-Perceptron-Artificial-Neural-Network (MLP-ANN) methods, employing 10 hidden layers and 15 images features as input variables. Statistical engineering software was used to create the models. Although the number of FFB samples in this study was limited, four models were successfully developed to predict intact FFB’s OC, based on its images’ color features. Three OCP models developed for image recording from 10 m under UV, Vis2, and IR2 lighting configurations. Another model was successfully developed for short range imaging (2m) under IR2 light. The coefficient of correlation for each model when validated was 0.816, 0.902, 0.919, and 0.886, respectively. For bias and error, these selected models obtained root-mean-square error (RMSE) of 1.803, 0.753, 0.607, and 1.104, respectively.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=542
%R doi:10.18517/ijaseit.5.4.542
%J International Journal on Advanced Science, Engineering and Information Technology
%V 5
%N 4
%@ 2088-5334

IEEE

Dinah Cherie,Sam Herodian,Tineke Mandang and Usman Ahmad,"Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances," International Journal on Advanced Science, Engineering and Information Technology, vol. 5, no. 4, pp. 314-322, 2015. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.5.4.542.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Cherie, Dinah
AU  - Herodian, Sam
AU  - Mandang, Tineke
AU  - Ahmad, Usman
PY  - 2015
TI  - Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 5 (2015) No. 4
Y2  - 2015
SP  - 314
EP  - 322
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - FFB, Oil Content Prediction, Recording Distance, Machine Vision, MLP-ANN Methods
N2  - In this study, the correlation between oil palm fresh fruits bunch (FFB) appearance and its oil content (OC) was explored. FFB samples were recorded from various distance (2, 7, and 10 m) with different lighting spectrums and configurations (Ultraviolet: 280-380nm, Visible: 400-700nm, and Infrared: 720-1100nm) and intensities (600watt and 1000watt lamps) to explore the correlations. The recorded FFB images were segmented and its color features were subsequently extracted to be used as input variables for modeling the OC of the FFB. In this study, four developed models were selected to perform oil content prediction (OCP) for intact FFBs. These models were selected based on their validity and accuracy upon performing the OCP. Models were developed using Multi-Linear-Perceptron-Artificial-Neural-Network (MLP-ANN) methods, employing 10 hidden layers and 15 images features as input variables. Statistical engineering software was used to create the models. Although the number of FFB samples in this study was limited, four models were successfully developed to predict intact FFB’s OC, based on its images’ color features. Three OCP models developed for image recording from 10 m under UV, Vis2, and IR2 lighting configurations. Another model was successfully developed for short range imaging (2m) under IR2 light. The coefficient of correlation for each model when validated was 0.816, 0.902, 0.919, and 0.886, respectively. For bias and error, these selected models obtained root-mean-square error (RMSE) of 1.803, 0.753, 0.607, and 1.104, respectively.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=542
DO  - 10.18517/ijaseit.5.4.542

RefWorks

RT Journal Article
ID 542
A1 Cherie, Dinah
A1 Herodian, Sam
A1 Mandang, Tineke
A1 Ahmad, Usman
T1 Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances
JF International Journal on Advanced Science, Engineering and Information Technology
VO 5
IS 4
YR 2015
SP 314
OP 322
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 FFB, Oil Content Prediction, Recording Distance, Machine Vision, MLP-ANN Methods
AB In this study, the correlation between oil palm fresh fruits bunch (FFB) appearance and its oil content (OC) was explored. FFB samples were recorded from various distance (2, 7, and 10 m) with different lighting spectrums and configurations (Ultraviolet: 280-380nm, Visible: 400-700nm, and Infrared: 720-1100nm) and intensities (600watt and 1000watt lamps) to explore the correlations. The recorded FFB images were segmented and its color features were subsequently extracted to be used as input variables for modeling the OC of the FFB. In this study, four developed models were selected to perform oil content prediction (OCP) for intact FFBs. These models were selected based on their validity and accuracy upon performing the OCP. Models were developed using Multi-Linear-Perceptron-Artificial-Neural-Network (MLP-ANN) methods, employing 10 hidden layers and 15 images features as input variables. Statistical engineering software was used to create the models. Although the number of FFB samples in this study was limited, four models were successfully developed to predict intact FFB’s OC, based on its images’ color features. Three OCP models developed for image recording from 10 m under UV, Vis2, and IR2 lighting configurations. Another model was successfully developed for short range imaging (2m) under IR2 light. The coefficient of correlation for each model when validated was 0.816, 0.902, 0.919, and 0.886, respectively. For bias and error, these selected models obtained root-mean-square error (RMSE) of 1.803, 0.753, 0.607, and 1.104, respectively.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=542
DO  - 10.18517/ijaseit.5.4.542