Cite Article

Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System

Choose citation format

BibTeX

@article{IJASEIT13079,
   author = {Ayman Ibrahim and Nazeer El-Bialee and Mohsen Saad and Elio Romano},
   title = {Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {6},
   year = {2020},
   pages = {2419--2428},
   keywords = {Potato tubers; quality inspection; image processing; automated vision systems; color analysis; texture features},
   abstract = {

In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LChuv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (huv) ranged from 68.92 to 96.61°, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system.

},    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=13079},    doi = {10.18517/ijaseit.10.6.13079} }

EndNote

%A Ibrahim, Ayman
%A El-Bialee, Nazeer
%A Saad, Mohsen
%A Romano, Elio
%D 2020
%T Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System
%B 2020
%9 Potato tubers; quality inspection; image processing; automated vision systems; color analysis; texture features
%! Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System
%K Potato tubers; quality inspection; image processing; automated vision systems; color analysis; texture features
%X 

In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LChuv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (huv) ranged from 68.92 to 96.61°, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13079 %R doi:10.18517/ijaseit.10.6.13079 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 6 %@ 2088-5334

IEEE

Ayman Ibrahim,Nazeer El-Bialee,Mohsen Saad and Elio Romano,"Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2419-2428, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.6.13079.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ibrahim, Ayman
AU  - El-Bialee, Nazeer
AU  - Saad, Mohsen
AU  - Romano, Elio
PY  - 2020
TI  - Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 6
Y2  - 2020
SP  - 2419
EP  - 2428
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Potato tubers; quality inspection; image processing; automated vision systems; color analysis; texture features
N2  - 

In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LChuv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (huv) ranged from 68.92 to 96.61°, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13079 DO - 10.18517/ijaseit.10.6.13079

RefWorks

RT Journal Article
ID 13079
A1 Ibrahim, Ayman
A1 El-Bialee, Nazeer
A1 Saad, Mohsen
A1 Romano, Elio
T1 Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 6
YR 2020
SP 2419
OP 2428
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Potato tubers; quality inspection; image processing; automated vision systems; color analysis; texture features
AB 

In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LChuv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (huv) ranged from 68.92 to 96.61°, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13079 DO - 10.18517/ijaseit.10.6.13079